{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this blog we will discuss about the inference for MLR. How to choose significant predictor by Hypothesis Test and Confidence Interval, as well as doing interpretations for the slope." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w17.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/167) 00:48*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The study that we're going to use is from NLSY, observing 3 year old children with IQ ~ kid_score based on whether the mom go to high school or not, mom's IQ, whether the mom go to work, and mom's age. First we load the data using R," ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cognitive = read.csv('http://bit.ly/dasi_cognitive')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we're going full model, that is using all the feature." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "lm(formula = kid_score ~ mom_hs + mom_iq + mom_work + mom_age, \n", " data = cognitive)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-54.045 -12.918 1.992 11.563 49.267 \n", "\n", "Coefficients:\n", " Estimate Std. Error t value Pr(>|t|) \n", "(Intercept) 19.59241 9.21906 2.125 0.0341 * \n", "mom_hsyes 5.09482 2.31450 2.201 0.0282 * \n", "mom_iq 0.56147 0.06064 9.259 <2e-16 ***\n", "mom_workyes 2.53718 2.35067 1.079 0.2810 \n", "mom_age 0.21802 0.33074 0.659 0.5101 \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "Residual standard error: 18.14 on 429 degrees of freedom\n", "Multiple R-squared: 0.2171,\tAdjusted R-squared: 0.2098 \n", "F-statistic: 29.74 on 4 and 429 DF, p-value: < 2.2e-16\n" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cog_full = lm(kid_score ~ mom_hs+mom_iq+mom_work+mom_age, data=cognitive)\n", "summary(cog_full)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observing, mom_work, we can interpret \"All else held constant, children whose mom worked during the first three years of their lives are estimated to score 2.54 higher than those whose mom did not work." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w18.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/167) 03:36*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Doing hypothesis test, the skeptical is there is no difference among the predictors, while alternative said there is a different. Using last line in our previous regression output(don't have to compute by hand!). We have F statistics, 4(number of predictor) and 429(n-k-1). What's left of is how to interpret it. If we reject the null hypothesis, doesn't mean the model is good, but there's at least one of the predictor that stands out. While if we failed to reject null hypothesis, doesn't mean the model is not good enough, but rather the combinations is not good at all for the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w19.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/167) 04:44*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking back at the regression output, we use HT as significance test predictor for mom_hs. Observing the related p-value, we can say whether or not mom going to work is significant predictor of the cognitive scores of their children, assuming all other variables included in the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w20.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/167) 05:49*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best practice is to truly understand behind underlying computation software, so we observe t-statistic. This is the same as calculating single linear regression earlier, with the exception of degree of freedom decremented by number of predictors being included.This is not actually different from before, where dof is decrement by number of slopes and 1 for intercept. Linear regression has predictor 1, we can plug to (n-k-1) and we will get df = (n-2). DOF in linear regression always about sample size subtracte by number of slopes and 1 for the intercept." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w22.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/167) 08:37*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can validate t-score and p-value from the computation using the formula given earlier. Recall that when do significance test, the null value is often zero. We're t-statistic incorporate the point estimate,null value, and SE, we have 2.201, just like the one in the computation. dof can be get like the one in example. Calculating p-value in R," ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1] 0.02826802" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pt(2.201,df=429,lower.tail=F)*2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting p-value is the same just like in the p-value row for `mom_hs`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w23.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/167) 11:03*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also can validate CI. Remember that,\n", "\n", " CI = point_estimate +/- margin of error, where ME = t_star*SE\n", " \n", "df we have earlier is 429. Looking at the much higher df, we would expect t_star would be closely approximate to normal 95% 1.96. We still count it nevertheless," ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1] 1.965509" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qt(0.025,df=429,lower.tail=F)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we get is 1.97. And plugging to the CI formula we have (-2.09,7.17). How do we interpret this? We say, **We are 95% confident, all held constant, that mom that work first three years of children lives are scored 2.09 lower to 7.17 higher than those whose mom did not work.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Selection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And last, we're doing what machine learning called **feature selection**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w24.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/169) 01:17*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two ways we can achieve this. Either by start full model and eliminate one at a time, or start empty and add one at a time. We're going to use p-value and R squared, although some method can be used." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Backwards elimination steps - adjusted R squared" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Start with the full model.\n", "* Try to eliminate one variable and observed the resulting adjusted R squared.\n", "* Repeat until all variable selected, pick the model with highest adjusted R squared.\n", "* Repeat until none of the model yield an increase in adjusted R squared.\n", "\n", "Let's take a look at the example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w25.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/169) 03:36*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we start with the full model. Then we take iterative steps to eliminate one model at a time and observe the adjusted R squared. It turns out, only eliminating 'mom_age' we have an increase. The lowest decrease suffered from eliminating `mom_iq` which may an indication that 'mom_iq' is an important variable.After we eliminate `mom_age`, we proceed to step 2, that are same step as before, but with `mom_age` excluded. Turns out none of the variable in step 2 has adjusted R squared increase. Thus we revert back to step 1, and the last formula with adjusted R squared increase is our final decision." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Backwards elimation steps - p-value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Again, we start with full model.\n", "* We drop the variable with the highest p-value, and refit a smaller model\n", "* Repeat the steps until all variables left in the model are significant.\n", "\n", "The principal behind this is to eliminate the difference that practically insignificant.Since we just need the model to be below the significance level, it doesn't make any sense to further include additional variable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w26.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/169) 04:56*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we start as a full model. We see that, `mom_age` has the highest p-value, since this is beyond significant level, we eliminate this variable. Next, we run again(mind that we don't eliminate all insignificant variable in one step), and in step two, we see `mom_iq` as insignificant variable. Eventhough using p-value and adjusted R squared yield different result, often it's expected to be similar." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w27.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/169) 06:28*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is an exceptional problem that use p-value. Here only have two that above significance level. But since these actually from levels of race categorical variable, we don't drop that simply because **at least one of the level is a significant. Therefore, we don't drop `race`**.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### adjusted R squared vs. p-value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So because it's different model from using both method? Which way to choose? We're using p-value when we're testing the significance predictors. But it somewhat arbitrary, considering we use significance level(5% is the default, different level could give you different model). \n", "\n", "On the other hand, adjusted R squared is more reliable option, since it can't drop a variable unless it need too. But why p-value is more commonly used? Because using p-value is like using feature selection. Dropping more features would avoid us from Curse of Dimensionality." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### forward selection - adjusted R squared" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Start with single predictor of response vs each of explanatory variables.\n", "* Try to add one explanatory that have the highest adjusted R squared.\n", "* Repeat each process until any of the addition variables does not increase adjusted R squared." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w28.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/169) 09:15*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start with one explanatory variable, and see the highest adjusted R squared. It turns out, `mom_iq` has the highest adjusted R squared. Then for each steps, we compared the highest of each variable in step 2 to the highest adjusted R squared in previous step. Adding those up, until we reach step 4, where it turns out `mom_age` addition can't get the adjusted R squared higher than step 3. So we exclude `mom_age` and arrive at the model, which is the same as backward elimination - adjusted R squared." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### backward selection - p-value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Start with single predictor response vs each of explanatory.\n", "* We select one of variables that have the lowest p-value.\n", "* Repeat until any of the remaining variables doesn't have a significant p-value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### expert opinion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In data science, you'll also comes up with domain expertise. Which domain that the problem currently in. An expert will know which of the feature is less or more matter, despite it have small p-value or higher adjusted R squared." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "lm(formula = kid_score ~ mom_hs + mom_iq + mom_work, data = cognitive)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-53.76 -13.02 2.09 11.55 48.65 \n", "\n", "Coefficients:\n", " Estimate Std. Error t value Pr(>|t|) \n", "(Intercept) 24.17944 6.04319 4.001 7.42e-05 ***\n", "mom_hsyes 5.38225 2.27156 2.369 0.0183 * \n", "mom_iq 0.56278 0.06057 9.291 < 2e-16 ***\n", "mom_workyes 2.56640 2.34871 1.093 0.2751 \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "Residual standard error: 18.13 on 430 degrees of freedom\n", "Multiple R-squared: 0.2163,\tAdjusted R-squared: 0.2109 \n", "F-statistic: 39.57 on 3 and 430 DF, p-value: < 2.2e-16\n" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cog_final = lm(kid_score ~ mom_hs + mom_iq + mom_work, data=cognitive)\n", "summary(cog_final)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If see here, `mom_work` status has high significance level. But we that from before, `mom_work` status has higher adjusted R squared. So we still not exclude this variable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Diagnostic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, we have to validate the MLR for conditions to bet met. The conditions are\n", "\n", "* Linear relationships between explanatory and response\n", "* Nearly normal, constant variability, and independence of residuals." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w29.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/171) 01:31*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check the linearity(only numerical, not categorical, it doesn't makes sense to check linearity of categorical) of x and y. We do this in residuals plot, not scatter plot between x and y. The reason behind this, is that we want to make sure that we have random scatter around zero. Any pattern that is occured will be revealing that's there's some other dependent variables. Recall that we want explanatory to be all independent." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/hvlyW385xOD4PpRCHWjEIJ/W12T8zF0q+VulW4C/UdoLVgMDfSgcKFcH/CtHd5dr58q5X7ECLue7E33L58AsFC4D/teTWX1CQUcwCLeHXeAGKJWepCFX2H56TmTAfC3J1HPrZMHPd37G2xBs2/+QxH9kIhQOhAPNzIFYATezG9rML6cv1+cvfPgzpwHgP7R/BKTlb2RekS4oYdp/YekU+ABcbc8DbWF3KIU2ohEnDb/lGvM/XVgB+kD8pwI5U2ITDjQXByIAN5c72fyvw9+unADzwofg1xt/E9l/gWcsNJbu4ES9YFF4FB6Cf0MpZDtJ8E3a+51EuyQT23AgHGg+DjiIhcKBSnBgNTq5LRh8lcFqJ7C8sfUeJ7wNqqBUwZempkwwRpnIaQm2/sMGDycFsQ0HwoHm40CsgJvPvWzuV/ILFzh15iJdGTanf3PWf8t4WfDT89PQBRaDzyAUDoQDzcyBCMDN7IamLqc16cHgP0LxA7iSehcqVffQcX+2+xi8Av5i0nDwH8VoLvqLC3GVvxBMC/7M+TsIhQPhQDN0IAJwM7ypuUu6lu2vcDz492GvhENhMlSi/M3mfWA8PAP+i1SXgz8Hbm5yghEKB8KBZu5ABODmeYNX4bL86zr9cpf3Otu9YBhUagD2UgxMC4PB1992llA4EA6EAxXpQATgirxttXZ6Rmq4UkzrJTKl/Acj0m03ZtrfCo6fiTam43GucCAcaBAHpmqQVqPRpnbgHTrQF6ZOdWQ10tm/4pLaHclwIBwIB8KBxnQgVsCN6XbjncvV7r3wDWwDc8ImuTSbUDgQDoQD4UBTOxAr4Ka+Aw13/vNpegD0yJ1iC7b/yqVjEw6EA+FAONDEDsQKuIlvQAOf3r9PKqH/OOC/XrU7+Fd8/Hu2/q9GlSb/7vPi4Lv7HPj3o0PhQDhQgQ7ECrgCb1oL7vL6XLv/UtSNcHCRPvh3a88F/3OGE8D/UOEiaAXlKAPs/NAt1bnpSftXrzbP8TNbf7wQCgfCgXCgrB3wH6IYW9Y9jM7V5MAgdl4P88FcYDD1r1UVKv8hD1eOaRmA+6cLyiQ9M/24Egy2t0AV+F8h+tew/Fl+os1IjAP/56dQOBAO1M2B0zlsybodWr+jYgVcP/8q5WhXU/4M2P9pp0+ldDrTz6Hkd4S3wX+UY2/wP2hYBArR31R6PVPxWfL+neJyUls64//29DgMho3gXRgB/nb7TZDI1fxXsEBSENtwIByoHAciAFfOvaprT73HY2Bp8OeFl8AJUGkyeP6Y6fSH5DtmyqrLvsEOV4yJ/FmqQd1gV07yU7mr2vNSnXLi4crfz+WS1hxkfk8XRDocCAcqw4EIwJVxn+rTy8M4+Ek4BK4GB3hXTGtAOas1nfNfvfKzsatC/z3rtSHRBiT2gX7QJSmsYXsi+66Cw2FjuBUuhKehnORKPRtQDbr+ne7JcBzojWWXgv/oyqsQCgfCgQpzIAJwhd2wOnTXYHtj6rg/SbsiXixVVm7J6ejQ+eBn5r3gczgd7oQDwdXhtXAMuLJ9D5aFmvQZO6eF72A+ODkHm7LSy/TG6z821auDSH8B9nl6mAB3w79gTQiFA+FABTrgzwZDzduB77k8A44/R0y0AonXkkwZbl3p3QcGW/U8HACzgL8B7P8Y1AeeBLUIHA9bw69QnX5mR/rTbnX1GrLcSe+u0B+mhofBwOrKV/k/Iu0Lz4DXZfmbsD24z1V/MVqNyv6c2zb0sTE0FyfxnB/Ct41xwjhHOBAOlLcDI+ne2PLuYoP0bgFafQRWgllhP3A11Q4aWgtzAlemMxRxos7UHZ+n/kWU+fm8N5yWZ//VlM2Rp7zcis6iQxPBACVeyyGQlZ+Y54HZszuKyJ9DXT9TG7Q/gP2hobUHJ7gDPLcrdCcaoXCgnB3w69qS5dzB5tC3lhqAvXfzgpOP6+E46AQNKYPHcLgOzoSfYGkoRAalm/JUtK0e4LXcCm0g0Ywk/GWqaZOCMt3OTb+eyvRNr26DOTPl9c3uTgMjUo34V5XuA78cNJTWoWGfs+TedCHtlxYngaFwoFwdaLIAnLwo5WpM9Ks0Drj62aY0TRXUyjBqTQ1b5mpfxlY2ho9zZdVtvmaHv1TkqvBU8BPsA2AAnxmmA4PxH+Dq2mf4KDgQDPSJ/Fx9JLgKbwsHQTb4UdSosu8vZs7o9X0DpZ48LEebJ6bO9Rvp88ByV+ANobVp9GT4M9f4+2y9B66Cy/lHHrnuxiYcaFwHpmrc08XZWogDBstTUtdq0LkSVk6V1ZQ8hp1Lwf3wHMwGXWENcIBfETaCbWEDOB7GQ6IOJPz540uwLuwGrgYN2E2pdzl5R/A6Erlq9BepnCSVUk5Gsp/ke1L2cylPkmnrL/KtM2WuvC33Rx5+Cr8U/Bo1M4TCgXCghThQLp+gHRQ3hfXBQNEcdQ0XNVfmwg4n7wq4GPmpfBJkf4Y8nLIBNTS0I/v81J6WkwIH/6ZWdzrgqlc/DobJYFmptRgNVsFCYFD0a4TndRXeUHKl6yQjOccipD3nPOB1ngZ+jt4cLO8FoXCgqR2IT9BNfQca6fyu6s6BG8AVwM1goPoX1EcOdH3hd7gNPoVCtSoVHTj97eHr4Q2or1yNngS7gu0aeE+A6aEYfUdlj/8lc5Blrqyq0zTseC2z8zPyxZ4/00RJsv5rVk4oVgGDkJOCb6DUeoEG94VzwRXomzA3/AgNpftoeBh47ofACZRfHeQjOAiU92ZtOBB2g1A4EA40cwdGcn1jm/AaZ+TcBshFU30YSPryVL4uybU4yAFvM9gBHNRd/RSinankJ96+sCZ4bD8ohfajkVfgZrgaXAXVRUdz0MWpA3uTtp+zpcqySQf8ByD9c9WjyB+RrRj5BnHAwDsvOBFSO8E2U1L//OH9u+6fbKTCgSZzoMlWwE12xU1w4qYOwCtxzSPyXPcdecoKLZqFil+CK5tES5O4EWpaIVp3TvgA0kGqO/nxUE6fxr2OKvA3n/35ryv89CSGbF65+v4K9oHzwZVmK2iu6saF+dnd53yLMrvI1eiPk7C0/wbkc8qsn9GdlulAkwXgNi3T7ya5an8pZvbMmQ10XTJlxWQ99kr4KHXQU6T9jVeD88ep8myyMwWuQOxXondI/AiuYLKffZM6jb31WvrCUjAdnALfQG1y1TwZ/Jnjq3A/uHJujurBRY2CM8BP7d5XJ2KHQDnoQTqxPngf/OzsPRkAG0MoHAgHWoADTb0C1uJT4VjwZ5GzgsHTFVpd5arHlaG/ZJPItt+CjklBNVtXwLeAk4BElr0B6bJkX2zL14F76ZoBN5ErTVecyycFZbJdj34MhT3B5zQUDpSDA7ECLoe70Ah9cPA5A26AH+BGGAt11bscOAFGw0nQDo4Bf25q+zXpE3aOg5+hL3isK6bdoFxWv0vQl8Xhe3Ci4S8ThfI78Gyq2JX+E+BXjnLS7XRGQuFAONDCHCiHFXBDWb45DV8IZ8IaRZ5kVer7s0N/QclgVy4aREfuhl3gEvDzuJ+gQ//rwGUUrZ4qnor0C5BeFad2/1fSXw708373/yqNTDjQchxoshVwy7H4P7+cUp/VZqm8akNDvWDeUjXYRO0sy3ldwd8EL8EKUJv8VL4vuJq9C/aHfHLl+yOkP1MeSX54vsqNUOYnXQPcJrBQPc+3DMd73btD+vrq06zPk6veQdAXXGWOgNq0HBXGgxO358EfkYTCgZbmQATgRrjj5bAC9ue+V8HlcAfcB376rTQ5eXDAXzjX8a5s/Ws/i+Ty1W1GscPAa+CZCc4Cg1FWAynwU3hanch4bGPL4OvXhYtgP/gSdoa6aAcOmgRbgNeth6WaiPm5+Qq4GQrpX1fqeX6Dt3LVPBH0PhQOtCQHIgA3wt1u6gBsoP0e0oFlDPnjG+HaS30KP1dvnGl0APljM2XpbA8yj6YLSLcGPzM7MUnLleZJ6QLSrjwfhJ7gcbVpEBWsfxu8CAaoumgoB6Wvy8/gtutKthjNR+WvwIlEog1IXJ5katj67Pj3mO+Bh0BvDJiJ/KW5a8EVrJMEA+taUJN2ZOfgTIUu5K/KlDVldlpOPgSGw65QyH2nWigcKMqBJgvA6Ze4qB5H5aIdWJQjHNxcSSXajkTvJFNB26np63eZ/v5Evqbfnp6G/f6GdTdoD8pfqnJS4kCblp9QDdj+/HcGWAIM1P7C2NHwMbiCrk5bsWMNWBfWh73BQJcOfmQL0pLUujRV00/j5pdPlRWSdOJwGqR9u5X8vAUc7Kp2QVgH+kMr8BfmEl1Bwt+EPhjOhLly6a5sq9Mf7Gib2Wmg956Ug+zbK+Bz48RDv58B+xgKB5qFAxGAG+82uipx0MvKAabSVEWHT4Qk4DooPgwToDr1ZYersgvhHZgfDJT+HPJjSOt3MtuDvxw0Bvz0fD2sB5afAgaa6gZj2zVA/QJqIrwJ+0J2tU1RjfqBvbNnath3JxzF6FsqG0TTmodMTRMJ6/YC39NtwOD4GxwGq8IsoDpDeuX6CXk9Wxqq0/3s2AycGCqfw2PhJjMlkl7vBU4MVi6yzSHUty/e60dgMDhhGQihcKBZOBABuPFuo59BZwYHz0QOTJ8mmQraPkBfx4IrlOG59J5sHfTzaTsKHej7wgC4A6pgP1gR8k1MfqV8d3AV+wIcCIlGkjCgLZAUZLY+1x6v/Gx5DhiM/NrwL1geCtXFVBwBrshtywDgPbwGitFjVP4MnDjY1rJwGewPNakDO9/PVHAyZyBulyt3VT1bLp1sFibxU5LJs7UvfhkYD9fmtveyNciVQnPSiO16b538VMHOUKjmoOLVmcpOGmw3FA6EAxXmgIP22CbucyfO/x7cAK7oTgQ/tVWqutHxlaBnLRdwHfuTT62u+PwsfB7sCIUoX1C4nAOT1Vu2jX0ouDFXeCHbM8Cg5YqzFzwEc0OhWo6Kk8CJh8E8G+womvLfIu7BdktoZUEe9aPMa3kS7P8KUJumpsI40LNEBm2DmhMCtQHcAnOB9Y+D1yHZT7JatWOPx81QbY267TCYe72JXGHbxwWTglq2B7F/v0ydC8jrcSgcKKUDp9PYkqVsMNr6XwfKIQDbKwdFg1FnMy1EfkZ2kE/LFfM26YIa0gYUJyyJjiDxPlQXYCx39VUF78FtsBQkOoCEn19LpbE0dDFsAgZLP5lmJ1ae80FYF3YDJwTdoRD5vFj/xBy3sp0J0jJAV4GrxBNgWmhK2Y+sDKrpiUR2fzrfkczncBIsA0dCoZMKqobCgYIdiABcsFV1r1guAbjuV1C5Rw6k6wYlPw2r3mBAKfRzYjvqGkRdVSXBzq8JtWkxKkyEXpmKp5L358mlkCveyZmGfKHTK7VFyDthcHWaaE0So5JMAdvpqNMHVoZ0OwUc2iRVXO0ukDmzXx7sf6Hyvh8IBuG9oAOEwoFSOxABuNSO5mkvAnAeU0pY5GfQM+FE6JlptxX5k+FF8JPwzbA4FKvZOcDVYBLICzneADkWkhWhq88foD2UQsNoZLVMQ17buamydUgbSLJyQtEQmplGz4YqeBwGQKnkytt7eT1cA3NDPq1CYRWsBAbia2EMhJq/A7NyiavCEhVyqRGAUzeqDekZU/lSJSMAl8rJ/23nKIpcafqpcHVwdetqLSuD56LQMbujgfO70P7zcB84AZgFSiV/HrtvprHdyZ+QKluedPYXinpR9liqTqmSTiycYDjRcKLip/+7IDtJoKhoTc0RP4LXZ5B3kJ0MPSCfulPoRGQU7AT2J9S8HfBZHw8ngWOCk7TqflTErrJQiw/A7bgNJ8KH8G9wAP8JXoIdoRSKAFwKF/+3jQUo8mdzTpwSzUfitiRTJtv+9MMV+llgui5yIPHnz7fD/eAK1tn+g7AtuBrcHHx+p4e0fP7eBj+LGwyroDfUR64yh8OxkKw2tiZ9MqTlPRqbK7C/x8E5cAB4TYXKn9sfn6nsp3x9bcnqzMWfAk40RsA00BLVjYv22U9PyG4gP6TMzWiyAFwuM1IHg4VhXXDgsl/+fNCVi7Pt9M/TyIbKyAEH9Gvhz1SfDDR/pfJNnRxMB1z5XgGXw87gs1WsLuOAtuDn9jXgJjgafG4NgAY2VwAG4u8hLVfKx4BBzPoOSs9CXbUdB14CTgImgG2tD1PDO5DWl2Q6gYHhc/D+XAA94VXwmELku+kn7bTeJGN5S9W0XPin4GLhGPA+3AkzQkvTSlywY/VbqQt3/F4tlY9kGTrwLn1yFplPDmj35NtRZNlI6iergCIPjeo1OOCs10CUnvUvQN6BqBzkp1IDjr/ElKg9iTtgjqSghq3HjYBJYFDNTgYNyvNDY8pJzwcwQ+qklnlNvi9VMAskGkriZDgenAikdQyZndMFNaTXZt/10CpV50jS+6byLS05LM/170XZIS3NCK53Czgoc91dyN+YKSu3bItfATt77FfNXVmP8i+q2RfFTe+Ak6eb4ScYAJuBq6uBUA7qRCcmwI+pzvxG+j2obZXi59mn4XfwuqpgOUhf21/kC11BUrUkmoVWxsO3qdZ8R1yJ/QvOhQ/hYPBezAdHgNebncw+QpmTlEJ0F5Vs90VwBX4WONk6G1qqOnLhkzMX/zx5n7uWJp+t1aFP7sKnZevE7+pcPjZl6kBv+vUqPAHXwEVwLfggvwxdoL4aSQNj69tIHJ/XAQfw88FA52fe7lAucgV7C6RXqa7aDVa1DZIOJKNAueozmLmK9LOv2gGceLQ3g/yxyXFgQNoVPKYhNBON3goG4kRzkfDTswFBGXQ3AgfEpB8G5EMhLQfNLdMFBaT7UsfP+htDa2jJ8uvBmRkDHiC/faaspWTn4EKfg9tBH5yolbuabAWcvJjlYJCriBWgK/g52hm9P196CP6GQuTPIJaupqKD0S+wTjX7o7h6B/qy60D4FRaCHcHJkjKI3QLjwPt1DtwPQ6Bc5HPlKmVbcDV7Avgp+UGoSWuy00+6x+Yqea1OCg24TubmBq/zc5gRHHSciLwCnsPg5Gfbf0OptQENjof+0AYugt3gbqhOTkbsm/fLAL4hGKjXgVDdHNB7n/dv4CpYHXwu9DZUGQ4YgMfAM5XR3cbtpYNYssKo7cwLUcFAmw8HKwedUHEO9KS6E6BkVevWwOWnR3U5pAcbJ3VXQj9INAOJRaArDIN7YBKcBd7fusiB7xC4CybCSKipra7s3yvHgmwL0axUug/se6KdSHg+J3pOGhNdSGK9JMPWVeWzcCN0hoZQbxo9HA6DxQs8gb7tDkNhG6jJM3aHCnCgFXU2B58vxx7zocpxwAC8ZOV0t/Q9nYcmHbR/BAe8HpBoKxKuruorB+j4BF28i8dyyIDMYQYaB37lvZlpSuqfP3YgmXyC60PayY8rYz/XPg1TgQP/cDgS6iJnrHdAW2gH9vNoKLV8MZ2AHAVngs9pB8jK8rnAvhh0J4KTDVemHr8oFCKD/mXgavpF8MtDKBwIBxrOgSYLwA6E5aD96cSn4KriUXgIekGo6R1wlfdzpht+yk9Wf9+QXiaz30nTV+DquQqGgKvdSeAKzE+ef4EBczmYBQrRdFQ6CXw+bMOA+Af4WdlgZ7CcE0opP0vNBk/ALTAYvP6sPqRgQ7gVVoTJcAz4PK8ErtZrk4H9ffBYJzm+DwuDE5py1TR0bBVYAZwMhcKBcKDCHHiN/qZXFQ7g74IrilgBY0ITai3O/RgkAdcB1xVdX1B+rTC/HSwBF8KdoAy8m09J/eczrqtBP5WenStz419h8mdmtckV83twBBiUrgdXwGtCIlfF8yWZRt46ifg3VIGr/cthAtwIThzuhdqk1yMylWYkb+AvRznZccJxAnjNTrpmhlA4UEkOnE5nnbw3uqZq9DPmP+ErFDvbT3QtCV/ouyBe6MSVptnezWmvA++RA+3VsC1UgXoLvEeLwM7wFKwPylVu8oy9SboT9AcDtjoIDMifmqlF67DfIGYfbOtP8PnYDdQgWB0+gKbQl5z0PngW+oJ9XQ0+BCeRelGb2lPh80yl38h3zJSVQ9a+vgZj4AgYAsPgVGgN9dGaHLwfDIQ29Wkojg0HwoHaHRhAlc/g0ExVX+jfYVymvC7ZkRw0ti4HxjFTHOjGn8vDvEX40Z2674ArVtUTDL7eh1PgBnCFV4i2ppKDfKK5SNjWuzAcXA13hqbU3Zzc4HMV+Jm6Cvyk7KSkEN9mp97zYCA3gF8AvgMXQnVy4ur7M391FRqofBHavSRP276rhXzRyHPolKIz+NMJ+CZwLnwA00EoHGgoB06n4SZZATfUBdWl3Wk5aNE8B/albKc85cUWRQAu1rHS1Pdngx/CKDBADYaVwXJXUYWqFxUfgFlTBxiQDbx9wOenqXUgHbA/flbfDV6E78AXvBCtRKXJ8AecDY/Cq1CdT05ixsKh8DHsAQ0lf7ywInTKncDJlKvfrOz/LNnCAvP9qfcytErVP4z0Ual8JMOBUjsQAbjUjuZpLwJwHlPqUeSKbhkoZLUzDfW6wkxQH23EwX569vPkaWDwqS44savRNRVn/AIeg/vheJgaroF8k0uK/0uuenvADGCwWxxGwAaQ1bYUfAJJsHIC8hCsAqWUK3oHqNGg5/qfXIt5Jx1et/Wq4GKoq/bmwC0yB/t8pQO9/lwKd4GTnS4QCgfq40AE4Pq4V+CxEYALNKqAattTx1XeyeAn1l2hsdSTEzlIrwfl+PPB8XmMOImyQgKjXwiyK/k9KTPYZnURBYtlCtcn74pRLQRXwn3wMCwMddElHHRE6kAnXQb6WaEtOLmYCAZEf6ZvIK6rduDAEzIH9ydvwFX+rsHfsAa0A7+iTIJCPu9TLRQO5HUgAnBeW0pbGAG4NH72oxkHwQ655lzdPg19c/mWvhmDAWunTDA4fA+zp8qqS57IjnSw84vBr2AwzeoMCvpmCnch79eB2cB7lAR9g69BshcUqwkc4D1OayiZfKvydJ26pP2acQu4Eta3deAR6AzqaBg4JfXPH07GjvknG6lwoGgHmiwAl+MKomj34oBGdcDVRx/4JXfWn9n6GXJ1qIKGkKuqlcFA8Cx8BnXVHBy4EvwOD8BPUEodSWPvgJ58A3vBpvAvqE0G4JdgHngONoFB8ApkdRUFDhzv5fC++PnXVeKesB1MAvUynAoGr3SAJ1urnDy0A+9zIn9xzolBqfUbDW4G9lEv/Pn5IEju93Sk9Tatj8m4Kg+FAxXnwFQV1+PocFM74CDpSiUtV8OWN4Qc/C+BzcGB9lNYAeqi3hx0I8wNpn+EuaCUepfG/BnuF9AatoH7oBDZn/ngHvgJXM1eC/nkRORw8HruhR2gJ3wNU4M+pfUlmWnTBQWmXZEavL0PylX2XuBn6IbQnzR6LDiB8DxvQqInSOwPrXIFbq+Gx3P52IQD4UCZOhCfoGu/Mf2pciQ48LvayCd/7mhASQJXd9J/Qw9oCI2lUVd0iXqRmABzgqu9DWEj6ATVaSl2GAhdtS2ZquTny6tSeT91XgIGtBdhe6hErUOnvYa2uc470f4CNs3li90M5YDn4XZwcjAKroRB0Ng6hxM+DQZng69fAULhQH0c8BlKjwv1aSuOrcaBCMDVGJMr9udrd8C6cAAYVJMgS/K/1Ifc6+DqbDysCmm58hsAG0NXqI8mcnCy+kraOZTEHuDKx8mCefvr6jGrwygYByPgYxgNyQqK5JT/y9itq/g/IAm6HUlfDwb3umoGDjwQjoW6Br+6ntv7+RocBNeBfaivdqYBPZkf9PpyOBgaWytyws1gpcY+cQOdTz+9Pz6jTp5CjetABOBG8LtSA7C/7HIGnAi+qA2hpWj0HUhWTJ5jczjXRB6tSdldcB88An7STdSehKujM8HB2cDYH+oqJwUzZw6+lLw/m1w9Vb4GaYOD509kP/302gYWhmvAScMOoAzsL01J/SdAZoPUvOwzeNVFfkGYAEPAvj0LF0NjalFOtjaUYnbvF4Y3YRpI5Mr6FuieFBSx9Z7MD/kmTUU0U/FVl+AKqmB9WBl+h/0g1HgORABuBK8rMQD7OXgSLAergcGsH5Rarib2yDRqIDPIZuWgbuA1OClXIf4y06xm0BWQbsvg/CDUZZDmsCn/jONYtgYA5efQT2E0ZOW5e6YKh5E2ACU6gYTB+zKYDUaBK1S1Dew1JfXPHzORzHowLWUG83n+qZY35eTFT9xpeb41UwUzkF4E5kqVlWvSPl6Tp3NnUuYErhjp65VwCdwED0MHaIl6iovulbpwJybXw0Kpskg2rANNFoCnatjritbr4YAv5XZgwH0cDGKuFg6AUutrGswOog4A+Z6PvSn3s+4HoB6BcbCJGWRgGm0ip4/Y3grZ9pP9tW1dsfpzR4N8FRg49WRmaAWJnDAsCwa1RD+S8Oe6iY4i4f7V4DywTV8+9ShsC93M5LQfW3/2mcjJx9WwL7gyPwmqk+eZmNnpdTghUSvDFbA7VMHBUF9NTwND4GjYtL6NZY7/krzBwc+/iXqQGAjvJAUFbNtS5y3wmd4FfG5eheHQEvUvLvqN1IX/SdpnLv3cpnZHMhyoTAcqbQW8EjYfl8fqm/OUlaJoDI08Ca7IBoAr7wUgqyspcOBNa0syBiV1C7haSusCMuvlCtqx3QtcIZ4ABo266FAOMngaiBeDr+AFeAi8ltYwO1RBPzBvgP4bnFwcAp7fAJKoLwk/AVruatVnZipQDoge29sMsvwx2MFMHnl9G2TKvXf+XFz/bKsrKPv2LGxkpo6amuPugwOgPxjgvFellL7ZbycNXrd+94Fi5L26OM8BPjeVoBXopJOlPWG6EnTY1W7yTNlcW3gT8r177g+V3gHHkSVL32y0mHbAwXRsuqDM013o300wbaqfvUg/l8qXOrkzDTo4ngELVtP4TpTfmNrXibSD8tK5MoOOgWYOaAOHw/u5tHVfBe+DK+LdwGOTVSHJgtWKmg6Et4NtGPAsU5YdNCX1n36YvxvGwHIwAZwEOMlxVXonJIHWoN0XloG0tibjoJuWEw3bzKeuFNqvQbAEnA0PgrKdraak/vnD4HZ+LtuBrb55/8dBdfeCXf9fI0ht//9z/0lczmbdTFl9s4vRwCVwFaxeh8ac4Hmvskq8yZY3RN7n0udzxiIb34P6Pjubg8+X99dnoD7yPbAdJ2Z9YCIMhVDjORABuBG8rrQArCXbgi/nmuAnxQmwODSlDFSj4RE4Am4FB6S0XMlNBvtrYJgeuoErJjkNfgCP8+E3UNRVfTnw+MzB7cjflSlLsieSMJimdRaZzdIFedIGzD0z5fOSvy5Tls6uSsYAfz/sDQ78alfw3qZlYDMw6e+LcAJ0BgdoB2VX7zVpNDvnyVTYhLxBI60OZJz4HApZH9L18qUNnl7LQNgGfDZXhmLUmsqXQfqeHUzelWBjSI+uBieRr4DnLkQLUekzSE+I9W8cdIX6yCB+NPgjjbXr01AcWycHyjYA+2C0hY6wLzhLq1RVYgDW61XAl9OBYj4oFy1DRwZAIX1y0P8FDDAGBQPRZHAAPBb+gGKDAYdMUT/+tI1EnmtzeAm6QlaXUtA9U+iEwee7Jrky9utD31wl3wu/FuyQy2c3BiknIAZa799fYBuqK7wFyWRqGtLXgteiP1dCWkuSGZMuyJM+kzKPTWssmbSv+v4OHAX94R64HQz6tak9FTw2/Wm0K/nxMBMUI8eT18EVvoHXwNMOGlrTcQInDck45jlHgfeoNm1AhQNSlbqStu8fwyPgPXNyEao8B8oyAK+Ijz/B3OBA8ww4qO0ElahKDcCV6HW2z0tQcAGsD5fACDgEboAh4L0xWC0KxcoVyd0wEDrBzVAF94KDbR9I60Qy2aB5I2VbpitVk16QcldBDrYGL1e1+TQ/hd+A/UnkOc9LMmxd2X4Io8G27L+y3s5TUv/8MT3JO/7J5k3NS6nX67H6eCo8Aa0g0cEkzkkyue35bAu59jmpNzZzrFkDf+885bUV2S/Hltlqqdid/cPAIL1OLXVr220QPTJTyUmRk5/atCoVLstV6shWrwfD5FzZnWzTATpXHJsKcOB0+rhkufXTF3NraAvfgp+fHEjvgkrUSDqdbwCpxGuptD4bkK7Oddp78DVsDgagKpgDDEJO8AycBqdi5KfaT8DgaNA5AlyNdAHbNXgkcsB38DwQlgWfi4mQDlRkp8gA6sB/G9wKBuD2YFCYFarThuw4KLPT9idkyqYh3xVmTJV77QbbqVNlg0g7calN9vdk8N11ctAO0jqXjNeQ1gAyh6YLqkk7CbgPDD6J9Phl6JoUlHhrXyfCJtAHfob9oa7ymdsnc/BM5PW7EJ1NpdNgR7gUHoRlQDkR9DkJVZ4DTRaA/SRVnXyZvwA/gX4ODo7LwfcQCgeKceB1Kn8KDvSu8BxUD4f3YSC4/3EYAU/BNfAWGKjvh27gCkRdANmBzsDbBSbBRmBe2b6fBxcDA7TyWXawPAA2AwPIIfA3pGXwegeGg0G4B1wE+8KzUJN8R+bPVOhKPh1U3W1Aec9ESk+Tvgvs737gtS8JW0Nt+o4KQ2uo5ETad/jVVJ0NST+fymeTXveO0AE87nLQk1/hSrgJ3oOGkOfaFV7MNe6YNAacpHnfitXDHDAe3D6TO1i/fOYK0T5U2hO2gelzae+XagV61NiamRPuAXrj8+oz+m+oTk6gZoWvwOclVKYO+JAZdH3pHCwXgtdge6hEjaTTYyux482kz224jlHwEDwLvvx3wKfgYGpwMOg5wH0AN8ICYDD1GAeOeeFm2BTyyYF5xswO66+cKSsk68TgjEzFFcgbFArRBVQ6FuaBJcCJhJPZRF6PkwYH8nxansLdYVvIDuwG5avhFvCd3A4K0WxUcqKxH/g++16/C65k88lJhN6vD8vBx2AAvBAuha2gIXVPnsadLA3IU15oUW8qOvE5G66D4TAVFKPpqPwAeG8SjSAxLMk00tbnwvuzGywN3psnoC3kU38KbwXvnfd9Ywj95xdBneSWnXzQtwAf0B7QDypVEYDL487NRDcMAvuCK9Y34BPYBpyZvwnzwQ1gwBoPBuq5QDnjd2A2GD4JH8BXYDDxWfU4g56DpEFwItRFe3FQeoC1DftwvYkC1Io69sn+GLTtb6JkILyMgnfAAFeoOlFR/9bJHWD+plQ+V1ztxtX/8XAeHAjTQHWqYsfiqZ1e0zWwTKqsIZMGyPTA6CTOBcGi9Typkx6vwQlGXTUHB/rcXgu3w6GgP4m8p07gTgDHzobQcBrNTr5OoWxgnpMtSJnPzZy5fbOyfR+cWLV0nY4B6eespfvRINc/klbHNkjL0WgxDhho984d4MDkAOYq93HoCqNhKBwLa8GRcD4sAoms62DiFxqD7erwMRwDG4EB+i44AtpBWg6MPgsngoNSdTJg3gntUxUMWMel8sUmXZ07qbDvc+cOdiB3EuEKphDtQCX7kVZPMqPSBXVMG9R3hg1yx9+bp50DKFs3T3l9i1rTwMFwNzwAVeA90CtXaiuB99XnoiHk/fDZ8drT97ymc7VlZxeYPVPJ5+4hWB7WAK+hD5RavhfZSYT3MPt8eN7DIJm0mVerQkP5OeUEFfJHkwXgNnkM8sGZIU95UuRLeVCSiW3FObAyPTYIGZgc7BzU6qs5aGBR+AkeqaUxBzfrORO/EgyEH8G+cCP47BmoNoHO4GBmoHQAUQ6Slhu0XJ39BR+Cx58AF8InsBN43O5wNijb8Nqt68rvFRgA90FWj1LwILwLR8HC4EA7EIrVVBxg4HIwnhfsrx6oT2EQGNSegto0MxU6wXzgataA7rXoWWswkHeA5+BbKFRjqPgjTIQhsBPYNwOHZcrxYmdwElVquVr8BfTBe+p59Hoe2AW8VgPOeKiPpudg2/wePsw15H05DTyH6K8BXw9r0h/sfD9TYX7y9nsh+DO3rydbry/xMVdc743PeX94PdWSvo1L5dPJpD9J2b9J+GyGmsiBNnnOa3Btm6c8KfohScS24hzYjB4PguPAl9EBfze4GOoqA8Dh8BgsA1PDOvA75NNDFF4PBlKPc1BaF06B3uCgNxi2Bwc4A5ZBZzuYCQwwk8CB9C9I9A0JB/BVwWsaCl+BAfgsMAjvCOmB0bYvgnwBmOIpg7L985iH4VbIDmIU/Y8chDeB9vAgOEgaFPXFa/43XAx3QDfw+goJlqtRT6/8GuBAazuHwJbgfbgK9OE7mAD2+1WoTdtSoQcsl6s4lq39exuqwIDyIRwKTpJqC0xUKVo+O15Dck8vJb0U6M0wKIV8do6Ez8BzXQfXgvf/Ltga9O4GGAdrwAdQjGal8jWQfk7eyuST9nwmvP8/wftJYRHbc6n7HswBd8OmMCN4XVl5jd7XleFzcJLhe7QShMrQgVnokw/i8/ASuFp4B7yJlaiRdLpS+14KvzvSiAOqL2iidiRuhxG57b1sDTgGUbUF+GIbNMfDNJBWVzJ/g9tEl5Iw+NWkPuz8HS6Em+EwUNvALjAnGGBMGxi6g/sMavZhM3AQSQKG/bUfX8MTYP20LiezG5yQLsyl8w1WeaoVXLQ0NSfAhuAAbr98dxJPlyf9Gjgoe+3rgHUchGeA6rQQO6ynN3ph8P0EHoZjweueDIk8zz2Qvt/Jvuz2JAocmNPakszL4LW8C96r9aChdEeehp00LZunvC5F83KQ/i2YO7gN26fgPPAaF4NEs5B4GnZOCorYdqOuE8xpUsfMT/rZVN7kAnATXALuOwNaQbHyHT4QfAYGQWuoTr4/TqTOhdvAexwq01/CcrC6E/aGu8FBz5nvIlCJGkmnx1Zix0vUZwfuK/O09RJlF8BUuX0OOufDVjAOZgL3DYRrwBc+kXWGJJncdnq2d2XK8mW9HydDz9xOBx8H/NVy+eo2zvId4AxgDqi3wjPwESwMVZDVQRQMAgc8+5fIcxscS6kXaax7qkEH4s/AgTnRaSR+B/tzOzgQ7glHQ3XyPdwit1OP9O9icIK8NjwEWd+HU7YG1KYjqLBTqtJspPX2slzZEmwnQvq6crtKtjmHlo5PtbYRaScZHVJl9UkO4uDBmQa8ngfgcUgCs1XmgydhLzN10A4co3/9waA3AfQw0cwk3L9CrsBnfxL47jW0fJ+91lkb+kQV1P7p9HXJcuvvtXRodZgOHFRUbxg1JVV5f7T0AOxAdgukB5oe5H+D7MvorNwVgANxWiPIrJ0q2Jj0Uam8ybnhzkxZvqwrs+/Aid4GYFD1RahJDtLvg3WvAAcxB679oTOoi2DzKan//OHz+yW44nAS6TFrwabwIJT6xbNvWVVR4CQ20fkk7McykHhvMND3fJqdwlPhyNxOB+6P4Wy4A16BD+BlSEsvsivb9P4kbR8eAu+DPl4H9q8jJPJeD08yDbD1XK/DaPBanZzMBaWSQTEb4HpSdiX4rOtha5gFnMh4/T4zdVVfDhwGh4LvWVqbkDkgXUDae5Dv2clUi2wDOOC4U+pxoKButqmhli/4vOAM0VWPs7avwbJQwzqwDs2vB/p+L4yD+uoXGjgZHGi2AFdgp8Cz8AWk5eDzF3ybLiRtIFsJ+sHPcBUMAgfuW6ETnAHVBRJ2/X99Q8pBZydwoLscHPiqk4HEgetssN/qRjAoPwWfgTJIfARDwWC9Ry79GltZEVaDX2FXeAuK1VQcsCOsAvpwJrwBamroDu+YQd7DecA+3Q1vgy/7hfAkJFqKhJ4o/TTI/gRe297wLujV8mCgehHWhaHguavgE2gPv8FRMABcWdcm77/n9PnYCuy/k5UfINHnJDokmQbYeq6FYHHwGp4HvS2VHqCh++AFeAI8hxOam+AA0GcnhF+C1+oz8hrUVVUcKPnk+yVptSZT03icrhvpFuCAL4MP5CpwKjwKzhR94StRI+n02Aro+C700cF7YegFrvT2hfrKoLEXOAg5sEwAB/1z4XhINJDEr2CQOykpZDsfOGhMAp+JzXN50xPB9mx7S2gIeT4HznlSjRsQfCaz/kxH2a6wHywLpZbP0bXg/VkeqqAPqL6gT/3BYO97cxCoBaE32D/LrwOD8WB4GCzX99ugLxwMtrUqqEXAQPUVVMFlcDU44TkG/MKhH96Hs2B6qIs25KA7waCgfHac4GxipoK1GH13YTEa7oFdIa1uZLw/roIbUjPT+FOwVu4kbdieA3vm8rFpXAdO53S+h2WnzvRoVvAB2Ru2g3ZQiaqEADwjxr4J06QMdhB0QO6SKqtL8mwOMnh2hGlhOAzL5T3naHAlZ2CZHezDS3AlHAEGjPfBZyHRMiTGJpkG3hqEXPkZ8BMZ4Oz7TklBA21b0e5gGAdONt6GtBYgY/BTa4KBcTJcAWtDPhnU9oAzwPswG+jnC+A+NQjcb3uJ5ibhfdggKchtPddWmbK6Zjtw4OXgyvoA8Jk4Fuqi6TnI61ooc3Ar8l7/rfAgXAA+6w0tr607NHSQzXcdnSj0/vlu9YBP4CpwMuBksVTy2g6FE2CLUjXajNsp2wDcnDyvhAA8L4b7Qmbl7HixbGEReQPqk3nq30SZgd2Bz/aXhKkhkYGgH6wHW8NQyOrebEED5q+j7Z9gIGwPBt/voZBJoQFcbw2Ux0ES5EjWqpOp8RgYJJ2EToKDIK0HyPiF4UVYCpaDv6GYAXBj6g+BRAbUA8EBOlE3EneDE6K1wIH8ePC8xVwT1fPKZ/B6OBcegmdheaiLFuagm8GJ3cPwOHjv5gI9PQemg6nhSLCsucrnwPs4Ct4FJ4/tYT7w/cwnJy+LQ9d8OynzufcZ2RVWANUJqmA36AvPQHoCR7Za2Y81IGmr2orNbEdZBmBf/OfyMKJCzR9Jv8eWed8diAyKBsJEDrBfwgxJQR223Tnm6jzHORhkVyZ5qk0pWpQ/DYBtUhUWIT05lTfpALAz7APLgvU3AweENaC+MvC9A67OLgS/GtSmvlRw8LO/DjKu6C8GV2G1yWDhe5DUXZD0eLgL3Kf6wUSwX65uEnnPrKcnhciBz0lCci6PfwSeAOWAbHBcD2YFA5j30He1A9RX09HA37B2qqHRpI9N5WtK2r+94TDYFmxrKTAQvwRPg8+Q5S9Da0jL93OBdEGFp30WXPH2Aa85eVZ9ht6DXlCderPD5+x8eAFGQFrtyDhWnAQD4TPw3bgINoK0DMAD0gV50stR5jvivb4FnGS2h5agsgzADtqr5HD14EzrUegLlahKCMD6uhj4su4EDmIOXPpfH/myXgObpRpxEP8WDPqF6hAq/gg+F77kBp2FYF44EQxq9teA4PPyBzwGPuBbg/vOhMbWZE7YNXPSC8gXMiGYn3qjMsd6b/4NQ2APeAD6ggExK4/tki2sIX8G+96B5WETeBwMVndCFRiAx8ClMBuUUn1o7ORMg1ORvzdTli87ba7eAWx9tj6CKvD5+gRWBCcit8Nq4P6OkJb3xMBTiHzmBoCBoxyllwbQ0fAN6Etam5LZN12QShu0/4ZFc2Xeg4dhYC7vxnfKZy9ROxK3wn3g5CytbcgMThdk0sn5eqTKvRdHpvLNOamXS1bCBS5LJ8dVQkfz9LFSArBd7wwO8rtA+qUgW2fNxZG+1MPBFYrBsxsUq/U5wNn4MWDf5oCHYXOoAoOsQUIZsF8Fg7RqBdfCumYK1KrUM6DvBh0KPCZbbQIFrTOFtrlJpixf1gByA/RJ7exH2snLUNgTZoGZwQFXPxIZvN+DaZKCArdbUs9AfDwk96gt6U/Bfnsv14BHYT5QfcEVkdd6C8wAxcogeVLmIAf2SZmyfNnzKHSSZbAYCHfAv8AV8ZWg9Oku8F68DkdAov4kfgYDeVpdyOwPh4OTErUO2P7RUAU+b+n724b8YDgW9gOvoTHVg5P9DV1zJ72I7ZewTC7vZgPwuvJpOwp3y+wwSI5Jld1IevZU3uT28CBsZCalO0n73lanzdjhc5WWz+y96YJmnK6YAOzAcE+F3ohKCsANZbGD2wBYEzqV6CRjaWe1XFsO/g6EF4KD5OVgEEkHOgcCA1chclC4Gzz+KPgLZoNidTYHOJlJZHD6HnolBbVse7LfAfUgcNXyBMwPWemt9bxG+/werAKFqisVh4MDgoNiWjuTOStdQNrzWLY4PAD2U/83BANdRyhG7ansBMl21VQwAk4yU4vGsX9WGAW2YTB/E/RjMkwDF4OrNtt9DX6Dk8FAPAEMXGktRMaAsg24qratA3LbdPC5hrK9QLUCz2e/V8ptP2dbrBccUmc58Uw8tJF1QC98F9Qc4LUsbCaPtqYsvbq1SlfwOhONIqHHaV1AZgjYts/70nAR3A41aV12+tylpb/3pwuacfp0rm3Jcrs+B5sXUvgA/QobQyVqJJ0eW4kdL/M+X0//XP2pm2EB2AEGwpnwFKwODswwYQAAQABJREFUiU4kkR1ckn3prS/Eh2BQSGSb54DB2BWQA8vKUJtmoYKrK8+9I0wAB/ViNCOVNwGff9urTg6qB4Arr/mrq5SnvBtlD8GmYOD4AE6BRPuTyK5sulM2Gq6GJSAtg8CgdEGB6Rmo9zLorUH8YJgKapMDvROmR1IVx5P+F3wNH4H3zet8Ao6FDrAarAX6m9WzFHgdV4LBx+fGMs+T1lxkxuUKXD0m6aSO9+LQJNMI2yM4x5qZ8/hFQx/OAn3pD9VpNna8CHqj2oGT2S3N5NSb7SRYDpz4HA7vQmvoBAZ7zzUYLKtJ3ocbYeNcpY5sHSuLfUdyh1fcpiwDsC+KA0FCcqMrzt1ch0ey9aEKldYBH96tck32YWtwc/BwFn8dOOg4kMwOe8F34IDioOAgtRnkC1QONntCWg4UHn8qzAge5wq5psGM3VM0NX86oOwMi08pKa8/vI5lU11qRfoqWCFXtjbb7PNrYBkKBhyDUFoGIv2uTb7no+A+eAac0EwF3i8H8kLlvXgdLgAnYQacyeB9vge2gCthNBQ6sL9C3eegC8wDF8HzcBaktRCZa3IF+jEgvZO0k6KzM2UNmV2exp3EOJlR08Df4LNuX2aG2qSHH8AYuBd2g6x6UuC9N3geA9NBXeW99t19CPyasi20FJVlAG5u5kcAbpg72plmHVwOgtXhDnAgvhb2gbnBgddgfAI4KLWBUXAu7As/QXZQXosyB9y0difzKTgYO2A4uO8PL8EuuTybWuXq+jA4HBattXb1FQxe58DV4KTACYJlO8JAMIgVqnuoOBN4HfrmID4ENoZEnusF2BxOAgfexAMDX6KOJP6ApZOCarbeC+/durn9XdjeD8vk8sVuVuOAN+Ay8BqcaPUDn4li5XP1A+hnorYkvocnwHZXhOPhZUiuYTBpn7O0vM9HpwuqSbeivCt4TtP10VYc/CE47twG20Oxas8B3pOZiz2whvrd2bcq9KqhTkvbdToX7JhQFnqQXjxTA6eURS+L70QE4OI9K/QIZ96HwHDYpICDRlDn4FS9GUlPgEVzZQ60C4FB/DhIBo23SRvcx8CbYOA1KPm8GoAc/A2CNcmfJdrOmmCQNwC5LVZJ8NqQA+eCHeAxsE+DYDew7UWgEF1FpSfBCcWW8Bt8AOlVMdkpq7u92LrCNcAp/XoAboUDwIBnf2rTnlQYkqlk4D83U1ZM1ufga1gbdoQqmAOKlQHibtBDA0Zv8Bl5Fgy8b8E34L0/H6x3CRwJ+vYa+Pz4rP0MU0NNmpad58FouAZehfqsKDl8yqfhxdjW5fo9vtQaTIM3wjDwXXFiEvrP71yUTQBegTuyCniz3oGDwAd+Z/Bhd2ZXiYoAXPtdm4oqO4ErukthKWgIuSKYNdOwz5fPnOWe/yK4CQyWY2E0OMg+CjOB/fwJnBBeB+poMIBVp07s+ADSA2Jn8vbHfcXoVCoPTB3g8fZneKrM4OmAV9vg7yH3g9dn4PT9ux289kIDOFWnfIHYlO2CZgqQ77b105qfzKh0QR3SAzjGe2H7s9fheA/pAN7/LUCvz4Oh8DnoycdggPSZfQ6cwOhZFfwJHjMaDKR6Wpsc285KVTqR9IVg++WomemUE7XtoUsBHVyNOj5f+qrawCRY10wL1+lcf5ME4HwPlwOcN2ZpGAanwWRwQN4bvOGhpnNgRk7dH1aG1qlutCO9GCyaKU9VqTXpPV4fHDzPhROgIV7Q72h3NkjLgf83eAcmwm6wCVTBnOAL8hK47ytYD+6HzcCVlroZahqMPOed8Ckk+oyEg3m2P8n+6rbTs+PJ1M55SD8IXkOiJ0gYlNMBP9mXb2uw6gpe2+VwDiwANWk6dh4DXvvu8DwYdArRw1TaFzqmKvuOW14f3cvBw+E0+FcdG/qF446E6+A98Lq2gTXAe3U+/AiOR+5zqxfu2we+h0Hge7ID1CS/YLwHepHocBK+UzU9T0ndxt7apzHg8zINvAfLQE3qw06901f1JxwC/cyEmsaBfAE46ckPJLommdx2EbZfZsoi23gO9OJU18Aq4KDyDnSCmcABe2fYH94CyxOZNoDNlxTk2S5FmYP9RmC7rioMbENAtYJN4UAYDOngT7Yo+alwJNiftuA5doE34Cpwf6KeJBxwLoWLQA+8Rp9Pn9+H4GdQa8K3U1L/+WNuNn3Ba3ffTuBA1A3U/ODKyoG92InGKxzjPUjkgO99SQe/9uSXB/fVJvs9CwyHQ+EmGADfQHXyHtiPX2FXOBVGwxJQiB6j0pXg8+LqUu/18jJYFPYCPZsRGkquyHzuvFdZeW2zwdfg/fYevgB6Yv98Jg1CY8FnZE64DarA49RXUJOH1vkb9DCrjhS4r9x0Hx0aBmeD78SCcATUdJ+8PicoaXUik++603Ui3UQOODi9C4/ASHCg+wgKfbmpWlbyGnxRK1W+LA4GDvKJfOnOgS9gSFLI9ijwxXSAWhLGgy/r03AmWJ7V6hQcnS0kf3eu7Ea2F8LacDEYLB0866pVOdBn6gE4Axw8FgP7mchrte8ToBe8CF7PPeBg4groDlgIDOB/QtInB+tb4Vh4Dz6GAXA+6OO+8AJYfgD4nI+DQtWOivqpLwbxyfAMXArzgf19E4ZAPnkPDOD253TYCCbCctANbOdOqElbs9Nzp7UimcvSBQWke1JnLfDcyr64et8W9gb9sk+llh7pn/3Vu9NAXwrR8VT6HA6HseBztD7cAj5PQ0H5bLw2JVXzH74XvjOJHC+eTTJltr0pT3+85mXzlCdFPUj4HjmuK733vnY308Ll++e4UnaajR7tAiNgV5gTKlW+UL6olaqV6bgDVFaTKLgiW0j+OlgafMkchJSDWxUMhqzmpeB2mCm1YwXSj8MW4Mub1lFkDkwX5NKt2Tr43QUTwQG2HRQij70cHFzV2nAfXG0GrQZfwYdwKxg4D4az4FiYGdQi4HW7Oloc3gIHZp9ltSd8C5eAASuRA7CBqFA5mDkpuBJcfXrO48D7YdsbQnUaxY5x0BvWA4+1voHX53QItIGatDs7t8tU8Jr1vK6aiwO/A9/9RE5mrkoyebYdKfNeeA8GQSuoTT5nXrMTMeUxk2FnMwXKCYj37GPwOVkJLgbb3Qqc4BiYF4ba5MTN8/ucjIFzYFpI1InE/jAMNk0KG2HbmnP0gJ6Q+Ho/6WkgrUfJLJguyJNegrJnYXyOlfPUaYlFp3PRS5bLhTvg9oHD4I08nEVZJWoknXZgq1QZTM/PdL4t+ZdgFMwKzoJvgztgEhhodoO0HGCvSRek0huRTgavPUg7eM0Dh0I2MPmyJ6svB2Bf5mXAYOQA4cDhgLEP+IAXKtt6HW4Gg/hb0A0SbUfCwaZLUpBna2DaPle+DluDg/XT1/05+fQAS3bKJNOBuxC1o9LbYLDfEvTcfn0InaEmrcJO66ald+n+pfdVlzbgOJjal0Te8xFJpg7bPhxzXJ7jnJzlk+f+Eg4En4Fb4QHw/tekjdnpMWnNSmZcqsC2nSw6Fg2CqSCfLN8JhoP3ejnYF3wO5oFi5CJD0pqGjNdkm2vAkzAKGlq+C04onGQ4KXgNLBsEV0NX8FnzPbFeqG4OOD41SQBuk6e/u1L2HrwN90JW32QLIt8oDjzDWXYGB9gLwMHJgHstzAGfwi5wNDhYGiCnht8hLYN2dXL274PoQPorbAVfwLfgBOBuSORA/QPMB/bjNTCIGBSugw3hJjgbHCx6wptQm2xzAfCa7LsB7h3YB2YE+7cWfAfV6Td2TJ/b+TXbpcC+/5Ers03TG0AS9HwXDAgDoRDNRKWnwWvuDx73PTgYen82g78gnzz2BXDQdHB/EK4Ay4vRI1SWd+FwmB96wTZQV3mvvadOnv7ONWK/LMungyg0QDiIqYdhJGwJY6Em/Tuz00Dqc61aw+NwB0wE/d0DVgGfi7Rs57J0AWmPrYs+yXPQ8ZTZfnI99+fy67C9ExpC+v8M+C4dmTvBiWxHgGOA78kpYD3f20sg1MwcmIvraQvOupz9OWutVDkoJC9QpV5DOzp+Kzgg3QMOSL6A+8BbYJn7XQkYpHwp74XFQXn8aHBwLEYGCQOLL35P2A4cnOfIbfuyddB3EvATvADuty8Oqg5eC0Nd1Z0DbX8j6FBAI7NQx+teOVf3XLY/w2BYE/SvD9jHQ2BdSHwjWZB8J+4DB8lOuSO8Vq/9Klg1V5Zv44D+ASwEXpv++HXDYFMXrcBBu4L31XtcXw2nAftj0F0MboMNIZ/01glTWj57B6cL8qRnpsyVZP/cPp/js8BnWe0OV0xJ/fOHAWfnf7KNlrIfc2bOthl5PW8odaPha/M07rM1b57yKKq7A04el6z74Q1z5Io062A6N1wMDjQvwU5QiWoOAVjfHWCHwCg4B2YHg0h2gHQ1ZJ0e8D74Mj8Au0Bd1IaDhsKFcDIYfA1iI8CJ2lfg6ulyeAMcuD+Ei+B7KEVgoJmC5XPrAO/E4V7wJdMvB/EFQE0H+8JhMACKlRORf4HtGUirQD+GgYE+n2ag8C04Er4B6/lOfQHLQ7loDzoyBrzfq9XQKYN1Oii2Jf8QGLSS4Eoyr3pS+hn4nHqfDoREJ5FYJsnktk40jsuUNUb2bE6SXXyMo8wgXCrNQkMG3fa5BruwvSqXTm9uI+OzHSqdA2UZgJ0Bbw2+UA6sfrZbAnxRKlHNIQC3wvhJ4LU4eK0Bk8EB8FbwXiVyoNorlzH4zQMO/qWUA6J9caCcCGvDbPA7XADvwNtgWTGyv4eDg9y5MA2Uq26mY4/BaNgeesHf0BnyycFzbG6HwfdoOAguhd5QaZqZDv8BQ8Hn4GVwUrIlvABOemqSAcdnec5MJSeVyWo42WVQTgfppLzQree5Brxnr0OhAdQVp/d0J3Bi6TP/CPg+lkK704iTxKvhQ3BCp/wi4FesRJeR8D1rznIyfwZcCxfDTNDQKssAPIardoB39vtGzoHl2F6XS1faxpcmGfgqre9JfzckcWOSyW37sB0Fw8CAtwsYtHxZG1ptOMEVsD+8CE4EHDSOgf7wOZwJhWhlKiUDjgO4A9Ek+B7+DQ54U0O5qRMd+hiccBg0JoATk+pkwLkB0nUM2t9AqSdINNko6sBZjoQH4SZoB4kMeOsmmSK2+votHANO/veFLyHd9oLkDfrTQm1yIP8bktW8Xt8Gq0N1Wpgdq4KTA/vjBOA82BPaQim0NY34ribXtR/p3+AL+AXeh/HgZNRn7Ag4DuriKYeVtTrSO+/R5uCkfT1wDCh2As8hRaksA/A2XMJL8CocDgvBa+AsvxI1kk6PrcSOp/o8iLSkNSMZBz21HGwL60CpZuc0VaMcfP3k+Bb48jhzddB6NJd3RlubHEzugqXBIGzAfQpOgNZwB7wO58NUUB8twcEOZteDbSYDMsk6a36OvBq8DzsU0Irvkl7tkav/PNtVoJSyH3rqvbkOkgGeZIPJQNIz07qD6MGZskKz7alowPGZcIU9PSifCYOhz5oB8VfoBTXJyeFemQqLkL8wU5ZknVDY/vHwNWwEDSHHpG65hruy9bm4BRx/fU4+h8NgMZgIg2B1+AyGQXOS17N35oJ2JH9UpqzU2bIMwF7kANgCHPR6QD+oVDWHAOwq0UHBASiR9+fSJNPE24Gc/50c97DtXWB/3qTeLLm6Bgv5EU4BByGD09twESwKdZWTAQc4g7CaA+6DpczUUQtzXBVsAN4f+30Q1KZZqaBfO0H32ioXud9BbDQYsFypDQG98z1uSJ1J4/qQ1slkdk8XlCB9Dm0cnWqnD2mD04ypsmzS4Lt1pnA+8ldmyswOBt+pVmaQ9+ppMAhmtSQF+m2gmDa7s4C8E7d5cvWuYDsO9GvbXJnv98swHrxO5ftvX5xgLQ/NRadyIStkLmZF8iMyZaXOlm0ATi60M4k2SaZCt80hAGu9M/L3YEM4EO6FjlDJcsafyPQv8BEcAgbMm+FjOB+WhurUgx0rQddqKhgYt8rsM7gb6BPNTuJIMHBk6yZ10ttHybiSSuR74iBqYG4qPcOJO2ROrnfZwS1Tpd5ZJxKTYTVwzNgXvoJ2UF/NSQMGqEfgd3gCnMAkOozEOkkmz9b78RR4f1UrMMg6AcpqFAXzZwq3JL9Ppmx78g+BwfJw8Fn1uouR1+A1TQU3wBiwnblA6eVbcDe0hengIvB+vgp6YeCaBSpd+uk9Scv3y/KGVJMFYG96dXKfA9ELcB+sDg6OzgZDTeeA92RX6AW/wGbwA1SyfqPzBk4HFwOsQcyBzPTZsD64AtgZXoJ82otCg+aa8BDsAVkZCL7PFP5Mvn2ubBa218PHcAcMheyAQNF/yfbSffqTvAEwGUD/q3IuMy9bV4sO9CfC1FAXLcpBTiCyg/63lPlspPU1mWxQTu/Pl16IQt/7RfLtzFPm149NYAc4A2aG7mCQqI+m5WDvyTdgQJ8Mn8Ng8B4pn50/pqTy/+Eq8lh4DfT8OngPLoOsfCayXs1A2a+pij1JuxL32RwDtrkNHAPFyEnFw/AcTA9bwEbg9TpJ8Pk3CH0By8Bj8CMon9cbYHlw//xQydJHv2IYb7aFC+FduApanHbnih+EHvAitIXz4AioRI2k02MrsePNqM/tuJatweDYP3VdDvTO+g+G+8EAlszuvyT9NtwDc4DB6jC4DVwZO/isBx5v+2oaeAL6mElpBdJPggOdag1/wYZm0GhYy0RKo0ivkcpnk9dQsHSqsA1p+75wqiyd7ETGvm4KBuKBcC90hGJ0DJU9twP/d5Dut4P2IZBoCRKec+akoIDtftS5Hg6H1+BoaCptwImPgdfBAXpX8L4YfGQoGHyzQZOi/1FnSlaE6u6PB/SBO2EmM6gfZP2zT4e6MyPHzLqoCwctBueAz2QVvAtOJpWTLfvg8+vEw4nIKFAXwY5wuZlmoHW4Bq/RbWPodE6yZGOcqJhzOBvRgGngxdyBPdk+kEs31MYBzJes1GrpAXgpDF0ZGsLbQu6Vwe5GMGBsCT5TZ0Ki2UgcCK/AKrlCjzHAfgxdwRWBA5AvzMywABi8xoH10jJoHpMuyKUHsf0EjgMH730gkflk0E3KBpLYIcnk2SbBbVP29QEnEMPy1EuKTibh4JLWQWSGpAtqSe/LfifD+qHmBH3RD2WQ9xqTCbNBoTcUqrWp+Au0zR3gO2kb6+XyDb3xerqB919tDl6zz4/yukfAd/Aa3AEG1kRev8HxFNgbknZIFqwNqWnAvwXGQi9Iy2f0snQB6S7wRKasLtllOcjr3SJz8ILkvd47YRQk9/960u67C0LFO+B4UnYB2IHiJEgHYAeKq6HUakeDJ8KH8G/4G36Cl2BHKIVG0ogvUkuTg+dZcCk4aP0FzrTrqo4caHsGPgf9YyEZCEhWq6HsOSC112OuhbVSZSa3Au//cuDK8mXYHZR1r5uS+ucPJ4UOSqv/UzQltTF/HpYpS7LzkFgBPDatc8msny4gPQHWzJRlswYMfTgZagtS51NnUUjL9ov5suTKt2u6AdLbQ+KTu7zverIOzArFyHexb+YAJ28+Pw0pA75jjhMh7/MHMAPMAY+CAcZ+qBPgEvgG3J/IseRbMIAtBT73r0B7KLXOpkG9mhscwO+DPtCQOp7Gz4OrwHfoHHgCVoTbIVS8A2UZgGfnOnwBnoIvwRfgU1gYSq2LaHA8GBimAx+sTuAg+TjsAYVoIypdUA3PUX5/IY00szpXcD2HgAPTfDAADCrFDsocMmUl8Sbb48wgB3kHob3N1KJR7O+eqeP9OjBTZtbg62B/GqwJibYg4cCaloP2Y+C9NbCqxeFvSPKWFaIuVPK4XWB5GA3XQik1hMZOyjT4AvmLYTD43Ncmg8qCmUqu5AdmyrLZ9hT0ACcMNelodm6YqeCE5thMWamz19Cgz2qig0mMAZ9dg+lv4P3xft8Cz8B6kNZhZIanC3J572lDyP6OA++JQbCh1ZYT6NP78BM4UdkBHoK5obHVjxOOhVvhFGgNlaayDMAdcNFguBP44q0GDWXuu7TdGfLJgfCefDvylBlUFqiG0ZTfAC1NVVxwT/BFvQqehkmwPhSrZTng8sxBDgjO/GuTgXqdVKVpSN8GrmJ6pcpNep4bwRWPg+38oNxaNpOZnLZi6wC4MvjF5KZc3tVzXWQA9Hk/A1xVTgWlVHsaewquBgPFp2Bg8bqOAgNMN6hJa7LTAJQE67VIe9yMUJ1s02fgSvAZOB1aQT4ZpJ8F75lBcHWw/WInNBxSlCZQO9sn78OSuVa8vrVhGOwNi0BW1l8sU9iP/JGZsuaQ3ZSLOAV8bpoi+BoTfG4XglnAyY/vc6nfGZpsUPkuJM9Yg56omMZHUnloMQfUo64D8dbVHH885Q5W9ZXX40yt0rQCHT4c9ofqJik1XdMd7HTwXD9V6WPSZ6byhSb7UPGkPJWr8pRlixzUJ4Mv7fxgsPwBhoD9M6ioecH8LjAAnIBNhG6gDLh/wK4wDMbDdNDUWpQOGPQ7FNARBygHzlfgZ/DZfxnehE/gAahNW1LhNfDd8f1I/CH5P5qeEj01UKtW4MRGD/PJ8ufB9p8Dj9X3hpbXYt/SOpfMUumCWtIHsv+gTB0nnjtmyiJbfwcm00R2UnY+ZWvUv+lGbaEsA/B+WDAGWjeCFb05x6vwBFwDF8G14CDgwNQF6qtKDMADuWg9WRe2AwfC7OyeohrlisAVV/tcrb3Yfgfjc/liNg7kBvR+qYN2I313Kp9OzkCmP6wK7aAzjIJPwPtre2oOuAcMvk4MHPRdsQwDr3kPcBKSyGA3GLaAQgJeclxN26nZ6fn/X3tnAW9FtfbhqygootgdYLeA7VXB1msndte1uwO7uxtRMVGxscBusTuwu0XF/J4H99w7d759Dnufs/fZe/Z5/7/fw8xaU2v9Z2a9a63ZQFKmpva1DpJoXFZ8ti4HR4yjoDuMSd6TtcEAYx1ng/PBvM/BYF4prcaJ0v553knhRlcysuzenwlS+Quzfm0qXa3Vozmx738in63vwWenVNkZ+wRsVJeE40Cv8zYqo8itku+end6urTpL8wcPY3M2PtgBWqf5w+pua80C8DjNWPEz23xxfQE+gD9A2VDuNXqtcn8M51Q9YXHoBjZwX8B58CDYQLU3GQyOhznAe6AcIR0Mjn5K1T3saONuI+R9lPnBxr5cWY7d4C3woTX4GYDWgqxmIcP79wjYCDi6mRm2Au/phpDIBvMB8BnYFk4AG2P1BBwInifRi6xIpdSDEx0Gjkb9jmfgvwLSMkAfA9NDJ5gOesOl8BTsCcoOit7aOfgOmtKPbDAorA93wiIwAjz3z7AGWJ5+MBZYRoPo9dAS2THIqlhgs14XwMjUztbP+1xtHckFBsMdYCdEf2aGX6FU6avvzk6wHLwD3tM/oTVypKdfIyBpC1ktSxOx96rgs+Tz/AZUQ76PvkcfwiqwM9wGldbznNDr+LyoSeEI6Gki1DoHZuLwxYowW+tOW7OjHaUMrNnVy7+wjYYPc1Y3ZTPGkJ6E7Q/BSqn9jmPdxq6lmpADHV04SutQ5CQTkGeDv3xqm43AxYX0XSynSm1z9UYwaFnWuyE5r42eDZ4vejVkwLGDZ4BTNpI2XCuYSMkOgsE20dmsnAPmJ2VNtnnflksSTSyvIf91sPM5FD6G3+Fe2B/OAsv1T1B2Ym6BZU2UKRtGG/z0M3AS6YOKnGdO8q6HdJ2mJ/1ykX2rlWUAtZ0Zp1oXKPG8i7PfAXAP+HzafniffKfK1RQcYMdiX9gevLfp94NkRbQgZ/Hc3nM1DbwLvUxUWF7jB7DD7Ps5DPqCcpvenQwHQzeoV9VsBFyqIb6M45e6c53ul7cAXKwh9GV6tQX+Oor4Gi6Dm+EQGBuqJRsBg1NWSefBHvpgmAkMLO77IBiU7akfA6+AAcJg8DZ0g2poB066RebEBuMzU3n6XmwEoZ9DwRFNWqeTWDKdUWT9KfJ+ggHwDXwBNu57wF9wKmwHac1Popiv6X2aWp+VDV+CZbYuNo5jQTHtQ+bP4H3sDXYKxlQfdmkoGSR9Jh8Dg6/3pBvsCVdBRyhHD7NzugNkx8+A7LKSOoKT9cmccEXSBsFqqBMn3Qi2gXkLF/B9eAD2hzthBNj+3Arl+sYhVVfNAvA4Rao2N3mHwsbgQ7cBXAy+rDeADcR3EKquA69x+idhJCwOE8BhsAuUq3c4YFqYGX6E96Elmo+DuoMvkw2K8rlYCDrDi+C2UdAF0rITN0Mh42aWv8D54DPmuWzYfoWHYDLYGiYHG61HYARUQ74DliUty5FuKKzj9+kdCus2NMPgODBoOVLfFTaD/aA5fcvGSWANMNitCW9AX9gK9C97zWy52KVkvcWe00M38Jn6AJrSyWx4GzYHy7YvDIf2otmp6JEwC9wC68K/wPfP5/I0mAfK8cRnbAgk+ogV32/P43ql5PskaWXT6W2tXfddvzpzEoP9pXAe2F657jNlJ+4g6AehIg7Y0Pji7QZjg42+jcA6YKN4AewFeZQvzcAcFnw9ynw2nAQL17D8u3PtQWCgNGAOAIOUZbsQnIbyRZ8flL1KG24DnLMnp8PxMCb5DB4O98Kt4PNm8K6W5uTEL8OMhQt4LUc4PvNpWU/rlOgIVgxq7n8iPAWD4SKYFMaky9jBzkUir/8JLFvImJflszAtLAdrwQ2wKYSq68AqnD7pQN3Ous+kuufvxeiOY6/CeqkLj7UNTetuEoumMyqwvjjnsA1PnsEpWPe9XADaSsaJtcF3PtFqrOwNjvrrTb7X5d7PqtThUM76OVxc4BaW36XS17FuI2FvJm/KawCuB58NCiOhU6owPguPgi9Vop6sDAVfegPvlTAMfOl2hqQhY7WutCKl+RJsMAz6dkCz6kLG83AtXAIG2omgpZqFA20Yt4dV4T5IvJyA9e3AIP0rPAMvgPungzbJ0ZqeP2corMei9Q4YFO1gqiPBZ707PA0+G1/BuFCO1mXnm2BWmBKuANvXamhDTvoenAtew+erLaVnx0I6TlxPemN4AOpNdROAJ8EZe/WdYWzw5u1cWDfdF+4spFnkShGAW367DuLQ7Es8H3kfQ8fMaQ8ssm9ml7pMTkqp5odkJNxUIQ2c0qGpHcrI95p7wcGwbOE4p7V9x/R8CHwOA2EOmBaGgddXE4Ajczs6g+BJ8N2tR9kpm6pOCuYsgp0oA9RiTZTJGZ2zwOf8UbCD5n3pD963lmgFDjIIDwbvu53Uaskyzg4TV+sCzZzXazvl/iLY7p4KBl7bBjuVzakHG6+Gu+FhmBuqrboJwFb0OHgb7O29Cl1BnQnfgNMzeZQPgg1ZqHwH9uCQbTOHrUj6HciOAs8gz4Ym1DIHbAy2LBx6L8suYIO0fCHPEdjGhXXfUUftiQwYvqfNyfPZINrAPQH9YCyoluxQnAyOgAw8NqrjQ62kv/pqh2ZleBf08XboDmltQ+JSMFDb0ekPR4Me1rM6UjjrN30NC+l97wffwVNwI+jdONCU7GA6y7NQYYd5WQ6DWQvpai3qKgBbyelgdUg3rpuRnh/yqgjALb9zjlzuh95gY90TfFH2AkcSvmzKQP0l2AC0R9nzXxqSBqQlHhhspy4caKPlyHgQOBpeDezgrAkzwg2Q1ZVkuK2YnMV6BY4vbByXpUF7l0K6GouhnPTS1Il9XtKdhtSmqq/OyRWeB33oCj7Ddmbug0XhIcgGYfc1gOjZAqBXHjcNNCXfkc3hMrgAFoS2kvf+WugPdrDsPHSAWslrd4Pm/ErK1o+V9ZJEYemzfngmr9LJmgXgYr2RB6ndxM3U0J7zPs1sj02N58BnVMkOmA2p06Xfgg2WU557w+PgPm/D7PArtAfZWK8Cdjjehy3gdZgLxoNVoTkv7ODqo3oMfgS9taH/FHwXr4VXwVHbzfAz+P7ZoLl/Vp2yGan0wqw/DQcU8n5jaSfK0d/ZhbxKLqblZJZx69RJHVVeATPBe6n8tlidhIvcAX/CTgUGslwPnoBTYTs4CBJtxMqbkHhmAP8G9oT9oJichXDUtjtMAMfA+XALVFNdOLmeOni6rXChoSytqx2tWugPLjqixAt3Zj+f+xVheRgFb4EeNqSKBeB9qKk946b0ZVMbIr+hHfiI2q1UpIb2HqW9aQ0qfDq8CCPgNPg32NCqS2BfsPEtphnIvBzuAYP13eDoxXP2B4PE9uDoqzeos6EbzAIG+p/gZPCdVQaQ2cDOQDF5He9jWgZhOxDVkO2LHZC5wcAwEtS4MPbotbb9YwSXs3M0FdioPwl2duYA9QEsO3rtv384q2EnKK1HSSyTzkitz8v6P8FR71+F/K1Y2umodgD2uv0gCb6sjg7GV7GsVQC2DKXqYXa8CAy8dho6wf1wBIQKDoyfUydsIAfmtOxR7PpyYD6KY/Az4C4FP4ANxWCYApTviUG1mAx4dmTXSm1cm/VrwG1TwrlgIDW4GjgTHcpKEpC7sO6IzAAxAAzQzY0WJmS7jXMfSLQ1K5a9GnKUaOB9FqxvTzgSHNWMBbXQilzUwHgSPA33wexgEP4KHgM7lInn3pcLIK0tSViPYvLeHF1kw61F8iqdtTInTI/ePb/PyBBXcqDpKOM34IzP/nAdHAV3QnPPNZtbJe93r1adoQoHT845bwBf8JfgFXgHBkIeFQG4/LvmC+0zYCAxIDhyCf09Xem0baJVWHkZHOUkL/IkrDcV2OZk2wDIyrzZUpk2QAatRI4I3ob0Pm6bsUApQW1W9jUAnQBnwKVgZ6ElcvToqMu6ZrUtGc4GzAAG4YfgS3A0NhHUUvNy8V3B5/pF2Bc+B+/p1LAV3AGdQU8HwVBYFvYEPwd4L4rJIDIYbD8TLcjK8CRRxaW+6nO6Y3ccaYNYHuTzZDCcD1YqLFmMfq+mdaVK8prJe1ulS5R/2mM4xIdwF7gL1oPnwIc3j4oAXN5dM6jcBjNDF9gd+kMHaO9y1OSoIgk8BrAX4EswGDp6ugR2gGIyKN1cZMPj5NmAJ7JRMFiuC8vAg2AAaK0m5ASO1BaF5u6n21aFjWB+SGT+iXAFnAy/wWbgOScFZUfd0ab1tIF7FOzIbw71pMUpjPdy70yhDiZtJyKRHhwOe0CxDkRP8q3neWDnxvvWF7YDnxffo7bQTFzEzoT35lawHmNDHjQFhfR5mThV2OlZfwNa2klMnarJ1boMwNdQ3OXAxvfFQtF9yC4rrOdtEQG4vDv2CLung4FHnwE2qu1dBpeDYDAsCPbcf4ZPwFGVDW668Sb5/3QYOUeBU2u+Y8Mg+X7M6n9kA2QjejSs8J/cv1dsWKeByTL5lUh24CT9wXu+C3wFSYfCd+kIUOOCfnwKZ4GBZz64trBum5HoWVauShJ1tBxAWbIBcm3y9i6xjEuwn/d8afBZ+AIuhN1ge3BU3ZbqyMVmh+z725ZlaOm19N1nyBHwauBsw5JQTdVlALZQTsWo18GX3N6VD1oeFQG4vLt2P7vbCKdlg7ROOqMK6zYem8KusAFMCPWm+SiQjcSBcDW8B9dDOTJ4Hg8G7FvBxjrrN1lNypHmRXADPAYG73KOZ/dmdRRb90/tYUfBsi5cWHYtbLuSpc/FnuAsmaP2oXAOfAIGaLUY6JkNar1pHwpkByfRWKy8AQaBUvQ8O3VL7ei99bnomcqL1dIdWIhd7XR6X+Yu/bAW72ms87ltc43TzBUvYZsv9ltwC9wGNo7lNjQcEsqhA8Mps43qyYWy24t3as2edbXk8+jI6TNwJDEX2GgbnK6AetGLFMSR547wAVi+wVCO/mTnA8o5ILWvQc1OsQHcxkMdB3vBSSYqoHk4x86p84xk3Xtg4/gDjAffwQywNRwCthXPgoHatuIFeB6ehgngn+DIv950NgV6EqzLnbAWeD+HQCn6mJ1GwIrgqNPn12dkMqiV9Hsi+AZ+qWAh5uRcvpfeezvp1ZDPi7R72eg6L2/DuAtsCr5YedRpFHpgHgteozJ35bqfwlngvXdUswZUUzbO9nwNuouBI5Eb4GFYCUJ/O7AIC0eYaXUgYfColC7jRAtmTnYC6c1gYxgA44O/E9kWfi+kWYz+17hWY2kQWxtmBZ+ni2E7qEc5at0QdoRlyyygHRO9t376cx/8DLNDLbQmF7U8V8Jb4PNSCXnfbwc7ei7toCQzHKzmVjUbAefWsRYUPAJw+ab5ctmAbgKzlX942UfYkO0LNuiJvP7xkA04yfZiS3v+Z4INoSMyz1lMBvgtwIaqPziCyYMcSTriTcsA8kA6o5XrNtp2upwCHA8cDX8HSQd8d9ZfgefA6dqZQO0BX4D7TQUvg7Nn98L20Ijak0r9BVuDHY9H4GHw2Wpr9eCClmWKwoW7s/Q9aO3724tzeF7frUQXsuJ7eTXcCnbQ7AjmTTULwOM049TEbDsX5oPkpXN3e1a+ZKHGd+A3qnhTG1bze641I3yUuuYyhXTSoKQ2FV21AXgBLoHloBM4ijeoO0JJy07ZvOA2n/FjYXIYCPWs5yjcfrAeOEOgDIgGvkrJKdm94Qz4E14EG/NfQZkvah8YDJ/BOzAHuJ/peaDRZUdjdTDIue698d1xRN3W6ssF7aAlz8K7rBskV4XToaVyRsoOhe9oIjtVPgO+o1+C79H5sAP4zITG4EBzAdiHqCvsBj+mzvN1aj1Ww4FKOmADcS2sAk6d+WKvA/bg74FxoRcYVIfDD5DVEmTcDUcVNoxiaXC6GQzAE8Oe4MiuN9hofgdqK7gJ6j0Aj6SMO8GbsAI4QvW93BgqqWc5mecfk05mB2mvMigZeE9LGbAd68Wez9QuVVn1WTD4p+U7YAezNfqJg6fMnOBA0o/Ba4V8Z6rOBd9b39nQGBxoLgBPx7GaOXQM54jN4UClHDCg2FO/FB6Cp8Eg4OjLAOqo9nP4Ax6AmeB9SMsG6ON0Buu/QGcwcH8A+8Mz4PN/B6wPHmODme7hk6xbfUTJJoU5wPq9DqHaOHARlx0En8ITsBRcCHb2KinfjZ5gB8zzu8zKUant9j/hV5gMbgM7rq3RjRxs59jn7HboDr5/jnrTcgZkonRGrLfMgTU57BqYsmWH191R9k7rfWRTd6bVsECTc+0FYZZCGZ5iuUFh3YWj5OthAhMpGZTuhkVSeTuyfgvsDkcX8j3/YNgNjink9WBZyUBmJ9b3aCXoAKHGdcDnzhkW28wzYUaopE7iZDeBz/1+8Bc46i6mvch8G5yV8BlfDSoh63grDAPPa6fj35DI7T/DbElGZmmHZHoYP5Nf6+QpFKBXrQuRvb7Gfg+ONhyZOM0gp0MeFQE4j3ftv2U2qGblPU0H2mT7PKzYQB0L54AjFEe/h8FykGhJVtzvHtihsJyVZSX0T05yPxwAXv9dmBBC4UC5DvicvgT+0C7Rpqz4/DclOwB2KJsK0k0dV06+AfdbOBGcch8K60MxbUjmXXA5+C4sCvWimgVgp+CaktMWTgFm9XU2I9LhQBs48CfXcBRphzCRjcxvSSK1fJl1p97mB3vkPsceZwdyI7gP1MNwJ9iI/AQ2ap9BazUtJ/DcjgTeKpzsEJaHwb6FdJ4XdlKOgnFhXjgZLob2JKeC/dY5ChyZ+gmjWpqJExskfAcSORJdN0kUWfppJvt5pshurcoyFkwJlsMO7q5gRyGrxci4GrqCgzrfS8u/LLwNoZQDT7DeGw6EN4pwBnl5lL3FmILO4537u8x7sbCht9EfC84Cn9Vy5HFOET4Aa4PnexTSIwuSrdZqnOHgzFm89rBMXqlJRxobwCYwQ6kHVWk/y/IXLF84v58AroM1Cun2sFiLSj4PLjcD/egB1dIqnPi8zMkXJ23gz4PsPCyUKajP8y6ZvFol62oEvD0ujAB7JsWm/b4hPxQOtLUDp3PBo8Hg+SMMhz6gxocDYAkwQJ8NN0BWNpQbgsFiengG9of0yIJkq+VoeorMWSYmnf1endll9C+ODbBfwi2FjY7yL4Ah8Cs4qrExs+y1kMFgD7i3cPGRLO2sS1LmwqaaLTpyZWcbHGk5MnOWZB3Qv9ZqKk7g/ZgP/EGgegUOheZGpO7XUjlLY7s8DHaG7uDzbocsDxqHQnoP0vJemB9KOeBI4LJC2u9ok6e25X01RsDl3cEF2P0EcMbD0WK9ypGlwehkMBBPDY7I1odayany/vBvsJExGDvy3hSakiOcAeA+BrK7wfO8DotCorlZGQyTJBltvNyK622TuaadGetXL7qNgvgMJNqNlVOSRCuXC3P8iUXO4RRrtWUQNvhbl1mrfbEKnn91zvUs2DlW04GdYWNMPUg/e9VDQTToB7CHfT7YiNnjS9OVdB4VAbj0u7YEu94Hy4INzs+wH9Sj+lCobOM/KXkGsFrKkZflegjugL7QlBydvZDZeA5pG9wbM/kmnQ1YtEh+W2TNwkVsTGcrXMwO0HmwayFd68U0FGBIkUJcRZ6zCa3VzJzgZrBzlMjn7c0kUSfLRSjHkXAcLF0HZdqbMrwFPru3wPJQL6qbAKwhx8AX4JSBDe/IDFeQzqMiAJd+155n126p3W1sHFE4+krUk5XlYKYko0bLVbnuIZlrGxQeyuTVc7Ifhcs2SD3IOxvuhfEgrcdIzJXOaOP13lzvezgVnOo/GOpFPo8G26zMq9SocU/O9Sc4S+Rg5XZYG+pFjjg/gj5gZ9rR5iZQa01HAeYDOyz1pLoKwIkxfkNZMkkUWfojmM5F8us1KwJw6XfGHmpWPg82vOoosEEz72vwu2qt5EvtaLdbqgAbs+40ZF7k6MDp6rS2JOHoZWsYADOC0+uOpi+GWmsyCuAoa/ZaFyRzfTuL+rNvKn8v1j8Ft1VKa3Gic8HGe6lKnbQC55mQc7wLzgQksp2+FXx+Qv/fgZoF4HH+f1n+k+P0RXOamY2VfKCbu1Zsa1sHfuJyC8NThcv6Am8LBl2nRW10H4VfoBdcDa/DcGhr2dM/Gmx0DGQGBstXy04Bly9Ll7H3IPgMHgKnl81zpPAN/Ah+4x4b7BxdCLXWVxRA6lF2vi6FBUFP9dEZgz+gUnIaWupNE1OgB+CTVMF8n9+CKcGOSKgBHHDaZcIc1SNGwKXfrPnZ9S9w2mpFMNjuBGoYOEW9AewC7rcz7A61lCPE9WAV6FTLgrTw2l057gKwM3MOdIdQeQ6My+4G3jNhP/gdfDY7QnvRBFTUjsFMqQpPwvqHMEUqL1b/68AprPb6bzIfaxGA83GfWlpKp6v2hcOhd+EkBjl/F7BMIe1iZXgCdjQRCgdq6IA/BtsjdX2fYUeDs6Xy2sOq76cd49XBDvQrYIc5VNyBmgXg5qagixc1cvPkgN9HnX77AFx3Su4nGAy/QXNyquqkzA6zkB4IB4AvtdN7ahFYafRa2/0xPpfaCqyfvfvLwUYnL3JUshZYj0fAWYVQ6xyYlcP3S53CZ/hacEr/zVT+8qz7LowER8y+E42koVRmdlgX/Ezoe/IEhOrMAb8phRrTAUekfivcHt6Fq8Bvuf5gxIDllGdzMkBsBtvBnIUdv2XpFJc9xnvhDvDl9vuv29pK43ChV8AyPgyrgEHM/LaSDduasAX8s8yLOjK7BrwH38NzsDaEWueAz+BkmVPMQdpAm+hQVvaA10D//b4+JTSa7HAcD8dABN9Gu7vUJ6ag6/emOhq9DmxYNoVfwSA5D6i9IDu6Hb2h8Mc0LO+CPWFrcGS5HKij4FyYDXqB+60MbSnLf2bmgieSPhvuATsHBuQpoBoai5NeAaeD/rwNR0CpGsaOy6d2npR1fzjUPZUXq+U7YEfsFpgW7CD5nHwO44JaBN6BJG3e5uB9DLVfB2o2Bd0ay7txcJ5G0KdRXqdP24POoJIbgJ2kQfAivAwGVNURDFJNyR7zMqmNBuQhsCRMAo6qLwcDsXltLf/+aY/MRfcnbR0NZspOiPWfyESFtQ/nOzh1TkfeN0PSSUltKrp6X5Hcg8hr645MkWLkPms1avAoOA3rczIxJNqIFZ/dtJwVsvMTar8O1CwANzdlZwOzbeaeOBL6CZy+2R3+hFB1HFiM064Ho8DR3XdQqjqx47VgkDJ4OjJw9GuAsCNiXheYCxy9OUJO61sSNmCJFmalG+wLC4Ajiy2gVvqaCy8Bz6UKYMNqZ8O6/QFDYH6w7npRSXneI1In/J31AaDfxYJratfRq7/x53TwUWqD9ybteU/Sy8IouBE+htCYHTCYNhVQv2Hb0plTzEk6TwOJTPEjmWcHmnvwhlGxX+A4WKuwtOE4FYZDUw85m0KtdGA3jr8DZgYbZgOiwbJUfcCONjZOt90DM4HncXRoILahHwkHwKcwJaTlc+FIV/WCwfAFHAULgdPXi0CtdDYXtgyHgh0CR4/Tg2X1r0bZqdgErG9XqLR+5ITdMyfVFz0tRWey08Vg2b03A8H79RgofzxzGXj/vgcDtfvWk+alMGdBf9gR8iA7ZU4/Hw1zgO+E92IPCIUDdeXAhZRmo0yJNiftA6uegqlGr+Xjj9Mopg1dvcuG7U/YMFXQS1l/BTqm8ppa9Uco3rtX4ROwIb+5wFcsR8D5kGhLVhxhjZ9ksDQAeIyNlKNvA/bVkGhZVgx+tVQnLn4InAwDwOfxCrCBdXrcgPYXODqdFRLZ6K4HrQlodoYegcXAjsoOYFAeD0qVv8y9Eq6BnaEDqGngM5jcREHOQNyQJOpgaf0fAL2cHY4Dn6mxoNbSq94waTMF0W/fC9uyuZvZL2+bbI99/0PlOVCzKejmiulLlW1k7Tna4KnnYKLRa/n4oyUBeBaqZjBzNHorzAjV1p5c4PHMRQy8n4DlaU4Ts9FguSs8CB+BwdvGyAZnexgGjnDTOp6EwSQtg6zB4SU4A5IAwerohvcwVzIyGB0FA8HrTgFtIetmULgF9OluGA4+q9bjXtCbc8CRp/58DLtDSzUzBw6Cm8BOSnMNPptLloHZ+5HVddmMGqYdSfbIXN/3ZMVMXiWTXTmZs28Gft+PNSEtn8+zwPt7LPwFC0J70ARU0s7E9eC9sa3qCKHSHKjLADwHZX8X7gcbmAfBxs3RhC/CzZAnnUZhB5ZRYBtUX+KVoBP8Ex6CGaGa2oKTv5W5QHfS38PUmfxs0qCXNEw2WI/Bx/AMbAPqTrCxSjQPK46AV08yMkunmt+BJJg6MtOXbAOsR9/BTuAobgVwlNgNqq0LuMCchYs8y9J7tjdsXcg7nOXZYMOsLOu28Bq4Xz3J98v7kb5Hev5mDQs5IdfeChw5+jz4DI0Hae1Iom86o4Lr43Kuz2EvGBt8DwbDqpDIDsABSYKlz7Xvq89io2s4FbR9SzSIleOSRCzH6MAp7NFrjHvVYIepuOa/wZGEDXTy0vlw500+oOUEYEf/SQOe1HV9Vo5KElVa2vD6QtmTnR7sxX8D18OY5CjJkV5am5PYLJVxIOs2oOpgeAZ+Ahs4v5sW0zpkjgAD3W3gs5CVgTcJcMk2j0tmTJK8aizX5KSvg4HV2YolwE5C0mE5nXX9nAX018bb2ZwTwQ7KWdAW2pKL3Ad3gb4bWIvJQPMHzA8GPOu0BtRCXbmoz8t+sDGMgnsh/ax0JD0CFoJyNS0HrAxLNXOg99d7mJYdYTsqie5nZfwkUVjuw3LtTF6jJWejQtcWqdQN5DX1fLl7T9gdtodsm0FWu9Ip1LYmAXicMdj8Gdtt1A0E9sB/AfXy34uG/rMLtcuORD8kb7Eq19qG10b3MRgONnj9oZSR2jfstygYbBJtyMrZSYLlSTAfeA99ea+CFeF3uAes8xOQlg2dgcNR8JfwLWTlS/xwJvMV0itl8qqRNKDaIL8Elm8A+EJ9CrvBZmCDNA1sBNZTTw6CI2FpWAWSjgmrFZdlWA5Wg5/BoHMpGNR+hLROJTECdgDv/7GQ9ZasNtElXOVCuKlwNb12/UqYCV6FbeECeBrK0TLsvD88AgvBEaAvv0JaE5B4O53B+tcwUSrvh0J6F5ae146WavS2ahzq+P3omv7vH3aK3FZMtgm+F2fAVGC7MTO8C6E6ccAH+DJwJPEV/AGDoBPkUadR6IFlFHxd9rW+Y6WOeYN1H95KaW5OtBR0L/GEBrl/wz7Qu8gxs5Ln/doUeoCN4u1QTJeTaXBMGir3WR9uhIPAQFWO1mTnKzMHOGrfPZNXzaQdhFngeHgGboGLYFLQjwfA+rnPJqBXNu5rw55QTd3JyafOXOBg0vpWz7KzYmOe1q4kfMZcHgDLQrmywdd/O06JzmfF82Xlcz0Mpklt2IP181Jp7+f7cC1MCI7YDeS+L40sg6zv8l6pSjqj81QqnV7V76/BdyKRnZ4BSaIdLk+hzr3qrd5OKT4EcxUK5gtj43VYIZ23RbkB2PqdCc+AL7EPqA92pXQgJzJAHQofwzbQnHxhbAx3g3XgWygW3CYj/0Iw8J4EHaCYTidzsdQGX2RfzFtgefBajnbGhlJlwNevvnAyPArlHM/uVVVPzv4BDIdzYGpQ3tetR69V7487OPVEmdN7/+z01LMGUbh5MgW8ifSKmbxykxtzgKPVtPRHn4rpX2Q6CNgfvF/OHowLiRZg5TV4EYbA+TAd3AeNrkmo4CfQHy6Gc6EzFNOyZB5eZMNdRfLaS1ZdBuABuL9G5g44isjrA30aZR+YqU8pySXYaT1YqpSdS9zH8/0ISXCakPUnwGs1pRvYsFpqozMRN0KvVJ6rNmy3wT5gY+bIy+Ca1aJk3A1JEDqPdUckjiITnc1KuQHCUfUO0BeaCv5sqplm4cp2DAwgjgYOgPehmEdkV0z7cqYrUmdz9kO/p0/lNbW6OhsOht1g/KZ2qlL+kpx3KCwIlvWcQppFq7Q2Rx+SOYP3o6kA7K7dYE1YDrLPlu+Onb6smjtfdt88p/XDwdLsMFYzFbG98LNTWlORsOPSnNxnE9gCvE+NpLoMwDvisCOotHYlcU06I0frLQ3A1aiigc3R2KQwM9jzXxUOgqZkD3W8zEZH5huk8jynjXrXVN7FrO+eSqdXVyDxKgyGN2EVSMupKQN5HjQJhTRIObLfG9KjI5L/IzsZjtbtZHpMF6i2LM9tYKfn+MK6QW1M8rm9Hrw3lvV3sPxj0rzssDEYvJOO3piOaWq7I+Ar4VrYEzpCa+UIzdmWtQonmozlILC8LZH3/yZwkJDI5/u5JBHL/zhwDGu2QbOAAflOsOPclOZgwytgx3obsI1ZCBpFp1ARfagr+UJ8DE/CqeD3s+8g/YCTzI1syAbWSWlPpBz9wAf/QvgAfCkMHE3pajZkHxJ79zYyiQzImySJwnI6ltdl8oolvb6927QMUr5w9a5OFHAk7AzzwUnwEUwAadmB2Rr2gFXTG9pw3Xu4NExawjX7sM8LkB7RbEXa+jWnzdjos7ULXAHPw/hQbzJoOqP2INgxWQ9aI++9wWFfcHQ9GErprLBbu9OO1NhO1XmwZDO1t7P1HiyX2mcu1vW2ayovz6t1GYA11CDstNfpsB/MAHlVPQVgg4ANhT1LZcNjurlp7kXY/khhn9lYGrgNwGltSWKfdAbrC8BVmbxiSaeiLYP3ex44HF6HDlDvOpACHpoppM+rdUlkPW6FfrAGDAc7GPWszSmcpGXgviGdkVn3fo+CiVL5jrgNSO1BPsfrwGrQpT1UuMp1nJ7zFxu4nEV+XgdjWctqFoCbm5qy170iPAeOGH6E5aHaDfI4XMOecR7ky34EnApbQqmakx0drQ4Ce5IGYIPFQtCUnIlwdOu0osHGALI6pHUjiRXAxsd7Oyv4cI1pxMQuo//Kjg2WIwanmr4HX7A/oN41MQW8PVPIx0innyOnb4dAP7gFekJnqNVImEuPUd+yx8KZveYm/WcmL530GdoJvH+JvP924BpF81GRk+F88H1I61MSN8MMYMfTmYCjoLm2js2hJhyw3Z8SnGVKpJfO4nyXZMSy8g6syylt9GcvnHpelk4TbV1IV3LhNMex8AHYuDgSGwkvgVNuldBpnGRgJU5UOIezAw+DIxQbtwfhBiAgmBkAADE0SURBVChFJ7JTtmFdk7w9Szl4DPtYrjtgKBholoNG165UsF+mkteSTj+rl5HuntlHz/fI5NVT0k6w99L3zlmJNeARcFTSlPqywU5hWr67BqVG0IJUwmd7WegBn8AJkNbpJHw3u8KEcBy0lxkAqlpxbcMZB8CsMCM8BLZhjSIHKb3qrTIXUyBHU2nZCNggVFpOBQ6G+aEL2PD48iwOT4CjxdbqNE5QyQB8CedbJVMov6eslskrllyezNdggsJGe+t2OuYqpCuxGIeTbAg7wIqVOGEdn2M8yqafF4IdDhuHR2FsSOT9XzNJFJbnsEwH6czmFien4sitwOd27haf5b8Heh7fkVNh5v9mF13TC4PtpjA+ONtyHzQ3u8Lm3OhJSjpbqrS2FY50k/pNw/rTqe3Jqu2LwSPUMgfW5rArQK+3aNkp6vaougzAh2LXyRnLdiJ9TSavEsl3OcnUTZxoMfKHNLGtnOxKB2B9sKFNy+kwe4ulyP1GgEHbxmFZqJQMPNfBibABDIdzoZHVgcptCweCsxLjQlrdSTwO/wLXnZIeAR5XSRkg74XtwU8GdqwqeW853RjVmT0GgCPn66E3NIqsU1b7kbFSIdNR2hXZHUhfBnZGQuFA1oG6DMDTUcpXC/jwPgMfwnxQad3KCTdq4qRHk39lE9vKya50APam9c0U4AHSK2fymktOycY5oGtzO7Vg2x4cc1DmOEf/pYzOM4c1VNLp+TPB51l/DFStkcF7b7gLhoLBwc8myWiM1X9MC34KaKqD6T6h0h3oz67LpXYfj/X3IJk96sj6NeB0fSI7XV9BpyQjluFAygHb8rqbgrZ8BoZ14QD4JziyqoZ6clKD/ZNwNVwAvkTPw8swE7RWlQ7ANqh/gcFuKegPlr0edBGFMLCnZfD1PuZFNppbQFuPHsvxx2fqBEhG0Tuy/g5k35PjyPP9CbXegdk4he/dduAzMhT2hLScanafw8DR8YPgrEd7kB2QvWAQ2B71gFDzDtRtAG6+2JXdak92GdgKnEbcFnrDWFAJVToAW6Yu4EjKBnZjqFRZOVWr5LfCtTJnsIw7ZfLqNWnny07YFvAYXAL1KMuWDbbOEtkhS+Qz8TT4+4lQZRxwJmMf8DPZMk2c0ndzlQIOJNqL7qOiftZy5sXf1Pg5pJ47sRSv5ooA3MwtcHTRqZntpW6qRgAu9dptvV93LmhwWB6mgV3gW7B3XO/aigI+minkVaTXz+S1NOnzpB9+dx8CR0A2iJJVkm4tstcd5A2DeWEW8BpnQ6g2DtgZ8ju49+Ey8IdpjSrf9+wz6TPosx5q2oF2H4BnwJsB8CPcA7NCog1ZuS5JjGG5PduHNcEH5D8I7UVOkdvgGLyOgokhDzqDQvoNtSMYxGYGRzlHQyV0LicZBhMWOI6lsxgtkb+idpozkd8dfwcbwovhCtgSxoJQ2zvgdz1HgH4f7gJbwk1QiQ49p6k7+bnQ6ee0OpAYls6I9f/nQLsPwE6Z+i1tTjgSPobZQZUTgP8+ovif7WkEXNyBfOQaaP2W6jS037LvhpfgYGitpuMETxQ5yS3kua1cTcQBfvM12Pr82rhPD6H6cMD7Ol+mKLYvG2TyGiVp+3k7dE1VyE9RMQJOGVJktWYBeJwihalFlj+m6Ak/gyOKV8ApoyUhVFkHOnO6hWFceBK+h3pSfwrzJjg1fBasAFfDeNBa+feu/bFfVk7PO0IqV3o3G/jsdoTnYSSE6seBjzJF+YC0sx+NqNeo1ED4GraGqaAP+PuUUB06MHadlMmA67RjIkc/Nr53wmRJZixb7cDknGEALAtLw3fQDepJ01AYp3adATkfFgE/UdhpaK1GcAKD8KqFE/kjL4NmX/CZmxLK1R8c8DQ8Cu09+M6IB06/V+JecZpWy/uyV+osdrLOhGdSeY22ehUVsi21o/0Z+GzbwQyFA006sCJbPoX9M3scTvpXuC6T35Jke5+C9lvQW6DXiVZi5QYYP8mog+U/KYPfZdNypubBdEZh3SB9OThb8jj0gDHJIPEX+EwNh4dhJjBw3AqNOjqialXV6pz9NnAGS0/t6I0NtZTPtZ37y+Df4HOyJYTCgbQDp5Dolc5oj+uOTOYrUvE+5G1TJL/crPYegP3GeWMR084lr5TAVeTQqmSNx1kHwe6pszsdfXoq7aozIwbS5UygOeEBmNvEGORI6AnYCLqm9vXHWGul0nldtbPlO+Ms0rEwKVRTvrfei/Rslfdw+2petMRz2wnwnm4KxdqXEk8TuzWwA6dQt3YfgLP390Iy/JFLpdTeA7CNo1P62VHJveTNUSmTK3QeA8Y74Oj8FjgUDCpp7UfCIJOW34tPTGc0s+5obZLMdoO+U3ZtJT8JOOVeaemZ709P2AwMjrNAteTo0gCXVjcSA9MZsR4O1KkDNQvATu3VqzanYIfA9/VawJyV6yvKez8YoAxofru8Avxu+TrUk/wRiT9uctT+C3wOWTm9+GEm80vSnTN5TSWHssHRv6NgNRc4yp7BRJXVkfMfCbOC615zSajEN+R/Fc6ZjD6Hk9aXfcBAWQ2N4qTpmQSvYefZ/FA4EA404UB2NNTEbpHdIA7Y0/sJHAnfCs/BelCPsoPwPhQLvpb3EXAU3MkEGgusk98fS9EZ7GQQt1NyQgF/PJQN6mRVXI7srZferwEPwklQiffRUfXVkNYTJMyvlgZz4jXBToSaGvyO74/oQuFAOJBDBy6gzDEFncMb14ZFNgC/C3vDlXA8lCunaZcCp73bQn4KuLvIhZwyrsSngGU4T//M+VcnfUkmr9JJZyseA+s2BLxmKBzIgwMOTHrVoqDj1OKiJV5zhxL3i93arwMnUvU7YHp4CJ6EcuUUbVvKkfoPsAFsBl3AUbcj/g7QWjm17sj6Zdgd/Pa7IawD1dRHnHzxal6gjHPbGbBT9RvcC99BKByoOwcqMeVVd5WKAjWUA4tRm+vAhvQmcASZ1ksk7oKWBN/0edpq3e+xBt2jYGtYuZA2IL8BldDOnOQQWBqcRVofvoH2oAWppFPizmjY+fgWZoJQOBAO1NCB07h2/CqzhjegBZfuzjEPQQ8YF1aDe2ASyLOepfB+f7dj4dRzf7gYekMe5F8VmwG6llBYR/zbwSC4FQ4G86ohy/MTzJ86uf/oytXg8xMKB4o5ULMp6GKFadS8WgVg/37qZeC3sYdhXmgPMkgeAMeBDXBLGt3zOG5JSGs3EjulM3K4Powy64cjNJ8PPwUdCqtAvcsR5o0wAN6CjaA5+eMyfwTmiFROBp+LamghTnpGkRNfT960RfIjKxzQgQjAbfAc1CIAT0G9/oI+hfrNw/IBmKOQbtSFI6RhYKBcFG4AR7Ll/ubARt4glZbfMvdJZ+Rw/XTKvG+q3LOy7nMydSqv2OrYZFr/nQvLYvtUM89vq5ZzvsJFHHF+DMsW0tmF9RkO2c7XLeQ5gq60fK8Mtml1IvEqTJzOjPVwIOVAzQKwL3Soeg5sw6m3hGGg/GGMf+VlS8i7elKB/uBoyAe4IyRyhHMpnAuOfvxR0OPQF8rRY+x8YOoAG9PLIS/fe1NF/5/Vw0ntBefAfuD0syP9T6E5Gbj6wGewLdwBHaCttAYX2gReLFzwO5arg58GiqkLme5r0E7rSxITpDMqtP465/E3AaeCnV87DE7xy7cQCgfqyoEIwNW9HTYyH2YuYePTOZNXi+RKXNQGfAgY6GaEUjUbO54JF8CO4FTktZAE4alYN+CmNZTEmEZ46f1dt+H0XHfBLuA1DoIHIc8ycOm330TfAIPaI9CctmKj/u4GN8C/4F3YFNpKzmCMylzsV9J2jIrpfTLHh5VTG33u1oQRqbxKrh7Jyb4GOzUnwp3g7Fe11YULOKqvRsei2mWP84cDVXfAl3Bg1a/yvxdYheR9MG4h26m4T2CDQrpWC6cQ74Ek6C7BuuWcEkqRgXCRzI7Hkt6wkHcYSwNzWteR2DidUcb6suy7LvQo45hG2/UEKrRYplKLkza/rTQvF3JEm0wfO/r2WVgNmlJ3NjgCPgj2hkfBKfdGkp0hO7P94W1YDkL5caBmU9D2aEPVc8Detz8MsdGyR74gXAYGo1pqFy5ug+gIRdkoXgPrwnkwJtmheC+z06ukJyrknc3Skf/kYGBfH6aBq6Elur8lBzXYMd9Tn/khPbOwNOmOMDe8Bn9CNeX07v7wLFwFM8PdcBs0JUfpfn9dCuyAGqwdoTaK7MzeDj7fn4IdjodgFfC9D4UD4QAO1GIEnBhvw+kLuVCSUePl5Vx/9kwZHJXvmclrKnk4Gw5ObTQI2Pg4Iks0Hiv7wrGwDSSzAKyGWuCAnRkb9nVgRtgBHFleAN7PN2AiaAtZlgUgGQm3xTXr9RpHUDBnaNJyRGwHN5QPB2IEnI/71OJSvsCR0lI5gjCITQivQLkjaAO/04eOogbDA3A82JirruBUYnZa2W3FdDKZ9u4NBA+Dwdu8xyDRL6yclCRyvnTKdFfQJ0f61vUPaEt9ycVs2I+ENWFtOAoOA2X+ifBvMDBXU5ZFQn93LH/NGPEb6Q6ZvEiGA+3agdOo/cAcOuCPOobCLmBP+wkYAKVqd3a8FQzgjpQMnOODU+IGzEPAoOw0cTmygdkItof0yLecc9TzvgbdS0DvvwH9mxn2gkHgqL9Wmo0LX1Hk4k7xT18kP7Kq50BvTv0SdClcYhKWdoBK7cwWDotFDR2o2Qi4hnVu80vnNQCfjlPJj5sS0wyeqySJZpb+aOcr6JTa52jWk+ljR8bLQ/fU9kZZtYPgqNVgeQ0sA6VqUna0EV0BLoWdwI6LHZhz4FrYBGqlblzYsmR1JxlTZTMjXXUHtuYKI8B31c6ssxOh/DhQswA8Tn48arclNRg8mKm9P3wpZaTTk/0cAY9KHX826wYR9fTfi//5syspR91fgFNpeZVB0qlBZw4mhJPBqfybYEwy4Nqo3gNbgMeJPp4P54HTwf4QqRYawUW/hx3gAlB2MtRnfy/izzZ0wE7a7WDn5xPw3QmFA2N0YOwx7hE71NqBbyhAdop3Y/JK+SXpj+znVGpa/nCmqcDqNKsjxjPhVZgJ8iin/+y46JMN4htgQDWwlqLO7PRuYUeP3xM2gp/AkbCdlNlhSqiV9uPCzozcCXYqLO+aEKqNA3Z8XoAIvrXxP65a5w6cRvny+A3YHzr9BdtBDzA4DoNSND47OSW2FUwCTjk7mjZ4ZGWwclqzU2GDQewuMJDlTctR4MOLFPreInnFstYg8xawg6rnI+EbOAkcaZ4OjjyL+Uh2m8r7470NhQPhQMscqNkUdIyAW3bD2vKo97mY058zgSPUV8DvtqXoZ3ZylLQEOLI9APYCR4RZrUXGITCqsOFJlnfAMoV0JRc+d35DPRr2BTsKldRrnGxRmDp1UoNyqdcx+L4Oz4HHfQUvw6cwAM4HR5uOjmstZ0LsHITCgXAgZw7EN+B83LDvKKbBsSVylDY5vAOLw8pQ7NuvQfFXUAadyWA6+BIqrRs44U/wGFi+72FaqNT03Uec6wwwQG4LXWFFWAdKlX7/Dgbyh2EjOA88lwF6XfgBQuFAOBAOtMgBG91Q4zpgUHMUdyQYiBYAg9CGkNW9ZPQHR3gGm7lhP1gEKqnVOZnT6gYyA/wS8CDsAZXUEE5mHcYCv8/1LSxZjFEd2ONZGAUGcjshBnWneieEVeARqKX08e4CdhCmqWVh4trhQDgQDjTnwGlsHNjcDg24bVPqtH2qXvOwfgw4vTxvKt9VO2MPgD/cOh1uhoXhNnDUXCn140R+056qcEID5P1gEKkX7UhBLoO94VQ4AA6GPaEetDiFuB0SD3uzbjCeFELhQDhQngOnsHuv8g6pzN42uqHGdsBgp5YFg+kIcOr0RXBaNtGfrLwOG8MVsCU8BeeAgbhScqRmsHBUqizftTCFiTqRI/PVwM7B5TABGIQdudeDdqIQ+0PioR2nW2FNCIUD4UBOHIgAnJMb1cJiDuW43WAZuAwMuIvCxWAg3AWcVk3k6Pc3eAa+LWTOyXJkYb0SC0ff08OdsBBsDo7Sza8XWWc7IyfD83AoPAGzQrU1PhfoB3fBEPB7fFbjkmEnKi3vl8eGwoFwICcORADOyY1qYTH9buk09LXwC5wFw+Fq+BTeg2Qak9XRf7XmQJYGRhvz9cAp2AuhUnKk9jH4jdXRttPiX0Alr8HpWiWDb3fQL7+B7wsLwGtQTfk+Pg6dYH1wpLsXrAJpDSNxUirD2YMB8FAqL1bDgXCgzh0Yp87LF8VrvQOO4Awe14Cj4TdAOa3qNOtRJgoy8GwDBp7fwQDtaNWRcaX0OSfaDgaD5+8M50M9BY8RlMeA5q+o1wY7CHYcLG815QzFC2AnSP0ABmHvhzMGiS5ipTcMA2c57DCtAS9CKBwIB3LiQATgfNwoA+ge0BU+AH+dPApKlYHERtwA+0/we69T0gZfA2Jab5JYOZ1RhfURnNM61asMvo/By6BPBrslwI5DNdWFkw/PXMDZAn95nZb/E9NG4OjcZ8Iyvg+h+nHAe2bnaTr4Go4HZ6FC4cB/HBj7P2uxUq8OOBV6DlwMO4AjpOug3O99N3DMwuCody3YBS6F0P934Fey9Goo2GGxA9MDDHzV1HOc3M7PlKmL9GXdEXgxPUnmPRDBt5g7tcvryKXfAp+jC8DO8jcwKYTCgXbpwGnUemAOa+5ozCCQVj8Sm6UzKrS+Iec5E46DqSt0zjhNeQ44lfwX7AxHwyDwc0EoPw7sTlFPzBTX0fDBmbxI1ocDp1CMXrUoSoyAa+F6edfsxO7vZg7xO+5EmbzWJi/kBP7o6gp4Bhz1zQehtnXgFi43J3wLT8PGMBJC+XFgEop6a6a4j5OeOJMXyXbuQATg+n8A/BHVDqliOr11MlTyBzeOsBeA9eEpuAHM2x9Cbe+A3+qvgpvB6ctQvhzwx3prZoq8K+lq/4gvc8lI1rsD8SOser9D//iHU+fPwgwwDPxVrj+oehAqpSk4kQ2+U5+JnmClX5KIZV04MAelWA68T3dANOiYUIdyFmko+FuNS2Ap8Jfq20IoHPiPAzEC/o8VdbvyMyWbF+4Cp6NPguOgkvqAky0BHVInXZz131LpWq06bTcNpMtWq7LU8rp9uPhg+AK+gxHgr6BD9eeAf4VvafB+9YB3YEH4A0LhQLt0wJFkHn+E1VY363Au9BksC5vAAzAjJJqHleXBZVtoLC6yOwyBm+B1SP86mGS7UVdq+inMkqrxXKzrywSpvFgNByrpgL8JuRtsC+6DRv2GXbMfYcUUNE9VDuVfQToMeoIN8PNgsGpND/sIjneq2wDsj3788c9HoPaGxcDtW8KVcBRUU/twcjsAq4IjiuXgAtgMfoT2pOmorMH27VSlX2X9W/DX6un81C6xGg602IE1OHIj2BC+hrXhcjDvJwiFA2U5cBp7N8oI+GHqchE4SuwABsODwcDcBxypdoFKaDVOYkBOOmsu7wMDYzVlz3uizAUOIW3d2pumocK3w7ipitvx8u+aTpbKi9VwoFIO3MmJZsic7FDSa2XyGiFZsxFwfAPO3+PjX1H5HLYDf4zzB/hi9AH/Cosj2N7wA8wErdVSnGBlcBSqXPYD86spr5NcM7mO9fVX4OXI7+aO3heH8co5sI72/YSyeG8HwPzgd0VnPY6HryAUDlTaAd+17zIn/YZ050xeJFvhQDKqacUp4tA2dsCAYoOclqNgA4zTyCcVNjzK8mTYHPwhV0vlsdlvP5OT90tLT1jiccPYzymv9Qv7L8nyaChnxDcp+18Mb4LP+jCwU/Ip5E0XUOAPwI7Xn7AL3AWh+nHgXxTFzwX+lsIOU571MIU/Af5dqISfg86CWQrpWIQDZTnQKFPQBuBrIQlMmrAD2Ft1Sjqt80gskM5owfpsHDMM5ikcuxBLe8c2NNWU0603wn3gi38TWJZSZafEgLVb6oBNWB8IHVN5sRoOVMKBqziJnaS+YMfITwY+g3mV78gwsCNxGNwJfaARVbMp6EY0s6k6NUoAtn7TgkHwdHCUew34zXRsSMvgNXs6o4Xr83Hck+BL6LV6QltpLi5k0PebZznqxs7XFTngUvI8Zy3kdS3T9eCofEMI5d8Bf7D4aKYa55PeKpOXx2TyCaraHe5aelOzABxT0LW87S2/9scc2hn8FvgrvAB7gt8EDwe/nQ4EvwO/Aa3Vi5ygVn/n9NUWFl5fOhY51obklyL51c6aggu8AjZoTu/5A7MrwG+490Aovw7MS9H3yxTfjl4jdLAeytQrkhV0IDtiquCp41RVduBnzv8YPAO/gSPhUeAo1emvZyE9TU2yXclOyvNwKDgV6LPuyFOP3oW21hpc0E8FBl/1PRwEa5sI5doB/1rcnJkaLEzaexwKB5p0IEbATVqTuw1/UmKDTei/DhzJ6jlwP9hheRScJaiF/Hb/bebCjtLHz+RFMn8OXEiRB8GX8AT4g8GzwVmOamlKTnwMTAszgmXwtxKhHDkQAThHNyuKWrYDf3DEjmUfVbkDunMqv18b/J3Kc8r5AfgIHJEfVEizCOXYAQOvMxzHwXrwBUwPfgKqhsbjpO/DznAJ2IkzAG8MfnoKhQN150Aj/Qir7szNSYHWoZx3w43g99g5oFpagRM78t4LzoUPwet/AyeBZdgfQuFAuQ6sygEG+7QmJ+EzFSrfgfgRVvmexRHhQFkOLMre/ip1I/CHT/ODgXFzcERaSTklaKCfAQy86gNYDAz63eA7eB1CzTvgj+bWBEd5w8DfPLR3dcIAR9lp+cPCidIZsV7/DjgNFgoH2oMDBtrDwOCrXgCnhFczUWH15HwHQBJ8Pf2J4A9zPocnoT0E3wWp5wC4Cfwrcx2hKRlU9GcRcF3NDt6jkTACnoa1oL3rMQxwNmW+lBHOtLyWSsdqDhyox2/AlmlCcKouFA5UygEbdb/FpvUTiWqMGgwY06QvxPoUkASWzKaGTPqr4NNgX3gL/DZ6DTgDMQrSmpTEeeB3TT26D2aG62ELeA7UJHA5OAp2RqG96hMqvgfogx0bp5+/hd0hFA6U7YA942PBl+pP8B+ZsBF7CZw2rIRsDAZW4kRxjlw64A9U7odk1mcq1n3O0qMIkhVRB85ioNgJnDqdFm6CtaC96DoqulCmskeT7pvJ0ys/AaSDx7akr4RbIasjyVg6m9lO03Zc9MIZl1DLHTiFQ3u1/PCWH+losx7kz+enBn9c8A4YfB2ZzA328PzVnz3kUDjQUgfsfPWA5+EOmB1Wgheh0vLX19vBReDI7wfw16q3QHuRbct7mcq+QTo74zATeQ/BGal9L2Z9KegKdl4+BmXnaU0wuIf+/m8CHwwjwoHWOvAuJzAAF9NiZA4ptqHMvNPYP0bAZZrWgLs7NeozlZ0ibsCq1rRK/bj6YakSOLXsD4f0Pi3vww3pjML63Sy3BN/9RWEeMM/RSigcqKQD7X4E7FTzMnB1EVf9kYwvbigcqIQDr1XiJGM4hwFjfegML4Mj3w+gPekkKvsC+Cvmx8CZgGPhcUjrExKvwwFwYmGDI9xfoD88A05JO5V/GRRrI8gOhQPhQEsd8BvGq/Ak+IJdAP5gw+lCGzCnqVqrGAG31sE4vhQHVmKnu8CpUpdOR/tZpT/4vbM9yfr2BQOov25uSk5X+83c6VQ/DxwOHSEUDrSFAzUbAbdF5Uq9ht95HQVvBQeCL21vGAsqoQjAlXAxzjEmBxzNdYO/YF0wCDnF+hDsBXlUVwo9B0yZx8JHmcOBMThQswBsz7Ne5JTT0FRhDMj2gm3IQm3vwIRc0tGc9+FRcBQXGrMD/qhrZjgaBhV2d3bnLNgXTi3k5WWxHAXdDT6EVcBpZH8kFQoHwoFWOlBPAThbFUcPjoi3zW5oJr092zZuYns38t9oYltk/68DU5C8DB4B/37h2+CvUh+GUPMOOOU8O/xZ2G1ilj7D/WECyJPmorD3wvTwEXSBp2EEmB8KB8KBBnDgTerwTYaRpEcV8gwGrZXfonZs7UnayfGPUc8VU3WdifXbYepUXqwWd2ABsp218QdIJ8OzsCkcCf4wKU/ak8KumSnwwqTzVo9MFSLZYA74aeQAOAYcuJWrmk1Bj11uSau0v999v4DTwL+rKQfBTYX1fVmG2s4BOz/+lY9E77EyHBwRhZp3wB8OTgvXww7wOawPv4ONRN6UbSP8pm0HIxQO1IMDk1MI3zXjxz1wFJwNoTId8C/oDwC/m2nqJnAxVEoxAi7dyfvYtWtmdwOyo59Q6Q50ZNdZYfrSD6mrPe1wvQWzFErl7wIMvosV0rEIB2rtgDHD3yakdRkJP1+WqpqNgMcptYRtsN/3XGNz2AAehCfA72mhtnfgIi4pzjz8AGfAz/AUhEp34Fd2NYDlVf54bDsYBnfCDLAxPA6hcKAeHJiAQvjJLC3jRy46vfUUgBMD/Uv4j8J54HReqO0duIZL/gT+clcNgzNdCbU7B4ZS47nBWanv4GsIhQP14sCXFGQJuCNVoE1ZPymVjtUWOHAhxzgtXSn15UTxI6xKuRnnCQfCgXCg9g74ecTPIluAn8icfr4BylHNpqDHLqeUbbyv09HjtfE143LhQDgQDoQD+XHgbYo6GcwGjnyHgZ8xc6F6nILOhXFRyHAgHAgHwoG6cMDPIofURUnKLEQ9j4Avpy6/lFmf2D0cCAfCgXAgHMiFA/U8AvbvUIbCgXAgHAgHwoGGdKCeR8ANaXhUKhwIB8KBcCAc0IEIwPEchAPhQDgQDoQDNXAgAnANTI9LhgPhQDgQDoQDEYDjGQgHwoFwIBwIB2rgQATgGpgelwwHwoFwIBwIByIAxzMQDoQD4UA4EA7UwIEIwDUwPS4ZDoQD4UA4EA5EAI5nIBwIB8KBcCAcqIEDEYBrYHpcMhzIsQOzUvYBcDc8A/F/A2NCKBxoiQMRgFviWhwTDrRPByal2m+C/0zsirA+HAc9IBQOhANlOhABuEzDYvdwoB074P9Qtj3cV/DgHZb9wP8KLhQOhANlOhABuEzDYvdwoB070Jm6f5qp/zekx8/kRTIcCAdKcCACcAkmxS7hQDgw2oGH+HM/MBCrseBOMD8UDoQDZTpQz/8bUplVid3DgXCgyg4YaK+F1+EcmB+uhKsgFA6EA2U6EAG4TMNi93CgnTtwNvW/B2aCe+FpCIUD4UALHIgA3ALT4pBwoJ074AhYQuFAONAKB+IbcCvMi0PDgXAgHAgHwoGWOhABuKXOxXHhQDgQDoQD4UArHIgA3Arz4tBwIBwIB8KBcKClDkQAbqlzcVw4EA6EA+FAONAKByIAt8K8ODQcCAfCgXAgHGipAxGAW+pcHBcOhAPhQDgQDrTCgQjArTAvDg0HwoFwIBwIB1rqQATgljoXx4UD4UA4EA6EA61wIAJwK8yLQ8OBcCAcCAfCgZY6EAG4pc7FceFAOBAOhAPhQCsciADcCvPi0HAgHAgHwoFwoKUOxL8F3VLn4rhwIByotAPzcsIJ4FX4vtInj/OFA/XmQATgersjUZ5woP050IEqHwfTwJewEywIL0EoHGhYByIAN+ytrXjFJuaMq0JHeATegFA4UAkHzuQkn8FmhZP5fw6fB2uDATkUDjSkA/ENuCFva8Ur5cjkapgSfGb8r+iWgVA4UAkH5uQkp6RO9Djrt8LiqbxYDQcazoEYATfcLa1KhQZz1v1haOHsd7E8H16DTwp5sQgHWurADxzozMrI1AkmYf3XVDpWw4GGcyBGwA13S6tSoW85axJ8vcBH8DzMZSIUDrTSATt4J4BBWG0Je8ODEAoHGtaBGAE37K2taMXsqDki+SZ11iVZvymVjtVwoKUOXMaBfuZ4Gt6Fr2EG+BlC4UA40AAO9KUOOzZAPWpRhfW56I0wK0wN14E/lAmFA+FAOJB3B/z9Qa9aVCJGwLVwPX/XvJ4i+/cyjwGfGaej/ZVqKBwIB8KBcKCFDkQAbqFx7fCwIdRZQuFAOBAOhAMVcCB+hFUBE+MU4UA4EA6EA+FAuQ5EAC7Xsdg/HAgHwoFwIByogAMRgCtgYpwiHAgHwoFwIBwo14EIwOU6FvuHA+FAOBAOhAMVcCACcAVMjFOEA+FAOBAOhAPlOhABuFzHYv9wIBwIB8KBcKACDkQAroCJcYpwIBwIB8KBcKBcByIAl+tY7B8OhAPhQDgQDlTAgbEqcI68nKIHBb0dhjdT4DnY5n+591sz++Rtk/d4fPgpbwUfQ3knYHv6f88Zw+652Ox/RvAn/J6L0pZWSDv5naDR/l3nRnz+vE9/QCM9fx2ozy/wDDSlmdmwAvifzIRq6MB2XHvjGl6/GpeenJP6T0k2moY1WoWoz16weoPVqzv1ubTB6mR1hjVgnfpRp94NVq8FqM/p9VqnmIKu1zsT5QoHwoFwIBxoaAciADf07Y3KhQPhQDgQDtSrAxGA6/XORLnCgXAgHAgHGtqBCMANfXujcuFAOBAOhAP16kAE4Hq9M1GucCAcCAfCgYZ2IAJwQ9/eqFw4EA6EA+FAOJAPByakmF3yUdSSS2kny7/b3GiaptEqRH26QucGq9c41GeKBquT1WnE528S6jVeg90r/279ZA1Wp6hOOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAONBQDviPU4zVUDX6uz7+5+fFlOf6jlusQjnPy/P9aMp636emnr+mjon8GjmwNdf9oAjTFcpzIMsX4F1wPS+anoJeAc/BnZD+z7X7kH4YrNNN4L96kwf5L/McBt6Pp2AnSGQdroM34UVYAupdM1DAj2CWVEGbq0cf9qv3++a/tnY9HABZlVvf7PG1TG/ExZ/OFMC8++F5uBLmgkR5aDeK3Y/ZqYBtgu/Qk7AwJOrDSp6fP+txIVzgSkHNvW/JPrGsogPjcG7/yb+EM1gfVLje+ix94PxnAacGg9kqkAddRCFtBNRC8DZY18nhY5gf7NGfCpdCHrQZhbwX/CdC5THoC8rgewjY++0Dn8L4UK/ahoK9Bb9COgA3VY883LcFqctD8DVkA3C59eUUdSEb6LPhc3gmVSLbA5+xqQp5W7EcUljPQ7vR1P3w/m1cqMeKLN8rrOf9+bMaq8JXkA7ATb1v7h9qYwccNY2ASQvXvYTljoV1F/uDPag86GYKuXehoHOwHAmdYGW4DxJ1Z+XbJFHnSxu4TVNltH721tX3kNw3045WVnClDuW/SWtdHG3YsM8CiZqqRx7u25lUwuBzDqQDcEvqm/hR6+V6FOBEsOOdDsDTkl4aEvVi5YdCot7bjebux2TUwU6s+hc4Q6Py/PxZfutlh70fpANwU+8bu7WtHB21d9lwOMVpD17NCLeMXvv7j09ZGKTzIEeDN8DisAjsBKPAOn0CiT5jxRG+wdnt9aw3KNysqQLOzbojEUcplj+5b6yOHp3U63888SvlW8lCZtRcPSZg33q/b7sV6tMnU6+W1DdzipolfYekT6YEH5OWRNuzcnshUe/tRlP3w+I7QlSng52PXUygPLQbTT1/lv886AfzQaLm3rdknzZbtvcA7I1xJOLLlshe08gkwfInsCHMg/5JIe3Jvgp+z+4DV0G2Tj+TpzpDvQfgpDH8krI6vex3bYNStk5k/cN6dXElR2quHpNSj/SzmKf71tQtaK6+TR1Tj/nbUqjVYdFC4bL1ylO7YRXszH4OH8KGYMciW6c8PX8bU37LOwTSAThbJzbXrt3wxxPtWZtQ+evAlyWRDf1ESaKwnu71pjbV1aqdqRPBl+dQMBivCEtCtk4TkvcLfAP1rgco4OGwATh1fjS8B9k6kTX6vuXhXlnWRM3VI7stT/ctqV92ma2T233f8nTfdqC8PofLw4egsvXKW53siB8LthfLFJbZOuXl+TPIOpq/H1aDuWAmWByydSKrds9few/Aa2H+Nd6BlHyhvFmJurHyQZKo46VTKx3g+UIZ/2T5DBi0rFM3SNSNlTzUyfI6DTYIloadCukRLP2GbQ93ekjUjZX3k0ROls3VI8/3rSn7m6tvU8fUU/4WFKYfLA+vQiLvVR7bjfEotx13R8Dqd3gDZoO8Pn8TU/a3wI7SQbAc9IDNIO/PH1VoDI1LNXzYpspUxx8eGMSmhW7wJiwEedA9FLJvoaAG3q8h+ebhd18fRF+0y+E4yIO2oZD+FRflrzJfB3u0yh++nAnjwLpgg+h9rXd9TgFnSRWyqXp4r/Jy386hrAek6pReLbW+6WPqYb0PhbATm8h36kfoDX4eSGB19A+W8tJuZO/HfZR/RyuBbOt+hbmhUZ6/fajLBZCoqfct2R7LNnBgDq5hgMpqLDIuBadnP4F+kBctTEH9dmNDIAavROuz8gPYq70fukAeZA/9TnitwA6pQndj/UX4COzx9oE8KNsAdqPQTdUjL/ftHOpQagBurr6cpm7Uh5KkA/BJpP8qQmfy8tRuZJ+/RSj/w2Dn9knYEBI1wvOXDcDdqFxT71tS71jW2AG/4dgDzKMsezE5UnREnEf5q20buWKaolhmDvOaqkee71tzt6Gp+jZ3TL1vy3O74TtWTPH8FXMl8sKBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgVY70IkzHAmTt/pMcYJwIBwIB8KBcKDBHFiQ+rxeqNP0LHctrKfzC1llLdZh7zfgbXgRDoN60jsUZoF6KlCUJRwIB8KBcKB9OdCR6k5TqPJGLG8trKfzC1klL8ZmT4P67HApTAS3QReoF01LQcatl8JEOcKBcCAcCAfqy4GlKc4j8DGcC+OB6g03wtdwM0wNaiw4Bdz/KTgG9gF1H2wJb8EnsBOoeeAuMCB53A9wJST5Bs3nYWJIZFkc4Xq9Q+BD+AgOBvPUF7AUGIDTmo/E5eA5vgKvPTMMgw9gT0jUVD33ZQd5GD6HA2F1cFT7BCwOY9K97DBHYae1Wb4EHr8b3AShcCAcCAfCgXbqgEHVILYVTAaOTPeA7vAdbAlOGV8NQ0DtCk/BnGCA/AlOADUC7ga3rQajoCssCK+Do9ZtwYBo0E3yWf3HnbCZK6gzeH2/624Or0FPWBgMYouCsqxe34BvIE0Cs/v9CfvBTDAcDKLLQQ8wKHeC5up5ItvtfHgtg/wfYN1mhYPgGhiTDLZOQRv8vebKMDtYHgmFA+FAOBAOtFMHNqDeL6bqbrA1QO0JjooTGXT+ginhQTCIJjJwpgPwCskGlga9uSAdaDcifSuodL6B9ubRuf/4x3osDdLqPtgfJiqwF8ujIZHneB6+h8FgEDYAj4REJ7NyS5JgaWfAfZqrpwG4PyR6j5VNCwmD6rvJhmaWSQDejn0eSu3nCPi5VDpWw4HcOmCvOhQOhAPlO+D06JOpwz5k3cDgqPHxVL4jTEdwTiE7mkuP3hwNp/VZKmEQHDeVbm7V4NsbHBkbgJMR5nSs7wsGTXG9JyR6hhWZExYBR5nKqe5EP7HiKDrRr6x0gObq6b7pc/xM2uurUTDO6LXS/ujGbs+mdn0stR6r4UCuHYgAnOvbF4WvoQNOsRqEE83AytrwJcydZLKcBiYFR30G7B6QyNFgWn+lE2WsO4K9H9YEp4pvAmWAPxAsg8wGGxfWDdrJ+2+wtPMwGSinjMek5urpsaWcY0zXcPsjME9qRzsxoXCgIRxIXsCGqExUIhxoQweGcq1e4DSxcnRpQB0CS4FBw/fLKdSX4Tu4A7YEg3Vv6APlaCQ7d23iAEe9R8LD4LXUYNgKJgGnl6+EPcGR9tKwPihHyo6AbzdRopqrZ4mnKGk3OxZ2WvTZb899IRQONIQD5UwFNUSFoxLhQIUceIXz+IMiR5mfgFPNfud0utlA6GjXdYPh2qAMgIuDU6rvg6M7p3RL1Qvs6Oja0eo2mYNuI30pJNPPbjbgO+IdAV/Aq3AC/AlbwuXgqHtJOBy+gVJlvZuqZ6nnKGW/X9jJsnm9b0HfQ+FAOBAOhAPhwOjvmcVGpXZukyndxCanh7snCZY3wE6pdCmrY7PT+KXsmNpnAtYlK8toGTpmN5SRLlbPMg4veVdHvxOBswvDSz4qdgwH6tgBX55QOBAOtNyB3znUUW5W5jsCTqszicFwEcwC80O5AdjRqz9qKkdOXReTZTwQyhmFZ89TrJ7ZfYqle5K5QLEN5PnDsBcz20aRllA40DAOdGiYmkRFwoH6d+ANivgoGHxdN/j5Y6Zayh+T1ULTctFpwG/TWZzS9zt1MRmE34LXi22MvHAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwoIoO/B+h3zxKTgg6gwAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cog_final = lm(kid_score ~ mom_hs + mom_iq + mom_work, data =cognitive)\n", "plot(cog_final$residuals ~ cognitive$mom_iq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall our previous formula, we have three explanatory variables, but only one numerical, `mom_iq`. So this is the only variables that we want to validate the linearity. We plot on the residuals from `cog_final` result in y axis, and `mom_iq` in x-axis. So it seems we indeed have random scatter around zero." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w30.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/171) 03:04*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we plot the residuals, we expect than random scatter is normal, centered around zero. We can see this by using histogram or normal probability plot." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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" 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rUOpvQQZcPdKRNRjCQpuAh1C2h9KABjRQYwMZWLUxpOv58RrXc8CqZgIeMJXuSAMa0EDXGshznZOAd4Knu9bCWFbcBDyWwtxcAxrQgAY+ZmBTlr4IO0JHPIP5Y6UfxgUT8DDK99Aa0IAGOtzAVpR/eUjyfanD6zLkxTcBD7lyD6gBDWigFga2pxaLwc6Qh2oYY2nABDyWwtxcAxrQgAY+sRsO5oYMuOrrTwl2vUYTcNf/F1CABjSggV4bGIct94QZYXd4E4w+GjAB91Gcb9OABjTQZQbGpb55rOTkkN/wfQeMfhgwAfdDnm/VgAY00CUGkit+CJnuBe+C0U8D/hxhPwX6dg1oQAM1NzA+9TsYPoB9weSLhIEIE/BAWHQfGtCABuppYEKqdSi8CgfC+2AMkIEqJuB0cUw1QPVzNxrQgAY00DcDE/G2w+EpOATSAjYG0EBVEnC6OHKCH4Vc2H8BMrT9DtgSDA1oQAMaGDoDk3Koo+EBOAL+A+1icV4cCefDzyAtZ2MMBqqSgE+gnAvBWpARdinXTPBtyI83bweGBjSgAQ0MvoEpOMQxcDsc14vDzck2+Qz/BXwLboCzIC1oowMMPEQZZ+ihnJ9l/Z97eG1sVm/IxknmhgY0oAENtDYwNatHQhJpb+M3bLhs08b7s7xZ07qqLh5FwZYYjsKlpVmFSFfzKj0UZG3WP9vDa67WgAY0oIGBMTAtu0lL9lI4ZSx2mUuIjzRtfy/L6c002hjIgKcqRL4tnQH5Eedcc3gF0g2yAKSMa0JvYh42mqOHDdOSfquH11ytAQ1ooJsNpAfyWEjX8Tk9iEiDbRmYANI9nbE6iVsglwlzi1IiCTld11/LgtGzgaok4JzAXMRPN8YIyH+GF+FUuATGNACATUbHzPy7ZDHfPFmKFa81r3RZAxrQQJcbmIX6Z8BVupIv6MFFkurx8AHks/kKWBSSiJNsb4R8/l4F60K6da8Bo0MNfINynzyAZT+GfaWVbWhAAxrQwIcGRjA5G1b/cLHHf3/PK/nJwTLSYLoc0m2dSOt4fdgKmq8Hs6rSMWzXgKti5T4Kkm9VjeQ2pLeLdb9m2t8wAffXoO/XgAbqZGBuKnMurNKLSl3NNuM1bbcfy729PNj01kotDlsCrkoX9Jacjlz0Px3S7ZxIN0a+SeWXN5KMDQ1oQAMaGBgDGV/zY0jXc2+6it9gu9xW9C6UMSMzaSQZfTSQboMqRP4D5Brt3JD/EEm4z0Gu2T5czDMxNKABDWignwYW5v2HwGHQm+Sbw50J6UVMEk58H9JIuioLRt8MfLJvbxuUd2Xk82bwdUh3xz/A544iwdCABjQwQAaWYD/7w4Fwy1jscyTbTg1/hxfgLkgr+j0wamYgo/IyGu/gAayX14AHUKa70oAGOs5AbiE6Hz49hpJPx+snQAZZXQvLQZ1j2K4B11lqc91MwM1GXNaABrrFwPJUNAOu5htDhcvrvFuw3bgwF/wFMh6nrjFsCbhKXdB1PbnWSwMa0MBwGliVg+8AuW57f4uCfIl1G8Bk8B7k+vBISOTBSBkI+124DowBNGACHkCZ7koDGtBAxQwkuebe3F0hA1qbYxNWJPkmyb4Jp0OedNUYT7MweeMK5wfGgAl4YDy6Fw1oQANVM/AVCpQHGu0CjzcUrnxgxqSsWwzmhSeL13dneiHkenEGwia2hrEZsDX6Tf4zZgMm4DE7cgsNaEADnWYgSXY92Bmeaih81m8EScwvwU1wLGwK7xTLNzO9DA6A+eED2A6MATZgAh5goe5OAxrQwDAbSHJN13OSb/Mvye3Eutzq+QIk7oLxYDW4GGaBFWENyGjoWyHPfe7t8/jZ1OitARNwb025nQY0oIHqG/gWRVwBknzLJPsp5g+DJNRF4XuwBySp7gsPwhuQH8HJYK20kq8BY5ANVOVJWINcTXevAQ1ooPYG0k2ce3Z3gSTfSSC3D2Uk879gHfgprATbQyLdy4m0dNMg2xj+DMYQGLAFPASSPYQGNKCBQTaQFm+u1yb5vgZzw+GQ24pehi/Az+Fg+ArsBbntKF3Pq8CVYAyxARPwEAv3cBrQgAYG0MA47CtdyrPBbvAmTAX3QVq6ub6b67zzQEY4pys6PyWYgVZ3wG/gMTCGwYBd0MMg3UNqQAMaGAAD47KPvSHXbpOEk3wT6XbeH66GjHL+LJwOaekm0kq+Ey4Aky8ShitsAQ+XeY+rAQ1ooO8GknyTZCeEdCfnFqIy3mImryfS/Zxrw3fD0zASXoTNwBhmAybgYT4BHl4DGtDAWBoYn+0PhHchLeD3oYzVmFkX1ixWHMD0EfgVJEmfDBmQZVTAgAm4AifBImhAAxropYE8JvLHkJZtntncmHw3YXlDOADOhItgfXi8mD+eqffzIqEqYQKuypmwHBrQgAbaG5iIlzOI6gnICOfGZJoRzT+CDLB6BRLTwIWQwVd3gFExAw7CqtgJsTga0IAGWhjIc5uPglHwE2hMviyOvqXoH0zL5Jt16aLOYKt0WRsVNGACruBJsUga0IAGGgxMzvzRkFbsMQ3rG2fz4I0MyEoLuIxZmcljKXMN2KigAbugK3hSLJIGNKCBwsBUTJN8r4EMpOopMvL5ILgFNofckvTDYv45pkYFDdgCruBJsUga0IAGMJBruBk4dTm0S768PDryOMn8mMLEMCNsBBeDUVEDtoAremIslgY00NUGZqD2afmeB2e1MfEZXkuiXh6mhoyOTkv4dTAqbsAEXPETZPE0oIGuMzAzNU7yPQP+0FT7OVheHfK0q+Ug134Xhlz//RrkM/1SWA/y4A2jwgZMwBU+ORZNAxroOgOzUeOMdk6X8yUNtU+Cza1HS0IS9HRwIiwFedzktvALyH3A6YLOrx3lSVlGhQ14DbjCJ8eiaUADXWVgLmp7LCSxNibfyVm+GdLFPBN8Gh6DGeAK+AByW9L5kJZxBmJNCUbFDZiAK36CLJ4GNNAVBuajlkdCup7/2lDjXN89B/JZnfUjYFX4N7wBr0GSbUhCfgu+D/eCoYHKGMj9c7mmYmhAAxqokoG0aNN6XaahULn9aEV4EH4HN0AiyTjzO8IDsA7kgRt3QxLyEZDWso0rJPQyjmK7JXq57YBu5jXgAdXpzjSgAQ2MlYHF2PoAOAj+CYmlYT9YBKaHSYrpo0zLpDwu89NCknO6nF+E0yBd03tDuqWNihswAVf8BFk8DWigtgY+Q82SLJNsc9vQ+rAWbAGPwF1wJ0wBh8JecC48BUmwe8KlkJaw0YEGTMAdeNIssgY00PEGct/ubnAs7AsLwYwwOaSlm2u598N0kGc5p6s5iXpnyPXfL0AGXhkdbMDrBB188iy6BjTQkQZWodRJvmfB2TA3zAtp1f4vPAlJriHXdy+ABSHd1GkVfwlMvkjo9DABd/oZtPwa0EAnGViDwh4NSagZFJr7e9Py/QYk/g4ZzZzBVjvATJCu6RshI543h/fAqIEBE3ANTqJV0IAGOsJAupFPg9kho5wPhrR2H4Z94DrYCDKK+dvwCKwE78PfYKlinolRBwNeA67DWbQOGtBA1Q3kMZEnF4W8j2mu4+ba7y9hXXgTboK14RXIwKtbYUO4HowaGjAB1/CkWiUNaKAyBqanJBlolSSba7wZYJXrvblv95+Q7uZc58013U3hOcjI54x2/j1kxLNRUwMm4JqeWKulAQ0Mu4F0Nec2oVkgyXfiYj5dzhmIdTwcCImJYH9It3SSsdEFBkzAXXCSraIGNDAsBq7lqJPAY5BfNZoL0rW8GuTa7w8hiTnXfjOf7Y0uMuAgrC462VZVAxoYMgO/4Ei5h/d2SFfzHjAO5NeM/ggvQ677bg4ZAW3yRUK3hS3gbjvj1lcDGhhMA0mye0NGM18DoyDJdTZIQn4eloNpYAQ8DEaXGrAF3KUn3mprQAMDbiCfp3lc5BzwZ0jiXRoy0vkGSOs39/y+BHODyRcJ3Rwm4G4++9ZdAxoYKAPjsqMMopoSzoTVIYOuMggr8SXI4KpdYV7w+c1IMLrHwDFU9Yzuqa411YAGhsBAGjHLwN/gcUjrNgOr3oAk3NxSlAFWuea7MRjVM+DPEVbvnFgiDWhAA20N5B7fs+EzkPE040Ai07/AVJBbkDLqOY+STJI2NPCRAQdhfaTCGQ1oQANjNDATW+T+3VUh3c2JtHTTEn4U8pma+YxwnhTuhbSIZ4e0ig0NfGTABPyRCmc0oAENtDWQluxF8B5MUEzznObXoXya1avMpwWcVvFjkAdsjIBj4QUwNPCRARPwRyqc0YAGNNDSwLis3RsOgreLLdLCzfrymu94zE8Ik0FauplOC2n93gQ/AUMDHzNgAv6YDhc0oAENfMxAWrp5cMYX4UXIdd3EW3AHzAVpEYfcYpTI067egWfgVDgQDA38lwET8H8pcYUGNKCBjwzkXt5FIMl0FMwKU0NawDNDupjT8k2kG/p+uALSVf0PyI8rGBpoacAE3FKLKzWgAQ184mc4+DQ8BbnWm4dqZJpbitLKDYl0Q/8WdoG0hA0N9MpARusZGtCABjTwcQNrs5jf8H0IHoQ8VOMWSALO9d58dj4NeeTkbLADmHyRYPTegC3g3rtySw1ooDsMLEw1j4Fc750c5oZHYT5IN/OkcDF8BTLIytBAnwxUsQWcLwXlQIc+Vco3aUADGuijgXV4X677jijePw7TDLjKAKsk27R+R0JayCZfJBh9N1CVBDw+VTgE8i0zowdzv1zurcsowy3B0IAGNDBYBtKyzUjnfO6cD+luzudQupQzCvpxeKKYzyCrb4OhgX4bqEoX9AnUZAZYCx6EJN90/SwIx0JGGf4cDA1oQAMDaSDdzRmxnBZunmiVVm0aJmkU5Prvi7AQZP3zkF80suWLBKP/BqrSAv4iVfkO3A6vQf4QMtLwOtgZ1gVDAxrQwEAbOJsd5rd509pNq/efkOu8GWyV5bSA0yDI9AEoH8TBrKGB/hmoSgJOV/MqPVQl11qe7eE1V2tAAxroq4G0eueFf0GS6/WwKOS6bxJuWr6fhikhjYE1wdDAgBmoShf0/tToDNgV8ofwCkwBC0DK6H98JBga0MCAGtiNvT0HSbRp8c4EuRacHrcsp4GSVvB28LtinomhgYExUJUEfAvVWRyWhRGQ68Fp9ea679WQLunexOfYaKkeNlyM9Xl6jaEBDWggX+pzeSvdzRM36FiP+VzjTQLOk6xWh1wWMzQw4AaqkoBTsQz1v6JFDfPA85SzN9deXmS7h1vsI6vSqk7XkqEBDXSvgXye/C/kIRtJtGnlPgK5vSi9bhPBv+Fo+C34cA0kGPU2MCvVOw3yTfMymBvK2IiZ35cL/Zgew3vTzW1oQAPdaWBDqp0v8km8Id3L6V3LNK3dPHIyt0Ia3WXgKKq7xHBUOd/+qhC59vskpPs4gx2uhgyOMDSgAQ0MhIHD2MmZkN60dDunZZsesYx6Tos3A7ImgJfA0MCQGKhKF3Sux+QacK7R7g93wp9heTA0oAEN9MfAvrx5T0gPW5JuWr3pak53dFo+edDGzJD7fDcAQwNDYqAqLeAk3LR+y8g31Tyc42L4VLnSqQY0oIGxNLAA2/8I0r38MrwAaek+BmkFp/s5yTdf/vMZdDcYGhgSA1VJwCdS27Mg31LLyCCIcyDXbg0NaEADfTFwEm/K9d6pYQbIeJMk3tkhPYDhRshro8DQwJAZqEoX9KXUeC6Ys6nmB7J8VfFa00suakADGmhr4CZeXRJyXXcyyK1F6YJO13O6odPq3RlOg7SEDQ0MqYGqJOBUOo97+1eL2l/JumBoQAMa6K2B89gw40oy4GqS4k1JuknC+dy7FZKcs87QwLAYqEoX9LBU3oNqQAO1NHAqtVoX0vWcz7gXIYn4DUgCznQTMPkiwRg+Aybg4XPvkTWggYE3sDu73Aweggy4Ss/apHAzJOGOD9eAg62QYAyvgSp1QQ+vCY+uAQ3UwcB2VCK3E6XbOZ9vk0NuP1qmWE4iXgMMDQy7ARPwsJ8CC6ABDQyQgSTbmSCDrBJp7Zbdzll3OawGhgYqYcAu6EqcBguhAQ3008CXeH/u9Z0Qkmzz2ZaWbxoZU0CesPd5MDRQGQO2gCtzKiyIBjTQBwPT8p5rYU5I0s1gq/ywQq735oEbiUfhc6Pn/EcDFTJgC7hCJ8OiaEADY2Xgx2z9NMwFub83keSbJ+s9C/mt3wzEyi1HhgYqZ8AWcOVOiQXSgAZ6YeBMttkQcqtRouxyTqs3T73K6Oe0jrOckdGGBipnwARcuVNigTSggTEYOInXk3xzT28Sb7qb8ySrtH5z3XcayPq0ig+F+8DQQOUMmIArd0oskAY00IOB/GTg3yBdzmn5JsGGJOB8liXpZuRzuW4k8/uAoYFKGsh/WEMDGtBA1Q1sQAEzmCrJNz+mkM+ukOSbaRJy5hPPQFrI38qCoYGqGrAFXNUzY7k0oIEYWBAuhBGQSKIdF9L9nGmSbtkCfpX5teFqMDRQeQMm4MqfIguoga41sCc1PwTKlm7Z5Zwk/C5kmsi134x6ni4LhgY6xUD+Yxsa0IAGqmYgXc6HFYV6m2l5XbcceJXRzWlAJPk+DLODoYGOMmAC7qjTZWE10BUG8kjJ30HZvVy2gMtu57LlGxlXwAh4EwwNdJQBE3BHnS4Lq4HaG/gGNczDM8qkm9uL0tJN8k0rOMk3r2W6EawKhgY60sCYrgHPTK0yojDPV/0WPAL5oWtDAxrQwEAamIWd3QjTN+w0iTafPWkJZz5kPj8luCTkhxYMDXSsgXYJeDlqdRnMB/vDUpB77PKUmV+BoQENaGAgDGzKTk4rdlR2O6dLeWLIclq+idx+9BW4OAuGBjrdQLsE/E0qtzXkWatfh+Uh2+fJMiZgJBga0EC/DMzJu/MlP9My8Zat3IlY926xPp87WT8/PACGBmphIP+pe4r8hFeG9q8A6Ya+AzLy8BUwNKABDfTFwNy86QxI9/H9UCZfZkcn2feZ5vpuWr25zze9bvmc+j6YfJFg1MdAvln2FH/ihWMhfwQjITfEnwo/BkMDGtDA2BhIEr0KcmkryTU0RtkCzudNIsuZTzI+HI4EQwO1MtAuAedban7Oa0o4G/JNdTu4AgwNaEADvTGQRJvkuQskCSeSXBNl0i3n83r5Wt73JGwKfwVDA7Uz0C4Bp7KXFjWegekoSJeRoQENaKC3Bkay4WaQxJpruu9BLmU1Jt/GVi8vjb7l6GtM/5gFQwN1NVB+I21Vv7y2L9wOGSixGpwP+Y1NQwMa0MCYDKzBBmnBphs5yTdPrcrgqiwn0spN8i1bvVmX8Sb5jDH5xoZRawPtEvA21HxVWK8wcDnTxyHrDQ1oQAPtDKzJixlHUkYS7fuQ5JvPnSTfxs+ft1jeD9Lb9hIYGqi9gcY/gObKrsCKXLt5ongh32CPhSRlQwMa0EBPBtLqzS8YJckm6eZzJiQJZ11JWr4Pw7KQlrEDPJFgdI+Bdgn4UTQkCTfGOixkYIShAQ1ooJWBHVh5avFCEmweqFF2MTdOr2X9ZDAC/gGGBjTQYCCPhHsEboLn4DpI8l0IOjGOodBndGLBLbMGOsDAdyhjupHTxdxM1r8GaQ2HkyAtXkMDVTBwFIVYYjgK0m4U9NMUKPf+bgizwVUF+QMyNKABDcTAjJCBmp+CcSCt3EyThBP5KcEJYHzI+nRNbw/5kQVDA11toFUCvhojU7aw8tViXW5N+l6L112lAQ10l4GDqe7eUCbe1D7z+ZKe672JJN+sS9wAGdSZ8SSGBrreQKsEnOSa2wV6inRHGxrQQPcaWIWq/wFyDTct3rR2M56kbPXmXt8sl63hJODcwrg+2IOGBEMDMdAqAedbqqEBDWiglYGtWfkLKBNupkmwZfLNclq9Sb5lEj6b+VzKKrdh1tCABlol4IxIzIPPl4MtWyi6mHU7t1jvKg1ooN4GZqN6vyyqmGRatn6TgEOibPVmPj+4kO13g6w3NKCBBgOtEvA2vD4KnoLLId1JZWQgRbvu6XI7pxrQQP0M/LOoUmM3ctn6TWs3keUM4LwEboHjwNCABloYKP9oGl+6i4WMXPwuTA3/bmB25vcEQwMa6B4Dm1HVJyEjnRNJsvnyHjJffo6kVbwBXADpSTP5IsHQwNgY2JaN013UildYn8TcieF9wJ141izzcBpYioOnGzmJtaTxcyEt4XL9HcznC/up8C0wNNApBobtPuCeBOWbba7bfBbKb7rlbQU9vafq603AVT9Dlq9KBr5AYZJck3DLJFsm3FyWyny5vAPz+QGF38ImYGigkwwMWwJOcm0V+QM7utULrtOABmpvYHlq+Oeilkm+6WIuE27GgSSSmLP+N5BRzifAWXAOGBrQQC8M9JSA89Y8jONnsDCUf3TMfiKjoHfJjKEBDdTKwDzUJsl0EUiCTSTJlt3OmU9CznXf8A7sA8dDEnGu/Roa0EAvDbRLwLkVaQrYCV5r2N8LDfPOakAD9TDwR6qxNiSxJuFmmigTcT4ryvWZpkW8HBwLebZz2WJm1tCABnpjoF0CnpkdpAV8RW925DYa0EDHGsgI5xkgibVMsmXXc1mprE/k8tTNkOc57wvpevYzAgmGBsbWQLqUeopzeWFTmK6nDVyvAQ10tIH5KX2eyzw9lAk2Ld8k3yyHzJeRgVZ5DsCWkK7no8HkiwRDA30x0C4Bz8QO14R8O74P7i5Il5OhAQ10toEvU/w7IXc3NCbZcr6866H8jJiN7Z6HjAk5BA6Da8DQgAb6aKBdF/SF7POmFvv1GnALKa7SQAcZWIGy5scUEmkBZ5Bl2eJNws18GbnWOwvk6VZLwP5wIOQpV4YGNNAPA+0S8KPsNzTHRM0rXNaABjrGwCSU9EpIS7dMuq8yPxkksq7shj6P+Q0hSXgZ2Aty3TcP3TA0oIF+GmiXgKdh3ydCbk1Id1S+GU8I14M32yPB0EAHGjifMifBhkT+tieAJNn8jWd9er6WhjKWZyYP5tkT7ilXOtWABvpnoLy+02ovu7JyYjgJHoN0Pb0Cuf5jaEADnWUgD8lIq/fzRbHLlm4SbubLz4KM8WhMvquynOSb2xJNvkgwNDAUBs7kIKvBpPCv4oCLM/11Md9pEx9F2WlnzPIOhIGt2Ml7kCSbBNzY9ZxWbyjXz858Y3yJhSTu5vWN2zivgU43cBQVyPiGIY/yW2+rAz/OytkgD+HIII1PQQZgZZ2hAQ1U38D6FPFkSDdzEm3Z2k1CfgvKSHL+LTxcrmD6FdgSdoHG9SwaGtDAQBhodw34VxzgOrgf/ggXQhJxvhEbGtBAdQ0k0f4vJAEnuSbKL9tZTkJOpOWb+Uy3gDLyvvVgZ3iqXOlUAxoYWAPtEnDuEZwP8s05iXhbeAl+D4YGNFBdA7lfdyook28ScuaTaMtEnMSb9W9AerXyd574BqTrOcn3WTA0oIFBMtAuAe/OMTdtcdxFWJcBGYYGNFAtA0mor0FuFSyTb6YhibdMxOU0X6Zzm1EZ32JmRUjy9X7/0opTDQySgXYJ+DyOeUNx3PzB5slY+cO8qFg3WJOUKfckvjhYB3C/GqipgbRYc+dCrvGWkb+nMgmX665i5qvQ+De2HctLwk7wChga0EDFDHyG8gxGF3SuLef2pkch3WT5wHgd7oAtYSDiGHZyxkDsyH1ooIIGLqdM+dtpJIk45GlX5frFmG+OfLH+OeSOB0MD3WZg2EZBt2sBtzoJc7ByilYv9HPdCbw/v8ayFjwISb6Tw4KQ+xInhHxAjCnWZYPVe9hoWdY/18NrrtZAJxvIE6pWgSTZMtmmG7q83jsO87nG+3m4FcrI+u/BbLAbvAmGBjQwRAbaJeD8YW7WUI78Qc8KGzesG6jZL7KjJMjGEZcvs5zBX/l2fiD0JgH/ne3yoxGtIuX3G34rM67rZAOXU/iVIb1GSajpTco0Ld9yoNXNzC8FjZHXkrgzWCt/62+DoQENDKGBdgn4HMqRBFhG/qAfhMEYGXkH+10FfgfNsTYrenvMbNfTti/yWj6cDA3UwUD+L+da7QRQXuMtW7xZLuf/xHz+hhojyXd/yHuThN8BQwMaGGID7RLwQ5QlDEXkw+AM2BUegHywpKt7AUgZ1wRDAxr40EAeipPeoiTSDyAt3jLhpgs683ntGWhOvkncB0K22wfSNW1oQAPDYKCnBJw/3nXgszA33Af5Jn01/D84HB6GgYpb2NHikG7oEZDrwWnJpts5x8w3ekMDGvjEJ65FQv4uy0jyTQs2f8tJvOXfdHqscudCY6TF+2PI5Z1DwOSLBEMDw2Wg/GNtPH4GPJ0Jn4Yr4Z8wJ5wPV8CkMJDJl92Njrf4N/s3NKCB/zaQlmvZ5ZxX86U0yTfTvJb5xuu+S7PcmGAzBuIweALyBTrvMzSggWE00CoBH0p58se8COQpOWX8iJl7YatyhVMNaGBIDCTBvg7pmUpSTUs3f6Nl9zOzoxNq/p7TtbwWNI52zpfmJN30ZB0DhgY0UAED+UNujjVYkVGRjck326wJt0Nj91fWGxrQwOAaSC9Ukm9GKof8UEqSb6JsySYhp5cqyfYyKCO38x0NGeho8i2tONVABQw0J+DxKNOMkG/KzZFrwHvB/M0vuKwBDQyqgVwCSqLN32u6kmeGXMdN0s26TA+Ar0LjiOapWD4O/gE/A0MDGqiQgeYE/C5luxsWalHGUaxL8r22xWuu0oAGBsfAvuw2STd/m2WyzZGmhCTeXPfNc9sPhMaYhoXj4XL4VeMLzmtAA9U1sCNFuwnmbCriaiw/CYs1re+UxXS/ndEphbWcGsDAtJCu5mZyHTjrzoJJoDlmYEX+r2/Q/ILLGtDAfxmo1KMo81jI3K5wZ8GzTEfA9PBlaBzcwaKhAQ0MgoFN2eepxX7T/ZyEW/ZYZfkZaJVg0z2da75JwH8AQwMa6EADU1PmDLzaAT4HE0Inhy3gTj573VX2U6humXTTxZzkm8FXSbqvQl7bDZpjNlacDV9qfsFlDWigRwPD1gLusUQ1fMEEXMOTWsMqXU6dyuSbrubMZ2BV5suu59eYb465WHEurNr8gssa0EBbA8OWgFvdB9y2pL6oAQ0MmoEb2fNSkBZvIl3OmQ9JxPl7zcCrlaEx5mPhMMgHyTWNLzivAQ1U14AJuLrnxpJ1l4GTqe6SUCbbJN90P+dvdHxIJPluAxkkWcanmTkY8gCd3G5kaEADHWKgtwk4oyqfg3wgGBrQwMAaGMHutoIk3/Kab8Zc5O8zLd9EpivB37JQxOJMfwgHwT+LdU40oIEOMZBv2T1FXtsX8vSryyC3IeVJO7k1wtCABgbGQJLsPVAm2jwMJ8m3vObL7CfehMmhMfl+huX9YT8w+SLB0ECnGWiXgNPVtSqsV1TqcqZ5BF7WGxrQQP8N5AEbr0C6mDPQKn+P6WZOSzgxLqTnKff6vg5lLM/MD2Bv+Fe50qkGNNBZBtol4BWoypHwRFGlPInnWEhSNjSggf4bSMs3rd0k3yTbRHmZJ3+bz8N0WdkQqzCfW5C+D3c1rHdWAxroMAPtEvCj1CVJuDHWYSFPwzI0oIH+GZiDt88K6XpOqzd/i2n5vl8sMxn9eqZlrM7M9pAEfH+50qkGNNCZBtoNwsp9s7kt4gswI1wHI+DzYGhAA303kOSbBJrkG3LdN5GHbEwMSchbQa79lvFlZjaFXeCxcqVTDWigcw20S8BPU60FYUOYDa4qyDd0QwMa6JuB6XnbA8Vby2u9WUwinhSSfI+HU6CMrzGzPuwET5UrnWpAA51toFUCvpoqTdmiWl8t1l3K9HstXneVBjTQ3kAGWz1SbJLru3m85DSQpJvI9HrYOQtFbMJ0LUjyfbZY50QDGqiBgVYJOMm17BJrVcWMyjQ0oIGxN5DEmiSbAY0ZfFX+/WUQ1gSQVnBGOJexBTMrw47wAhga0ECNDJQfAI1VuqFxgfl8a58dRkE+OAwNaKBvBlbibRntXCbcXO99AyaBJOYFoLzE8x3ml4Yk7ZfB0IAGamYgIy97irSC/x+8BrnRP9NfQrvWMS8bGtBACwN5mly6knOb0YtQ3nZUJt8tWZfbkhLpbs5TrjLgyuSLBEMDdTTQLgFvQ4XnhEVgsmL6KaZ7gaEBDYydgZFsnkFX+SKbuwryt1eOcs6Aq5GQVnAuAc0DudUo2xoa0EBNDbRLwHnU3RFwd1H3fDs/CNKNZmhAA703MIJNV4ck4DzR6kl4G9IaznXfoyB/i/lyOzPsAemaNjSggRobaJeAL6Pe34Wpivpn0MjmcFWx7EQDGuidgYvYLNd2M/J5VkgLeBSkZ+kOyJfb/SF3H+wJb4GhAQ3U3ECrQVhllSdnJtesnoV/Q7rF8uzaPEAgt0YkloR8ozc0oIHWBtZj9fyQlm7uAU4rOK3buSCRe3zTs5TX94a0ig0NaKALDLRLwBdQ/xvH4KC8hjWGzXxZA11pYA5qfQ4k6eYOggkgyTfJNr1Paf1mrEW+xB4M5QhoZg0NaKDuBtol4Eep/BOwKOSDo4x0o91bLjjVgAZ6NJAEm6T6KiTJ5vpubuvLCOgk4OsgI6IPgyRlQwMa6CID7RLw5/CQb+/58Gi8JnUpy7uDoQEN9Gzgy7yUcRP5+3kYFirmM9I59//mS2w4GgwNaKALDbRLwJvi4wcwsgu9WGUN9NfAHuwg3c75G0tXdC7XvACzQeJEOGb0nP9oQANdaaDdKOh8ay9HQHelHCutgT4ayGDF5SGt3yTh/MhCup0zAjqxN5h8R6vwHw10r4F2LeCj0HILfAH+3aDoNuZPb1h2VgMa+LiBXL7JaOZ8wU03dKZJwOl+/iXkmq+hAQ10uYF2LeDtcZMPj9yjmGvAJflGb2hAAz0bOIOXXoGp4XfwF8iX3bdhWzA0oAENfPRrLK1ULMbK78F5rV50nQY00NJARjkn2Y4HuWNgUUiXdJ7p/BQYGtCABkYbaNcC/iNbrAXttlGjBjTwfwbmYjYDrfJEq/QepQW8BMwHuRf4MjA0oAENjDbQLrlOyxZfh9fgPri74FimhgY08HEDK7KYv5PcYpR7etMKzuWaVyDJ91OwFxga0IAGRhvIh0RP8SdeuLnFi/mGb2hAA/9nII+YvAqSaHOdN13Oue0o3dBJyFl/A/jkOCQYGtDAhwbaJeBH2SQ0Rz5cDA1o4EMD0zHJE6+SZEOuAb8OuY0vfyvpSUoifg4MDWhAAx8ZaJeAp2GrPCxgHsgtFOmuznWt62ETMDTQ7QaOQ8B3IX9HSb75O0mk1TszTAa59Sj3A28BhgY0oIGPDLS7BrwrW+WD5CR4DPaHXM86BAwNdLOBtHJvhZ0gSTf3/JY/pJDrv2FyyN9XLuPkMZQvgqEBDWjgIwPtEvBcbHUUjIR8mz8btoTdwdBANxr4HJV+BHJPfG4vSqJNCzct4EzL5Jv5xBywNOQLrKEBDWjgYwbaJeDH2XI2yCjofOPPKM4MwMo6QwPdZmAVKnwVlI+TzHXedDuXJCmnJVwm4TyAYxQYGtCABloayDf3nuJXvHAd3A9/hAshifgsMDTQTQZ+T2XXLyqcBJuYBNLtnNZuyICr8hrwM8x/EwwNaEADPRpol4Dv5F15gEA+ZJKIt4WXIB9Ghga6wUCS6oMwA5Qt2/QalS3djG4u1yf55m8lX1Y3LNYzMTSgAQ20NtAuAefD5anibfMzvQ+uhneKdU40UGcDadXm/39+ySj39j4BGQuRRJvX8reT5Jv5TG+AZcDQgAY00CsDra4B50PmJvhxsYetmWYk52FwO+RasKGBOhvI38UdkNuI0trNpZc5IMk2CTivJ+km8oX0R2DyjQ1DAxrotYFWCTjXfvNt/hCYGPLoya/B4nAR7AmGBups4HoqtwC8C0mw6VoOScahbPXuyPwE8EMwNKABDfTLQD5Ycq/v9MVeVmf6WDGfyefh8oblTpo9hsKe0UkFtqzDYuAvHDWt23Q75w6AjHLOiOck3szntedhBTA0oIHON3AUVVhiOKqR61iNke61rHuzWJmEe1kxn8mUkARtaKBuBtKSvQ9mgbR283dQXu+dkPl8OU0cDzuPnvMfDWhAA/0w0NwFnW/5t8HWkA+ijeAcSOQDaCu4JQuGBmpmoBxk9SL1ynyScBJwvnBmPnEzmHxHq/AfDWigvwaaE3D2923YBR6GtH7/BDNBbkvK7RhHwmBGWh5TDeYB3LcGmgz8nOX07qR7OaOec903/9/zhTTL+T95DSwLhgY0oIFBN5Bn2ZaRD6FcDx6/XDHA0+z3EHgUyutsue6WkahbwkDEMezkjIHYkfuolYEfUZsk3iTdR4r5LL8KScD5/3g7GBrQQD0NVOYacKPexmu9GYzy58YXB3j+BPaX1vVa8CAk+eYLwIKQUdi5BpdWiqGBgTSQH0nYCTLgKl3P+aKZSDLOk64SV8Fqo+f8RwMa0EANDTxEnZKAW8VnWTkQyd8WcCu73b3uZKpftnyfZT6t3STjXPNNKzj3vhsa0EC9DQxbC7jVNeDhUJ2u5lV6OPDarM+Ho6GBgTSwDDvbDNLNnDEHT0IugSTxJhG/BPuBoQENaGBQDGRwSRVifwqR67O7wgOQ7u8pIA9DSBnXBEMDA2ngcHaWRPs0zA4LQ/7fjQtpFS8CeRCHoQENaGBQDFQlAd9C7RaHZWEEpDs6rd5c970a0ioxNDBQBpZnRyvAy3AFZKDfm5CEnOSbOwHSGjY0oAENDJqBqiTgVPAtyIdhc6RFknLm2pyhgf4amIkd5P9Zkm0etbopXAJzwAh4DH4HhgY0oIFBNVCVBDwrtcyPP6wH18F2cD8kNoCs/3oWxhDb8PomPWwzF+sf6uE1V3eHgXyZy8M03ofbYCqYHlaBxASQa8P/yYKhAQ1oYDANVCUB70olMwhmKUgCTbfzynAvjE38ko1Dq8go6HzYGt1rYF2qPhGktyXjC3KrUVq/S8PUcCA8D4YGNKCBQTdQlQScQVaLQ67DZUBWnkKUW49yrc7QwEAZ2IMd5VJGSGv3GZgHcs95xhwcAIYGNKCBITFQlduQknDT+i3jTGbycI6L4VPlSqca6IeBPGQjI5unLUjrd/xiPtOvgKEBDWhgyAxUJQGfSI3Pgj0ban408+dAuo4NDfTHwJy8+QXIE9XyiMm0fnOLUbqds+4QuBEMDWhAA0NmoCpd0JdS4wySygdlY+Sa3FWQ1wwN9MVA7id/ADLwKj83mMeqZjDWDMU08weAoQENaGBIDVSlBZxKvw7/aqh9BlNNDlfCr8DQQF8MnMGbcsvRY/BbWALy/yr/98PxkKdhGRrQgAaG1ECVEnBzxTdjRboHDQ301cBqvPGL8Abk/9K+8E3I06/yfz/XgfNjH4YGNKCBITdQlS7oIa+4B6y9gSWpYVq3T8ErMAoy2v4USLfzOPA5yO1vhgY0oIEhN1DlFvCp2Mj9moYGxtbAsrwhrd0k33/Dr2G5YjndzXnQxhxwLRga0IAGhsVAlRPwdzCSlouhgbExsCIb537fH0BGOs8ER0AesHEh5HrwIfAIGBrQgAaGzYBd0MOm3gMPgoFc890evgf5GcvVIQ/duAfmgzz7OfcD57Y3QwMa0MCwGjABD6t+Dz6ABtZgX1vBLpBWcO4ffwLegSTdXOvN//dzi3kmhgY0oIHhM2ACHj73HnngDOQZzxvDPpCBV+tAupong8thRhgFeexklS+7UDxDAxroFgMm4G450/WtZ34lKwl4d8iDNsaH3HaUB24kAS8DeaTp7DA35OlqhgY0oIFhN2ACHvZTYAH6YWBT3vtF2BHyxLTxIA90eQnypKt0Qaf1uxZMB7fCj8HQgAY0MOwGTMDDfgosQB8NbM37cmtRku+fYUHIbWtvQ+7zTSJOCzgjoaeFm2BpMDSgAQ1UwoDXwypxGizEWBrISOfPwC7wM1gCHoU8XCPXfvPc53RF5+lXk0CefGXyRYKhAQ1Ux4AJuDrnwpL0zsBubLYQ7Arzw8aQruUrIa3faWBmSHd0EvBfIdd/DQ1oQAOVMmACrtTpsDBtDKR1m4drJJlmwFW6mJOEX4ap4KtwM5wPeYBL/m9vBp+H3IpkaEADGqiUAa8BV+p0WJgeDOSabm4xyq8Y7QFp3R4CG8FLkNdzvXdRyGMmp4T86MJvwdCABjRQSQMm4EqeFgvVYCD/R38Ime4Fq0ISa1q9adkm2YYk4sdhEdgKTL5IMDSggeoasAu6uufGkn04kOpgRGRg1Y/hp3AhJOGmC/oUyL2/b0J+ZGEEXAZZb2hAAxqotAFbwJU+PV1duAygStJ9AX4C90Ou/+be3mdhFsgArNMh65OUF4DDwNCABjRQeQMm4Mqfoq4s4ETUOkk3txYdCRl0lec5vwhJtOl6fhjS6t28mOZ2o0fgcjA0oAENVN6ACbjyp6jrCphEewTcDcdBBl5l+Xq4FhaCpWAEJNI9nft+H4QvgqEBDWigIwx4DbgjTlPXFHIKanos3A5JvonPQxLvbLASTACjIE+9SvJ9FX4BK0JawIYGNKCBjjBgC7gjTlNXFHJqankMXAlnwpdgA/g65P/pG5DbjDJ/G+S+4Ivgy2BoQAMa6DgDJuCOO2W1LPC01Cot30thcUiXc1rDub/3P5CemgnhXzA+pDV8FuTHGAwNaEADHWnABNyRp61WhZ6B2iT5ZpTzUTAVJOmmazkjoDPg6kVYGiYuyPJmYGhAAxroWANeA+7YU1eLgudWouOLmqQ1Ox7kvt60fDPaOfOZPgV5pvNj8HyxnCRtaEADGuhYA7aAO/bUdXzBR1CD3GJ0I+QJV0m+adlmIFWe5TwTLAxJxmtBknCScR66kUdQGhrQgAY62oAJuKNPX8cWfm5KfjicCOfBPwqWZJprwHdBEvJ0kJHO70K6qu+FDMrK64YGNKCBjjZgF3RHn76OLHyeVpXkezSklXsuvAzzwY6QnxNcAnItOMk314GvgrSC0yI2+SLB0IAGNNBJBo6hsGd0UoFrWNYk0LR4PwMj4Gp4GJ6BPNs513Vznl6DO2ARMDSgAQ0MpoGj2Hm+9A952AIecuVde8D8B/8RHASPwJmwArwBub0oSfg5+BrcAmvD7WBoQAMaqKUBrwHX8rRWrlLLUKIMtNoX0rK9EPJEq4xszu1H68KdUN6CtBLz6X42NKABDdTWgAm4tqe2MhVbnpLsBnvCPbAKZLDVdXAJ5DGS+dWjjHCeBh4Dky8SDA1ooN4GTMD1Pr/DXbtVKcAO8H1ISzeRhHsOpAX8WcgTrtIdPQ8k0jo2NKABDdTegAm49qd42Cr4JY68FewKGWhVRq7/5nrvfpAHbVwPafnmiVe5Hel0MDSgAQ3U3sD/1L6GVnA4DHyFg24Ju0Bj8t2O5cXgNUiXdG5JyutTwA9hEzA0oAENdIUBE3BXnOYhreT6HG0j2Bkeh9zrezQ8AD+FXN/NsP/JIYOyco0429ryRYKhAQ10jwG7oLvnXA9FTb/BQdaAJNR0QWc6P+SL3kSQ7udDIdtsDJfDQ/BrMDSgAQ10lQETcFed7kGt7LfY+4qwIyS5bgYZ7XwH3AxLQQZe5VajdDtfBnNCkrOhAQ1ooOsM+OHXdad8UCq8HXtdDjLgKok3txXloRp5kMYs8AjcALnX9zuQL35zw1chrWRDAxrQQNcZsAXcdad8wCucBLoAZDRzupQzyGoU5NnNx8IqsCbkxxRyLXhBSMJObAvXjJ7zHw1oQANdZsAWcJed8AGs7jjsK/f3piW7CBwIz8Jj8ARcARvCiZAEPTMcBhl8dSWkpZx7gg0NaEADXWnAFnBXnvZ+Vzq/0bsXzAWLwzQwJeR6b1q0aeXOAe/CQZD/Z+l+vhT+APdBRkgbGtCABrrWgAm4a099nyue5Ls/TAVbwAWQ245+DvNB1qcr+lZYHaaA7eEsMDSgAQ1ooDBgAva/wtgYGJ+N09Wclm1au9tAupdDWraLQ7qfMwp6asgljs3hfDA0oAENaKDBgAm4QYazbQ1MwKsZ3fwynAJp0U4Ik8FX4DuwKOS670RwHORxk2+CoQENaEADTQYchNUkxMWWBpJQD4cMskqX82NwF2QgVrqZ0xrOU61WhAy+mg6+ByZfJBga0IAGWhkwAbey4rpGA5OyMBJWgnXhOrgJ0vrNiOYbIQOq5oaMal4HXgRDAxrQgAbaGDABt5HjS5+YDQdp0X4dzoPPQEY/TwLpYj4bToa89iCklWxoQAMa0EAvDHgNuBeSumSTXONdDMaDlWGFgoeYHgPpUk6iPRe2hm/D9+G38B6kZWxoQAMa0EAvDVQxAadMGdjzYi/r4Gb9N5ARy3lgxsOwOUwBeZRkBlotD09BRjzvu6aecQAAFN1JREFUD7m+exScBImF4d+wZxYMDWhAAxroLAO5veUQeBQ+gP/A65DBPVvCQERacWcMxI5qto/c1/sk7AhrQ7qV/wHPQ7qac4/vC5DEewv8FHJ+doPrIQnb0IAGNNCpBtKgWGI4Cl+Va8AnUPmFYC3IwJ6UK78jm27ObWE7MAbHwAh2ewXkHOSa7wjIl6HcbvRF+CM8DrdDzs3X4V5YEXKb0algaEADGtDAWBqoShd0PuiXhXR1lpEEkOuKO8OB8HMwBt7A2+xyPEjyzSjn9ED8FZ6FDLhaCu6Hf8GskB6JLBsa0IAGNNAPA1VpAaereZUe6pFu0SQDY2AM5PruvDAtfAl2hbkhreCfQK4FvwT5cvY3SBf0a3A1rAwmXyQYGtCABvproCot4P2pyBmQZPAAvAJJFAtAyrgmGP03sBK72A2egA3gLdgJloSMgD4eHoYMsLoN0guxL/hADSQYGtCABgbSQFUScAb3LA7phh4BM0Bavel2TsvrP2D0zcAkvG12mAauhBHwDqwBid0hSfku+D18A14EQwMa0IAGBtFAVRJwqpjWWLpBy5iQmfHB5FsaGfN0MjZZHSaCv0OS70GQEc1rQUY4p4W7IOT6+ozwCPwDEhlslYFWJuDYMDSgAQ0MooEqJeDman6NFavA1s0vtFnehtc26eH1EazP6N26Rlq4I+EaSNdxuvIT6V7+J+wK28GmkC7olSDJeR5IzAufh3RJGxrQgAY00CUG7qOeaXU1kvuAM0I3634N/Y0N2UFuaapD5N7dJNIk0HQfTwBJvGn9lrE9Mxnclu78xAIwCq6CcyEu0ruQkc75svMQLA+GBjSggW4y0PX3AW/J2X4WjoEMBgp7w3nF/B5Mjf8zcAGzy8BfYEqIu/fgz1BGup1HwaeLFbnGeyosB+/AOpDkm67/WSDJO0nc0IAGNKCBLjOQa4+nwTmQ7tQMBjoZBirq0gJeHyEXNkn5Lsvpck4yLmN6Zl6BdMsn1oAHIV9s5obchmRoQAMa6HYDXd8Czn+AJIvN4H/hasj1SOO/DaS1+sum1Wn5JrnmC8vsMDUcATfDgfAX+BWcAofA/ZBWs6EBDWhAA8Nk4H+G6bjtDptbYb4IaQU/1W7DLn0tiXP5provxXIGWqWL+TgYCbdBPOYa8ZvF/MFMDQ1oQAMa0EBbA2nlpVt6oKIuXdCfRMi1kJ6CJWBruAYmhubYhBW/Bbubm824rAENaOBDA3ZBt/ifkO7o3AtsfNzAeyymBXwl5Dr5TPBVeAMaYwsW0gLeEexuRoKhAQ1ooEoG0poyOs/ABxT5522K/R1eWxp2hpfbbOdLGtCABjQwTAaqnIBzPTO3yBhjZ2AnNs+TrnaB18burW6tAQ1oQANDZaDKCTitOKP3BsZh090ho6Az8Kq5S5pVhgY0oAENVMVAlRNwVRx1Qjkymv0HkMFWe4A9B0gwNKABDVTZgAm4ymend2XLYyn3g4yC3hPeAUMDGtCABipuwARc8RM0huKNx+sHQJ7pvDdkhLShAQ1oQAMdYKCKD+LoAG2VKOL4lOIQSIv3h2DyRYKhAQ1ooFMM2ALulDP18XJOxOKhkCeF/QTSAjY0oAENaKCDDNgC7qCTVRR1EqZHwiNwGJh8kWBoQAMa6DQDJuDOOmN5NOfRcFcx7azSW1oNaEADGvjIgF3QH6mo/Ex+ajDPLL0eTqp8aS2gBjSgAQ20NWACbqtnWF9chqN/H9LlPAXkeu95cBoYGtCABjTQ4QZMwNU8gbNTrLR0F4b8kMKvYT64EgwNaEADGqiBARNwNU/idhRrLXgB8vu+6XLObUebQAZeGRrQgAY00OEGHIRVzROY24zybOck33Q5p+s5yXhCMDSgAQ1ooAYGTMDVPIn3UqyT4ZdwEUxQTK9hamhAAxrQQA0M2AVdvZM4L0VaDa6E3HJ0OpS/7fsX5g0NaEADGqiBARNwtU5ifsf3EMjTra6DxWEa+CPk3l9DAxrQgAZqYsAEXJ0TuRhFORAOgpuLYt1STJ1oQAMa0EDNDJiAq3FCl6IY+0J+VvD2ahTJUmhAAxrQwGAaMAEPpt3e7XtZNsvv+O4Nd/buLW6lAQ1oQAOdbsAEPLxncCUOvwvsAfcNb1E8ugY0oAENDKUBE/BQ2v74sb7A4rawGzz08Zdc0oAGNKCBuhswAQ/PGc5TrraAtH4fBUMDGtCABrrMgAl46E/4VznkhrATPDn0h/eIGtCABjRQBQMm4KE9CxtxuC9Dku8zQ3toj6YBDWhAA1UyYAIeurOxOYfKE652hDzX2dCABjSggS42YAIempO/DYfJ7/sm+b48NIf0KBrQgAY0UGUDJuDBPzs7cIiFIQOuXh38w3kEDWhAAxroBAMm4ME9S7uz+zlgV3hjcA/l3jWgAQ1ooJMMmIAH52zlZx7zdKvpIQ/ZeBPKyGv50YX8tu8d8BYYGtCABjTQZQZMwAN/wsdll/vApPB9eAfKmIiZ/MTgOPABbA2zwVNgaEADGtBAFxkwAY/5ZK/OJjNB7tm9ZAybx+cBkASbJPwuNMalLIyEXxUrr2R6PGSEdGMrmUVDAxrQgAbqbCDdoUbPBk7lpfUhiTSt2bOgJ2fj81p+y/c92B+aky+rRnc3l8k3y7+HxyCDtAwNaEADGugiAz0lky5S0GNVN+aVXKv9NpwOq8Ir8E1ojlzPPQxyi9FB8D60inRHNzufinWN3dSt3uc6DWhAAxqomYHmZFCz6vWrOovx7oxeboxfsLBo4wrmJ4YjIF3UP4Zc2+0pruGFA6H0nhZzfo7wX2BoQAMa0EAXGSgTQRdVuddVTWt3vqatF2c568uYjJkMqroPkoTHFIezwbRwFfwB8v6loKcWMy8ZGtCABjRQRwOfrGOlBqhOJ7GfsyEjlG+AFeBEmBISU8BRcCOkZdybSKLdtjcbuo0GNKABDdTbgAm45/P7DC+tC2nZbgZZzi1DL8PUcCxcDiPB0IAGNKABDYyVARNwe1350YStmjaZjuVj4AI4s+k1FzWgAQ1oQAO9MmAC7pWmjzaakbkk3/+F8z5a64wGNKABDWhgLA04CKv3wmZl0+PhN2Dy7b03t9SABjSggRYGTMAtpLRYNQfrcs33l/CnFq+7SgMa0IAGNDBWBuyCHrOuedjkJ3Ac5PYhQwMa0IAGNNBvA7aA2ytckJcPhyPB5Nvela9qQAMa0MBYGDAB9yxrEV7Kk6rCtT1v5isa0IAGNKCBsTdgF3RrZ0uyej84AG4FQwMa0IAGNDCgBkzA/60zz2bOLx/tA//+75ddowENaEADGui/gXH6v4uO2cNilDQjmG9pU+KJeS1PucpTr1r9QlF8rQqvQzdEfOR3iv/TBZUdjzrm/LY673Ws/iRUqlv+H09EXd+Gdj+UUpdznEbVuEV961KndvWYgBf7Oz5nTvbxBXi83YF8bfgN5D/3ZcNfjCErwVkcaZohO9rwHmg9Dr/j8BZhSI9+5ZAebXgPNpLDzz68RRiyo6/OkX4wZEcb/gNdOfxF6HsJHITVd3e+UwMa0IAGNNBnAybgPqvzjRrQgAY0oIG+GzAB992d79SABjSgAQ302YAJuM/qfKMGNKABDWig7wZMwH135zs1oAENaEADfTZgAu6zOt+oAQ1oQAMa0MBQGphhKA82zMeajuN3y5e03PM8+TD7HsrD57etuyWmpaLd8tCh3PM8ZbecWOrZTf+Pu+i0WlUNaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMaqJKBPLRhnCoVyLL028B4/d6DO9CABobdwEaU4Bq4E34HU0BdY0EqljreBn+FDaHuMSsVfBzmqmlFV6Ze+f/7EJwHU0HdY2MqeFPNK5kvGEcU9UxdD4Xxoa7RTZ/DdT2HY12vOXnHk5BH2yVOgqNGz9Xzn0up1mZF1WZi+jRMXyzXcbIVlbof3oE6JuBpqNcTsAjkA/toOAXqGvly8VN4Bm6uayWLem3N9FzIeQ1/gKyrY9Tmc7hbnvM7UP8J02r4NDxb7PCTTMct5us2yf+Nn0FawIl8cL8KS2ShhpHWwtdhTXiphvVLlZaCu+B2eBdOgPWgrrEaFXsDNq9rBRvqlV6qPSDnNaSH7nNQx+imz+E6nr9+12l+9nAW5D95uiy7IfJh9jx0w4Pe02KqYwt4G+p1OpQxMTP/gQnKFTWdrky96t4Cbjx1k7DwMHytcWUN5zv+c9gWcN/+V+YXR+6FCWH1vu2io941L6X9DewAdW0ddtQJ6WNhP8X7Xm9475vFfBKxUQ8D6ck5E26Ec+pRpR5r0W2fwz2KqOsLJ1GxXA8MmW+OtArTHV2HEbP5IlHW9cWGiuZb5hOwbcO6Osy+QCXK+n6pqUJ1bQF/m3qWlxRS5cmhTMJZrmusTMW6oQWc5HshXASZ75ao0+dwt5yzXtVzNrbKNc8wOywJ34Ey8luU6cKrw0jSfBiXdV20qGAGOzwG2xfLdZqkjmV9U/fGqGsCXoNKXtdQ0UWYv7dhua6zK1OxuifgjEc5Fy6GCaHOUefP4Tqft37XLQk3XbCzQLrvfwwZ/FDX+DsV+wlM3UA3fLOuawKegPP4NKTFkPlT4VCoe6xMBeuegHehjvksymdU+fc6KfN1jG77HK7jOexznXbinWk1hN/DQlDHWJpKpXXfzOZ1rGxTneqagFPNDeBVSM/G5VDXD2mq9lGszFzdE/Ao6tj8t/on1tU1uuVzuK7nr1/1yjXfyfu1B9+sgeEzkO7KOlw2GT6DHrkKBvwcrsJZsAwa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0MAAGxh2AfbgLDWigvgYmpmr/A+/Xt4rWTAMa0IAGqmJgUgrySsHrTD9oWL6Y+aXhbhiOmIWD7lgceEmm9/SjEE/w3vl6eP/arP87PAmj4K+wIgxW9FSvYzngfoN1UPerAQ1oQAPVNfBZivZMU/GGMwFvTFkuKMozPtMZm8o2Nos9JeAN2Mm9sA7kGIlV4Vn4fBYGIXqqlwl4EGS7Sw1oQAOdYKCnBPwAhT8BXoCboGxJjsP8vvAYPA77QNYlZoYjIYnvdkhSSywKI+EiSMt6ElgJboOX4FyYBmaCvPdVOB0WgkugjB8wk3LlfVsVK9OFfCKkLCnrWTAZJHpKwNfz2oajt/j4P7uxeGGxam+m2zW8nDp/p1hekOkV8DI8DLtCYmE4FY6D5+FWWATa1asxAU/LtnERJ6lj2SL/JPOnFOtzvD3B0IAGNKCBDjfwWcrfqgX8H9Ynuc4Ap8F5kNgM7obFIS3lO2AZSFwFv4Yk4m0giTTJJ8d4Dw6AdP0m0aQL/JuQFm7ecxTkWuzWcAlMCkvCPZDYBHLcz8D88DSMgL3gMpgO8iUh22cfiVYJeDzWvwmzZ4OmWInlJ4t1jYkxq/JlJEk58U/YA/JFYj1I3aaG+Hgffgip188hCb1dvRqPcwHbxsUMsCXky0ZiQ7gaPgULQNzNDYYGKm0g//ENDWhg7A28xlt+DE9BkkL5gb95sZzkkGSXltmXYRZIi21fSGv0l3A/fBUSb8EBkISUpPVv+CO8DjnOmvBBsfwu0xy/MbKfM+AGSCJOIk8izbp8KciXiBzjPkgC6ymSKCeEF1tskOQ7PSRJt4t8uTga3oZRkHJMC4kkxwMh+zoTRkC7evHy6Ei54uBweAPOgXyBSAs6PmaF5eBByLHi1tBApQ18stKls3AaqK6BfPiXkS7RJK3EzLAH7JKFItIinB0ehSTfMq5nZia4GR4rVzJNsl4YksAbI/vuKebjhV81vHhjMZ99HQcrwbOQ5Fm+xux/RVrOYTa4o+nVOVh+BJLw2kUS4N9gfrgVcrdF+WU/XwTKeJ2Z3n4GpR7/gcvLNxfTJN18mUlvQOo/PpwGOQf5AmBooLIGyj+KyhbQgmmgogaSDFpFktteMGPBPEw3gSS/JNspoYxPM/NQsfB+uZLpDXAtlPvINAmmMemz+LFIizVJuIxVmEkSPxFeKOZzvOx7HGgX2Wajhg3WYz6JLV29fy/Wp9U6QTGfSZJuIi3VtE6PgtR3NcjxymP25I1N2sbdvPoypE6ll7R+T4GUozxeXH8ZtgBDA5U2YAKu9OmxcB1o4A+UeUuYCpJ0Todd4QFIst0asn4xSFJNK7g5/sKKZWDx4oVvMr0E8veaVuMU0Bx5z7owGUwEJ0OSXa6LXgNpec4CX4C0gtvF93gxdUgyyzE/D0mAX4e9IZFWcsqYuiQhrgyJXJtOpDxvwcaQ3oExHbOnevHW0fEO//4VtoeUaQa4E9LK3gh+D6nvxXAPGBqovIH8RzY0oIGBM3ARu3oKRsF9kO7Xn0BauJtBEsjDcGkx39zNy+rR10yT6P4GSSZJiNtC9nE7LAjp2m2MY1h4BZLks8/fFdMjmB4M18M5cB7MA+3iXl7cBLaD5yAJ7hHIvneAieF0SEJ/HNItnMSXyHanwm1wM6wBOfa80C56qlfjew5l4RuQcqSH4EjI+34DSeD5kpPjp3V+Bhga0IAGNNCFBiahzqFVTMfKtBzHFEneacE2R744T9S8slienGm6ixsjx5qmccVYzKdFO2GxfVqxO0Ljscuu52KTjyapexL12ES7ejXuJ8ds5S/lTP0NDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oAENaEADGtCABjSgAQ1oQAMa0IAGNKABDWhAAxrQgAY0oIG+Gvj/2qq9epnziTsAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist(cog_final$residuals)\n", "qqnorm(cog_final$residuals)\n", "qqline(cog_final$residuals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When looking at the histogram, we see the the distribution of the residuals is slightly skewed. And looking at the deviation of points from mean line, only little in the middle, except for the tails area, the conditions are fairly satisfied." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w31.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/171) 04:35*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the residuals vs predicted(not vs. x, because we're going to observe explanatory all at once) and observe that the plot should show points random scatter around zero, with constant width(not fan shape). We want also observe absolute residuals(convert all to positive side) to identify unusual observations." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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nMSWKwoECODCQOvwnVl21PgeuUv0s/QzUVnNyop/39gH/isd18AJ0hFA4UEoOOAGVUCUOxAq4EnNiVzhQDQdacYyfhJeE9Pv0DXn/A4NnwV9AORX8pazaakZO3Bk6wOawAnSC28CVcCgcCAfKzIFYAZfZDYvmlpQD/tOF54P/acHMYFDsCt+D+hVGTkvV/Y9uVHEGeE1/DjwaZgP/moe/jBUKB8KBMnMgPWMvs6ZHc8OBBnWgJVd/Bx4Afza7C9wF/py3GHKl7ap6XzgIDLwGZP+KRygcCAfK0IEIwGV406LJJeHAErTCVejVqdYcRXp+mD1VVqjkE1TUB1YBV8DjYSP4D4TCgXCgDB2IT9BleNOiySXhwG+04s88LZmVMvcVWp9Sob+A9TT4HwT4KdrfMj0cQuFAOFCGDkQALsOb1kibPA/98h9tnwX8tDsIfodS1X9pmAF4Vxiea+TxbD8EPxUXQy9Tqb8JvTj8CLH6xYRQOFCuDsQn6HK9c42r3f4D7gau5+Es8JeaxoG/+VuqMvgeCvuBn4NvBX9Oa0Aupr6lclfBEXyL6XLUHQ7UgwOxAq4Hk+MSVTrgytFfLro+d+SbbP0NX//aTSn/f6w/0T5/69m2+tnZ4BgKB8KBcKBaDsQKuFo2xUFFdqAd9T+UuYb/YIU/Ty0HfUkjI/iWw52KNoYDJeRABOASuhlNuCmueLfN9P848paHwoFwIBxolA7EJ+hGeVvLrlOX0mL/SUX/Cs9Y2BQ+hjEQCgfCgXCgUToQK+BGeVvLrlP+LHUpeBQWg3tgewiFA+FAONBoHYgVcKO9tWXXsT9o8dCya3XtGrwAp/lXrtrDG+BfubL/pahlaJQ/i38PpkAoHAgHCuRArIALZGRU0yQdWJBeXwj+i1j+trYBtSp5zI3wCJwO/rvRfnZvDQ2thWiAXyL861TKn8OfBD3gcdgNQuFAOBAO1NiBIZwxqsZnxQnhQH4H/M3tv2Az8B/H6AP+JndV/wzleRyzCaR1Cpnd0wX1nG7J9c4G/xrYcPDvYft/GX8HySS9LWn/CczVIBQONCYHBtOZFRqiQ8nL1RDXjmuGAxU5sBY79gZ/M3q6ig5q4PIjuL5tvBU+g9vgOjBwVSb/16TsP6LxLGX+C2ANpZFc+CPw5+7+QyKHgavfdcB/cEQZjAdANzOhcCAcqLsDEYDr7mHUUFgHTqS6Q8B/anE9eBlmgFKTgfSFTKNeJ295ZfJnvltnDrDPljeUOnDh81MXH0b6C+iSKjPpJMFfmAuFA+FAOFAjB+ITdI3sqtHB/uZyPzgSlq/Rmf8+uDvZ98FPoolOI3FskmngbTOuvzT4ucrV7+WQlv94iKv2yjQTO6fAReCn6GFwDTSkxnPx6TIN8OfUfnKeJ1e+Bls/uRusQ+FAY3KgwT5Bx29BN6bHqGH6siqXPQMuht/gOdgY7oSaqjMn+GnXehJdRsIXpKE1PQ04B1yN/wp7wiS4C/yE6+fal+AGqEzfs3NR2A06wS1wOzSkHubi42CDXCMOZGt/nEzcB/6DKH6RcPLxKYTCgXCgAA5EAC6AiU24Cn9z15979oJ3cj7MztYV3dPgz0Zrom85eInMCQuR/ylT1hDZ27ioXJq7+H/YHg5T4RgwOI+A6uh3DrqqOgfW0zGDuI7B9SHwZ8E/g5ODL8HyqtSKA9YFn4dn4D0ohpy4eI034JdiXCDqDAfCgeI4EJ+gC+/rXFRpAFZzwCqwGJwNtfkU7eryDtgTrG9teBTmh4bWxFQD2pP2c+wY8JeSVgZXkQtDOcv7uSBMV4NOzMSxfq4+A5yQ6Iv3rZByoTAQ/LpwNThRmxdC4UAhHGiwT9CFaHy51BEBuPB3qg1VjofNwc+oBt7J8BUsALWRn3q9VzeDg21nKAXZzyQwueLdB66ElUBtCgaJpiY/Xe+Y6rRfLO6FQgZIv6gcnbrGFqS9Hwb/UDhQVwcaLAA7swyFA7V14AdOdAD255gGoE+gJ3wNq0NtPkX6afFQqEx+ivQXsxzk/cWofeEdKKYmUPmpcALMCjuDK94VwMmHPswITU3t6PCoVKe9DxPBLyAfQiG0CJXslarI5205WBH8bB4KB8rSgeZl2epodKk5YDBcA1wJnQT+Mk8vKIYMfm+Ag75B/0QYDh2hmHKWPBv4SfxgmDvHqmxnBwPwU9DU5C9nzZLp9NLknZyl1ZrMYXAl+PNyP+NXV9aVXSy4+v2zuhXEceFAONCwDsQn6Lr77yC4MWwHnXLV7cm2by6dbPxsfEmSKfDWgJu93taUuTqtDzmxGAn+4tKLsD+MgPegKUrvbwR/fmyQ9ccQL0AzSORE/2U4DRaHXeEvWBCqI7+ITEwduAfp78EfV4TCgbo6EJ+g6+pgGZ3vYLQFzAefwfXgYFTqcrAbCh/CB/AqOPj6szgD0NMwGfwkORA87ix4BYZDoTQjFb2Vqcz2+Mm7PuRvME8BP0XfDN5HV3UdoSlqDJ022I4GvXkMVoP0M+2XkefAyZN6Hb4FA+shUJUu4AAnfI/DFPCvgS0Ev0AoHAgHysCBUlkB34lXF8Im4KD1JLSEUpdBxtVeIj/H3g9LwlLwBjgYPwgG3b2hK3jMHeAgXQhtTiVjIanPrQF5G6gP+enUScfaqYvtR/quVD6S/3bgILJb/rto2i/pDc+UVZWdgwMWgOSX4ao6PvaHA9VxoMFWwNVpXGM5phQC8C6YeXfG0EHk982UlWLWQJr9rdMDKHN1k5Y+b5UuIH0RbJYpq0v2PE5+Hry+q2/z9anFuJgrvLPhErgc/ELQFOSEZwfwPvtJeVaoShtywLDMQX5GHpApi2w40BAONFgAbtEQvW3C11yEvp+R6b+r4J0yZaWYnUqj/Dnfm6nGLUP6nlTepAPyU5myieTnzpTVJXsIJ/vLT/PCFfAI1Kdc7c8My4KfQ5+FP6EpyFWr48b5sDh8CU5I0s8F2X/JrwNbwDNwMnSG7rAphMKBJuuAP48M1Z8D33Kp5TOX60H+60xZKWYNdA66HaE1HAuubPy8nNbHZNKfZ93XFz4xUUD56f4WqO/gm3ThOxKPwtPQVIJvT/q6ELgC1n+/PqwPR0FV2pMDBoCTtm/AT9JOXkLhQJN1wJlsqP4cuIpL3QquJh2814JzoA2Uuu6jgX/ANbmtqxkHU8vS8tOkwXZOsI87gz+zGwuNQV3ohCtfA/DtkO0/RY1W3tPhmd55j/fPlFWU9dkPhQPhQM6BWAHX76PgzN8Vw6LgqsHf7OwAP0I56EEa6SqoF7gCNghl9SkFTihmBD87+nl2I2gMchV3JrQG+/QKlMPkiWYWRB9SS/brxsqU/VxF7T7vK0K7Ko6L3eFAONBIHXBlNqoE+zYPbVoa2pZg2+rSJH/e7Wdrf+nM3xr202V9Sj8vAK/vav1UaAa1lT86cMIxU6qCU0j3T+WLmVyJyjeD1WtxkRac4y/LHQzWURfp6WvQDXaBSeAksiI5UfPrh+d9C/7sPhQOlJIDg2nMCqXUoMbYllIMwAdhtH8t6TL4AhrL4DQrffG3hHtDS1gFHKjrKwj7yfstMEAqA5AB4EAztVRfztsjc+4s5O/IlBUj6y8uOXn0eXkWLoHqqjkH+vPas8DgOwFuBMtrK330c/JwqOyX6/qx39+AbwbKieZzsICZUDhQIg5EAK6HG1FqAXh7+nwNGCzUMjAZFjRT5jqB9u+e6YOD/+mZsmJlV6NivU2rFZl70wV50gYKg4bH3QNPQ1tQ28CAaal//nA1+iBUFoT+Obp2qU05zZ+pJ8+JbbwZtoXq6CAOGp058ArylZ2/EPuHgj68AGtBoi1IWK5PD8NncCLMBFkZ6P1N9bT2IrNzuiDS4UADO9BgAbgus+AG9qzsL+/qcCD8keuJwXcQ9Mzly3kzI41/K9MBfzva8vqQwdagldZvZFqnCzLpuch7PxaGDWA9cNV4OVjfbbAUuAqeGXYDyz6HR8CAVAz5VWQHSJ6Tv0jbrq5QHXXioOzE50bKLM8nV/VvwxjoDU4ATga/YiwJBn/bszq8AU5UTPt5Xg/T+pGM3qXlM/BruiDS4UBTdSACcMPd+d+5dPJpLmmFAcIBttz1JB04DJLny36OBMvrQ89zkS6QnszsTd6AkNUMFJwL58OeYGBLgsZNpN8Hf9HoFzDwGBD97OwK0TpdSRqYPXdjqErzccA6sGJVB+b2/8C2Q+bYBcnn60vmsGnZb/lz+cwOfTFg5tP2FLpqviu38z22J8IuYJv3h1XAYLs7HABKb46clvrnj3EkDf7T54rWZXse3JfL12bjs+Qq/FDw+tNBKBwoSwdalGWrG0ejx9ON22BZcEWwArjamhvKXfZrLTAQXgEGLQf00VAfMugcDK7Q/Lzkqstn3c/gWd1LwetgIH0APGcQGISUE6Vk5fwzaYPugeDPNseC+gV2gj3AoFORNmCHAew5MAi/CQaRP6EiDWfHKDAQPgF6eSNU9zmxnU4k9MTPyZuAbZgT8mlGCl0BpzWVTBv4EZYAn9fLQSX1PEh602kl//zhdeeBl8Brq8Xgq2mp2v3hM/Q1OAnyfhr0fXd+glA4EA6UqANDaJcDWSnJWbwDvqupW2BZaExahc5sDms0UKfa5q7tyq15njYYHG5PlbtauxRGgKvMnvAXtIO0DGAG7LS6kDkvXZBJL0Peujqkyr3ne6XyFSUXYMd4uA9GgnWlZWA/EHaEbD9dIe4OD+c4hW17qEirsuMR0DvlivMS6AuuZG+GwXAZLAR3QXfYEK6CfNK/+aBVvp01KNuaYydljj+Z/GGZssiGAzVxwOfZSVyoiA6UYgC2u3PAIuCn0FD9OmBQMKAlcqX7KLwPV4IBcmHIyrK3YOncDleHN8LauXy+zX4U7pzZYZC/PlNW0+wVnDAUtoLh8ALYj0QG7guhF1wMrm5d5VYmg+3ncCxcB2dCIs+9AT6DL+Bg2BUM2rNCMWV71s9cYAnyThBC4UBtHWiwANyiti2O8wrmgAOdhOrfgQ+55LdwFhwNfmL2U+oucDVMhu8hK4PYTnA33A9+DjaQZldnFP1PP5Ga+X+5vxMzsfktU1aTbB8OXgmS2ftNpAfA/nAubA9/wkGgXEEfB04GHHQqkgHdgOoK1/49DYl+JLFtLrMb22VBD50AfAXF1DdU7tcMfU/UnYTXD4UD4UAJO1CqK+AStqzSpm3J3vPhDOgI5SqD4H9gNFwFrijbQnXUhoMMUrNV42CPMQCumTt2TrbjYI1cvjabYzhpo8yJnchfmSs7ne0YcMU6HPzasjR43wqhWajEz+4PwROQbQtFBVUbanNFvy8sCjvAX+A9DBXGAZ/TtaBLYaori1piBZy6Ta7KHQCnpsoiWVoODKQ5y8MJMDu8A6uBg3C56XsabFByQFeubl01Vkc/cJB9r46+5KDd4Fb4AqYDP50+CrXVN5zYGe5MVbAKacsNSq52x8ABsCKMBVe0hVgx+vPcN+A0OAw6gJOXPyC9QiVbMOn3JnBUDseIecF7GKq7AytRxcnwCpj2OdoGfodQI3bAl9mVlD97c/BzVuvLNhn6QiEUK+BCuPj//t9yVON9aZ6qbhnSt6TykazYgUXY5arRT9yHQDOormbmwAvhLngGzoV7wHdkfugDvju9wEArH8BFsBRcBr5f/hy3rtqSCrx+WguRuRGcQB8N58MJ4C9vhf7tgAuNA2EU+CwsDg0pnx+fnSVTjRhN2slVY9dgOrhCQ3QyPYg2xPWTazqouArZCBxkbNc8sBf4ucmfWYVKw4HZacZ1kF4lGpALMaiXRg+L1wqf6WvhATgL2oBeTgdVyWNegi9gQ1gVDN4G4E5wOqwDDuoG2g/hdnCFOBP4DlnHVfAj1FXe7zczlXxFfhZ4B34FP4X7Ln8J7SD0jwPjSDqZPR78cqBXa0BDaXUu3A9eTTXAZ6ZnKh/JRuqAL+xcFfStK+UOMnXVECpwthmqmwPO1G+C1qlq/HnRI6l8JPM7MJLiHpldriK3zpSlswZZPyE/Ch+Bz3BLUK6i7p6W+vuPWdm8C279dHhtLu0n6vbwEPSFQsjnwPrS7+3h5CeBk4u0nBQcky5o4mknSuMzHrgAuTlTVp/ZzbjYsZkLzk3eSVxjV5NfAbuC6lHBXd6Y8s8r2BfF9e/A61zyQfgeesO2cB7sAaHKHWjF7lcyhzxD3lVjRTqDHZ3gJDCgPgeDQP0B6S8Ps5P387Qr0THg/tvAydIl4Ep0KBRCPgcOXO/DkXARGEQMytlB+wnKnBSE/nbAyVB64mTpa9CQXwn8KuMKfF1QM8DZMMpMqHE7sDzd89PHU3A9XA6j4UX4DywIdVWsgOvq4D/nO1BcChPAVV0h7g/VlJQWpTVngj8e2b5ALfMzsZ/1EjUj4TNe2Wc+A/R00BbGQW8YBp1hX0gP5N4Xg98ckMiV8BfgthhaiEr7gH1oDk7Esivg4ZTtD6G/HViGzR3QJmXIxqSdLDWk5uTiz8Nd4O8P7ApNQU4kV2gKHa2sj87Se0BfOBb2hG7QDKorZ3D9KsBg4YMVqp4DS3HYDXAz/Bc2BeWg4Qr4IHDQfRqGQmOSK85JsAmsBP6s83yoq1wFfgMDwLpvhKrqTQ/KHTn+L3CQvAWcBE0PaW1ExmPWy/E2Wwf3+pKfx58EV069wL6+CE4iQv84sA/Jn8EvSIfCvRBfCTChATSYazb5AFyR77642UGmomMNGv4sIx8OZLdXdGKU/8sBZ8IO4v5ihko+ba5F2s/N2RXh1ZRtCKodOMOf10wZqRtt9flwlfkNbA6JnASOgMSPpDy9nYnMIJgIT0F6pUv2f/JZdt/RYLCsSv7SlJOdRAbT32A1cMWZ1UIUeM4jcD2sCPUt27UrHAO7QysI/V8HulLkgmEv8L0JNYwDTT4Az4/vDnDfw32wKCTajsSNSaYO2yGc66w8VLUDBgg/JablDPF88D7Nk95BehvYGwxQTnQugdfAIFMO8nlzwpE8d4+RngidoSfYr4OhD+RTcwpfhwG5nQey/QhehOdgNqitPHcq6H1/uBcWgXxamMJHwAnoyvAGXAihcCAcqNiBBgvADhyloENpxMfg577HYRJ0glDDOOAq7ZvMpX8mPwMYDFbJ7PMzml8qHoXDYX9wFWz5FlDq2ocG9gQ/NavJYEB14mf53jAIvoS0XBk7UZkAM8Kf4LHLw7LwKRwD14Cf7msjr9kBbgb93Rregnxy4uO7dCv4o4HFoC345ULZph5QUQD3mFA4EA7UkwMt6uk6VV1mQw5w0PoJToJX4B5YE0L178BDXNLPyhPhC3CidizcBQaBd2FPeAb6wuxg8NkZkiD2O+kdoR/cAqUsJxbpCYeB7HnQB4Ork4qnYEtwhZloAIn14UxYEVrBUdAV9M2g7Ip1FegG+lcb/cpJTkqroxczBxmI54STYUlwcnEd+J5dBaFwIBxoIAccWEtBr9AIV7+J/A3oC2E8zJYUxrbeHDD4GFxehdPhRnDgvh7eg/bgKmovcKW4LhiAs/L5+itbWIL5h2mTQTR5H1xxKvu/NlyS27qqT+RvGvsz3FXAQO0n+lEwE8wNu4NfC5RfD6afliruH1Oofp3UJezPPrAEOBHaAbyvnWAPWAMau7xPe4P99x6FwoFwIONAb/KfwNGZcmftzv4NAHXVECpwgAxV3wEDyWrgyqkqLcAB/szRLxmqNeh3LzNloEG00UnGCTAR7gODps+kn39vgJchUUcS6efJwd3JxvvwNowAV9b6YflcUGwtzAW8lkG3B9wO9ssJRBdIa2Myx4GfqJ1kjc/hSr4irc6ODWDBig4osfL5ac/9sC9sA3qzBYTCgbQDg8k0+cmZPyPrnHYll+7O1tl6XRUBuK4OVn2+g/d7MBzugb5QTvL56w6dwE/w78A54OTCSaCB1QmJagnXQx8zOW3K9gd4HO4FX2x/Hpvvuaa4KJqFWs+EobB/7grnsl0zl0423pvDwAmDP6eeGZxoPQgG2rSakTkProHjwT5uBqWuR2lgut96cwcsVOoNj/bVqwO+p00+ABfb8QjAxXb47/pnZNMRZv8722B/tuLKy+aYrhatcKX0ETwCBlNXUdY3GhIZmF1VnQyHw2OwOKjlwEDmoF+f8ovDWBgIL4MrYAeXB8D2qrXAdjuxdXWclpOF69IFpE+C01Nlc5K2r0ulykox6SQwK79w6FELcAJiQHb1vx+EmqYDDRaAfQhD4UAhHfiRyqYUssJa1DUr57himwr+TLY3LAPfQHXl5+PT4NLUCU4u0gHV1X5b6AYG+U3gS1Av/r2p1z87czUnC3PDJ3AcGHhfgP4wDt6Hr8HVrqv55yCtD8i0TxeQ7gIHpMo+I30F+DNk+38kuIJuCbvBu1AK+pNGzANOpBJtQMLA7CTD9u6Y257B1iCcvt9kQ+FAOFAIB2IFXAgXS78OA+GncGiqqX42vRyap8qqSrqCNWD5o5FEDtYjkkwJbg+iTdtm2rUEeT+n55OB1YDtJCWRgfaCJJPbDmdrPWm5Kj4EXEk7uVGrw0Qw6JWCkmC7Io1ZGG6GYeDKfQKkZTD258Xt0oWRbhIODKaXfiWqd8UKuN4tjwsW2QEH2vvACVei00lcD/PBe0lhFVtXsH6eNJi7MloQ/OtFSYDzE/tK4G84Pwx/QE3kb+f680nPmwjfQl31KxXMkKmkNflfMmVJ9gUSN8DH4CrWn43az50hrWvJnAO7g6tfJyKngIF6PZgM6jG4BnYAj29ojacBfglwUtEK7oKhYEDOfqH4jbLvQb++gVA4UHQHmhf9CnGBcKB+Hfi9gsvNSrkBqiZy1dwTXCH6WXUb8BeQXB0b0FcBg9Eb4KfY6soV2O1gsOsMDvim66pxVGDw9GfVanY4FVzBVqSr2eFEwH49AZ5vMErrfjKXwb3wIHSDucFVowE5rc/JzJguaOD081x/T9gF7Ouf8Dro96qQyD4tAV8kBbENB8KBwjngimhU4aprVDWtTW+OBT8pztIIenYefXDVo2aDN8FA8RLsA3WRgfwvWCFVyUmkvaYryO2hD7jiyspA7j6D3UapnRuTfhCuAQP7gVBbLc6Jr8BYMGBuCcWSkw+v0yx3genZ+kXASUupy0mQ9/EI8Nl3QjQXhJqeAw32CTpWwE3vYZufLm8O64E/Lz0YXCVNhl/gK1gYyllH0/hN4T7wE+R/YT7oCmuBQbK2MvD6Sfu5VAX65wrKlaarTq9hwDdYJ+pA4jroCXo9DgyWypVkJ7geDAaWW2dt9Donuaq+GSaBQcX6iyH7o79PweHgBPcIcDJR6nKSMif49cJP8U4m7Esh5T0/Gc6GXQpZcdQVDpSbA0NocH2sgJtznd3gIhgIc0CpyCDxABwJV8EU+BDSPzdcn7wrsXKW/TkTHFg/gmMgWaXNTtrVTm21OidemDl5EfJ/wgKpclfgPgPKiY6TAIPvwuCqcbXcti1bg8FksG2JfFaXSzI12NpP77Er8h65rb/Z7Oq7WPJTrs/NMsW6QDXrnTfXhnbVPL6Yh/kl6WHoC95rJ0NjIFR6DjTYCrj0rChei+orAI+mCyPAldK24GcuVzQNLVeAtqVjqiHXkp6QyifJ+5NEGW4NQM+CAciV/tFwMewPakaoS/8Mpk5e9gGvZQAdD3dCVg/mClyF3pbaeSLps+AKWBe+gVMgrVPJdE8X5NKuqpeEDnn2WeRKLjvB0AMnXWp6WAoWNtOIdBB9GQfe6y/ArxANqcu4+MaZBlxKfpNMWWQb3oHBNMHxOlREB+ojAPei/dmA1oOyq4vYr+pWvRkHOhCn5Wru43QB6cXgyUxZTbNzc8IS0BArkZW57g25Bs/D1lXIspAEXVfDBr66qBUnjwXrvhvOBAfctFwBPZsraM/2Pkj+1oGB+yQwUNwLj8KGkMjgalB2RZdWbzIGclfgU2B3yOooCnwO0zLgOiGxvuvhSrA91tUSaiMDiX04DNrUpoICnrM1dQ0HJ0fKZ9ivCouaaSBdx3Xnylx7e/J7Zsoi2/AONFgAbt7wfW9ULZiN3vjipfU0mfSnxfS++kz/wMX8mVdab5D5E96BztAdDE4HQm21Fyc64TgYXoeeUAx1pdKBYGDZIHWB1qSn5PJ+fvZnky/AIuDKdT7YD+qiXzl5c3CVtT4cB+pIaAvzg9caBOprMNhdALOAAWs5eAIM4i/DnXAo7Ayu5Nx+CImWIXEP7AMHwdLg9daBtKaSWSNdQHoj8P5/ADeD96gX/ALZSRlFVUrPdwXb72Tke8gGG4rqTfblTPgjd0Wf6wFQrGcvd5lKNx+zt3vqiOVJ9we3s0IoHGhSDgyht6OK3GMHvpHgCieRq5brk0wDbltwbdvm4NsSDMY3wdZwCIwHV44GhtpqXU4cDdPnKujI1iC8ZC5fqI2rRdu7OqwMf8G+oNrBHbCmmZzs80uwalJQhK2rr4vhPrgF+kBW/SgwiN4Jp8Mk2A7Whs/BiYL+bQFZHUmBgTStVcgkQT4pb03iNXAi5cp/T/gdDEbZY22z7VEG/BFwLeS7PsXT1I0/nTDM9nd22p878ee5qXx9Jy/hgk5I0upLxr43lObmwj6XTkRPBO/v4+Dkz/LFIFQaDgymGSuURlMabyuG0LViB2Ddc5B7C3rBHjAR5oBSkIOzQfYhMAhsCRuAwcxVnGX3Qyuojc7npOUyJzqY12VFnaluWvZN/kx72oL8rbDQtL1//0axg9wAOAdGQhuortbiQAPoitU9oRbH3c05XXPn2W59fxJOANvuJCOtI8hsmi4g7YTvzEyZ2Q5wHTgZuBD8AuOxZ0BaLck4CbD8UVgAFoFhsBdk5fH6/Bz4zHjODNAOnMwl8n7sAkfDbtAciqmNqdxnwraoLqCH85ipo5xoLAXZr0fVqXYmDnI8+AmcICY+9CA9CkKl4UDJBuB58ceXri30g82hXDWEhtfXQ+/gfQo4ANXmxeW0etESXMWBaq7U1a4ifWQqX5OkHmdnkttRdkBNKqnGsfnuo4FopdS5s5DuBWuCz3B1dREHuhI8EF6D06C2MhBtAjtAdoXmynM6UAa0VcHV0mZgwLwN5oNEnv8OLJwraM/We5fus7s6wv3ggG8Q9BgHfCdfBs+DIJHP6NUwGZLg4L5WYPA2+KRlkB0Kl+YK+7J19ekKfkyuzHoMzGeB/g+HFyD5KkKyKNqPWt8E791Y6AJ1lRMe74OTmHdhJ6ip9CbfM3RHTSuK44vmQEkG4NXp7g/gIHAFOEj4ou4B5aj6DMDl4s+uNHTvTGMd/G/PlFU325sDHQSd+avFwQCwkJkC6hbq6pyqz2D0MfjZry4yUL6RqsAA6leB7Go0dUiFSYPYKPDl3h9+hPQAfi15V9pqHDSDZyFZFZ9Cuhuk1ZOMgeBKGA9bQVYvUbBKqnAe0gZeJ1ne2ylwM3iPTwQD1cWQ1XUULJgqdEJjUFbW9xAsC04kbLfjhHJ8SILxtAL+MAAZIIstJwy2OVkJZ6/XkYKTYCBsDJXJiaTP7qy5g9qxdUKW3LNccZWbzhxxA3h/Ezkpt65QaTgwmGZ4v0tKzmy3B1cPX8My4Mvqi1+OigD8f+/adhQdmylelLwriNpqd058G1xZOVCvBoXWylTo4GgA2ghehGNgJNwFN0J29UZRlTIQZV9EJxX9qzzz7wNcMV0PBu1n4EBI1J6EAcygpVzR2gffsafAdl8AysHasuXMZDQz+YXAgJBPrtiyOoOCJHBMR7ojGASU9XifljKTUze278H0SQFbj78pld+L9GXwErjSTXQqCSfvaa1E5qx0QQOkfa4nwZbgqnQqZJ99iv6n40ml++WONWGAiRrKoO8iZnFYHhxDN4BQaTjQYAG4RSX998X8HHxxP4PJsCp8C6HG4cA4ujEaNgE/iXWAc+E8qK2u4USDgM+Pz833UGg9TYULgoOpgc0AYz98Pl+GXeEFeCK3PZPtn1CVvuOAuTMHdST/U6bMax8JC8Dv0A8MWpvB4WCfn4QlINHXJG4EA7xB6z+g3wbpD8E6DQgGivPgOXBikZXvX2Xv4IzsnwUMMMpgbiBxNa7+gCkmcvqGrStU27MXGKAPAicev0Ai7+UnMBCc7LgKbwXbwkRIZD8NwI8lBWw3BMuz8lreq85gOxwIvQfFkBPCQ0Bf1RwwErrAC5DVXxTYvrRaktG/mspJSV84GnyWTodHoL40KxfaD9rD23A5VPQ+JM//xxwTakAHduDaBt1X4ThYCl6DnaEcNYRGjyrHhhe5zb6c98NEuBs2hXLQDDTyHLgFXK09Cq1g4Vz6PLYnw1Hgfa9sssnuaVqMP10lrQAOvuuDA7EBLZETC8v0aSZYCxxMXwf3JbqexK2wclLA9hLYMpVPJ1clY1AbAbuBgbM22oaTxsKS4AThTnDAVc3BT8UdzGQ0P/ldYEeYK7XP5+MysB4DlYHYwGVf3M4GabUl8wUMAFf5+8MP0Bqy8nmzHj0yQOir7SuG7s1TqZMlJwf51IXCdyFpz5ykbZ/l5STfk19gH1gJroOnoCWk1YbMheD+O8Bn2nObgpz4+c6XnHrTIl9oX9xFoQeUqyIAl+udy9/uxyk2MKhrYBCcCWPAFdgmcCwoA/UW01JV/9GJQx4Eg8MVsBCk5WTUoJLW1mQ+htfAVeJXcDK8DQY1B7cj4UNoAcVWTy4wHJx47AG+v+3BAD8anHDdANlBmKJ/qRW5L6FfrtTjL4UTYVlwfz5ZfgxcACdAegJDdpo24887c+lko4++p8WQgcVJTiI9eQlWTArybJ2AvQNXgW31mVL6sC+cB/3B+1uqOpWGHZVp3Fnkd8uUPUfe/iTyWTk3yTTybckG4MbkewTgxnM3DYo3p7qzN+mrweA7DlytfAbrgjIIGoiqq0U40OAxFA4DB+tEZ5DolmRyW1eNv4GTAleXW4GBy7KJcA84OWgLidYjcTYMgC5JYQ23S3G8fd8ZXI1XpNnY8RGkB2JXOSdVdEKuvCdbfUjLYDM+XVDL9F6ct13m3LnJX5spq222PSdeDHfBs2A//gLvzRrgc2IgqkreswWhXe7AZmy9z947g/fBYL2bwTbgs1NKuoTGLJ5p0Ibkj0iVdSR9UyqfJK8nMW+SacTbwfRthYboX3pgSa4/icRLleBqIlR8B5yt6/VFsHnxL1dWV2hFa6emWmzwnQPWBgOEAc/Zuys9VyunwMzgwFuVkiDgJ8szwPMdiJKVq4P5AeBAnMiB/j3wnu0Hc8K38CEcC+/A/OBEQHm+g/8YMJg9D7tDD1gOqqM+HOTK7BeYD76D7rA8TA/KrX3wOr+DK7rWoDaFVaalKv7Dvn+e2229B4HBvl2urC6bDzjZ/qbll4up6YJapm23XyP03WDTFXxmjoLO4PvkM3M0VCV9fRe+yR24Pdsv4FLwWbgMHgPvxcnwJhj0SkUf0ZBemcbsSV7/E/ks+7xmpWfuC9WjA76UDlQVsWw9tqWQlxpCZaMKWWER61qPuu8BByQHvj+gH4T+dsBgOBzSnhjQpsCu4GA5CLaBh+AN6A+3gINlEqBI/h9Zb49MqROhbVNlDrBPw17gQO5g5ue7GWFr2BEOAMvvA1dKrox8Bh24DQxJICQ57a8nfcL2TLBe628GFWkBdnwPHXIHWNfd8Cp4PYPtqeAk4npwdn8u7At64D4nBpOgMs3KzvvBlfIjYPC5Hf6C+aGu8n18FJw47Q0ToS3UVd2pwIlrWt6b8ekC0gvC0jBDpryi7MzssL16fCO8AIkfD5JWy8GHcIiZElA72jAVToc1wed0AqSfr+bkL4cBkMjjnk8yjXw7mP75jpSUZqc1N8GLMBlegbfBl6YcNYRGl0vbX6etc6ZMno60A6ADpi++98KZfLlpXhrsiz4RnoI1oLYyOHwJV4BB7UqYCVQr2AP6g8GiEyRyYD45yeTZjqZsrkz5duQNXmk5mG0L68Ch8Cl0ADULeN3vYT5I6zYy6eewC3mP9brzgAPjONgNKtK67Ej3YSL5veAT0AcDrO/sn+CXASccXtNnx2N2hKHgdat6jnpzjPXom3VfAFuBE4+6aBtOvgtehdfgeJgNCqH1qCTtj3X6Dj1sAunxMXAH+Px8BUtCZfKcN+EG0AvlpMbV8Fg4DRKZfizJlMC2FW04HGzXbjAdZNWWAscdnxOfDd+pNtAUNJhOllwAdsbkC3Ig3A2+dC/AMlCOKqcAfFPGYAdJB8uNcuWLs50AHXP5ctjMRCPtg8+RWggegFXN1FItOM/n0VVMvkFlS8qPgLRc7dyXLsikHaQOTpU58L4E66TK0sllyVwGH4H9M8D+B56FfCtM638ckmDTj/T+8BbMCKoTDDNRgfRseGrf/aS3h+9SZQYW2z0wV+Y5v4PXsY2+D2vBSKhMvdl5BjgeOFBtAqoyD/8+ouI/d2CXgUwPvId7gu0wXQg5AfLZWiNV2SGkx+TyPhP233ur1gQnhHOaqUCLUH49eI7+WX83+B6mQGtIdA2JZ5JMGW3t28I5Em/KqPm1bqrPdckF4NE0ykHHgfNlUMvD0Gmp8vujnAKwK5X0A3EB+S9g3pTt25F28C4X7U1Dj840dmXyBopiaQMqNnCk5YpwQrogk25HfioYuDYF3wM/x+VTLwq/gQfhbDAAfwQG8JnhKtgMEln3V6AXHuuE6iJ4BdLBwnvvl4LKdA47baPPxEPwEpwFiQwCd4CTZuW7fA98AotCovFJIrVdiLRByYleV3gf+oPB10BlIErqJVljPcoZBt+07I9+FkpLUpEee1/08mJoBcoA6nOQlvdsi3RBJq0nQ1NlO5I+Bbzfvps9QR0PX8O5ZkJl4UCDBeDKZpwfYt0C4EzPB9cXxsHDstD/daAzRQfAzOCExcHRAaA2Op2THOB2gu/AYOtD4j1J9BOJZEBJykp5OwONm5JpoANXsurL7Kp1dnrO3A18Tg2OS8BWcBO0hfPgaqhIntMBdgcD0LVwJ2Q1EwUjwOMNbt7rM8D71h4OgV9gLLji+gAOgn5gnc+AAedTsI7fQc0NA+AEMxXIYOC72x0M4r+B76Yeq1NhbTgJLoVh4L5OsBR4rFoUsqu+XSjrA5PBlepb8Br4XFu2N4yDy6C20pevMyd/Tr6Qz8Kr1OeEx/fSd+V5SN5H89NBWq3JJPvT5Ul6Cokf4ELwPl4HQ+F1eATug4/AY66EYyAUDtTaAV9UB4a1wFnk4/AslOuDNYS2j4JiyIFtEnSDjnAsODhnX3KKqq15ONJB/Gg4CnzJk/pmIe1gsRKUi1ahoXrUNtVgnysHs6yaUbAX3AZOAC+GpO8kK5THuBJ0ZdIVzoLPwOtOBOtyUlMIGeCfg/Uyld1M3qC6I/g8fAgG8H5Q0f3qyD4DssfdDZtARZqdHU9AH1gEfEZuhWvgS/DaY2Af8JnxWfFdXhJOBN9p78X6MAFWhkQ9SPhczZAraMH2E9gDvE++P36x8Hk8DGqrszjx1NTJ9sPrzpsqK2ZyNyr33vm8qHXA67c3U4mcIHifxsJoGAytQTkhWxH0OVReDngfVyjFJs9Fo+YAX8QDwcGrFZSjihmAb8eQ7A08n7JNCmjUydTlrP5McMDdFMpNu9NgVzpOKobCeZBPDs6XQDswGBg4BkJV2pMDrDetQ8l4vULLFeErcESq4rakf4abU2XNSbtaMujVRdtysoP+FDAQWm+iAST6wuHwKDwFBjnf36y2oMB34QxYOrOzP/l1M2XXk78tU3YxeYNybWW73oVbwMmqE6O1oLoycNr25SE9HhlIbesEGA+zQUXSs5fgWrgRFoHqam4OlGLLZ8yJnsG9kPLZ8euGHvj8+o41ZZVsAG5MN6XYAdjBN609yPg5r5BalMq6gqvjclUnGr4xrFlBB1yFvAAtMvsNOq7kKpOBumfmgC7kfcGKoe2o9C84BtaHKWA+OzgfQJmBT80H54PB1E+Vs0JVOogDDBTWa0A0AJ4OiTYn8SY4oTEwOVG2HQtDTXQsB2+WOcGV/AfgPbM+j5kC2ftDUbXkCvFWuABegElgkKmu2nCgvunBCPgJ9oN14X5YFJqB9+MecBJXkTqwwz5NX9EBDVi+Ede+C/yy8Q7Yv4pkfxeDxWG6PAd573aH3rl9Y9n6JWNt8J35BSqbrDRn/+rQC1yQNTaVZAB2Nu0LkmVQmbpfzACsJw4CiZyVvwerJgWxrbYDc3OkKyPlCsfg5WTmMqjoEy67pmkv/jw1l042A0kcnWQy25bkDaL7QHeojdbkpBfhVRgHtt2BLZGD45PgcU7SDIxbgQOZg+zDMCdUJutunTvgBLZHgROSzrmyt9k+mksnG+s2SNVEi3Hwg9Apd5LBzPZ6H86Hq8DJRhuojWbnJOvrmjrZYHBYLu9kZH7w/UnL4HAwGPz/C04KloPJMBz013q3gbSst2+6oEzS3lf70yHX3o5s34fkfueKp21m5E/vs/dmBHwA7SDRzSR8d/RsEnh/74O09iRzWroglfYduTTHKWxt16rQmDSYzqxQah1amAb5WUgcUByoHofuUI4qZgB24PgWzoCtwYHYgSpUcwem45Rh4KDxCOyQS/viJ4GBZF65knkJhkJXOAI+A88zEF8Eu4BqDjfCWeA1Xoczoa5akgps6/ZgG8bA5aD6w34mUtqd9PGpfL7kbalCP0c6yD4H9u9amAg+e1fAdeA1DOqjoKbqwglOug2MXrcb1EQzcLCTDTGdVh8y2feiLWV3wgFwD3hPpsACoOaAB2B/MIj8CkfDJ5AEAgPQo3A3pCcH3lfrLTcZ6HpkGr0B+ax3HvIMXGgiJ4/x+fc9cvIxCdJ6gsy56QLSej0sU5Zkh5M4NMmwXQIegrlTZeWebLAA3KIS595mn6Rl3pd+Yrow0tN+q9SBYmfwwTwbfEhDNXfgD065BBwoXHUtCL70/cCV355QkX5hx/LgMZvAl9ADroFzwMDlSsGg8g6kB6PR5B3814X7oTJtys6F4GsYCbY50askOoDtXQVughtAGRyen5b654/XSS7+TzZv6htKt4Yx8D1sBA68Bjjb7HV2BQfpn8BB2D5+ATWVwdcgXJG85rGwOrSCyyAJ9LOTvhrs00rQHRzsTwTb5f1x4pNWSzILQmfYGH6DzeE20DuD7gjwmWgP46EnPAVPgnKF/CBsAwvAqzAzOBmxHbXVFpzo+d+DQd77UB8yeP6euZC+tMiUzUX+IzgoVT6Q9JWwKCwLfslMS/98TtJakYwLiHzSz31TO14jfSesCremyiNZDw5syzXuqYfrFOMSQ6g0GSiKUX/UWTgH1qOqk6AXbAgOsOqBvzc1+nMcR3fNnGGQuBcMomltSeaQdEGe9FDKpA9cDg5IBqXqaGcOcnBMy0mAQaYyzcvOv0BPDgYDj0E3kW1wYuHqvzcY/D+B6gQf++GE8RSYGypTM3Ya+H2XpgcnndeDK02DwwewP7wLx8AR8AL8Bt7DtnA7dAdl8L0UXoS5INE8JN6EC+FZMPAdBsp23g3PgeefBx7TCr6DCXAkeN9tV211ESc6uekG+4H+Lwj1Ia/5H9AvNQt4/ez9nJMyJylZjaZgMTgVdsrs9J7o7R2wAuwGj0A7yCfvl/curQvIrJ8uKPP0YNqvFyUlb9RLKd4g/TNsDuUoB40IwOVx57rSzGGZphqEHHRrKgeaGTMn7UPeiaTBKi1fxD3TBZn0ZuSfypT1J38LjIVbYTuoSM3ZcTPcBzvCZTASqqOZOMjrOwmeL3PCbbm8+w3S/eBcWAMq0+ns1Ic1YUtwkF8OKtLa7Lghs7MdefuzMDjwG4AdoBNdQ8I2ieoAb4B+ed6BoH+zQ6LxJC6ErcF31ufB1b9tawEfw3swBZxMLQk+GydCd9gUFoestqJgGFhXP6hI+uFEwwlHIut0FV5f6suF3oHz4Hbw3ubTIAr1PZHHv5DLzMvW4GqwNFj7bHqPfZZ8B4bAyeA9qUjbs8Nn1HOUvn0OTngai0oyAC+Eu2ukWJW0M95ylQ9bBODyuXu+FK7MOsJyYKBYF2qqizihT+okB1UHqG3hMVgLHIAOg0/BgcUyB7yVIa0jyWyULiA9FqaA78b84M9kHTwr0wbs9JhCrSJ8rtdJXdCB92uYK1WWTXah4D/gpCCR/X0c/DowCcbBDJDI9iaBNClz+zDMDU4EjocekOgBEuuB71+i6UjYtva5AoP23bm0G9tgoDBorASPggH5ABgGTmKUwWEEXJ1Ls6lQ/dhjIOkI+mMwPQbyaTsK987saEM+mehkdhUt63O5DPhsVaTp2eG7oX83gpOfJFiSnHbuMLYG6XNhLqip9uKEF8B76fs0CzQmlWQAbkwG2xcHAAeqUPk4yZnO0AAAQABJREFUcChN9Z5dCa5KaqMFOMnB3MG7F9wCvnAOUifAc+AAfyq0g4thGBgU3obxsBu0BgdlA0yipUi8Cp4za67QgHV/Ll3VpjsH9IfjwHbWRK04WH9ugLGQ9HFn0s+AQb4yGUyPBAOcwcm+HQQfQltoBk4SroUWoBy8HewXM5OTwcoy1R9uhvPBIKvXN8GlYFsrkpOAK+BJ0MsPYCtINB+Jj2Ac2E7rrokM9K+DniWyf/q2CDgZcQJ2IHgfeoJBPS2fP4+vTAZ2nwmDdX3LyUptgmu6nT67q8IqkPYqfUxjTDserFAqHXuQhjgoVcRZpdLQGrZjCMePquE5cXjjcKAd3TgaBsAm4KrBVd5R0BtccY2EHcGBWu0DzvifB1cPP4KD8MNgcO4IJ8H34MBsfQYfB67boCrtywFPwTqwORhAHfiqqwkceBHMDyuDk4jTYT8wCFSlzhxgwJsE24LB2DYYvNM6l0z3VMFqpD3O48+AMTATKAOjAXQqfALXwCnwBiwHl8BwOAjyycBuMOwGXmMDWBa8xjCorfTIe5iVq8VDYDJsBt4Tr3sdTISXwHuyFTwC1lORnGDcCU4kvoDloZw0B431+R0IPlffwOzQFDSYTpZMAPYFWwt2h7fhCFgd9gSDsjPectQQGj2qHBteD21uyTUOzvlzNVsHwsas0+hc30wHryI/DlaEZeBzcCXTCwzc98CroD8/gwP6V+A7sgQozx8KHleZFmTnuzBj6qClSd+UyleWXI+dt2YOsM0GqupqUQ78Ab6GtWEL+B5ugLROILNhuoC0Kz0nDQZIJzNZNaNgJzgGnMgYfB+CHuB19d9A5XEVaT52ODBeDrtCZceyu1I5KbJfrmwT6dd38Cm4cpRH4CiYAM/Aw+C44f1fACrSnuy4DJrnDliJ7QtgHxpSToj6wG7gmF6RfP+/APuRaH8Sw8A6GrtKKgAnZjtb3TnJ5LYGYmd55ajGFIAdUNaEdWCWAtyMe6nDAWQhWBUmgROxYsug0xXaF/tCmfqvIm8QTGszMvqwMWwDB4EygDiYOjDfB+pAeB8+gInwFxwAh8MnUNUKVG8d1LOqbgB1xWob0jIQJu2z3HvpO+xEwXvbDtIyqB4D9ncgnAQGu3cgkf2wb3MmBbXc3s15XTLn2qb1M2XFzC5K5falH+wFz8OO4OCrrgffJzUCnGRcCU7AqpIT+wUzBxnMrL+h5IRlOLjK3wVegUGQT4tTODLPDst8jhq7GiwAJzO2fAY7O+yY2bEM+S8yZZGtXwdm4nJDoQ/0BFdhi0Bl6sTO5aB1noN6U/YL7AvvwJNgMDkUfD62yaV3Yzsd1Ea+xN3A50dZ72lwPBhMPgJXDbXR3Jx0NPQHV2TV0bcc5EQjrS3J3ANHgZ/jlgSDlIHCleIdYP/bwYXggPYMnABPg33SvxfBVVVl+oydS0Cr3EE7sL0OesBiubLKNq+ycz1okzrocNKzwx7QAd6Gu8CJhP26DWaFRN+QcOAdBwbiU+E8mAuGw8ng4O19sb111euZCpzUtM+UFSLrPVoU5s9U9ib52eAT+Bl8fyaB70YLaAl+4ZsFXGj4jDwK+lGVfs9zgO/aX3nK66voQC70FvhlawT47unL+pDVLxTY/6ychOhVqAEc8OV0QPEhHAI+rB9AdiZLUVnIPowqi5ZW3sjH2b1d6hA/LTm4tk2VJUlfKmd318IVYCCZD9IywDpIp2VguA8cnC+CjWAoTAYHlprIto4FB/SXYCCcBmdBos4kngRf+JrIvjwEu0Jv+AKOg6rk4PwXGKyWhjPAgGC/DwUDnEHHwLoaPAYTwGDkpGEfuBjGwwGwFdinvuCx1ZF12IYx8AR4Xx00LesOVcnzfwKD9+1ge3eDAfArWFda+5E5AtYFvZfr4WxYGOzbvdANuoMDtRORQuh8Ktk5VZE+TwGvWUgZPK+GoaAnD0JVz6u+6PkoOBKcaHk/lWNfz2mpyv/Ylt0+20kQ60raOgvlH1XVWNdwRqfMWZuQPyZTlmQHkfD5aA7TwTgYDU1BjpErlGJH56RRe4E3Z2+YB8pVjSUA35/nBjiIrpGn/AbKDkuVb0naF6tNqmw50gbwGVJlfUg/Ca6c0jqVjDPq6srg5UDUNneCL7ftfxZcjaR1ABkHsprIFd7aqRNakL4RVkyVZZNrUmB/9eFT8Hj75EB9EwyErcHVkB6MgafgGVDuvwC+At8Ptw7aL8MlkPaRbKUySH4C+rps7shF2FpfdbQqB50FUyE92D9AfiSk1ZPM7fAarALJvXGgvg4uz5WxKbg6UOOfcCJsAeOhokDArv/JQLAL6I/PsYG7InnsR3BU6gBX8vpTlTbiAAO3z+q54DOlJ3pTXR3Lga/ACLgZlgLVDLaBk6AfTA/F0qJU3Bm8hpOeDSGt/mR8z/LJyYMTsgng/TkBLGsKGkwnVyiVjjrodAMfqP/mwRtbjhpCo0eVY8MzbTaAGWjSuoHMSumCXHoi2+aZ8jPIr5Up24/8D+BAcSjcAwPAQTstg0RN7r+D7CbpCkj7BcXnqiP4jN2VYyjbzaEmsp3Z/h1EmROItNqScYJiuYNsJ1CLgwPOYuAAfySkZRCz/Q7uV8GjOd5hOxso78Xr4PkOtjVRLw52RZ3V3dmCSvIGmJdgItwCbcD7+S6kNZnMZzBTqtBPwPo/S6qsWMkZqHh/0OPuUB35rPu8GRBPh6+hHeTT0hQaRLPSE/vnc7It6LdBqBVkNTMFx4GTLN+Fmt5PJ0FOoOxrohtIXAnrgO/eN6DvhZTP4FkwCvTgW1gTfF7dzgp7ws/QGkL/dmAw2RX+XVQ/OW9cVntTMAXegnshq6nZgsjXqwO3crUhcAT8ChdAR3gWsvqJAgeeP1M7HIB/T+VNXgovwiqQBOJdSBvUH4REPUg4CFZXv3Cgg1paBkMD8JNgX7aD7eEyyPe8UVyhvmTPymBdiazrqCTDdiHQL6/poPo9OAgPAAPnebA1LAoOYmmNJuNKxhWpA9jsMAMcA/fAFbAaPATnQk01hRMMBgZNfVfeg6xn03bk+cMA3h2897ZPL0eAAWRGuBPsg4P/K+A9tp0GMScRJ8IH0B6K/V77LF4C1ZXB8kfolzthEtuP4SAYkCvLbn7JFpDXWzUGvshte7O13wbL7yCRgcsgWVt9zomSaFMSc4F9UQ+AfTgE+kOhZIB/FY7OVXgX2+NgDzgVpgPH83nhZwiViQPesJbgoOmLsDmUq4bQcGeI5a5mdMCX63G4DxwwDAr5ZJA+EzxHHQxfQSszVchAfS/sA51gV/gLkgGNZJVagCMM4MvmjuzI1jp2gpfgP2AQHgnLwd1QE1mv9W0GzmAdZK+CRD637jcAee+nwjs5LN8EesJpcC70gbQGkdk3XZBKu7JwYO2RKqtN0omObdkUdoUJsAhUR/q1MAyF46FjLj2Z7TJgnbvDurAEfAnbgO/13nA/vAszQ6npMBq0UaZRi5G/LFOWZA0y+nByUsD2YBgHjlv6mtahZE5IFxQhfQB1ZsfMBSm7usDXeoj67H9ap5Dpli6IdIUODGaP40e9q0UlV1ydfQ7wi8NJsBI4cM8KhX6AqDJUTQf+4jiD6sewFjh4zgdvQFZOOi4GP0V9BN/AovArVKXvOcAB8Chwxv41dIBkpUaySr3HEfuBwe9DcJDoDT5HN8DpkFabdKYa6Zc4Zh6wfb3gVvBaibqQ8OVqDV53KKwMrpSuhPNhIVgH3gHP/QmcGGwNBi/rz6dH8hXWomwE57wJa4PX3hE+gurqcw60nUeDz8X8cB0YhCXRVSQGgr478fgUHHSugG+h1GS/9OTOXMNmYXs4LADLgvc+rT/I9IPnwa8WvicfwBawP/gcpHU7GT0rpqZSuV9IxqYusjxp3626qD0ntwM98iuBz43vlh4kclxI55Py2JaJA5fQzu2hJXwNzqgd0MZDOWoIjU4PzuXYh6TN15AYA65qVocJ0AMq0uzsmBuys+SKji92uUHC1Vs6uDlQ3lvgC69CfReAkwCDqhPO48ABywH5KzAIJ5qDhIFqJPQHB7lEzUjsAzeC/neAhtZJNODsVCPmIm3gWSxVliTtk8FraTCQyaFwMJSiHHeeAt/Z3uD9ewv6gn3sA/nUnMKOMG9q566kT0/lTW4J52TKqso6kXOS46RgEgwFn6mK5L4nYTR0hp3gMTA41lY7cKLvjtd+Fwzwh8FDkGhvEt9Bq6SgDLe+u8NgLAwCn4diybFghWJVXtt6r+PEdaEn/DdXyapsnUGXoxpLAPYePJ65AYuQvz1TVorZZjSqL1wNt4ID6QFwMtwG6YBHts5ywmGwdDL5NhigzoVL4WhwQO8F1dFdHPQF6P3nYNvvh4Yc5Gbg+s+D7+oR4ARmY8inYyk0YCcyUPnVxHe8VGUbnTy9DwYcJ5JqNjAIdoTqyHv0EgwDJyA7w2MwE6gNYQ9Y3EwlMsj5PCnbdgKcYqYSedyBcBGcDvNAWsuQOSqHQboyGWx97trmDhrF9neYCr+Az4ITxGHgZLJc5UJvAqwIc8Ih4DNe2WSH3bVWSQZgZ1qT4VU4DpaC18CHtxw1hEb7wJa71qcDBo+sDAalrmE08CZw0L8HXIF+Cb4ABpNiyHodOL+Hn+BC2AUc1H22DdJVaRsO+A302IlCM3BA/QgM6A2p+bj4CBgLB1XSkBnZ9yZcCduCA7WrubrKwJWsCEeSblnXCvOcn+/ZPp7jfBeqK++zA7n3fwA4GTM4PgDvwZPwJxwO+bQYhd77rJxI1jbY9eLcd2Aj2AAMrutBRfJZMyipc+Bd2BMM8PrxGSwP1XmmOaxkNZ6Wdc60zv72yZQVKuv4U3IrYDvXGxx8fFAXhR5QrmosAbgTN8CBoE3qRqxJ2iBTyupK4x6DWcCBxmfLvjh4O4iZL6YMmgafD8FB73xIe0i2Qg1kzyR4HU4EB/4F4C3wXphuCM3LRW3XdrA6OKG4GCqSA/NOcACsU9FBNSg/lmP1tC20giPAAFdo3U2F9jUtA6fPVF10Cye7yEgClu/RD2BgzMoFyBXZQvLWMU+e8sqKWrNzH/gSvHeJ5iLh8+Q7kk8+s8vldiTBdnPy/XJl97L1WU1ku1aC+ZOCMtneRTtnyrR1b/JOnIuhkg3ASWd9MFokmTLdNpYArP27gkHMl88ZsINRqb9kzvCPAl+igyGRq5sl4aqkoAS3J9KmH+EJsB8OfteBA+iN4EQiq5kpcEBPVizZ/ZXlF2LnCeBg6uqoIt3Mjm6ZncPJ98iUpbO2x/NGw5tQl4nPC5xv4E3LgFzo1YQeuCoy+DjZ0fsxUBe14+SpMA4MbO1B+RzqYVb20+v2Te04lbSTMhco1ZX13Abnge/tFDgZEjmBWjzJZLb68CJYx6ewKjgOLAHKZ/Giaam/v3KMJT0C7Ocd0AXKQT73h6Ya2oa071qhn6vkEoOLWHdyjRpvfagcBPzZycvgp5FbYQ4oRw2h0aPKseEVtHk1yo+EA2DOCo4ppWIHFZ8fP5clM9k1ST8IC8L1UKpylfM5fA0OcE4gfgdXnD+BK6O1IZF9vR0GgcHuMZgBqiMH04dgS+gO38MRkE8OqvlWCulVVfo8J2kO2MvmCjuwddW0Si5f0aYZO3aEQ6BL6qC7U+kk6adC72uh1ZUKR8IN4DOfrFpJ1lgGMO+nQWwn6APeI1fZ3utLIZ98z7wfF8J5MAxmhprobA7eG9rDeHA89RnpBm1hClQ2xnoP3oS3wbb0BOXWe7s66L9p+/YGrJfbWrYJlLra0cBP4FxwrPA52w+KpcFUXKzgXus278uZDo5+ejYAt4SL4XgoRzW2AFwq92AbGjICHLgqChRJW109OAg4gHisK4D5wAHPwNyQWoSL7wW+8NkB8C7KWsM4+B2+hC9gKvjiLg23gl7MAvZxDUh0EYnTkkwV24fZv1zqGN87VzbLpMqS5GUk1k8yue2TbLtnypLs4SS2TTK57ZpsB2bK0lkD3ePwFjgx+BNOBuV9MyAkWpWE+x1AS1kG8LOgP3wAHWA7mAjeO8e8imTwXgmWhxYVHVRJuffS66mtwOv1BydrrqZ3g6rkRME2fAZOJB7JpX1+lfenD7wLs4JaC86DO6E9lLr0eUfYB1YucmNLMgBfR6c3hBnBAKwWAwfNYsqH2kGs0BpChaMKXWkTr8/BdyQsAHODK4OqJmjO0K+CH+F8uAGSAZ3kNHXkTwPYwtNyxf+jG5d4EQy+TjwdFJeERL4LrsBUS9gCfgBXFol8Zu+DXnBqUpjbGsSq+97ckznX7JGQvlZyyCIkbOseoK96eQVUpOPYsVFmpwO5A3NFcvXxDDTPHdCF7Xfg2ODg7mTE+3gCPAhORkpJM9AYx60k6Nk2fehtAunXL/ApfAvrgnIc2gvOAZ/PNlAI+eyvnarI4PI83Ardoabyme0HS6VOtH8+E5ekyuxXf7gcFoXQPw6UZAB2VnwmpAPwEeRH/tPugqWc7ZwB78Of4KDiADcZXDUVQhGAC+HiP3W0I/kaTP9P0bTfDr6ZvANeVZqLA5yVZ1d2u1Pmatog9g4cBjWVQdBV61h4HtKDE9l/ycA5FdJtNviPAZ9LtRz4TG6aS49n+ypkZd+7wVmZHQaBRzJlFWVHs2PF1E4Dn++BgS+fbP/JcDa4Aq9Mq7HzWWiXO2g6tvZrk1w+3+YDCg0SaV1NZkSuQI96wkYwe66sVDZL0hDvyVB4BZwkqK3gmmmpv/9oy+YmODxX1oztBBgCK8EhoE8+s3WVz/uj4LM/H5wE/wXvRaHkNWz/veA9WRBs/5owBZL7TzKEA4NhhVJzwhnje/AMfAGPw8ewNBRazspug2VhJvAF8CFxwHgS9oPqaDMOurQCXqD8/upU0kiPmZt++SLqbSHk4HFjnopcDaUDSJ5DKiwycDpQGLBUa3gMepupppbgOOtYIHd8MhjZ93zyxbsgz47rKZs3Va5/vqg+q/3gVpgHEm1B4hPYC24CB3llgLoYjjJTDS3PMbbf+pwI3ANnQKG0IxV9CNZpH/aHyvQGOzfMHOAE5LJMWTqrL/rt5KGhZODRx9VyDWjB9kXYAXwHRsEk6AMD4TlI2rs16bGQ1s5kzkoX1CHtszkURsPxMCMUWgZ477MeOHYfCW+Dk8hiyufda90OemjQL3WVZAB2EDQY+injFOgJhZylUd3/9A6pimaXXdnnIFQdzcFBDsD5GEa5A2NTky/EILgRXNW9BTNDTdSegw1SE+BZ2A2s1wFkPUjUmYSfJ9slBTXcns7x3TLnrE1+YKassuw57MzWsR1lh1Zw0sKU3wbJ4Osz7gD8CWTroeh/8n34C7YHvfkGjoB+YPnzcB846dsfaqJ5Odh3zr5sVpMTq3Gsk6ObYRL4PHQE72VFOpYdH4E+KZ8l+2cbs0qeNScvPm8O+PmeBQOgHp8M3pfWUGhtS4UHZyo18KW/4PXJHWNwbZk69gDSToDSWpDM1emCMkkvTzsN8idAbSfGNenqQxx8CTgBWhKcrK0PpaySDMBDcOyYenLtDq7jQJZPAyhMvzT5jqlOmf0ZVZ0DG9kxBpfDUn1yYL8GqjuZclD9HBwoVVsYDX7udFB2MLb+feEZWBmag0HPwXs/cPWRlYNwT3AQ7JTb6SCxUS6dbMz3TzLV2F7EMStkjtuY/HGZsnT2SDJXgv3Rr//CCPgI0t6R/ZeWInc0/AhOPhJtQKIQz2xSX1Vb27EKtKviwMXYPwm8R3vCZHCV5KTMwbIinc2On+AzeAOcFOeT3iXPiftPhGGQvf/X5srXYXsSWPdsUEj5/O2TqXA+8j67VclJj89RWtY3KF0Q6f/jQA9K/KqSlpOeO9MFJZguyQB8CEZdB9UdqOvi6/Kc/Co8Bc6eLwdflBfhP+Dss65qqgH4vjzGXUzZMnnK8xVtSOF5mR1zknewVe1hK/DT3vyg3HcJrAue+wHMBIkMvpeC9/lw+BZ2AoPAg2AgVIuDAd7A0Q0MbHNAZbIt1pE8t04YrGM1qEy7s/N5eAI2yR04Pdu7YI1cPt9mbQoH5NkxLk9ZVUW2eR6oLBjNxf4OuYqasz0NfFf0+UfITj4o+p9GkjJ49oIx4JeQk8CJ0v3gtesi68jqQgqWTRX6PD2SypvcG07PlNU1OzcVeD9XyVWktz6Te+XylW18Pn2G7c9acCBMgBmhsciJte9VXe952o8+ZI5KF5D2GX0oU1Zq2QYLwC0qccJZqSsHB8f34Q9Q90Blq4JpB9XwD1+U5cFBsiM4yHwODtKTwAE0VDsHvs9zmi9fddWaAz/MHPwd+eQz9tekb0rt34a05+yfK3MQcyV5AJyVK3Nl9Cn0z+WvZuuANxmOgTvgNfD5XBcMEh7vs3gXdIEXIZ9si8/Sy2CQ8djt4HGoTNewcz04HD7IHfgL2xGwEjyaK8tuvqFg4UzhLOQXzZRVlZ2VAwZBe/D5fxX2heS9a0N6IDgBcVKhx0+BwcL+qeFwGWwOep5VSwqs72x4DXaGt6AdjILdYTnwfdPDI+FWqK58LrKynWnNR+aCdAFpJytnZsrqmv2YCnaBB+E20FPTfumoSvbfYKI/PhM+d1vAj1BT6e2cMBW+qOHJPke9QQ8NYlOgEFqASnzWfoUlwefoIPgT6iLfuf3gGkj6qo/5xiCKQ5U5sCA7u+bBWVM5asj/b+8swK4o3j78IQIWYqDYgI2KiR1gd7d/A7u7u7u7BQu7ExO7O7DF7u7+7hvP6rKet857Ys/7Pr/rutmZ2Zr97e48M7MHoNI2Mu1NR3PBg1IXvTNpg5nBrTmywfTlTwcUj+FLVkw22stkVsxA/vRUmUFy8lTepI2dL29Wt1MwMFVoQLWse6qsWHImChcCn+Pm6hw2XDizsf5tnCnLZg+h4DToDbPATbAqNFfei89gl9QOeq6XiZ4jYU89kR0Dg+cESUFhuSPLNTJlSdbRsp3pe8F7ZD3tZOjTNvAXzANqQrgFFjHTTB3JdtYrUbFnbXVW+i6mtRwZva+ExuKgM4LPcUNanhV6vSV0bmijEsoXY58bwIHECNgImivfj9tAD7cA743tcWtl581jrZg60AOkt03lW5NchZ09/mawD9wMdir7wMFwKriuG+RFvldz5qUyxerRkcIxi62oo7L2GoCT3rMNwWAYBNlGm6JGtQBrfakOAhuTs8HRVDFtSuFBmRX+GOaIVNmFpGdL5U36cm6UKTNrIMrKbZfMFpYhb3C/H+aAccBG6RvoAk1pOza4DM6DJZraOLPeoKevafnO2dFQ08AVI1Oj/vEOWQN+WvuRsREsJjtNb8ED8BrcCMNgK/D+7gFp2Sidki5oIq1Pd4GB/ULwfONDWl7XPeA2PgPrwEPQ0meSXcoiO05DYAU4Fj6DcgQHg76eTgnKGaPXYVEzzdALbLNQajufAd/h7qmyUpLLsNNBmR0Nyh67XPKZ3ATWhbFhLrgXToZHQF++huZ6waYVVa4CsCMHG5IOhUtei+W38B0MgnI8nBym6jqRM/qitVf15MKnBRvAUtSDnZYGG4XRGjmAjfAr4IjGZ2lD8IXzRUzkMe6EXuCxBoIjMTt5fWERmAKU240zMvXvHxeRdJtimpfCFcFgWoqssyM/OQayAYSisssOzlGZo/r+3Vco876dm1lv9kkwMOu5Wg30OuuX69QlMDm4nSOTN8BA+CycAqtDWjOTsbPVUnnvGnvWvDaDvsHvSLBOtdBSnPS5zIl3JH9QpqyU7C7stFJmR0ewR2fKGsp6f7LyefTdaY18h/fKHMDn5fZMWTmzz3OwjcBntTP4vHq+t2AGqLVqFoCzDakvhi/kI2DaRvM8GAi94FfYFEL158A7VNkG948Sq/4J+w2FB+HPRo5hILXhHg5bw/QwCfwAiTzGYXA53A39wJGCI7B9YQnwpV0N7DSdAl3B59VANSd4jKyOpWB7mAbc72BoqV5mh+ULWJ+vWnqAErY3ANoQrZPa10DweSH/Nkuvff9C3sWpYLB1JPE63Aqrgj5+D8XkM7ACXFtYLsOyFxwOF4DHnxhUR/Be3GmmhXqf7Rt71qz3WbAd7A0fQC3kc+JzmNaVZKZLF9QoPSbntf1Ny47at+mCEtKPso/Pt53URPuQeDzJVGDp8+s763NmDLGNMMYMhsWh3Wr0zJXvR96GblY4B3wZfVmWK+A0it9KJoPdIBQOFHPgDwqdcWhM97FyvtQGm5P2uVoXfOYMMI5CLbcxMhD8BE+Ao9xsJ2BtyqQnuP9pcDMsC7dBnvUjldsGDFo2SF3A4KsXSj93gWfAQP0b2Ii9BgbbM+BIWBgMfg3peFa8B/r8EGwINrxXgY2yx38TzobeYCN5NSRy9KK/1veDpLCOl3Ze+oHXn2gWEj8nmVYs72DfG8ARth0f21X9XBCao0FsdD7sBT+B9+RteB5ao2/YeQN4Cnw/xofHwOenUvqdA08AHQsn6MLSZ++0Qj4WBQe8GTYCY4E97hth20LavA2cjZnpepMBwVFRKJ8OXEi1ps9UbR3yO2TKGsqezIp5MisNZtkRTmaTXGVtmPpC1oekkr5308I0cAXYOUlk2d0wRVLQwHIMyneE/WC1wjYG8EtgFTgXPoHZIC2D9mVwMTwM50FHqKUm4uSTQ6n16MS+djAcVOj5yvAI9IDGtDQrDR7nwJqNbLgk696Ds8B2cz1oibwf1u8asB0u9TrZ9T/yWZsRpvzPmvIX+F7aKbZz6KzLg3AUWNYLislncyjcD3eBsalSOp4Dz1mpg7f0uEeyw5vwJAyHbqBOga/AEUU96kQqPaQeK95O6mwQmC1zrduRTweZzOpRsj63K4xS8vevl/fJlLWV7H1cyOiZi3G0tHKmrKms7/MzmY32IL9vqswOuY3lWqmyO0nvnMqnk2OSORrc5hHw3pSz024g2h9uhOvgDRgPSpH12g0GwYnQGxrTQFYaVO0o/Q9GwCtgPeYAOzhp6e+Z4LHnT69oZ+mpuN4b4EuwM2K6IT+WY51+dgdlR8R77XNYCeUqAHuBk8OK4JRzog1IzJpk6nDpCxABOL83rj9VuxUc1ShHDzb6E5hphqZjG3vLs0MHWBzcf0KopWbm5IfAMWDDUi7dwoGSBspjTgL+fqO5HRb3Uc4wrDYy9e8fNpYX/Zsd+WtVg2haBhrvV1Z6/xScDKbtJBwHu0C55OeJg1MHs+NxJXROlVUi2Y2DOjgZB2wL7wbv78vwIrwLn4OjabU+uP0CYLDxeVwbisljHwjOLNhWpdtesm1GPrPTgh42JJ+rnpmV+5NfI1NWrmzNAnC2B+0F2Yg11pu8g/W7uWEoHCijA/dxLBshnz9HNN+CQfVLaI5eZyODz1nwG9gY2hB+Ac2Roypf8CngM7gE/oTWaB52dqrN4PsdeG07wmnQWg3mAKfDNjAnGJQmhh6FvFOWxepvkBoAjlKfgG9gLrgWEvUh8WOSYWng+CmVN2lZdrRnuYHJ++d1qt9hT7BRPQHKITtZA1IHSjzWh0dT5eVOjssBHdF/DwYEOxVLw8+wMThoGQsuBkfHh8Ns8DUo978CDNwG6kTekxFgAL4Q5oDbwefxQ2hL8rrT117s2ny29HgLsINjW/AVdIE2pWIBeDeusFMjV9mUeY3sGqvCgUYduJG1UqpeZcdFS9zZBu9FsIO5DuwM88KvUKrOY8dV4K3CAQx6l4HneK1QVsqiAzvZaNvYvwCTwhlgoPsBboaBcAGkNTaZ88HOiQ3a9bAwrAoGTPfrCx5nJUj0JIl9YW0wgKi94PmRqVH/MCi/nyrymnuDy3LpJw5kI52WHajR0gUVSH/GMfXcAGnb+REsDT3hPfC6DRJDYAm4DZLgS3JkJ2wEywkg3Y5uSf4sOAXUCPA824K+tzc9wAU/B9eAHZIl4XhYCNq9yvkiVdPMEzmZL0YoHMg6sCEFBp+0jiCzTbqghPQNRfZxlLNIkfKWFF3KxoNhQfCZNhhNAYkMeJckmdTSToYjs0QzkrgTJoF9YBCcBO6f1eQUGIAuhuvgYDDoZWWQvxqWhenBRvQe+B58BztAa+W5L0odxI7Bn1CNtmk2zqPfV4HPjAHW89shcbRv0D0O1gPvf/q+mDZQd4O09iNjwE5rFjKnpQvaUXodrtVnzQ6i3ujpQeCzWQkZ3OesxIFbc8zu7OyLZE/kRXgZ3oJ6DWK+/PVad6peVONQegTYsDqqauhHDawKNeJAsaDoC+mL2Rpdyc6zpg7g6PBtmCZV1tKkI677UzsNJH0OJKMnV80Mg01k9EAmb9ZrX6ZIebGizhTOAFMVW5kqc71BSq6AC8HAbL23gKbk6G8+mB2KBWx9fBgehHPhcpgMqiVnHLaCd8D20en6EbALbA5e93JgUDW9AiwPb0Ixr9ei3OtIawiZHdIF7Si9JdeqJ87O6KMdFzuA6U4X2bKpZgF4tEYuYWfW+SL4YLwPB8C3cASEau/A6FTBTpH3ZA3YHg4FH9pQyxz4hs37ZXZZivxXmbKWZo9mBxvo1WAxuBvsxdsQl6pJ2PG81M7+GGs6mKlQNgFL39ELCvn04icyfodMy4BqAGmOfmUjp/nfbWJj168Lp8FxsCk4Na4P+prIQOazuyIYoJXB+yKwfE94CZaEKUG5ncd9CjzPerArfAjV0kec6CzoBVfB1/AL2NlaGgyyXvNd4H3pU8Drt7OcyGs6BhYHv8MPg5XB58Z76nW2Rxlv9OQBuBXMLwRfQLuRvUpNcJTldyY1Bwwamaq/P9raCHh1bsGZmdswM/nLMmWRbdoBg5LBcRMwILn8HcaE1spGdl84DAwkrZWNe3a0tCFlX8LNYKNv8Cqm/1F4KUwIneBYeBaKjTIpHkUdyTnKNMA3R6uw0a6ZDScmf2OhbC6WD8NOcBA4UpymsFyWpdoPXoOH4FOw/tbXoJtoYxJeU+ekoMrLNTnfDjA5TA9jgN76PDUm74HX7/4G6athONiR9jpHh/Ys76n+LAbbwj1gLKqEjuegc1biwK05ppXy4Vb2en1gekJTDxab5FJtLQBvhMtbZJwen/xtmbJyZO14+RIkDU05jpm3Y3SmQvvCGXAITAR51cVU7HGYHwy+D0B3aI58bh4E3+PDoTmdDI99PhgkHoWTYTRoTAZq62iHQbn9KeAzNB78DLNBIoPqLWCnQK0N34H73QHd4Bmw7lnZEZ01W1il/Cycx45POjisSv6aJs7vCHq5zDZe+zqZsvac1Qvfxd3Atq1SymUAtlf2DSwMPhiPwFOwF9Sj2loAtvG6E8ZN3QyD5GmpfDmS63IQe6JD4UVwpHIltPceOhbUVI6SfC8PhEkqWBM7JrYDPlvK0fJxsKOZJuSI9mOww2DHcBdQ84HvY1b3UXBSofAsln3B58y2R3nNPoNZ+Tz2yRZWMT+Qc/0KG8Ae4LRpN2hMF7JyClgANoe1wNHw1pBVRwomBTsuedTEVGpFWBp8XupNuQzAmuiL7UjAl2A7WB/q0WCqPfKFH2KiDckX/ifYBmyML4Ny3h8b0K/hNTgClI3Ek7C/mVCbd8AAkQ2WPmMGmeaoCxv5HKU7CQZWpxjTcur2JRgM68HJ4AjfYH8UqE3AQO7UtB0BdSr4PNZac1MBg6czDOM0ozKHss31BZzFOB9+hFUgLYPbILgGngLfw+TaSdZcs1EDO057wQnwFTj7UU/KbQCuJxObqquNyJCmNqrD9TNTZ0cGK0OnMtd/WY53EgzOHPdu8jdlyiJb3AEDz3lwB7wAdmKzmpaCM8AOlI3BmJAXDaAiB2cq44jMRrc1OpKdD4du0AMuBEfIY8NjBT5j6QjfaegF4S/QT7f1m6DPoAF6LKg3LU2F/wA7F8vAYHgG9oVEdkp+hk0LBQ6E7HBsVcjXeuG0sPdkjlRF/MTwKHhvroOFIe/KZQAeD9cMWDYar6awQa5HnUil22IAruS9cPTji+RoJNHkJOyJOyUdatwBA+nvsEFhMz8XXA0rF/IuHC3YYC0HBpeNQG+bM4pis4rLetwOK6bOtAfpS1L5UpIG1WPAa70KHPVmtSQFL8MNYOekD7QVbc2FeK+3g31gVfD++3wkGkDCjkpa3o8b0wU1TBtcj86c347ZO9AdZoBbYVnIs2oWgO1RNSRfsm5gj+b71EZfptKRbNsOPMzlvQU2hLvBnWCjeS/MC6G/f8HqZ5oP4fWMIcuTd5R3caH8W5a7go2W3u4ITjm+BveD75mjO0eEBu0zodayTlvAK2DQHRM+hY2hNfqTnW1jGpPP20yNbVDH63wWpocDU9cwH+lfUnm9Tre9rvoVDMJ50I9UYsJURRYj7XXJ5wW2ZHk23AahjAP2QhuSI50zwMb2iRRvkg61HwcMEpfDUYXlzyzHgqWgvctgehA4EjBY7ARpjUHmk3QB6e/A2aWPwHfpBRgGt4KBXL0MbpMXvUtFJgDbg8PA6/4NQqU7cA27zg5OL3vfFwBHYvtDImdGloDFkwKWu8PrqXw22YkCvynvCY6wG2vjWd0qPcXeP8Ie4LPuCN7O4wWQ6AMSthehFjrgNNnlMHEL98vr5idSsSF5rVwd1KszdZwNHJFU8qWuAytGVnEl/vwJRi9U2AboQUg3lr3JPwI9IdEhJIbB3oUCg7Yj4s3AHxepl2CFkanS/rAuzlpYx7by/pbmRL738j6dAtfCIDAgZzU9BXZ6zwDbr2OgIxST76XP21GwFFwMT0NnaI2s51QwfpGD+Pxb93vhbngeukMip9ZvSTI5XdZsCroxP7ZmpVMJf4A9rlcKnMSyHhUBuB7vWn7r7Es7f6Z6Bl8DbFqLknFa8TBwZOBzeAQsCKoLOJK4HfzWeQmcCqWqGzv6TdXz7QV/wawQql8Hxqbq/WDGJi5hc9ZfkdnmcPJbZspakp2Fja8Bn0vjQFPHWoVtfOa2AL9t3wQ+k3lWzQLw6I24cjPrniyy/ssiZVEUDrQ3BxyVjJu56AnJOypOy5FBT3Dm4Dvwc45ThOvBQ2Bwnht8114ERy13Qqm6jR2PhesKB7AT7fHuh77gt1tHSaHaOLAMp50DfBbOh+zzQtF/9AMlxdri7IaOUrMDpOspcxRainqw0wswHzwGTiW/DB+A8aGYPN/MMC+8BSeD9Q810wGN7g9Okb1WBA2tRznyGFKPFY8659IBRwb3wNSF2s3J0p7/ZIV8YwunEG8Fp+ZWAxvNO6ADtFZ3pQ5gHa3TqeA73ROcJpwBGtN0rNwI1gIb3VB5HDiawziaXBH2h99gIiiXduVAu2QOdhD5PTJlzc0OZENHsmk5Cj87XdAG0rkaAWv4CHgTbBSy+ipbEPlwoB064GjVKd5r4T2wl+/I5kNwlLM1OPX2MWwCP0KiP0gsBwbfSeFBsOE0WLZWo3GAMcAR+jpg4PXY1u8dMNivBI6SxwZHRwbZJ+AZWBIOgguhN1wGk4PX0Z5kJ2k30D89nQAWBX0sRQuxk4G3L3j/b4L3weC4O5RD53KQEeBzdwVYX4NydyhFzpD6HKVlp6FzuiDS5XPAB21Q4XBOI5R648pXo/Id6UQONaR8h4sj5cCBTtRhKzgFDoeuUGstTgVugZ7QBTaCqwppFhXXZpzhPBgPToDr4H5ItDQJf+ylVzcX0gNZ2rBuC06JO5WZaH0SHq+9yZm+I8A2Ua0HF4BBqRRtyE4bZ3b0HtmBK6cMjvuAsx7eZ89RqqZhxxdg+sIB7JQYHzYo5NvK4nguZM48XIwN2nfQD86CNcHvAGnsXdWjIgDX411ruM4dWPUQ+PLMDo44/wJHa7XUUE5uw5WWDbkjzWpJL+6Dp+EDSDomvrt6NB/YKfD9TjQuicfg9KQgtbwzlW4vyQe5UANOWieRsW0sRSuy02mZHRcgf02mrFrZnpzITqvXZCerIS3Mii/Aut8Ezqa0NR3PBeUiAGusN+UzsEf8E/yQ4WLy9agIwPV41xqus98nr8ysXoO8oz5lQBkABptsQ0pRxXQ7Rx4nc3RHlo6gaqGdOOmbcAxcB6uD8vt1p5Gpf/+wIbr33+zI1Hj8+WKmrD1k7Uhl5bM1f7awmXlH0h7zDugDy8JD0AuqLWc47GD4vswDz8P50JB8BmYDg3ZbVK4CcGLwASQWSjJFlqdS5rejelEE4Hq5U82rp6O8JJgke0xBYjBMC7eAnckL4A3oCtXQwZzEEW+iiUk46pwhKajB0oZzLpg0dW6DcTK1mBQ7Krb8UJgIpoFrYW1obzqOC3Z0mGgVEt7HMZOCEpd2xnwmDebTlXiM1u52AwdYMHMQ67RYpqy9ZGsWgBv7nnFIE+5PzfpqjiyaqE6srqAD9t5tgJze/QRsqG2Maqn3OfmKcE2qEv1JfwOvwwBwGlYZFA3GO5ipsI7m+I+DXj0Hy4PevQq10jucWNI6hcwZ4JSiP7Dyu6F1ttPtu2+D/F1heTPL9qYhXLDPeV9wBsDOix08ZwVbo2JT/K05Xin7dmCnZzM7PkrezmKoThxwhNG1TupqNWMEXPrNuoldbbD9jmnAuw86Qa11GRV4GBaDHeEeWAIcvWTl9F+1ZIdlWVgTajXKac61Oio2yDjq3QP80Vjo7796cz1G7AyPwV0wNrQV2fFaMXUxBuSnwM5Xe9TxXPSc9XbhEYDr7Y6VVt8N2c3vVmmdRGazdEEN006PHgQ2lt1gbrCBSWt0Mk+nCyIdDjTgwIKU/wLpz2uXkN+ige3rsdhPEs5gbQOLgp2wU6G9qmYB2IYplA8HpqYa9rL9wcyPZa7S+BzPF80Rzv3wATRXfrs8JLPxpeTz8l3wikzdDLSbw5ZwNnQGR8Q3QiVl8N8ExoNX4DLIm0ajQs5idIf34FYIjerAAmTXhfQ76PO/L5wDag4YAD+Dswf19nek36HOPqcG4MXBZ/VaCFXZAV/IUG0d8B4cAPbCtgeDo0GvXJqSAxkwe8OE8D7YgDQkR5AGr/WgE3wNs0FaC5P5Nl2Qo/Qf1EUfV4H7we+Xr8LBUCmNyYFtiD33vWDDdiVUUv5AaiNYC8Zqxok6sI0Btz98BXuCDa/loX8d8BvvJP9mR6bssFiu7MAMAjswln0Es0C96RsqfCTsBxF86+3uUd+Ygi7PTTMw+CIksoH0u6bBsrWalgO8C2ukDmRjcT3YA85qawr8azQ27CfAJ9AT7gUDci8YCH9Bcxp9NmsXOpqrXD9zpQbgYWAH4DmYB8qlJTiQP5pxWvRA8H5MCo3JWYHbMhsMIr92pqy9Z33v/Oarx3aOZwY7cnZ49PhzmAgSzUei0p2t5FyxrIwDNZuCbs3l9GJnH9B60YlUdEgOK+uoJBsMd6VspVbW1cb5GhgOb8HukEgvsgHBUbGNS/rHJruQt3NgsD0KzivkJ2aZR3WjUtNDOTovLbk+A1mv1A421AbFYwtlNt7+QKwcIyVHZ468poJEdo68N43pZFZ6j9NajMwh6YJIj3SgB3/6XhqIfYcWAmWwTXeWRxbyx1VJIpZ16UDNAvDojdjlN4/NMuttVH6EV2BH+BNCrXPAxrRj5hAGvNZ4uxT7nw1OjTpT4XSsDcoLcCf0L+RZ/CODg/f8h39K/v7mZePiPd8rVd5YcgFWrg7uYwCq1lS117wtOIW/LOwHTr1XQ19yknlhROFkA1k+Bn4b7Alvgg23gXIfaI36sPOZ4D0dGx6EIbARNKbvWDkTPJPayDpX6/6kTpv75CfUcLkitfQ+Twe+r35uUHb2sh2bkSvij3CgNQ4syM7DYQuYrbB8leUmYEOdfpHJ5l4nUkMbqrxpUyr0FCSzCSuTtqPTFUrVEew4oLCznajBsCrYcL8Dr4GB9WmwEVc26MeNTP37x8wkb/s322RqZ7b4Cq4GA5CdiOmh0urLCfTM0aFyRuENWNhMFTQ55/D824KNse/GN2AnSI8vhI3hVGitVuAAH4HPzUrwO+wIL0FjmoyVD4H7W1/f419hTGiNVmPnM+AkmKc1B6qTfZ1Jsh3x3egHt4LvbKh+HajZCLgxy85h5bqZDTYkf0qh7AmWPTLr85w9kcrlLQA70t0OhsEIsH6XwVTQGh3Azja0idYn8TD8Ao6IkxG3jWZyP50NccS4E3hf54JhMDs0R25nwDUoJLqYhIEhOV9SXu7lHhxwezAY9C8c3A7kEYV0NRbjcJJDwKBrMH4W7ETprwHTQCm9oTW6l50vB69tInBU+z7sBk1pAjawfhfB4eD+rZHPmd+iZ4E54UZYC+pdE3MBA2DuBi7Ea9THk6BSnTzfxymgO4Qq60AuA/CRXPP+mes+jPxxhTIbmHEz6/OczVsA7oRZNpxO7S4Kd8IwGANaqxk4wD0wbeFA87A0KDi1ZvkCkMiAn4wcrdMxcB0MBvdrrnZnwwcyG3cm7zSsI65Kyh86PQjbgQHqCrBhzAbg+Sm7AK6C/aADlFtrcEBnAsRpX+tmYHoOlgFnFFrz3tzF/qOBwf4mGAJeU7EpU4orJjsSwyHduRqf/B0wNuRZPgdXg14OhfTzuRB575kdFNf7LqSvkWzF5ftoJ8nn9EmwzfWehyrjQC4DsI3422CDbaN8P3wENuonwPVQT8pbAN4D8/QxrZPJrJsuaEXaEayN/g3wMwyCC2F6MNgvATZE3tfJoLXanAO8mjmIz8q3MEGmvJzZxTnYN/AWJA2pAdgOR7oD4bXaoM4JbncgnAXlDsKrccydQDlLtCj0h2tBHQqWqW6gbzvCfNAcGRAc9ab1MJnm7p/erzVpny8DQ1Z26CbNFjaRt/OwBawO5b4f2VP3psBno29hxQCWtnETQ7LOZSI7ODsnmSos/STwC2xUOJd++BxtXcjHovwO5DIAe5lORXrjfdFWhGR0NjPpelPeAvApGNgnY+JS5B1JllNrczDP5Yt8BjhtZkB4D16A9+EDMBi0Ro4SngdHeAa4fvAOXAyV1N4cfAWwEfd8Z4OdirshrQfITJMuIH0m2BEppybkYE/B0nA7jAX6vhWo02BBcDtHizbua8GvsBk0JUdoBpBFwffwanAE3JB8Z48E74u+6I/3qrVy+trO3SSpA01P+g3okiprKjmYDay/HlwD90EnKFXOLiwGety5yEFOpmzxTHnSCVqf8myg8z5Vc7Bh3Y/O1G9s8o7KqyXfEz2RKat10hqep2YBeLQmLvoT1l8JF4MN2s+gXvp7EX+2woGv2Tc7almDsq9accxiuxoADLQ22gZ3g6/30nIDgKPU08GXvgOUqj/Y0aBrg/ws2GgZHDaESuonDm7AvxVmhWPBRtZrTus3MiPSBaRfhdZ2PDKHHPl/p65N4WCw8fZdeQ3Ogl1hTXgcBoOdQvEdGx9WgqY6tw+yjQ3kKrAT3ASbQkPyHk8Ey8EA+Aj2hdbqMw5gZ8Lj+dyuC3ZoTP8CzZFezASbgB6sDk9DczoibPYf9aLkKvAZN6B/DBNAWmOTsc5p2c6NBda7KxgE7Qw4YzIImmon2aRsGp0jfZc52u/krXc1tAAnuQV+hF/hXZgLQlV2oCPn8+H7C74AG1gfyi5Qj7KhczqpHOrJQfpD31YczEZRT3crHOdAlsNB3xvTmKy0obPhddnU9galO2E8ULPB43C2mZSuJT1xKl8vye5U9F5YAmy8+sFfMCWk5SzAxqmCrqS/hD6psnImfU+mgVPhCfB+6bmBVt0N2Xu3M2WrubJMckRqRyirGyjIBqbsNs3Nz8yGO4Aj/F7QEh3AxotndvD51LOWKgmgy6Z2tF7nQDqADiR/BSRyhsDA6/s8LjwIT8J04L16tIDvXTXke3oPLJ062f6kT0/lK5X0nfgEZkqdYBbSPkPV6gCkTl215PGcac6qna2ZJ9qG7R6APoXtp2Z5LfjS1KPKFYDt4V8HB8Ez4M0rVb7UB4N12wmaesltsL8DG/T9wN7pt2Dj05g2YKX7bQ8XwhtgsErUgcRDkASHpLxay76caEVYqMQT9mC/G2EoXA4G4azs8NjQHgLrg43cplAr+Qwl71ZSh9tJLJZkyrCclWPYScvKADxptrAG+Z05p89kWpuROSJd0Mz0XGxXLEgZPNIdy9HIXwQG2l3A9VtDoltIPA8+S3aS1gPf0XWgWpqaE30Pdh4ugyPBd7/SmpETeM6szqdg2mxhG8ofz7XMmbfr8SFdKVOp2cn7UNajDHKtHQHPzTEcXdlTVr7Md8C6Zqogg+eHmfM8Rv7iTFmxrL1aG5EV4DTYDgy8vtiHQbEXj+KKa1vOYDCyA/IAeC36WgnZwdkYbHCLBelKnLOhY9rZGAbzQS84D26GcqozB7MBXy910NVJvwwNeezz4PZ7wiZgvlLqxoHtCPn++BlhNfD9srylciTu+5GWdX8cxksXFtL6b6fPEV5aN5GZMF1A2k7BppmySmd9VmeDaga+STjfreBzk8gZgtfBDmwx2Z7oz3LFVtZJWS4D8FaYd2zGQHurjjDqUSdS6SGtrPju7L8qdIVpoDv4AlcreDlK3QLSMqA8nC5oRtoXzJ7+MLgNbGwr2dBy+KJy2u9n6JJa6/PlNdW77OzY0fEd6gXF1IfCC+BSsEM0OpRbNqq/wSlgQ3Mx+PwWUwcKh8FxsDQMhhcgfX/IjiI7dgbsFaGhoD7KDpnM2OR9Ny+Ek2EqKFUeZ1ewHj7jXq/X0hLtw8bpds/grX/OJrQH2Tm9BHw2Z4bhsD0U01UUngnefwdm5n2G6k0+J7kbAdsL/BDsQZ4A98E34Ci4HlWOAOyDeBhcD+fCm3AEnATVkEHz6syJHEEYROtRe1Pp7CyLDbre5kU2KHa6dgCnrpsTZAwk3itf6iXBUd38UCs5ipkXrE+nRioxkHU+22kdQsbOQTGtTeFQ8L0YBMNhLKiVvDY7cMPAd8J71pz7xWb/SK+eAo/j/l6fAaY9aRUu9iw4E7LvZ+LDBiQeSDKFpZ3J/2XK6iGbywCscQZhH0IDzB4wJdSryhGA5+PibUxXLpgwA8sfYPNCvtKLnpzgT7gSloHLwPqMB/Uon62sd4tTZgDLi/T6bLAhugiegcZGhHMVtkmPBBxJXAt514FUcJFMJQ3axUaRdpQcGU6Q2t7O6cGpfL0mvXfLgR0MR4Kh/zpwLEXZTuUClB39301zX1KzANxY79CHcCl4FnaC72EJqPRU5eicY3zIo2xcd4YDwO91BvU9wQa2GvqCkzhFNi/Y25wUpoWvoR5lB8LRxQDweesLd4G+5kE2wE7hbgk3woZwWyHPoqjstCYdo2SDl0iMlWRyvPQ58tlKy47eV+mCQrofSztQX6bWHUN6nlQ+78lxqeCecAr4XnUGZac2+RZqQLkHnJWpdNvHKepGxoMZMrW1ffwuUxbZEh1Ynf0eh+kL+8/C8g7YpJAv58IH/wh4D/4EXwBHli/CxlAO2agPaeWBnIpbI3OM2cmfkCmrRNbptfdhd5gbToJ3YEyoZ01O5Q1qfkO6BrK9aopqpv05s53OtOwknJ4uyKQdMXkdTmUm8n7dn2RyvByHun0KR8JMsAX8AsWesVUpd7u0piNj4KoH2eZ8ATuBHeiDwQ5IN1A7wmBwMOD1G6B950J/O9CDxQOwPNhJXQdst32G6k3HU2FnenIlf5W5ZKZGPqiVeMHO5rg3wKzgDXQ05ItgY/wYbA2tVTkCcB8q8RpMXaiMjexVkG2kC6vLurChOC5zxL3Jb58pq9fsxFR8INjoZ3vWFNVEertb5sw+i4dnyrJZt3F6dln4H9wLvaEaWoyT7ACO1kcv4YQGJkeFp8JBkJ5iJvuP3O5qsEM+Fhh8h8ICUA/alUpm76NBd49C5e38pztRFp8P/UyERjrgs2HbPQQMYpNBPSqXAdjef7bB34ayyyvg8Nsc015UMc1HoS92a1WOAGwdbOA+gjPBzshmUA0dwUn0Iq35yRyaLqjTtB2aO2ErMHDYk+4PtVZXKnAHGGSs47pg3ewkNiWfkwPAAD5FUxuXab3PyJWwJjhKd4akOXVls5I0JntdBH42MBh7zRqLalkAAC6sSURBVPWiw6io709azlQcVShwViYr1y2SLYx83TuQywDs1ODwAoNYPgXvQ18ot27igDZuxeSLckmxFS0sK1cA9rTdYEZoqNPgNuXWdhxw38xBzyK/baasHrM+ZzZ+iaYkcT30SApquOzCuQ+Bc+BYyFMv389EdlzsCL4Gb0FHSLQ3CTvSof86sAVF2Sl0OxN2AtXZkKTNzwZ/QkMzAm4Tqk8HchmAtdJA40u+FywIo0ElNAcHtRF+HC4DH/7L4Tl4CXpCa1XOANzaupSyv6ONN+A08F4cCHqWbnDJ1qUMtlkdTcEC2cIa5/V9E1gV/ExSSw3g5DfC+IVKHMfS92XCQt6FnYUrUvlI/utAZ5JPgJ37JcDp6GcgaeO6k/4e7HQ5Lf0AzAltQf25iMFwLdhJq/WzTBVqqpoF4ORha+jqv2HFNXAUPAT2ACshH/w5YE+4A0aA01p+y5oFnEpr7/oJA2YAg+5S8Ano2R9Q7xqHC7Czl8gGwUbx66QgB8sDqMPOoN9rgZ1FG/FaaTNO7PvyVaECz7L8EJYv5F1MDz43of868CtF84LtTD+wc2s+aeM+J21nxvWvwJrwNNS7bDsMunYstgffvRMhFA4UdcARXpeia1pW6EM2pGW7xNZVcmAdznM1zAjOdtwOjvTzosWpyPvQKVUhG7BdU/mWJjuzwz4wFO6B9HQn2SblTNFUqa18R56CS8EpfBvah2EKCLXOgXXZ3W/rt8Fh0NTAhU1yKzsRzoykdT6ZhdIF7SxdsxFwXny2wbgInPLxm9a0kMjG2Ye/OdqCjYY1wHuU3w+hfDqwBNW6EAwgm0CepsUc+a4Bdganh6mhF1wCpcrAOxj8tDA+nAc+v82V26an7icl/xecCXp4FtihCbXOgZ3Y3bZpEvD7r59GDML1qnuLVNyO4HJFyttLUbsPwCdwp32wbTAOgQ/Bhk61JAD/vUfxP2MEXNyXKG3agc3Y5GC4AM6Fq+DVQp5Fi+Wng1sye41B/u5MWWPZ0Vg5CB6Gw+EmsBMTKq8DL3M4701al5OZKV1QR+lzqOt6qfo6ze7Ap3eqrL0laxaAR8+J0/a+bJR+ggPAh94RQnueFuHy27264sB88Bs8Cj9DLeQMjN/cb4BtYBawEf4DSpGjXn9cmJbX5nU2V3+y4cYwO3SDU+FjCJXXATta2efOAcK45T1N1Y5m+/oROIvjrODmsAm8DaEqO2AvOg8y4PZLVcTGzQbFby720ELtz4GeXPK1YAC2g/Y19IBayG9m14B1cKp4ICwNBr6JoaUazg4zgL+q9RinwROwMDjS7gDN1bNseB/kJfg6Fe5I3PvWFuTocPXUhcxKemt4JVVWT0mfE3949S7YEfRa7GCG2rEDS3HtPhh7Zjw4kPyvUI4HJKagM+bmODsGdfsOlk/VcSDpi8DvsNWWn0Oces7qdgq6pwqt760wDO4Avxk2pJlZ8RcY1J16trPhsex4OsquR/Wn0jeDnQi9cZq9E9SzJqfy3qf9YQd4FPpBqO04ULMp6DxZODaV6VukQgMo27RIeUuLIgC31LHabe8oo1jAsyM2RQ2q5UzRWXBS6txXkDbYJvJzifkpCwUrFPJOozckt3GWx9G0oxHVBfz8Ug/Sl4Fgp+FMMFBNBYkGk9gtydTx0hHjyuBfRXI2JNS2HIgAXOR+nkPZuEXKSy2KAFyqc9XfzxGnAS4tp2WfAEeJtZAdRKd7r4er4GjoDIks65NkCsvdWf4vU5bOLkbm0HQBaa/zgUxZObP+4ronOMvQWtkhGgxOpZ8GBmCn1hN5rpuSTCzDgZw6ULMAnJcfYRW7LxtSuB98W2xllLVpB17j6t6CfeFY6Aj3wsPwOdRCP3DS2cFvnH/Ap5CW79JX6QLS1jUZ2WZWjcw+zZ97QH/wO67aDrLHGbmiDH8M5Bjrg597FoHl4QUoRUuyk50hOxFqajCoez2bgrID/efIVPwRDoQDdeXAz9R24jLWOEbAZTSzCocy6DoLYuB1atfA5JRnXrULFUtPmxucHBHO3ESFpytsZy/8bHAqNz2yJlsWOc09BJKR77ykX4VSp/TXZd/NINE4JLxPjxQKxmN5DaxQyMciHMirAzUbAefVEOtlYxRT0Hm+Q1G3tAP+2Ggo+MOjvcEfIa0EzVFXNloA5oJKdTJ8n7KdgYGUbQylyG/eF4NT5onWIvEJ3AN3gPlQOJB3B2oWgPM8Bb1l3u9a1C8cSDnwG2lHmQtDN7gCnEZvjr5jI6fXKylnFHqD0/qOzn8Hg6RT66XoQXZaGd6EzaEXbAgG+c+h3jUWF7AcjA2PwqsQCgfK6kCeA3BZLzQOFg6U0YG1OdZ64Mj1ddgWDGjqgb8XufvzcWrkD6IcuRpQ1oTLYH4oVbuz40OwCPwIa0BbCL5On18Eeub1vALLgrMaoXAgHCjBgfgGXIJpsct/HHC0dylMDI6S9oRTIT0VSzZ3OpIaXQ6O5E4D/1Wvg2AvqLScir8O7ga/k3eChmSnZnLQ21rpNk68Wurkk5B2tqBXqiySbceBmk1BV+p7U1u5NbNwIfaE7wSn3PpA6L8OGHwcFR4LB8IE0Fa1HRe2BXwKjvqOhi4wL+RZTqXa0CwF58BG4Gjd8krKQDYQtgcD8Yvg92inxLNySvtKOBxegAWhFjL4X5s68cek/a7dN1UWyXAgHGiBAy0dAffg2P6KNWkEZiV9H0wLoVEdOJes3zydztwY9G0aaIu6q8hFHUrZ4kXK81S0LpW5GuwsKT8/fQHLmWlAM1C+FQwER/ylyOnu7L4nU7ZE5mD+FS+fG79PKzu774N1qLYcATvqTet6MgPSBZFuMw7YMZ2zFlcTI+CGXXeUY6P1UGGT51l6oxw55F02rjvCNeC0o9/oKqUBHLgvOAJ+BAbB0rAntEW9zkVtnbqwGUnvBz4fedZlVO5LeAx8Ni4GO6W3QjEtRqGj0W+hM3wCM0NL9TM7+B01rY/IZEfePj9+n062HU56W1geqq0zOaGzBFOD/5jI6TABDINQOFA2B2yoQ8UdsIH4MLPqC/JjZsrymDXwfgM2YDYgx0I38Ec45ZYjhQsyB7XTsn2mrNSsjb/B3MbZqcEnwB//+I9h1EIG27dhGvD5WAXmg88g79qCCjpL4Yj0QXgKimlCCh0t94O3ChvYwTgY/ge/FMqas3iGjXaB4wobT8HySOhZyCcL26Jfk0xh+RtL73+1dSMn/BEcqTtjoFc7Q7nlt/DJ4HuwbQmFA23WAXv7Q1pwdSuw7e2QdFKcLbCRXRXyrAWp3B2ZChokvZZKqD8HHZw5sGXpb2iZ1S3K2mm4AjqCDdahsC/UUl04+fKwOkxey4pU6NxzcdyTihzbUbQBoyWy4+eI11HkTjAMloKslqHAYD1GYcVELP+COQv5trbwubkUfLa97sMh+TxAMlRFB47nXG31OauijY2fqqUB2KMdAi/BbmDjcyDkXctSwWIB6t4KVvxUju2U4YKwHtwP5QhM03KcbGeCov9zhJL9rmh5qDwO9OYw14GdzkTOpLwLNlQtDRR2nFaCtcGZg4a0HSsccdsg3gB2ctqixuai7FysWbg4O5cG4U0L+VhU14EIwFXwu5QAbLVmA4NavfSQelJXv+ulA9TS5IdBJbUWBz8GDoByBF/rOgucayKjq8lPmSmLbHkdsNM5COwEOW39DvjXiC6B52EcqIR8dvqCI+C2KtsTZ3LSGo+MnY5Q9R2IAFwFz0sNwFWo2j+nsGdsINsYZvin9N/EvCT3hd2hFzSkFVhhD3sTsCF1+rkeG7Qu1Nsfka0GiXYk8TY4asijDEzeo/PhBOgG9aoNqPhg+BlOgUQ2WM56hEpzwPfTdzitrmSGpgsiXTUHIgBXweq8B2B7wDeBP/LZHAyg6Sm4dck/B34rMyC5fj5oSDOxwuOsD/UcBBwRea1Hw5FwHUwIeZRTrR/CHtAbnHZ9BPzhUSnakJ2uh7vgdKhFp6Mv5z0PsrqGgrzeh2xd85bXt4egf6pijoh9xkPVdyACcBU8z3sAdpS6SsqHHqQtsyGfFGzYDdKJZiRhMEp+tJKUt8Wlv35eGBaEPF/vltTvJEjLjpOjRzsPV8Fl0JyA7AjJUfQE4MzIBfA0nAt+Z6+WZuBEZxc5mZ2C9PNYZJMoasQBff0OzgKfi0Mg/c2dbKhKDtQsACe/8K3SdcZpGnFgTNZdn1r/Celh4AjkA/Db29eQ6BUSP4Kj25+Twja69DofyFyb161nn8EfmXW1yhqQ7DSl9QKZC2FHcEq6D1wJjm7fgIbk+rngV1gd/ITgc3ATrAEGcb+5V1qvF05gfS4qpPdi+Tmkn8fCqlg004FX2c5O9nTwAzT2LLA6FA7UtwMnUv0hOb6E26jbZJn6+aOMRaEnmO4MibqTsHF0dNie1IGL3RmGgp4MhwkhD1qVSjhqTcupxjvTBaSXB5/HxmSgTWTjPD7YU58fHCldA46iqiGfsSfgFrgOnC7tBKFwoC04ULMRcFswr7nXkPcA7A8zbgZ7xAaUs+BeSLQ5Cb+FzgsLgQFoWWhv2ocL9l4m03V+EzcYORrOgy6lEg+C338NVJ/AypCW99gp5cbk+oGFDby+fvAbOMpWR8N8I1PV+8PnMjl/9c4aZwoHKutABODK+jvy6HkPwFbS0a6B9R7YG9IjXrIjf4B1HMvDYB4L2qHslGRH/QdTpnfl0AAO4vEM9FNBKTLg7gQDYUXwG18HSLQviR2TTANLpyd/gSPhWXgOZgc1CXwBbhMKB8KB1jlQswAc34Bbd+PKuXdHDtYf/B4ku8LF8D4kup2EuK0jv3XhRXgB2oscBf5euFh/kDUrTAzl+HHWVhxnEzD4doV3wBmHx6Elcmo8LTsH3qOjYE4wcK4PjcmRsyNcA/VHsAw4Cnba2c7ZQHCbUDgQDoQDuXcg7yNgf6BzYMrFNUn77XCcVJlJg68/hjkJtoOvYFNoL3L06Ld8g64jyyvAqfkPYDwoVT3Z0YCbHl3PTP6aUg+Y2c8g7H1aHbyHTWl+NnAmZHlYDLzG82AH6AvV1NKczO+/dv4egVJnBtg1FA7kzoGajYBz50QFK5T3AHw3154dxe1PmY1vWo6idksVGKDvgrlSZcWSNpp7gEHeYFCvclreYOBI+A4wQE4DjgrPhtGgIc3ECr+dngZrZTYy4B2aKTN7dZGyahS9zEl6pU40JulrYYpUWTWSdkLsCNpBUUnHIKa///Yj/qx/B2oWgBtrrOrf1vq6gp+pbodMlQ3I2bI+lF2U2u570peA05MNaXpWuM878Bg4snI0Vo/6lUrvBU7zGnQ3hDfhSPDvzDYUGPx+eiZ47YPhVPBbeqJPSRig09/dpyyUJdtUc+mU9YjUCX8i/QpMniqrRnIbTuKsg8+OcgTsD83WMBMKB8KB0h2IAFy6d+Xe01HszamDOgXtt8hHU2Umv4HJMmWzkP8hU5bOXkxmJ7gCnEZ0NOXUpoG5HvULlbbj8RSkr9tpaQN0MZ1D4ZYwFJ6ESWFqmAOUQdyRnseeH5aAQeA+tZCdL+uXyPy68ElSUMblqhzLmQSfDT9tdIREY5HwmUvrazLWJxQOhAPhQLMcyPsUtI2e3/geBgPmECg22jE4OIqbAWwEt4cvIT1yIzuK0oE9WWFwXyzJ1OHyZOq8NThDoHcGz4ugIQ0tsmJ3ypbJlC9H/hg4BGbNrKtmdgAn87vv0rAwPA5+/y23NuCAV4Gj/W7gc+G/3JVoIxI3QDITMy5p6zUPhMKBtuBAzaag24J5zb2GvAfg5DoMutNCYwHVxs8Rs0HnOBgPGtP1rDRgJxqNxPNQywCT1KXUpf7cBnfDrbAvjA4N6UJWLJpa2YX0m+A3zrzKvy98EBwJS0El9BwH7Zo58LnkFyiUGXjPhsdgPzAYrwWhcKCtOBABuAp3sl4CcCWsWIiDOmqxEZ8bHA0eBZWUwXBHuBYcYfWHWmpGTq4HG4E+OIuwB7R32YHJytH/kplCf+S3OEydKY9s+Rzw3bRDfTrEN/by+drUkSIAN+VQGda35wCsfb3haPDHR9V4uR11XwL+KGp6uAlWhlpqIk5u0D0IsgGGonYpR7ebp67c5+R3mCRVFsnKO+DszB1gZ3k28Ed30UHEhCooAnAVTG7vAbgKFv9zivlJZUdWBj9H3q2VU6L/AzsSR4DHDZXugP79CHbOtoeHwGAQqq4DwzndZKlT+rsGZ476pMoiWRkHahaAnSYMhQPldqArB7wvc9DPyHfKlJWSvZCdfG7tUDkd+inYSPlXdEItd8D74l/f8sdn/qjPzs0ICFXXgVc53YepU/5B+iXoniqLZDhQtw7ECLh6t25KTnUrOP2cyCnfB5JMiUtHZo7Q0rJscLog0uFAHTpwBXWeL1Vvf23+EfRKlUWyMg7ECLgyvsZRa+TAe5zXv8ryMWwG/krbALw6tEaOBi7OHOBR8rtmyiLbegecAl0TJgUDgQHCH7GFKuPAYRzWv5kwEL6AA2BvGAGhNuqAfx0lFA5UwgH/UYeZoAMYiNcGp4uLyQBtQ2+j35jeZ+WC4DETzU/i5yQTy7I4oL9+w58b3gDv3b3g1H+oMg68wGF9B/wc0Bd2gMEQCgfahAMxBZ3P27gj1RoK18GrMDE0pqNY+Q4sAf6VovvAhkvNCJbX899v9jpqrc2pgL9iT8v3Z5N0QaTbpQN2zraB28B37xroDPWsmIKu57sXdW+WA/bsD4NpwCB7Cfgy94Tlwb/6sjicA+vD91BMe1H4CAyAH8DRmSNsG4VF4WlYD24Btw213IFp2cWOTlpOQa+SLoh0u3TAd7grrAq/gh1o/+6yy/hEgQmh4g7Ygx9SfFWUVtgBe8ifw06F84zB8nxw2m0FWA4mAbUfLDEy1fw//L78G3Qp7OJUtqPq1n5zLhyu3S325oq3zly19yU6NBlT2mH2Sa45+6noXMpmr2MvYgRcxzcvqt60A4uwiVOaBtx54THYA96DZcARrCPWBcBedEuntBZmH0fRv4D6A/YHR8JOkYVa5sDZbH4dfAveK2cW9LMbhNq3A59w+b5faTkTZac61EIHRm/h9rF5OFCKA33ZaWHw17RzggHWxt0XtwdsB1eCMxRzwYTQEnmcbHDwF9M/tuQgse0/DnxJyo7RvuB98+8KTwo/Q6i8Duiv74fvw2WQDW4U5UrvUpvd4LhCrRZjuT04QxIKBxp0IKagG7Smoium5OiOah8Fg686Dz4HA64j1LvhVHgRtoSWqhc7DINkGmxm0p6zJ4TCgbw6sD8Vc6bBmZoz4W0YG/Ks8ancp3AaHAxDYTqoZx1P5ZO2qarXESPgqtrdLk82P1e9M/ifHzwHx4DfkMYCvwn7MvcBGx55A1qqEexg4B4E34Aj3/ngHQj93/9tigk28t/DDLA4fACh2jmwMKf21+bTwq9gZ9QZh13gUMirvqJik4PvVyc4AXznQuFAow7ECLhReyq2ciWO7D8qoCYAG56lwWBsozM+dIADwRGAwTlUPgfW51AXQvKNbkHSt8J4EKqdA1tz6v9lTu/nmKsyZZGtvAM1GwGPVvlrizO0cwecXnZ6Z0Xw2+LTMBCOgDvAKax7YVyYFf6AUPkcWIND7Q3J99uHSDsbsSiEaufAt5x6xszppyHvaDjUThyIKeh2cqNreJn+QGpjuBmcclYXwBUjU/Gv/RRsqOjil8zRDcZdMmWRra4DV3M63wFHwn4HNhgPBmeB7CB5j1aF7L2jKBQO1J8DMQVdf/csalyaA35LXwFWg33gekg0M4m/wO94odo60JnTHw1OO78G/lDRTzB+ktkGToZQ5R2IKejKexxnCAdy48BC1OQGsOEdDn6XLZf8K1yXwhwwNRwOznQ9ACcU8n4S+ABCtXXA6eY9wR8Qfgz+sMlPMHaQzoBuMD2E2qgDMQXdRm9sXFZuHfA7n8FwOvAX31PBINgBXoLW6k4O4Kj39sKBzB8Ke4AN+yvwNbRHTcJFrw7+Av9+eAzyID8H+LcBsvqNgjGyhZFvOw7Ej7Dazr2MK6kPB9ajmstD8tet3iV9LDhdXA59wkH8hjg3OKIy4L4Kdrad4qz34DsO13AwOK3ud9QZoDnqxUb+6t6/ovYm6MU6kAd5z74AfyyXaAMS64L3L9RGHcjjCNg6dYWv2qjncVnt2wFHNP4wLa2fyJRrpOPozkAzAjrCffAQXAb1Lq/nZTgTNoXecB44e/AMNKbLWel2jxc2GpflJYX8W4WyWi3+5MTOULwIfjbwF9LOjJh2mjoUDlTUAX+McAS8Bz6MTpXZSPlAbgzlUPwIqxwuxjFa68ASHMAgMGbhQC593i1vrQxQTmU+AROBo0WDr997fcfqXY4Iz8hcxPzkB2fKimWHFincj7LFi5TXqsj75/f5eaBcHbJaXUs9nbdmP8JytJkH+c8QTgJOzdkbNfjaQ50JTgIfRnu9oXCg3h24iwtw2tSRnCPVGWEjsLy1mowDPAi3gMf+Hfyx1/QwLXjOepYzY49mLsD2wvKm5PfUXjACVAdYGfQqL/IHWE/npTJRj/bjwNtcqgG4mOajsFjvtdi2jZXFCLgxd2JdtR3oxQnnhZ5lPHEPjuW30axupWCabGEd5h3t3gjp0fw25P2G3pSWZIO7Qc/t2N8E0anHhND/tfsRsFPNi0Kx71T+fcbP4iEJB2rggFOCA8EpQUdMF8HjUI7vciM4jpRT/pjnOdgfjgA/51wMXsebUO96hAu4D/zh2t4wAziy94dtTclfg78PW8NYcBXoTSgcqJkDNip5kH9n0Smz78CGwh8hdIM+MDosB+9Aa+QI2BFCc17W1pwn9m07DlzHpXwFfhLpB87G+CviWeE9yKMMtmeD087W+wk4DMrRaeAwuZD3oS/YTniP2tK1cTmhKjvgCPhSqPr0v8EtD3qGShiEnWLqBU5HO+p1iuh+8EcqoXCgmg4swcn8EdNw+AN8NheBg8CR8MpgAMibrOtmeatUI/UZjXVOwztab24n2+/A2W/BFIXCgfpyIC8BWNd+hntT9o1B2m89EXxTpkTyPw44PTwjfAN+HyzX8zIhx3Kacg1YDZSjSUeVToU6CrsDQqU74I+njoMuYGfHQNwffoRQONDmHbD3mVetTsVOaGHltmD7YQ2wKuXdIdR2HNidS/FbYCdYBx4DG/Ny6F0Osjj8kjrYoqQNwJ6jQ6o8ki13QP+egk9hINjR8f4dCaFwIByoogOvcy6/taWxobPxs2wQtFZrc4CtWnuQ2D83DixMTfxM4SxJIkdTuyWZMiwP4hh+830efHbuhfXhL3DEFirdgWnYdUiR3S+jzE9QoXCguQ5MzIZ7weHgwK2l8hvwnC3dqRzb52UEvDEXY2N6IsxeYB+W1xXSjnRC4UDaAZ+T7SD9A5yTyPdLb9TK9EHsvx78BL7gn8AK0BO+h1DrHPi5yO5jFymLonCgIQe6s+IqMH74S/dD4TSoC+UlAD+IWzac04LTzo5+PwcbuXcKaRahcOAfB/xO2Puf3N8JA2OxRj2zWYuyt7L1vLAgHAAbgtPTodY58Ba723lKf2Y6l/x48DGEwoHmOODzcxScD8NgJrATtyjkXnn6Eda3uGXjthbcD34P8hedoXCgmAOXU3g1GAxvhj5wDDhFXAl9UImDtuNjOo2/GzwE14IdJ2cYloZQONBcBwy2j2Q2Nn5MkSnLZTZPATgx6EoSD8OZ8FxSGMtwIOOAsyRrwimwLvgr6O3hbQjVhwPOcM0GNpZ/wocQCgda4oAzpQuAM1WJ7IQfm2TyvMxjANav9+EjcEQTCgcacsAGfJOGVkZ53Tjg+x4KB0pxwBjxBgyEl2Eb8Ie7t0PuNVqOa+h09Bg5rl9ULRwIB8KBcKC2DrzJ6f07+9OBI99h4GfMulBeR8B1YV5UMhwIB8KBcKDmDnxJDfareS1KqECeR8AXcj3l/kVrCRbFLuFAOBAOhAPhQPkdyPMIeMvyX24cMRwIB8KBcCAcyIcDeR4B58OhqEU4EA6EA+FAOFABByIAV8DUOGQ4EA6EA+FAONCUAxGAm3Io1ocD4UA4EA6EAxVwIAJwBUyNQ4YD4UA4EA6EA005EAG4KYdifTgQDoQD4UA4UAEHIgBXwNQ4ZDgQDoQD4UA40JQDEYCbcijWhwPhQDgQDoQDFXAgAnAFTI1DhgPhQDgQDoQDTTkQAbgph2J9OBAOVMuBhTjR9TAU7ofs//dMUSgcaDsO5Plfwmo7LseVhAPhQFMOTM0GD8CM8CrMAxeA/8B+/F/MmBBqew7ECLjt3dO4onCgHh3wv5FbEgy+6nEYBHXzP9tY6VA40BIHIgC3xK3YNhwIByrlwFgc+LPMwb8gb3koHGiTDkQAbpO3NS4qHKg7Bx6kxodCh0LNDbw3gdPSoXCgTToQ34Db5G2NiwoH6s6BIdR4fngSrgK/AW8B90MoHGiTDkQAbpO3NS4qHKhLB7an1v1gfLgOku/BJEPhQNtzIAJw27uncUXhQD074Ag4FA60CwfiG3C7uM1xkeFAOBAOhAN5cyACcN7uSNQnHAgHwoFwoF04EAG4XdzmuMhwIBwIB8KBvDkQAThvdyTqEw6EA+FAONAuHIgA3C5uc1xkOBAOhAPhQN4ciACctzsS9QkHwoFwIBxoFw5EAG4XtzkuMhwIB8KBcCBvDkQAztsdifqEA+FAOBAOtAsHIgC3i9scFxkOhAPhQDiQNwciAOftjkR9woFwIBwIB9qFAxGA28VtjosMB8KBcCAcyJsD8W9B5+2ORH3CgXCgHA505CB9oBO8BL9CKBzIlQMRgHN1O6Iy4UA4UAYH/L+ET4LfwFm+gdATPoVQOJAbByIA5+ZW5LIi41KrFWBMeBiGQygcyLsD91NBA/AlhYo+wvIU2BBiJFwwJRa1dyC+Adf+HuS1BhNSsStgikIFX2a5bCEdi3Agrw50p2KfQBJ8redF8Dn0MRMKB/LiQIyA83In8lePq6nSMXBboWo3sxwEfk97t1AWi3Agbw447fwHdIC/UpWbgHSMflOGRLL2DsQIuPb3IK81sAFLgq91dFThNPRMZkLhQE4d+IZ6PQn7g8+wOhF8bl8xEwoH8uJABOC83In81eMXqtQjU62FyX+bKYtsOJA3B46gQlPDvXAjGIgXhPSImGwoHKitAzEFXVv/83z206ncebAbOKo4CpzecxQcCgfy7MDvVG5gnisYdQsHdCACcDwHDTngyMHR7qHg36W8D06DUDgQDoQD4UAZHIgAXAYT2/AhhnFtEgoHwoFwIBwoswPxDbjMhsbhwoFwIBwIB8KB5jgQAbg5LsU24UA4EA6EA+FAmR2IAFxmQ+Nw4UA4EA6EA+FAcxyIANwcl2KbcCAcCAfCgXCgzA5EAC6zoXG4cCAcCAfCgXCgOQ5EAG6OS7FNOBAOhAPhQDhQZgciAJfZ0DhcOBAOhAPhQDjQHAciADfHpdgmHAgHwoFwIBwoswPJP1Ze5sPm8nCzU6tb4Jlc1q7pSg1gk5+b3iy2aMQB/+GZjuC/cx0q3YGx2PUniH9buXQPbXvHhB9LP0TsiQNd4A34oBVuTM2+S7byGK04fexaDw4Mq4dK5ryOi1O//XJex3qo3lVU0v93N1S6A/qnj6HWOeD77Htdl4op6Lq8bVHpcCAcCAfCgXp3IAJwvd/BqH84EA6EA+FAXToQAbgub1tUOhwIB8KBcKDeHYgAXO93MOofDoQD4UA4UJcORACuy9sWlQ4HwoFwIByodwciANf7HYz6hwPhQDgQDoQD4UBFHZi0okdvHwcfg8scr31cakWvcmKOHp331lmsf/oYap0Dvs++16FwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBnDvQgfp1zHkd66F6o1NJvSymTsUKoywcKMWBa9npvQw3FQ40Pssr4XV4ARaAUHEHpqD4YngWboP+kGgAiQfhbbgO9DVU3IFVKb4XfN4GwViQaG8Sz4M+mg6N6oD/2tVVsNeoxSO9KuZbvN8ZowrZKVl+ANMUWb0uZU9mygeQj/c7Y0pkm+eA/6yajZxMAAbbdUAZfPcDe4ID4GMYE0L/deBcipKg0I/0m2Avujt8CLOCPecT4AII/dcB/5m/r2B68JnbH64GtSbYyHWDScCOzrIQ+tuBuVg8AF9COgA35lu83397l/5zUzJvwK+QDsB2Vk6DT+EpSBTvd+JELFvtwHEcYXDqKN+SNignsue3ZJKJ5SgOXE9u10LJDCx/gC6wDNwNiXqT+DrJxHIUB2z8bkiVOBL5Dewkng9bQaI9SZyTZGL5f6fggcH2dEgH4MZ8i/d71AenM9mhYAfQQJsOwGuQPwbs9KUDcF293/EPqnP3ciofuq1h50L97PEZQOxRJ/qYRPyD7okboy6dKdgcHLHdCdvALzAVfASJPiHhKE5vQ6M64OzLtKmimUgnswhZH30We6S2be/JHTDA6eesGvIt3u+sU3+Pepem+LX/rhr5Xu9B+U+ZdVl/c/1+RwDO3L0cZdenLjeCU4BqQnAUl5YP3zjpgkj/48CCpJw2HQ4fwAAweGR9TF7gsVgXGtUBp5j16wzwM8j+8DP8Dlkff6RsbAg17kBDvmXLPUq83417WWxt1sdcv98RgIvdwnyU/Y9qDE5V5XPS46byJs1/mCmL7N+B1umpJGgYjJeChSDrY1fKDCpJR4dkqODAnyxXAEcVerkn+C3OUUXWx3gWMaUZasi3bLmHCk+bYWhmk6yPuX6/HRGE8udAX6rktOidqar5ndLe3BTwfqG8F8t3C+lY/OuA03kd4blCkYHE70R+79W7XpCoF4n3kkwsR3HAxut7WKFQ2oelU81/gT72hES9SISPiRsNLxvyLd7vhj1ryZp4v1viVmxb1IG1KL2vyBp/wOGPO0aH1WE4dILQfx2w87J2odjA+yUk39kcwS0Ofve9EI6E0H8dmJii5Buaa/1rSI6ClT92sYMzGfSC16EfhEZ1IPsjrMZ8i/d7VO/SuU/JTJMuKKQHsLRznch3Ot7vxI1YluSA39rOLrJnL8peAL9pvgEDIFTcgbkpvgUMErIpJFqTxHdgb/keGAdCxR3Yi+K34CW4HDqC6gAXgFP3H8FBEPqvA6dTpIeJGvOtFxvF+504NeqyuQHYveL9HtW7yJXZgYnKfLy2fDi/oxWTswiOiENNO+AsS0M/sNJfRx2hljnQmG/xfrfMy2Jbx/tdzJUoCwfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHAgHwoFwIBwIB8KBcCAcCAfCgXAgHMg4MB75rpmycmQn4yAHQqciB+vRQHmRTaMoHAgHwoFwIBxoew6czSV9CEfDWzAblCqPM0Nh53NZPgYjwOPOC2oCeB1ehm3hVVBTwPYjU3//cTCLzql8c5ILs9HTzdmwGducxDb7N2O72CQcCAfCgXAgHCjJga/Ya/rCno5Yi41Wm3vgJADPwQ7PQkcwEK8Ah4JaEp4bmfo7wE5aSK/L8qZC2v3+gjEK+eYuIgA316nYLhxIOTBaKh3JcCAc+L//WwQTHgKD2hmQBKP+pK+FL+F6mARUBzge3P4JOBx2g8Y0jJXd4BZYFC6CqaEvXAgnwxdgMJ0V1FhwFnwA1uEq6AppjUdGkjrfTNrRpKPrC2A6MAi7HAQGfus+AC6By0G5TXeYCLzmr8EyvUm0GokXYASsCsV0JoVrpVasRPqcQn4jlsPhe3ga5oas7qegd6rwQdI9C3nvh3WybtbR+io7NY/Cd+Bx54dQOBAOhAPhQM4dMKh+BhvDhODIcCcwCHwDA8Ep28tgKKjtwcA7IxiUfgSnlRvTuKz8AQwWncCpYoOkQegPOBAcoRrADKJqb7gTJganm1+FzUAZ/C3rDMPAqWaXSfByZGsgfBgM0HOB+48GHuN2GAdc9xfoQwfw+gcV8nryJig7C5/DKtAXDIQGu6zcx2MkupLE5mAHwMA7B4wPBuWhoE4COw3qQ/C6En1Mwn3tGHwL68OkYB3tSKirYR8YE7w3xepFcSgcCAfCgXAgTw4YpF5IVchgOzvsDA+lyqclbaAyGDpKSwIhyf+7DZoKwG5nAOphAqUDsFPTifqTeLGQ6cnSYKNMG5j3M4PSgaoT+XXgS/gVNgG1HFhXlQRg0+tCEiTTU9B+M7Yz0AfsMMgDMCtsCfdBop1IPJ1kUsvxSH8DXcGA6LV5XI81M6hu4P7Pm0EnQVMB2PM/Ah5HvB+OppWj+GtgdhgN9CMUDuTSAR/QUDgQDvztgKOtx1NmvE/6WTDgOa2Z6A0SX8BkMDU8A4kcDbdGn6Z2dpQ8eiFvMDwZPoFbwJGgATOr3yhwpGkQcoR4IpTyntv5+AvugVcLGOgWAJdPQaLHkkRm+TX5e2F5WBYcgX8J38Ha4HFfgdWhJXW0bn0hqZcdg/FgctgFDLreR4OynapQOJBLB1ry0OfyAqJS4UAZHTA4GIQTTUliVXC6daakkOWk4EjubbChd7SVaLYkUeLSoFdMZ1Fo/Qw8s4DndZo4LQPZ3qmC20g78pSWysDo6NXzeb0yK1wA70Laj97kG5IjUj20bqbVhrAGWOZxj4XstVA0cgTexQSys6Hnyms3mCf1cjkXOBPwO3jcSUDPLoIJIRQO5M6BCMC5uyVRoRo64GhtTuhTqMPuLA2oQ2FhcNrUd2ZzeAkMULfCQDBY94cBUAkZRB6ET8ER4JLgSC+t78hsD65XjjLvAkfSDcl13QorHWX/Usg7fX03bAteswHtZZgRnH6eD6aHMSA9yhyf/BKQ6CYSC8IAuB6UgdTv1E6vG3gHQvZaKBo52vc8yoCdbOM1zQtzgFofbgfrORg2Azsrl4LXUyy4UxwKB8KBcCAcyJMDO1GZ78EA4QgyGT3tQdpg9S68AE7DKgPQ+fAZPAUG5EOhKXmOHoWN0t+AX0nt2I90kl+N9Ah4FB6DM+FKUI78HLkbaM4Ag8/X4CjRQKWWg/tHpkb9BtyLMkf4zxbWec2/gZ0NA9xr8A5Yxz0h0XYk3O99uByeBrUIZAP+ZZRd48qCerDUq+fgZdgH7DyMBSfB/qBWB31ypuFq0PfpQNkxcN2rYN2dGldzgfnh8AXsDqFwIBwIB8KBOnFgdOqZjArTVbZ8wnQB6cWhd6rMQLFNKl/OpAG2ezMO6AjzkmZsl2ziyDE9TT12sqKwnIil587KEWnXbGEL8nrpuRuT53BUXUwdKczej2Q79/F+hcKB3DpQ7KXKbWWjYuFADh1YkTodDufCNOBIcyFYEIoFcYpHjga/M1FBTcux36jg8ePQ4UA40EoHmup9tvLwsXs40OYduIkr3AAcbT0O88On4LvVEKyquCL4VtziOEE4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAOBAOhAPhQDgQDoQD4UA4EA6EA+FAO3Pg/wGZAK9K81Xv6wAAAABJRU5ErkJggg==" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#in plot, fitted = predicted\n", "plot(cog_final$residuals ~ cog_final$fitted.values)\n", "plot(abs(cog_final$residuals) ~ cog_final$fitted.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we can see that the plotted residuals in y-axis, against the predicted(fitted) in x-axis. We want to observe whether the plot random scatter around zero, and doesn't have fan shape. We see that the condition is met, and observe the absolute residuals plot, fan shape will get converted into triangle to shape, which also not appear in the plot. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![jpeg](../galleries/coursera-statistics/8w32.jpg)\n", "\n", "\n", "*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/171) 06:36*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to observe that residuals(observations) are independent.This is specifically concerned when talking about time series data. We can check the residuals against the order collected(scatter plot). If doesn't met, think about how the data collected met independence conditions:\n", "\n", "* Random sampling when observational study.\n", "* Random assignment when controlled experiment.\n", "* Less than 10% population." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(cog_final$residuals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that there's no increase/decrease pattern, so we can assume that the residuals(observations) is independent.\n", "\n", "So remember, the conditions are:\n", "\n", "* Nearly normal residuals with mean 0\n", "* Constant variability residuals\n", "* Independent residuals\n", "* Each numerical variable linearly related to the outcome." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In summary," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can test the model whether it's significance using F-test. We have a skeptical where aare the slope is no difference, the alternative as at least one of the slope not equal to zero, and using degree of freedom(n-k-1). Usually this gets reported at the bottom of regression output." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that **p-value is arbitrary of each respective explanatory**. It will vary based on other predictors that are included in the model. As long as it below significance level, it will become a significance predictor, given other predictors included in the model. You need t_critical(based on significance level and degree of freedom) to be an input of both confidence interval and hypothesis testing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use stepwise model selection to make the model become parsimonious model. There's two alternative to do, forward and backward stepwise selection. Using two method, adjusted R squared and p-value.\n", "\n", "For backward stepwise, you start the with the full model. In adjusted R squared, try to eliminate one explanatory, and select the model with the highest adjusted R squared.If it's not get any higher, we cancel the elimination steps and choose that model. We keep eliminating the variables until we met the condition. For p-value, we also observe the regression output. Try to eliminate variables with the highest p-value, until there's no insignificant predictor, and the p-value of the model is still below the significance level. If p-value of at least one level in categorical explanatory is below the threshold, we shouldn't drop that explanatory.\n", "\n", "For stepwise model, we start with empty model. We try to add one explnatory, and select the model with the highest adjusted R squared. And we keep adding it until the adjusted R squared is not getting any higher. For p-value we try to add one explanatory at a time, and pick the model with lowest p-value. Then we keep trying to add one explanatory, observe p-value, and check if it's significant/insignificant. If any of them are insignificant, we should stop adding the variables. Say, why in stepwise and backwise we don't select all at once explanatory variables that below significance level? Recall that p-value is arbitrary for each of the explanatory, depending on all other predictors in the model. p-value will change if the model change. So that's the reason why we add/eliminate one at a time.\n", "\n", "Model Selection can also depending on expert opinion, you're domain expertise. If you know that the explanatory is significance, you should add those irrespective of whether p-value is insignificant/ adjusted R squared is not higher. When facing against two different model p-value vs adjusted R squared, you should also include expert opinion. Adjusted R squared will play more safely to select the model, hence more reliable vs p-value that have less explanatory overall.But p-value is more commonly, as less features are good, to avoid **curse of dimensionality**.\n", "\n", "Finally, you should validate the conditions for MLR. You want each numerical to have linearity with the response, and validate that residuals are random scatter around zero, constant variability, normally distributed, and each is independent of one another.You use scatter plot between each of numerical explanatory and the residuals, to check whether it has linearity. You use histogram or probability plot to check whether the distribution of the residuals is nearly normal. You use scatter plot between residuals and predicted to check whether residuals has constant variability.You use scatter plot of residuals and index to check if each of the residuals is independent of one another." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **REFERENCES**:\n", "\n", "> Dr. Mine Çetinkaya-Rundel, [Cousera](https://class.coursera.org/statistics-003/lecture)" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }