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vincenttzc/gist:12364c1e4a8bc1cfe6128e0ac8c654b5

Last active October 26, 2017 15:12
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Real Estate Project Regressions
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gistfile1.txt
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"Attaching package: 'dplyr'\n",
"\n",
"The following objects are masked from 'package:stats':\n",
"\n",
" filter, lag\n",
"\n",
"The following objects are masked from 'package:base':\n",
"\n",
" intersect, setdiff, setequal, union\n",
"\n",
"Warning message:\n",
"\"package 'sandwich' was built under R version 3.4.2\"Warning message:\n",
"\"package 'lmtest' was built under R version 3.4.2\"Loading required package: zoo\n",
"Warning message:\n",
"\"package 'zoo' was built under R version 3.4.2\"\n",
"Attaching package: 'zoo'\n",
"\n",
"The following objects are masked from 'package:base':\n",
"\n",
" as.Date, as.Date.numeric\n",
"\n"
]
}
],
"source": [
"## Loading library and working directory\n",
"library(ggplot2)\n",
"library(dplyr)\n",
"library(foreign)\n",
"library(sandwich)\n",
"library(lmtest)\n",
"\n",
"\n",
"setwd(\"C:\\\\Users\\\\vince\\\\Documents\\\\R Scripts\\\\R data\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Model 1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data1 <- read.csv('Model1.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t1009 obs. of 14 variables:\n",
" $ month : Factor w/ 24 levels \"2009-04\",\"2009-05\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ town : Factor w/ 1 level \"YISHUN\": 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ flat_type : Factor w/ 5 levels \"3 ROOM\",\"4 ROOM\",..: 2 2 3 4 1 3 2 2 2 2 ...\n",
" $ block : Factor w/ 159 levels \"201\",\"202\",\"203\",..: 20 20 21 24 135 141 119 123 83 89 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 1 1 1 1 2 2 3 3 4 4 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 3 4 1 2 3 3 2 2 4 ...\n",
" $ floor_area_sqm : int 108 103 121 146 74 122 84 103 84 84 ...\n",
" $ flat_model : Factor w/ 7 levels \"Apartment\",\"Improved\",..: 4 4 2 3 4 2 7 4 7 7 ...\n",
" $ lease_commence_date: int 1988 1988 1988 1988 1987 1987 1985 1986 1987 1987 ...\n",
" $ Age : int 21 21 21 21 22 22 24 23 22 22 ...\n",
" $ resale_price : int 275000 260000 302000 399000 230000 373000 272000 315000 248500 270000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 1 0 ...\n",
" $ Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n"
]
}
],
"source": [
"str(data1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data1 <- data1 %>% mutate(ln_resale_price = log(resale_price))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t1009 obs. of 15 variables:\n",
" $ month : Factor w/ 24 levels \"2009-04\",\"2009-05\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ town : Factor w/ 1 level \"YISHUN\": 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ flat_type : Factor w/ 5 levels \"3 ROOM\",\"4 ROOM\",..: 2 2 3 4 1 3 2 2 2 2 ...\n",
" $ block : Factor w/ 159 levels \"201\",\"202\",\"203\",..: 20 20 21 24 135 141 119 123 83 89 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 1 1 1 1 2 2 3 3 4 4 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 3 4 1 2 3 3 2 2 4 ...\n",
" $ floor_area_sqm : int 108 103 121 146 74 122 84 103 84 84 ...\n",
" $ flat_model : Factor w/ 7 levels \"Apartment\",\"Improved\",..: 4 4 2 3 4 2 7 4 7 7 ...\n",
" $ lease_commence_date: int 1988 1988 1988 1988 1987 1987 1985 1986 1987 1987 ...\n",
" $ Age : int 21 21 21 21 22 22 24 23 22 22 ...\n",
" $ resale_price : int 275000 260000 302000 399000 230000 373000 272000 315000 248500 270000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 1 0 ...\n",
" $ Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ ln_resale_price : num 12.5 12.5 12.6 12.9 12.3 ...\n"
]
}
],
"source": [
"str(data1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fit1 <- lm(data = data1, ln_resale_price ~ Treatment + Period2 + Treatment_Period2 + Age + month + flat_type + block + storey_range + floor_area_sqm + flat_model )"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"t test of coefficients:\n",
"\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 12.22611439 0.21543958 56.7496 < 2.2e-16 ***\n",
"Treatment -0.05262565 0.06160419 -0.8543 0.3932157 \n",
"Period2 0.26158300 0.02098015 12.4681 < 2.2e-16 ***\n",
"Treatment_Period2 -0.00240641 0.00701322 -0.3431 0.7315932 \n",
"Age -0.00997825 0.01002102 -0.9957 0.3196768 \n",
"month2009-05 -0.00851718 0.01334307 -0.6383 0.5234437 \n",
"month2009-06 0.00726140 0.01075635 0.6751 0.4998167 \n",
"month2009-07 0.02145882 0.01114722 1.9250 0.0545731 . \n",
"month2009-08 0.02782083 0.01092816 2.5458 0.0110865 * \n",
"month2009-09 0.03844152 0.01260835 3.0489 0.0023713 ** \n",
"month2009-10 0.06025886 0.01116603 5.3966 8.914e-08 ***\n",
"month2009-11 0.07206849 0.00986152 7.3081 6.483e-13 ***\n",
"month2009-12 0.09208223 0.01188033 7.7508 2.725e-14 ***\n",
"month2010-01 0.10714692 0.01339642 7.9982 4.336e-15 ***\n",
"month2010-02 0.12927993 0.01321633 9.7818 < 2.2e-16 ***\n",
"month2010-03 0.12663570 0.01311602 9.6550 < 2.2e-16 ***\n",
"month2010-04 -0.13031842 0.01328654 -9.8083 < 2.2e-16 ***\n",
"month2010-05 -0.10901725 0.01373511 -7.9371 6.855e-15 ***\n",
"month2010-06 -0.08683590 0.01371673 -6.3307 4.033e-10 ***\n",
"month2010-07 -0.07500373 0.01299760 -5.7706 1.123e-08 ***\n",
"month2010-08 -0.04969070 0.01301705 -3.8174 0.0001451 ***\n",
"month2010-09 -0.04963867 0.01424242 -3.4853 0.0005180 ***\n",
"month2010-10 -0.01005684 0.01367995 -0.7352 0.4624591 \n",
"month2010-11 -0.02029548 0.01480462 -1.3709 0.1707881 \n",
"month2010-12 -0.00752882 0.01528011 -0.4927 0.6223434 \n",
"month2011-01 0.01180924 0.01022103 1.1554 0.2482716 \n",
"month2011-02 0.01698615 0.01077128 1.5770 0.1151878 \n",
"flat_type4 ROOM 0.10960666 0.02041057 5.3701 1.028e-07 ***\n",
"flat_type5 ROOM 0.24368173 0.04732465 5.1492 3.285e-07 ***\n",
"flat_typeEXECUTIVE 0.40070902 0.06899822 5.8075 9.092e-09 ***\n",
"flat_typeMULTI-GENERATION 0.41411943 0.07024639 5.8952 5.481e-09 ***\n",
"block202 0.01113482 0.01840117 0.6051 0.5452715 \n",
"block203 0.04321747 0.02011724 2.1483 0.0319863 * \n",
"block204 -0.00339845 0.01647525 -0.2063 0.8366267 \n",
"block208 -0.00219865 0.03336699 -0.0659 0.9474792 \n",
"block302 -0.08246983 0.01854850 -4.4462 9.955e-06 ***\n",
"block303 -0.09325851 0.01957594 -4.7639 2.248e-06 ***\n",
"block304 -0.10255292 0.02072323 -4.9487 9.084e-07 ***\n",
"block305 -0.07545846 0.02931827 -2.5738 0.0102358 * \n",
"block306 -0.05557037 0.01748312 -3.1785 0.0015363 ** \n",
"block320 -0.12188705 0.02838781 -4.2936 1.970e-05 ***\n",
"block321 -0.14047020 0.02023120 -6.9432 7.842e-12 ***\n",
"block322 -0.12081839 0.02837882 -4.2573 2.310e-05 ***\n",
"block323 -0.13351881 0.02394400 -5.5763 3.345e-08 ***\n",
"block324 -0.21949268 0.03108698 -7.0606 3.559e-12 ***\n",
"block325 -0.21244246 0.03252965 -6.5307 1.153e-10 ***\n",
"block326 -0.21375126 0.03111821 -6.8690 1.285e-11 ***\n",
"block327 -0.17055788 0.02350795 -7.2553 9.357e-13 ***\n",
"block345 -0.20047829 0.02231115 -8.9856 < 2.2e-16 ***\n",
"block346 -0.18940183 0.02024486 -9.3555 < 2.2e-16 ***\n",
"block349 -0.19193766 0.03151645 -6.0901 1.739e-09 ***\n",
"block350 -0.18063840 0.01986364 -9.0939 < 2.2e-16 ***\n",
"block350A -0.34474208 0.03092293 -11.1484 < 2.2e-16 ***\n",
"block351 -0.18944277 0.05902253 -3.2097 0.0013810 ** \n",
"block352 -0.21834242 0.04201027 -5.1974 2.559e-07 ***\n",
"block353 -0.20717912 0.02805676 -7.3843 3.798e-13 ***\n",
"block354 -0.18455666 0.02649069 -6.9669 6.696e-12 ***\n",
"block355 -0.22523073 0.06306563 -3.5714 0.0003760 ***\n",
"block356 -0.19954363 0.02989866 -6.6740 4.605e-11 ***\n",
"block415 -0.23633437 0.06432615 -3.6740 0.0002544 ***\n",
"block416 -0.24653410 0.06363214 -3.8744 0.0001155 ***\n",
"block602 0.01337838 0.06297512 0.2124 0.8318178 \n",
"block603 -0.02010018 0.06209277 -0.3237 0.7462393 \n",
"block604 -0.06707885 0.02549628 -2.6309 0.0086765 ** \n",
"block605 -0.04849469 0.04909316 -0.9878 0.3235398 \n",
"block607 -0.07856484 0.03307368 -2.3754 0.0177586 * \n",
"block608 -0.15766587 0.06908769 -2.2821 0.0227403 * \n",
"block609 -0.05502935 0.02053524 -2.6798 0.0075167 ** \n",
"block610 -0.05955927 0.02293084 -2.5973 0.0095647 ** \n",
"block611 -0.10147916 0.03790694 -2.6771 0.0075768 ** \n",
"block612 -0.06202115 0.02370826 -2.6160 0.0090613 ** \n",
"block613 -0.04122462 0.02037926 -2.0229 0.0434132 * \n",
"block614 -0.13676909 0.03078467 -4.4428 1.011e-05 ***\n",
"block615 -0.02586824 0.02098217 -1.2329 0.2179816 \n",
"block616 -0.15507412 0.06109324 -2.5383 0.0113242 * \n",
"block617 -0.03118114 0.02061387 -1.5126 0.1307626 \n",
"block618 -0.12067583 0.05914066 -2.0405 0.0416243 * \n",
"block619 -0.01653293 0.01756656 -0.9412 0.3469029 \n",
"block620 0.01487387 0.01840095 0.8083 0.4191427 \n",
"block621 -0.03818097 0.02086230 -1.8301 0.0675947 . \n",
"block622 0.00774242 0.01819826 0.4254 0.6706223 \n",
"block624 -0.04574146 0.02263369 -2.0209 0.0436127 * \n",
"block625 -0.09605905 0.06767681 -1.4194 0.1561718 \n",
"block626 -0.01424370 0.02425989 -0.5871 0.5572796 \n",
"block627 -0.01390744 0.01904010 -0.7304 0.4653383 \n",
"block628 -0.05336545 0.03114381 -1.7135 0.0869986 . \n",
"block629 -0.04216641 0.02089682 -2.0178 0.0439361 * \n",
"block630 -0.04016115 0.02370337 -1.6943 0.0905871 . \n",
"block631 -0.12372751 0.06748210 -1.8335 0.0670957 . \n",
"block632 -0.08684083 0.02275238 -3.8168 0.0001455 ***\n",
"block633 -0.12722577 0.03632521 -3.5024 0.0004862 ***\n",
"block633A -0.07857247 0.05661575 -1.3878 0.1655721 \n",
"block634 -0.09007066 0.02097134 -4.2949 1.959e-05 ***\n",
"block635 -0.04592858 0.01806832 -2.5419 0.0112085 * \n",
"block636 -0.09214070 0.03011095 -3.0600 0.0022858 ** \n",
"block636A -0.13368438 0.05800757 -2.3046 0.0214400 * \n",
"block637 -0.08645777 0.02952963 -2.9278 0.0035086 ** \n",
"block638 -0.08205566 0.02018179 -4.0658 5.252e-05 ***\n",
"block639 -0.02653494 0.06132694 -0.4327 0.6653620 \n",
"block640 -0.10011469 0.02347244 -4.2652 2.232e-05 ***\n",
"block641 -0.02797737 0.01656180 -1.6893 0.0915509 . \n",
"block642 -0.00162586 0.07116284 -0.0228 0.9817779 \n",
"block643 -0.09123746 0.06663119 -1.3693 0.1712866 \n",
"block644 -0.03709788 0.05491819 -0.6755 0.4995429 \n",
"block645 -0.05970215 0.01622312 -3.6801 0.0002485 ***\n",
"block645A -0.10132188 0.01671390 -6.0621 2.055e-09 ***\n",
"block646 -0.00255534 0.06115890 -0.0418 0.9666827 \n",
"block647 -0.01920514 0.06075644 -0.3161 0.7520074 \n",
"block651 -0.02122250 0.06398114 -0.3317 0.7402018 \n",
"block652 -0.06243371 0.02810375 -2.2215 0.0265888 * \n",
"block653 -0.02171961 0.06238429 -0.3482 0.7278115 \n",
"block654 -0.12689007 0.02622691 -4.8382 1.568e-06 ***\n",
"block655 -0.02149023 0.06189559 -0.3472 0.7285299 \n",
"block657 -0.02349166 0.06237962 -0.3766 0.7065751 \n",
"block658 -0.03882759 0.06439862 -0.6029 0.5467262 \n",
"block659 0.03124704 0.05983644 0.5222 0.6016682 \n",
"block660 -0.00886575 0.06171092 -0.1437 0.8857999 \n",
"block661 -0.06531878 0.01851806 -3.5273 0.0004433 ***\n",
"block662 -0.09476211 0.01842470 -5.1432 3.387e-07 ***\n",
"block663 -0.07742896 0.02332249 -3.3199 0.0009405 ***\n",
"block663A -0.08155031 0.06266705 -1.3013 0.1935153 \n",
"block664 -0.09701702 0.06380846 -1.5204 0.1287889 \n",
"block664A -0.12161447 0.06288288 -1.9340 0.0534619 . \n",
"block665 -0.16298844 0.06092227 -2.6754 0.0076152 ** \n",
"block666 -0.09076318 0.04532691 -2.0024 0.0455720 * \n",
"block666A -0.13164039 0.05669707 -2.3218 0.0204889 * \n",
"block744 0.02009318 0.04056870 0.4953 0.6205308 \n",
"block745 0.05980442 0.02099184 2.8489 0.0044974 ** \n",
"block746 0.06116385 0.02194053 2.7877 0.0054321 ** \n",
"block747 -0.02788897 0.02324765 -1.1996 0.2306261 \n",
"block748 0.01110084 0.02552066 0.4350 0.6636962 \n",
"block749 0.07495201 0.03790677 1.9773 0.0483483 * \n",
"block750 0.01727836 0.02549918 0.6776 0.4982152 \n",
"block751 0.04755720 0.02482002 1.9161 0.0557051 . \n",
"block752 -0.00755357 0.02685779 -0.2812 0.7785954 \n",
"block753 0.03172561 0.01679121 1.8894 0.0591915 . \n",
"block754 -0.00504570 0.02680454 -0.1882 0.8507350 \n",
"block755 -0.04518959 0.03308449 -1.3659 0.1723529 \n",
"block756 0.01354794 0.02658021 0.5097 0.6103998 \n",
"block757 -0.02294981 0.02245644 -1.0220 0.3070990 \n",
"block758 -0.04036008 0.02231191 -1.8089 0.0708354 . \n",
"block759 -0.03076629 0.02951620 -1.0424 0.2975581 \n",
"block760 -0.00962031 0.02204173 -0.4365 0.6626197 \n",
"block761 -0.09543680 0.04188342 -2.2786 0.0229477 * \n",
"block762 -0.03269640 0.02949579 -1.1085 0.2679692 \n",
"block763 -0.03781595 0.01800692 -2.1001 0.0360295 * \n",
"block764 -0.02909733 0.03189679 -0.9122 0.3619159 \n",
"block765 -0.01689426 0.02928869 -0.5768 0.5642216 \n",
"block766 -0.02551098 0.02849082 -0.8954 0.3708327 \n",
"block767 -0.07847527 0.01773378 -4.4252 1.095e-05 ***\n",
"block768 -0.05302585 0.02705579 -1.9599 0.0503522 . \n",
"block769 -0.08141839 0.04166833 -1.9540 0.0510481 . \n",
"block770 -0.04511636 0.02592880 -1.7400 0.0822357 . \n",
"block771 -0.06531017 0.02783774 -2.3461 0.0192103 * \n",
"block772 -0.04249821 0.02768522 -1.5351 0.1251605 \n",
"block773 -0.09571678 0.02025351 -4.7259 2.699e-06 ***\n",
"block775 -0.01748292 0.02319159 -0.7538 0.4511590 \n",
"block776 -0.03014635 0.02423393 -1.2440 0.2138682 \n",
"block777 -0.04492610 0.04313156 -1.0416 0.2979039 \n",
"block778 -0.05597494 0.02706318 -2.0683 0.0389270 * \n",
"block779 -0.01689535 0.01590029 -1.0626 0.2882873 \n",
"block780 -0.13018511 0.05185787 -2.5104 0.0122518 * \n",
"block781 -0.02788292 0.02057250 -1.3553 0.1756828 \n",
"block783 -0.03390580 0.01880484 -1.8030 0.0717528 . \n",
"block784 -0.03506767 0.01620322 -2.1642 0.0307364 * \n",
"block785 -0.05123899 0.02734741 -1.8736 0.0613396 . \n",
"block786 -0.00312934 0.02000469 -0.1564 0.8757325 \n",
"block787 -0.01946772 0.02049174 -0.9500 0.3423807 \n",
"block788 -0.04571196 0.01580153 -2.8929 0.0039192 ** \n",
"block789 -0.10629593 0.05029477 -2.1135 0.0348656 * \n",
"block790 0.00598430 0.02030583 0.2947 0.7682917 \n",
"block791 -0.05231492 0.03607549 -1.4502 0.1474020 \n",
"block792 -0.09674087 0.02764263 -3.4997 0.0004911 ***\n",
"block796 0.01655018 0.02222200 0.7448 0.4566286 \n",
"block796A -0.04220034 0.01530171 -2.7579 0.0059482 ** \n",
"block797 0.03284712 0.05351799 0.6138 0.5395466 \n",
"block855 -0.04516120 0.02458537 -1.8369 0.0665876 . \n",
"block858 -0.02327158 0.01957443 -1.1889 0.2348354 \n",
"block859 -0.02432097 0.02073953 -1.1727 0.2412651 \n",
"block860 -0.04387838 0.01970669 -2.2266 0.0262491 * \n",
"block861 -0.05038137 0.02626885 -1.9179 0.0554721 . \n",
"block862 -0.03779830 0.02055825 -1.8386 0.0663394 . \n",
"block863 -0.02098530 0.02195752 -0.9557 0.3394965 \n",
"block926 -0.12031147 0.01281927 -9.3852 < 2.2e-16 ***\n",
"block927 0.01277839 0.01484445 0.8608 0.3895913 \n",
"block928 0.02518105 0.03995131 0.6303 0.5286798 \n",
"block932 0.02457313 0.01445558 1.6999 0.0895310 . \n",
"storey_range04 TO 06 0.02951227 0.00428169 6.8927 1.099e-11 ***\n",
"storey_range07 TO 09 0.04901559 0.00459801 10.6602 < 2.2e-16 ***\n",
"storey_range10 TO 12 0.06255231 0.00428314 14.6043 < 2.2e-16 ***\n",
"storey_range13 TO 15 0.04556167 0.01277661 3.5660 0.0003836 ***\n",
"floor_area_sqm 0.00438598 0.00064126 6.8397 1.561e-11 ***\n",
"flat_modelImproved -0.02099025 0.03644279 -0.5760 0.5647894 \n",
"flat_modelMaisonette -0.03324039 0.01404871 -2.3661 0.0182111 * \n",
"flat_modelModel A 0.05618216 0.01414740 3.9712 7.785e-05 ***\n",
"flat_modelNew Generation 0.05403110 0.01629095 3.3166 0.0009515 ***\n",
"---\n",
"Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Robust SE\n",
"coeftest(fit1, vcov = vcovHC(fit1, \"HC1\")) "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 164, 168, 177, 355, 512, 648, 699, 813, 818, 973, 988, 1009\"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 164, 168, 177, 355, 512, 648, 699, 813, 818, 973, 988, 1009\"Warning message in sqrt(crit * p * (1 - hh)/hh):\n",
"\"NaNs produced\"Warning message in sqrt(crit * p * (1 - hh)/hh):\n",
"\"NaNs produced\""
]
},
{
"data": {
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eNfX1Mf+y5b0Cuc5XU920rlfO63aBu1H0jc3HZFK6Vro6fEaBSkluTrLZc5T+M1\nHrgd7f/IqrcUmlY62p+pEVb5laPGKNdt+DTIbiBx2LoXSHcbz4/RjiC1jK3CmdJCp7lkF20h\nrAAUF64Tr43/xqpuuaO91khirtgDpMfa6DU42hGk1rGVntkKko/zt1krpVrp/UlBagm/Rfvs\n55Feg6MRkFbj6lbbp4KU2bUYK7M123hkZ7Tir9gtr95Gi168UVOI/xTqB0ktY6zLLq6RhkxX\nwrOc/cEyW7OdsmvXmKpnHdtZkdfgqB8kXnzvUnZh127ItjCQ2eNOKzDa2buPkd1AipaRfHrT\n6CdvJC0N2LmURkGyaJldGzfaPm8AaaMT3FH7gbS3nZfhaNwjHVr2tmIyO8+ZuzC8MWuNVMro\nRnrNSG8kPZtNbSbWESANxd9mje987lFJftX0PCD1jOBamm3xtRN/penJNatHMouzHoaUuXvH\nht6N9iCzZZ49Ek5cgvUUrb2Rp988eQWOZgXJrpQUJA2VV3eW58ioKqsqDbmLgBRnfZ24zo+A\ntHU/sym/LUhe/o6sMq3fNP/u3T6qktxt7NjQLuuNXoijfpA6TK+14wEghY0GUY0ySDvXZU3l\ncsZ27Zodqb1HejmM9gTJJQc9FhtHwUpHxRBxqRtA2mtw7AHSZv5b8sfeaNlkMPodgYtoN5Bc\n9rDdYlMfrOCWH5qlNVJjKbutmKxBMqnpSNHuBR3SvCC1l7EWOybXVfe29LVMs2OgZ7lGOgqk\nxBuF/e4X4+glQdq1zE22DXftehZJdTN96RVGr8PRtGukjiJWYjvjzlzKPHSQDBXlTFZzNQOp\nNwp/8eL1OJp11661iDUrO3TmYwNjrWDzIk9SX9H096FfC6KbdgTpCItndBiNksOKnhCkjDe6\np3avidHlQTpJk4NkuGvW65F87a9bPLP2BqmW67rtPTlInGeXNVLeG4Xkr8kRQBrT9GskF/08\nqugXxQggjWr2Xbu9QKp4I//CGAGkS2gikFYTv2qnAqQRHTzzTrJGqnujw1eOcwm7dgM6euYd\neyBrsgvdY8Ed/HhtKgGkfh0+807wHGl1bfTA1l23VzcKIPXrFUFaS7T85oTFX2m7pgBSvy4B\nkuED2Ya1kQs0XbVPNwsgDegCaySr6rXYcfr/1xRAGtH8u3ZW9VvzRktZj6DO4qvuryqAdAFN\n7pEWjF75cSxAuoRmB+ll/gpkRQDpAhp/IHtK0S8pgHQBjXikU36N4oUFkC6g2Z8jQQDpEgJI\n8wsgXUAnP5CFGgSQLqCxXTuTx8boo0YBpAtoHKRjfx/plQWQLqDB50gWLgl91CiAdAEBpPkF\nkC6gwQeyAKtKkAgAACAASURBVOlAAaQLaKihbD78hj5qFEC6gPZuqIp99FGjANIFBJDmF0C6\ngDZ8+ruateFPG6OPGgWQLqC9QApX4ZG2CyBdQL0N1f5H9N3aU1v0UaMA0gW05y/2OQeQLASQ\nLqB9GyrzlxbsvhbmZTQHSOiwqvob5xGxNbZqLRn6pVE7gkT9uLojZPI55SdWf9OHFzyQPUr7\ngXTvx+paVu8socuKGmn6jk9/19KgVxq1G0jCGwGkjQJI82tvkOgXY+pJ+u2/koZAam9VgGSg\n3UHKbQqlSdBjNQGk+bXvGulxsP7xE+zaVQWQ5teeu3ZrOQ/opJjQaxI70vRG8fIVm+sUzfEc\naS/FMeNFY8iB50iOXw8u+kX11CDFs/JVdzVOrPH1GuskvShIy4dfXJi55x4v54N0rfY6Q0eA\ndNoaicChbzgNsZ1A6gIB3wQg0atqL/fy3+ZCemqPJBGhRUN0DiA1FL18ftVFLfhwUudWcBY9\nN0hitnz0N8cmwjdNPxTmAInaLwR5YkNj6uY7RMeDdM5H9F3yg2N+gLRWdNJe/AXmAOmhHUFa\nxeU8kJZDxzPr1CPhdJA8xXRhqcnhHUC6az+QXHKw1eIWFUC6/weQGorOgYQ1Ems3kFz2cIvF\nLXL6pwxQENqtF+3yIGHXjvWaIOkHItP/RvXpIEUPkBzcUKLXAUlu19GIOLoeYzofpNJMBAW9\nyhpJLIplMHeNmXVCkKBIr7NrF4OzeKMrzKwTgAStaEeQTrC4WloC0iUEkObX64Fk9hsGxwkg\nza+9QarlOgEk7xw80kWKvpZeDSSP0O4qRV9LLwZSvGt3DQGk+fViIF3zV9MA0vx6HZAuLIA0\nv15n1+7CmgukC7v2HQWQLqApQArI5D8i8vICSBfQDCA5+nfhJ9s7CiBdQBOA5PgVIOUEkC6g\nOUG64EdEdhRAuoAmBOmaHxHZUQDpApoAJBHLXfgjIjsKIF1AM4AUfZMpdu0iAaQLaAqQolN4\njqQFkC6guUCCcgJIFxBAml8A6QICSPMLIF1AAGl+nQoS1Cjzpkcfmau9SXfsrk7rfVW5pOnJ\nPcEew6Y55amFb+8ZgHSkaYA0aeEA6VqmAdKkhQOka5kGSJMWDpCuZRogTVo4QLqWaYA0aeEA\n6VqmAdKkhQOka5kGSJMWDpCuZRogTVo4QLqWaYA0aeGzgwRBLyKABEEGAkgQZCCABEEGAkgQ\nZCCABEEGAkgQZCCABEEGAkgQZCCABEEGAkgQZCCABEEGAkgQZCCABEEG2gGkxaT+83qlP7aX\nTVysVsn09sQitTy5alonXrnF3Omp1P4XEXv+eGJLwj3+FmN7HXtKL5nYaiC16MgwG9fvVhL7\n0jcvlk1n0nfVw6DWNrd4qoqtsyFl251a2+uz2VN6zYapxPdjs3X9biUxnbY3XUqsUju/knp7\n4uItnqq4jispm0fpejpre3022+97tTg7uWi4VMsqJc63VT51OVIrJs6N9si11FLnExdqU0o8\nI0h3dVSrlTlTkJrsxZYtE+6Uv25yBaRC4nJbZdlYWcjEpgu2XXJQ6d5M4vz7UuLO4XCc9hik\nth7prDrubaBmcp2jbOIWkHh0FtnIe4KOzYZVkBJjtXroPY85QepadBtO9ruB1Jh0ys2GzSCV\nvUZH/FWoR4NHGjVdStt5i2ervVpPBFKf0V3y10w2DLJMYldOvDn+qnRYNmpcNa0vt4aYtVs8\nQ+qbgKr1EilX6t+e0lf7pZjaNuXm/tgRpBaOMolrX/GUMb0+2psSW4C06kZFlNf3LVZHqjUa\nsrS4E0hdDTwtSE0c5ROvjcnUC1RGe1ti6SUaQYovV28xtTUfRj3D2Xau3wekvnSzgtTGUekm\nqiClpis+pjHxMtz19brpTOJmy7X0p6m8b5Mm7bLblsTa0bTb6yp9a2mdJjl4cepdQ+JKtbKp\nq9vfrYlF1CXeNn5EqOEWY8szglRpnTRdR2jaks7+I0I9dZxz1w6CXk8ACYIMBJAgyEAACYIM\nBJAgyEAACYIMBJAgyEAACYIMBJAgyEAACYIMBJAgyEAACYIMBJAgyEAACYIMBJAgyEAACYIM\nBJAgyEAACYIMBJAgyEAACYIMBJAgyEAACYIMBJAgyEAACYIMBJAgyEAACYIMBJAgyEAACYIM\ndF2Q+EuG6E/1ZxKVMu9YsReSo07o+xP41a8mCIZbDc3Rl3PUYkRN39cCkHZX77cLlVNGX5Gz\nZnKur/WYoxYjAkhzaAeQXPS+nnyOvpyjFiNS85f4Mkn+6lKOOPTXGC1Z+Ap9ddGsX0c5s0I7\nOtGKXh44LztGBIIyYfbLsZJecqIkMjRHF1534OhAgFtUHcQgOf7pkrxNAQUUKQzgcJz0hKu0\nNDe546bPg0SpZPLcv3O68LrjJvpOQPEvnsr4kryaprxuW5wpp18rB/w231NVkPIHmX49Sdcd\nPHmPVAfpfugAkqW2gRSMOKc7K5dZpgJIZiqAJPfEU5AERdz4cnl13fY4SzEnSQcsB+WHFXpq\nK4GUnQADSOd34XUHTs0jea/6934Q+6vCLHbdBjlJWY+UnlHn8z1VBSl/4PwsXXjdcVMDKdd9\nKyAlvQi1KQtSqX0Tj5Sd0R6uxef8Wg2kU7vwuuMmD1J0oBMtLwKkZLPiwg1ykiJO0p5wPrmW\nXpdrpLhv+GJpjTRBF1533EQgOf24IpyKkoeHD04ccxaskQYUg5R5jqTfJs+RZKdwWuf1cyed\nyrGhOboQAweCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmC\nDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmC\nDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmC\nDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmC\nDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmC\nDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmC\nDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmC\nDHQVkP59e3Pu84/idZe/kcLpnH51pn8xuYc+/66kyB0W0zSV2ZP6XF2kqv8+Pfrx079Cgs0g\nvbm+9K8mF1QkCSBdQF/d57/e//3svhUSbAbpSp12hpb2+eY+tyfuuGCQ+lxdpKrO3V3Rv94e\nAkhWCu3T1E4AaVbpJv326e6gPtY1Xz6ivW+c4Meb+/SjlO/j4tuPkoF71CLMPFI69/eL+/R9\nl1u6mCKQuKV/ff5YOf2iKx9N+81zU95fo26iHDf9c2/3n28fU6W64JPeuxUok3MlPubZN/dF\nFiQqkhkWO+giIH1zX//Sm89htfT9EbU/QPh4+fJYD4t8ois+88WMAQkSp/xIdTsESXFoxy39\n49GEP2TbfdEgRd3EOe767G49+/fDWHRB9R4VyMlFJe5FfpMFPSrytTAs9miffc2b6aNd3r49\n1rk/3ed/H4um++j/eXt7u4fby6/bhX+fXXZO++k+/fF/Pj1yFAw8XkVKd0v5Y5kEX1u02fDH\nq5b+dDvx89ZEsu0USFErc467ft7nqe8ftqILsve4QE4uKnHvJ1XQL65IZljs0T67WjfUr683\nL3JrjC+3jaN/7lO4Qj305b6Q+nfz8eraXV/uDfnrMZMVDAQzlPKxR3WlUH03he3vG0eypR0N\n0Efb3RrsVxTa0eWFKz2k7+S8ZS6o3uMCQ3JVid9RrtCJ+WGxg640Rn5//3RrMDmu//76/ln0\n0CK+HvVjSFcwoC7nBsML694Ib59+LW+opb99hFV//oQUhbZTrcw5Hvr6Eaz9vcUH8QXVe1Qg\nJRfnKGHUnaVhsYOuNUb+hBBi0WdqId1i6vRDeZA+RykBUkn3Rvjt7isUNTa/35aRn/7W2i5q\nZcrx0O+PYO3b3aVEF/IgUfIMSHF3AqRI1Aiag6/u7cevvwIkTt8GUmQAIJX1aIQvjwBJt8iv\nb29hgsu2XdLKIceiT2+3/zMXkt5TycW55TAtKA5A9tM1xsiXZSvnvrD5TEucexNxw31J15Pp\nGulLxYBeI30BSEKPRvjz2GxIWjoM2MeF3zR++UiNb3X04V9+iI3RlI+owJBcnBPYLAWpNdK+\n2wxLFQ4oY7s++uPHx4rx9+cbUD9uuzDfHlHyb/+HY+L7ltHH5exmg9iLKxj4K82EXTtt5IW1\nNMLDJYmWfnvslC0eSWyWvX301b/PD5BUN3GORR9D/74fkFyIem/p2pBcnCOQqCBRkcyw2KN9\ndrVupm9h0+j2hh4DhbNhB+IRIosg24vwOPccSRh4c+Si5HMk7wHSXUsj/Hu4JG7pn7oL7s9s\n7o9v7k+Fviy7CzIN5wh6e3RLciHpvUfXLsnFuaVyoqCwXMoPiz3aZ1frdvrz9WN2+fzz8ea2\nvXNvlq+3jyOLIOzHBw5fZYPJdeaPT/zJhtTA7zcCiVMCJFJohG+PmZ1b+v5xBH5K8J0+UPBx\n9PVxFHUT5Qj6uQRf8QXVe9y1ITmfC5Xjgh6fXvldGBY7CGMEemLt/XkGUdJRBUHQgbp/yOHf\nl+JvC9gXeFRBEHSglo/dfVpPaSSABD2lftw/nXlceQAJggwEkCDIQAAJggwEkCDIQPYgOahR\n5k0/0kf/nXb7h+m942ys9ia17yRzi0+qM0Hiw//Oq8UFBJAuoDlAgmoCSBcQQDpO74P5ANIF\nNAdILxLa5UhqoOt1QAq/iOLEj4sIIB0ogLRSmAtFOvrhL0LUHCC9isZIehWQnJcgUeni/cQC\nSGcLIEWlRSC5E+oxoL0rWLH/eqFdXqskvR5I8ndZAdKa/dcEaWDr7sVAugEEj0R21x/MT982\n+6ifpFcDiQ+wRtJNoc4PfPLlqQSQVkrDrl1kWUS4a0W/UGiXI2mFrRcDCc+REtsOIOWUcAOQ\nrq99G8o1gvRaAkhPqJ0bquad0UekOkkA6QKa44Hsa4V2qQDS5QWQzlDfzh1AuoDmAOnl1EUS\nQLqAANI5eq++1QJIF9AcIL1aaOdjdACSp2f012T3iiBd60ldm2okPTlI3J3io6qP844vz97p\nc4DUnW/uRh3Q64KU/NZEQMbJy9N3+vVAusingXtVIWkOkHZyCaI76bPfKVwAqa3ontDuSUGq\naAqQ9hrJMUiO0FkiOw+Q2ot+UZAaN8FnAGm3Vi+DtJxRPM2rOUBqSa2WpFO3abveC8daTw0S\nd2coQsd18Ei2RcuGnH0Dp10Ayftoc0H9zgBA6ix6NbR7onhOqYmkDpDMW2j3NVJalMv9VS6A\n1FT0FpCexj/NDdLx7awfIM3ezXOA1Jg2l2P6qapdJZI6Qer5JovVVE/StPvrIiAVeXnWmE9q\nyCPxhleL5WK6525ZQ80BUsP2d2FQACSVMlnSVPO67OFg2S+uy4BUNXLp7n7PHCkBpAtoDpA2\nWrl2bwOkZ9DlQZp+O2dd78mB1m4gYY1kp5GGalrHdhX9gr+PJLTmksY2G1yLr8aunZUGGqpt\nsusq+rVBWnNJczxHgqqaA6SOTJcP5PoFkC6gS4BEn8Zy9Msqr8RTN0jtD2QNy35xzQFSPbSj\nAm/f93EnyfnSGHlGwo7wSDrjy3/TQb+GNhua1rE9RVdBCo+KHl+b84DJOT1q5CeIr9j31VUS\nQrsL6ALb3xmQ5F/FCEmcSHo52YOE0O5YXQGkZWX0GBfhnwSJ+LksSASQFUjOyDtfsS1P0Rwg\n1UO7EMY9DujIF0C6ZGBfc0kbQFrLS22FTzZsVG9D7bMhVAPJxT5pqUb0GJ9m4JVvZSqVIqyd\nAmLFJQ2C1PjJhvpfZQRIjZrDI60n03FbDLJYH439tU4x5pyrfqvTCdoNJKfTbiv7xXURkHh0\n50dIZr3UXxVCcDKSxjYb2kHiWWpL2S+ukYY6NrQLsZ2LGCoUvxGk8HvNffn31RBInhaUDYbL\nM8dUDTGzBhpqhw2hlQeyYctbxW21ro+vtY2ok0F6Vz+ExkBqSr+aESA1ahykzW3cMT40HStO\nJ6WmcdWdKepQvYtXqf1Aitu0kgCqawykltFmtbNKUaTYsyvnzvmelmhP1Pa07fOSSxrabMAD\n2WO1G0giTlovuhLakR35waTkow1x3TIFtd7piY+hCi5pg0eyjL+hWE5v7vTnb98QqqVrAEl+\n7DvZ2M7YziOz7FbUqju1toR28Ej7KbPe6LfQuiHk4iJ6Hug6Xvyr7W9x4OIMyTnvk4+4XkwA\naUrpwbZXQ4nxPrxGcpRd/NpEAlLqXzMgjX3cYRIBpCl1DEh9O6v50M6F7I4/yrCsjcRnhZSl\nfCh5ndjunV6EANKU2gxSY2i2Gj22gcQfqwuFLp8Al9sZKx+U69vTPhM5M5CMNu0AUkXb10gb\nczYboJpK7+OYJPq0w9pnejhIbKvWuSRZeqSt2q8lrhEiVLV112571tb8emEkdvDooZIT/1ft\ntE7PhWXWUcq5pKcEaff56mBQ5wCp/ByJQhR9IB7TCtdUKaq53+Qva5yhjEvqBknsjE6wRipE\n20bWi6Xuaz5b3vFZ4/xFkOg3YvO6J3G0iqqX1RbbUdA4j4Y8ktFI3d4Oxf0fE+ulQsd+m2ZL\niadkbc0f/sqJckpeg9S0rg7bFA1FLlhORNLQZkN33q1lVw0IwFs+eLy90MM/M7lh1+6Aogt+\nKIRyi0cSv2Xh40iiu9/knuAkClVZ/xL0+UFS+0f7cdSycDYv8yw1hHaliM7xZ+3Ci8t2UHe/\nOfpTRUP3ZKH3GJmlKu9PAJJ4t2P4vATyhwboVwMpfFqInJAEy2f7jahraVhH/52mAkjvXR5p\n0jXS7nsMbL85mDcssye9HNS7F13wRsyPejqcB4mwaHJKYUk2dksmKnmkPpCMbsOgIZw93qtF\nHleULHIszwGTnaZHEaxiO+Yoiho4WA6nWkg6e30U+54xkGxkbfCg5m3vb7MSx7MYeqRCaJd1\nSOHjdU5/dEiywz59cUecoIneDTdloWcG6bjmBUgyRQYl5ZbkIsmJ3QcmKfOh16vpqUA6Tsd2\n9xwglVLkCVLPlrz47J0PkZy0TmzZLBpOUDdITjSdUdlX1KHdPfcaKUXJR3sMEV+eNtzEApf9\n11SPh5oFj3QBDTWUzdw+tEYSnogBUj9yPp2n52s4Jf3IyIWT9GeN3wvPlADSaTqxobaBlD0r\nd/PyhloeEZ0PWw4k8WnWomuKNpoNbuTslmjW2Z02B0jFJCWG8v7Ii31PXRKl4kVUtV5nb4BX\nQSqHePqTDRY3sm9DFEd/NxaHfdC4VMz1QCr6I6IpMaw2J9YKPnjbNKsVkEqR3dVA4ucYuQst\nn0Phhx4HTX/FYnrL3mdDqPOzdvoTDfGl9KbEDvhTgORLXulaIHFLP7aJqPYLYLw/XPECixHH\nr3sqlJaLek7TljVSBar8DEfI1RpDVGumtUGyRvJGIFFLlFPtDhI99RO1deJ/X/ZPLrFyDEil\nqOcsNSz6W1DSHyRaHjKlppTLqn6icYI1ktYISOHZ2bphfimXvY8USHL+0nNe7J+iukm/1l3b\n2hxSqnI+jJwbpFaEJCEcfgYj4Qe3WkiVK7K/dffWSGjXbJidQKXs0sWNDSVCMx0I0Iy4XMtv\nEqVZxivQkSP/WbORlii52gEziwp/jqtXnuIUwmb5p1PxHx/K3dnW+7JQ+TnSu5dPlCIdA5KY\no7aSFLZ/yCSd58CCnxrmKkcT30BdVJkdVc7lGmgIMT43yR4kQsXpX89zCh1XBGmkYfdRDqQG\nybHk1qfoMZCo/+llm8LcJ0pTYLjwSeUUF0enx0oWr53ZCnHmkB1DkEoJ2qBZflL8FmYvMc9F\nOxLCRac1eg6QSsGrTp5+oCpOUjjjLFuLHFxSxuOHc/L3LV02sz7XxvfoHeTMzw5Su1vyS+8m\nILkQzSmHVG6MKUCSJI2A1NpJ7A0K1wtn9gApOhO5JxnrKc+bVoNyrwFlMYxVNbqzHBLadcV2\nIjk3u5jIKJWv7CcYNuw2HQbSqsXCmTCsTfZlcixEznXpV0rI1/MUEurrPtlGI3bWZrH+orsf\nyGqGxG9SEEiO17Ge23at4nYNa6i5QFLzvVFzrdSUO/nxzvObXGZHOXx8aT+dOG5aiu7xSU4m\nJyZka4YWvphGQGpaIw2WTU1oOJ9nLTlyQemvnImbizPrvSSA5Js54tWoeE+lCN9kN4ceqSGQ\nOv1FNBTljHS4mJ/g+piMZZKszxJL6vlBYr9pVPTYGslHx3JnzguSxC4pD43yGJmCta1rJCvt\n2hS6pWN+5LvQecuplRg90GayAmnU0BrJPGoofT9So0daUFqAcerRMyHmVdOW2/jI1i/LYrPB\nQju2BE1vXJLiRzgU7lzVm0XLZOK4OXGgoB3XsVGCLvn4rRdrTv2qV065Wp1PktBzgUR+JVro\nhNccSBxKUAdWETlhMnwSkKIoz1M/+e0gvW/VtmZ6MpC4B/iVC1L86EH24EgGG7VSYs4afdO4\nC5sDJIPnSC7s1C1zF89hLnj5KLJbXnfnYJDEbEOtyEXjbrusQRK8RCA5+XEf8kNRzlaQcsU2\n5NowrEeyaZ9cTEXDe73obc+RHH0YKz5F4V0HJ64yLY1PWL1yQ0wIj7Ta/D5qr0KS5rJLJUTl\nBatheuNr9K5Qm1oksXajhWxjySp5ezOt9ZA0XEzY5KDb5dkhPY4VLkkb1WnJX2u5bxupqXHI\nIzUXszXFWm6XebccUWs6uUtUtiZjCtWDa55kVpD67A5PduxISn4oAil2NcK5uAofuWrlUtLa\na3fpOuwHUuvY6rGYuFKXvBOOx1Hk3Via6FFGaq2qc4LUlKUIEo/8/z4CuuXff+JY/Luluf17\nf2/75x3nudsXZbjFXq4c9W8tTYuNrf90GTuCtB6gD9iLXGkEkvI7NDXJz3M1FxS/1iPUFkoL\nyRrqdThI2fPZNVLvYyTto7z4dJbv8SYVj1S9HUsd5pEaa5KWUyio5IPSd/EpudXQXjH1Wsve\niGg+Wc5wnHIvkAzWSH0guRApuBBMi1jOufbQrpzwuNDuqDVSW0WyJwuE6Uw6oXznxLAXkV5I\ntXYjMjHlKVe4UYVic4aTJhgpt5EkGt2jZno5cpTFh8PFTEcMXosQ6vdjKlnQbCCVRiy1jpOn\ndAJhwsWuhDKnozQz/eu5hia/DS1QDOxSw+mpEY+0ikijHT60eY4kfxH23j0LPAtXrRxFdYsq\n1HOHRroISOQWKpVwckKjj/3IBxUUjou2jo1GCZaxqIymZa6q2PN7gWQle5BiqJZSZGc11urE\nZknVDZJqB/uyuZDobLFIJ1yVGPNRr4TIwdEbVV6mAiqbCArjEZ71noXbSmufMTErSMUkvfDo\nt0spj+PGO30GkDjT5vvIGsj6nsqHrSN+dOVo+KuecjqDtqxIc/QurI1V4nw75KpaabEUu8TA\n84DkNUhcit6+a6rWVBwNgeSin7Zly2lKlx17GU6k+GEUNTl0VYeAcU1kAgGSCD+C6TweRZ/a\nMUy279pZSRRt+Fm7cBQb6ajXKQuhiuYDyRdGt089gkwq8rhlR4iaW6EZcZqOcBVvPE5wGYKl\nOkhJRTf0/OQgDS+TRFu7pdPmI6Qiq99HsgXJxQU4fZHGtXZV5F1cfEUM+bDVKuy7yISuGHcr\nl7r0tQQo52c0eyaaA6Riik56JEdeBBDeFVp0Wm3+DdnsTNyvcuyTcRPeqwjtUSPHrZ8Q4cTO\nd8BI5qxNfSJuo2Tc/dLpCPopoTdrIVGdk9RQdCdGYUaSDxXEL1c2FjqDNv85rjA7b1USu2VG\npVdnoqCMeqRsnbcIeBrkbbi0BGE54VIPgPg63wRlPQskNXLNit76axSh/biJpGcXmL0QSDaK\nQHKVMsKQEMOfcmT9EaelnAsGOd8W8gc2PR1yfn7Vz+Tlvejf7iwA3t+QIy1vHzWYgCQ5Up7d\n8+R1lVXSpCDJAZ4rW+8bPEZ6eNWBG+di1yKmw2CQarBcCSQHW2Kl5L3KJ4DU9yLMlngpE1bW\nQMu76OeorEM7aupoXuTL1wFJaTi0M/C/UegUnEypaEaI/ALHbey0dCWTPxSQghQCMuF82DYB\no7xToUoqUe2eO9tuapDa2AkHovOWzKEc8kmXCe2UhkCqjPnxsuM5Pik6Xsw71fTqed7SG9SH\n0ZGMwXjB4wQOjsiibTqaMDMgBV+mI9QcURcGaeTPcSUzGTd1bIcafWUqmlQjIDnxz7Dssslc\nOObF0OYoj2mkE48+WsY3Uce5gxOheI39igro5fKJypCOjcISV2mio0AaK6ha9La/a+fFK81S\n0g6faxpbE8C2+WtddgKp0ja8RtIjhCY5fqXw20UX0g4MOHFgIQuS3e7VKa/Lltu4AuXCSB5p\nuKGGpglgk9YHtG8gidaeYdpLcQnzUKj0SsEW42+ryt/YtxxN+K3mPCiCe1hewv/UXey/2O2I\nQ1FwSMyuR3ilUCh5NZ/vekcFynCxApIqofXuO9Mbar3odYxCM1IwzoZdZCfMhmsFG/nbTXrP\ngCS+Obb4F8L2XiM153FOD1funWXMB/chOlm4MDXGyVdRYBiBRP4s33lclIrzFFGZu+688zlA\nGtz+VsskHRrnQGqDZH6Qyn9pzyU3vVVNs10mC50N4zecpfe8dRDmQRnHOZ1aoBhOLtaFOxIX\nZaXIF8WsUu3yN93XeENNHYbtNm0ESa+MZMgQtwP1m+qDerWmBqn4VyvtF3erBtMWjRpwQYLe\nSZKWS9ybnjuUIjIOM5KwkGwHlNiQroAgT99VPA1E3tAXE+baoVcHRg0rLolAkgvSpHND/CBD\n8JV6m449aAAAH9dJREFUnctRpJxHWlsj2Za9dt3VTklHwn0h0/B+Az1GoiQSi+CnfCBHpCGU\nfGxdd2i9d9kDZm6q2hYDLe98g91GO6tJ1giidzq0joJoFSGsl2o/HjcpBsn7dZBc9HNb2WvX\nU5fkordi0IRR75xwS4++jqKMYMeJLFEYKEsXKy5dKdWhtd4Nd5OMlPUwZQ6QiqFd7JOoub08\nR5fSOgnnTzPZ1TQEkgpltpa9dj1KpQcutb0nUIRbIVQYl+WdDsedwiRk18segePYrdPdxLQ9\nAUg+ZonWoRolz/0l8lM8rfpudr3ntr/7QjtaNG6symr+hpgnDHwvGfLKoXBHxseLBaJJwcUj\nntOpHOyfOu42k34XkLwL/29TU5TlZcPGPkjxxBGAyB1HdRabJPsrC9LyHOndyydKkXRoZzzb\nFYKi8kBlHoieNMQQk6CmSEBAsyffGsPp5Su7peXmOWHlHvVCIJu6Yb4YUB/mm4p2ScsK5y/C\naReeE1HTC+AdxQ5X5IjqvP4FM9EayeB+dYzWZW5xENRL3EHcY85xqvCG+pvdqu7a5RSHKzJ0\nkR7PqzGRraPTC6LyyF4b8ScOrIbQzgcIJE1e/JPzmvA+0g2JwdSEv8UcsU0FkBq+qSkCqWky\nrs+KLjrqCZNUcBWQ0YEFp/LETiDLs7diJoKP4fCQipJVDM5KgJS7T+d9knXUuYxkslETSASJ\nmKm0519eZOTsKGs/Fxbx0EblQYrP5pSAtHIrSfJyivogy7SydDQcIAhguGv5mFFLgOE4XbGh\nr6jKKJBym1HSw0X3mIGuPpDmAKmSSHkeNZtJZ8RzlZyfNBSyJYqtwp07j0JtekDqMuzLndEG\nUjr7SBSE2xNU0bJHTZWZKVJOiApKumNmLqmWIjQmqQxS5oYqjVRumKraJrt2O9U0eYxoKlKx\nso/aOGInqnppSBQ65UTNAVK10WIT5ESCcwlD2S99qiOMaH7k6CIumfwQnxCW04ppKLMg5QZG\n/oaqQ3YEpNAg/VlLRdeeI8UkedHaj+qQ+ycEMn0e++5C/cNsORNJ3SDRMF25jz6Qqm48Z033\nmKeGlYM72o0I/6I+cK7UfeSR4irRKGVXloDkRaVUvoNAUt5wXG0gycWpCorpFLdP2vJxaasg\neV7vnqX3GJndPBLfZDFfk0HmRp8gpL3cefACDBEACMbiGYCpoJDR6zO5GqkplZCO0uVb7CiQ\nam6+z85aEkmQ2BINb3m+4ZoVBlMzSKLjT1H6uHU/kHxm1EYJ2syEslWwwvNcmHqdk3WMZkct\nL3IoiyFQ9OpMFOOHWixGyEc33Y2XRVaCnCRHp0TlNqkHJDoS7oiXRTKkKRqWV2qtotZZJyhx\nSAOhXX5iH1Fjfh7GnM3JTqMZUeAT8okTkiMKBNkwZ5QFL7OrWHlpkCjC6GgM4QSp7Gr2UZAG\nZr9K0aU/WZxOVNzQ3AOe26m29pTDqtwqIno4RZnP/7TXRQb7nVlLFjtTsqegztHvadzLaC/X\nveSNJEii24PdxUWFQoTD4rx8Yuz+W+b8UdurWRXTK0WX//Z3tqmppcSqNLyKbJny1m83n/c4\nGYHU2EnrFjtTCpJjDNhDMQ6VidJTQBYmRz7kHUCmxxNRzCB3eqdDKt7VSsIdxBFXA0ilFBok\nhiZcjnd3dHCsC2hsEqOZ3FJHgBS1lWj09sKDmWUYEzx0jadB6Z7k9Cg7mpihaIPpDBWj0I1X\nTz5YoltRPzsl3WHVwl4jRnijcZBEX3BTewFoOMsZQnumJbQSMjp37adreCQaaUunsW93PBJl\nRCEWvH4J0NihMGkS6RhCCt28tpoG58OBBk8P9dboNS3vbq38kH696OJHhKgcNdGERuROYJth\nHotK6HHSJwZ2WY2AZOVZ+w3wXOfklOelA1JhBqHl2J1oojizAMk7tiuMh0Gie1E6unKlK/ez\n2p47eyRPg7padO2zdjK+E8sfeez1+HH5257P1aR6pxehIZBkeLNB3RbEOobDKi86SpHknYDK\nqwtLHrH+USnFnE7BHp+hbHwbwuPp+rqmoXEWSIKkDaFdsBAtlpa5KMQQaj4RE0hcwHSuJlH+\nrwSNgWSj3tDOqRGv1jPC60TTow7h2KOlSSPUhKuimw9uJ5pUOHxR96QyrjeEHUjR/a6WnBTR\nnD/OROiEaJligHSaoWQXU9YhbVojbVXPbOc99QatWIL78CHs5tEfoOBgjC+owC7GTOxaeOGq\neKw7+ic9IC/c4ttrASnK+f7QQENFJg26qim046Ts49kTeZ7wLohNosKfrdsNpIZZsbVsHqRq\nrUNQUFghVz7xPl5MSPBwEiKZSIYkdA88KARmXtgdBOlu5v09IaizoTJZtg7ddpAkQT4EDHTK\niGsu7DRfdjRIDakayxZ7b467iLoq4EUuquhzvAwyyJFI8ERJKuZjfyenVl4vEWtxZBd8Yf0O\nC/z0NlQuS0vWWpqO8cHLIS+bStoxi2Wi8HoCjYBkRVIrSKnvkMwkIVbsk6KFklevIkoMU6c0\nKwJIbSe0iFofRX2r2csouKC16fUKIIU2pYkv3xgm4uafR0MeSY6mDbabPZKnAa8pIP8joj4n\nfmj8GCTvQ0johdUQvpGjkyY4mAv5JWOFe6k0kfRBq2NsZMR0uIBGkKqhneNGooklSbFaEZqw\n1tLx68Eq/oX8IY9kpMggzfPJeXlRug+mhiM08YZ9TnxOvKrobimf6PRkgoc7byqwLyTS15Rb\nB60P+aGWb5nq1s03gSTnrg1RHE9V+SIY19NAeo9+suYBKQzdzIUw8zNUgow45MssjgonOXiT\npQp/5rkwOdeGOhBnkq6CSvsIvmXg7TxithbNa1SRJcm4irWgI82s7Z62Rip/ZcsQSDwQN8ml\nb1yuF5ZOosZkmuQSqYSMCvdkpwdHEtjhmwuX6Tyb4IBPcFZYI91U3IlL7ry1odpkNcoa7Igg\nQWSJM67Grz0gyf46UmWHNLZGammWFovJG5eARH5GBE/iXYpO7py67MVGH21QBJfDJRIa7J04\nkhNrpSXr8j/zxACtttNqY84BUvlXzWWreO+5EWI763e5BtIZ+LD2AWnzTSUgESvigJNppyF9\njQRCkCS9VuDG0/TJESKBoC+QIepex0UvtYxA/HhVLkgPn+Isuja9Tg6SmJHCmWRuyJzKllVI\nxn3QVu3jNQiSiUsS+R+7wKG9KIwrzWPa/RARyenIMTFmIqIjMhjLcF3VJbgp6ZeE93ILQqrK\nyZuxJhvJZTTiWsyQ745yibyOnHbVELV65tIMHqmiSUC6v+UtLZ7xs3XUmCiEpFdSnokIiK95\nxicY8NKcmCPFToMknh4HcStlQGoIbhobqikLTyqb1ASSdNMilwKLfMqmqkzL0RBIYW6x7iRq\nfhqYRECcTjkN6ZYKPipck53OiTKMeWaQbl4GnI6xfxfwxOyLZjoWJCuJoivb3z6qYwak1fh1\ntRpng/SeHEgNgeSLHrhLqacJJx9X1EMXLk+EV4EBHetF2w1e7S1Qck/uKKN7+veqGEyqmJP9\nTbSqu31KkNLBEA36DfeuS3g2kGyUGqRhSbO70yP6cY38C79jKiiIkEyIQ+F7GAQiywlf40Um\nWUUxLnKzpOZJ3+5YG47k4qllk0bzRyWf7U0MVOdoMpCET+KYjjshCqdcqJdzPObFyBcuQ+Am\n0AlXHu+E7VA6UyOaQQW2peGam4OHh/VANmc0eMcNpPPPtWUNkgqAtml1+CUgLZ5GIfUuwzFm\nQNRURHC1WE3eF7+I9Y5AiG+e975FKotgptpQLVlMYqG20K6Q8ersSK1+M2yzJb3Z0JW1ZLFw\nRhfE4yG4CfIq95P1lUwBFunBgm0Z9nnehZAQu+TO6flRVOMJQDIZy6Mgmd7/FTQCkot+2pWd\njlPiJVQg2RfTaFA4lu7aaWeqnY9cK9FeuFfg8iwvXVIEEj+YtJyR5wBpIF/P6Lo4dFOB5Fy5\n73nk6gGitw94/CcMCSCYJU4rPJ3IFuK2UDNNjzh0XJtAvN3QGDEUt9N+RWdvtBOkpREvTNNM\nIAV/UkxOsz2HdpqLgFJIq11SfOj5v8CM2rcIJXnBi+aJ8ukmsR4OQ/ZctTEHii7+7e9sBbso\n5mablKT37KHUCEhWw8Xl3lasioYWHsHHwDAAhE3CUJKBWEmvBQvk/XRVtRH2SIY6cXStglTs\ntR6KFxdesHS+1jkaA8nzyNmiTpDYI6kZLHEh8mwEUhy8hcueeMhFhWqHfQkkKZ+oia5jUwu0\nteIcINUSbKzhy4Jko26Q6FUe6pgtRNo89FNn5JMsFNn5EL855oWTe/ZbqqZRzTpXBm3peqRn\ngk06BiTREjOC1MDRTCBVxxUHT5xThFaeAq/llFglxYuoWJ68CP1znFu6OS6fPVmmOn2OpiHp\nSMvbh999a6TeckQXzCxbkPaa7ZRJ/SZUQK2RpL+Qe21OVJGgIkLyjircl7g/9m5yxlS8KoRU\nZZvvn0ltb6hW2zauYlH595GsRr+dpeM1ApJZu61djKd80dA8gyXb1RSYMQzB68TwOKc24oT1\nwByjFOAJwZ9nALOVbb1/F2UvJuzSDiBBNc0Kkpj+fWFshpOBC08UcezFPkmuepLtBgbTh3KF\nV+N1lA9BJHtIXdFiZYs36RoyAKT5NTVIAotwjkas4yHMHkeOd3Y86V5cJLbCpbPrUmWFV+WM\ncrfV1EguXnxVGqNTHbVYN3NX32ftnkXvlXdSM4MkPjUgnIIPUPHiSHDCvKgjtW6Kry8BoUQz\n/HS0j8GgssOSqZO697aDMUgi8NyiVweplaPBzYbu+vSXLUbq41XM3dFIl+ug4h5dzFHMkxcO\nzdOJJJZjlye2HRKSOkfwDmskK716aLcrSGJwrhmupqqXzQ7C+zDK5bKIgrd4SSP2FRgxBVkO\nI8Fj1nS4GeYu2mzYoqa2PEUvDlIzR2MeqSm5fhkqmyO75X9yTvxpA7EY4rWRU+ejo/hEcDni\nudHilsIJJw7VaoxhV9NMV0M1aGiN1DDZ9RX9kqGd0mkguXoZ6wZ5XIcBHyqyvCfPEyiKVz56\ndZQP77xgJbgmL/BhKkOVxPZg7mbVeqvxJqspVm1YZFmzA5BqGlsjNcx2JiBxeZ4B4kWTQkTE\ne6F2ES+FVZK04wOPulTebfAiub7PAL08ubb48W1p5gAJqmlojdTS+5YgxQx5vXbRS6bYz3B8\n5oV7UsGcCPMEbewHOYEXCyeqW7KKC9VsWUC1p+lSV5ZKYoDUqA0g1fOGMTi+RnKyRB7SZJu9\nIl3Ubob4UYgpp0VbE1SE9nOeYjUfQszMJOL4/wJIkrxcI1wBpFcL7eIVUW2FNAxSW9Di6qma\nXZoenp6cipfD3ksC2Oew71LuRHIkjMmVGC+EhCsLqeQd+mBA+2rHDVBph51AasqjJp5VMy8G\nUsLNeSCtWly/KEhyCiR6CaM49jfBi0Q+SMZxIrLzEkQfHJlckbGL5WHHmEjvxeGgivdqJK35\n5vrlbJYVQJRhhHYZ9TmkIZBW5th2i6sX1dwvHJxTSQJQvPVGPkJ4IXItIqGjjMSj93xIJCw1\nEaGaqEV0NlRXhHR1r7M22vcczWJyOrroydXpkMZAEnPwFq2CRAsdr0e18Ei8lUAuRgVv0leJ\nVRDHZORGRHzmBFscodFWBFdPtIvwUxppTrhPQ20UTxBrRb9WaHcMSH3SGWWYVc/FoRehQ7FI\nFECRv9JRmyPiOIiTSHA1mA8JnwszhiNchJNJPBIdyMjTq9RDGsna1MaL9SRRto9eC6RejYDk\nop87lU0uJQRoog4cXpGnEY5JQKDXTiKWcSL+4rpweXL9JHBdYkHFh6gM062iuo3+eyCv8oVr\naRHabdfEIHle9nsO9ega+4PIBdGrJw/C0ZwO6dRKZqmTXicFlyhwDrhwRuHO2A8R3Kq+uzRU\nPguxvkWvCdJKGJdTN0h6yt+k1fzJcj+Tw0Uc0VDnJY0TQPAqKJAS21MACqfIEV7hztnlBZBk\nLLplTI6B1FxoLc1LhnYZjlbR2uKRVjOs4LYOkojGpBNyUaJo3KsQn9+HMc6uxhFIyqaK6chJ\nieVWobLMTmBRrpE2kASQjlVKzbqLGgGpMf1qEesGXYINUbEchlfFjg712K9k04eaxHSSFbXa\nKc4MaikldyhE3tX7LTfDSB5jkF5GIw5pBCS5LGgyXErZUHZaTFh9eDnrSheiaBJQ8XCOfFJu\nnHNcGNyLWxmXXK9oBjkJpOwk1G/+FUFK1bBm6gcpP4XXDG8AKVcLF8ImGdBJeU9ksQNTV4XH\nCe/yIC1vsjFlejORGYFPq3Mo295RCO3WtAdI0ehoMbwPSLzx5hkH2injFUsU3wV/SpsIIQzT\nIIWl13Lj5I3LLAkfpk55ER4O6kS38HIgDWzY3bUbSCZrpFwe3kaj/+SSRxJEr56HsiApQMl7\nCXSrFCH6UADVuHw7KSvkorahMJDbRT8PLPrSGuVoEKSmmF/EUlvLVpnIs4TQbimL6NBY6X2F\nAIdwUfwq6iV8Ft9x9bZLd7pxfTSYGSANaZijPUEyLFvXI/gStRIK3id668iJOdq5o41sH6Xh\nakkKw8mUuPhmsldOAElPJJvUF9pVy9temd2V56iJrolA6mxop/6p/W4vwzsNBS14wi2wZ9OG\nk3iOqFrDJbqPcz3SVnWBVI98axcnURaZNi/VDZJ3JmMjNdDY0DxMdTQXb955HdOFIR7hQIA4\nYdNLz0V2HkfEmEtrr+HuvbHaPW/KfVjRSxvkJ0SjMbOrNjikAZDEXtZGjUzc0aikkUv7AjmQ\nuNIhEYV2XjFBzsjxDp8slBdOxepn7mNzTNOfPecady9aTybFixdT47JpACQzDYAUpaL4LCyc\nBEBe4sT0U9CWWUHwlKpM8MQRcMzR4uUYsm2rbmti1rArejW0ExNsWi7PNLRvurVuU+miIDnZ\nX+xzxJJHhWMKn5Ax7UrqYeXL5EwbrGQqq6u0du9d6rUm6rC1Ij0g1ScS0XQ5l366xjfs7uoE\nKVmH2JVdbdpQWCBHpRcba7SRJ5wKuR8XVkClgjjyW8wEQoV3ctXRsnYfY5oDpJbUup+Si9ro\nVCBt5Ggej1R19nrwis5SLkNHZAJ3F/ASrilbKkV8Il+aRVUoU1nzlhoCycY19udvmEguBFIz\nXxOBtJaQ4zg549KugIKHceAlknBN2rTGSub1HCrmA7lKnQ3baw6QWj8itN40S7KxGu2jrRx1\ngWR966uBnE7oiu8Jj/BxIN5hCJGe+o/LEMcyFHQEz1o0WKqzANQ6Bm5MfxpIzTanIimvXUDy\n6Xy+TcXYKL6WgpOkUCDJ9ZH8yEO0d+3Iq6l/lD4siYpD0vEGVAROtErZ2GTd2WuV3rnoDpPz\ng9SxcOoDaTkywqlgghYnOmUMTpKNFkMP5ENI5hfAyCEJNngNtOxJiABQ7H0XWBAwxuA4maB8\nq43qzy02LLfJfrDHTTODSsDsDNLyfq+IJbu4rxXmeNgQ42Kl5Omj3M4JaDwhG8ALJ8gvRXFg\ntvpyyMbgnAeSmcxDO9FBJvYstHXH7qZRkCzaoQRSbjatlEfDVe8TaLyYkAUl73k5FI7ZeYmX\nYvFVkLhStVtt1DOBNKEsOBoEyWY2KRl5DMJ6bCcvJXtqigJeyFC4Rk5PvDJlPlrlUJ5s9eMo\nLrwl9AyCmDlAelYVOeoCbBCkniK6y+bFiU7pVBJKKhYlOouTI1sFa0tMF0CSu3a6hLCBl61u\nsOTr4By/a2eoFwCpqANAsmnf9WBNp3RJgvIixCkX48MewuOAAzbyN2kVxEZfbtVGhSSk2Ef/\nc4D0tKFdQX0R35QgJWMxgoXCp0fa8KIMBLcT7DlPdEVuR+V17H2kAzOIz9ZVJBAgnaFDQDJR\nh0U9kCOQkjUSrZs42hI7RRE3KorUj4QIJNoH33OrqQzrHCAZGp1ox66spwRJt70CKR6Ay6JG\nrlpoKy9ee+mdCrERIUoIy6e0JFvlQ9TyyWNkUbRefKpmdF7/TAqP8xnLZMPurquAlObkf2Kz\nOzgOL9FzxFWyIJJxYTCimJFbEJWhbqDpQRoN7eIbc5mzTZ7YvhnsOLooSPHeNk9y4bQnPsIW\nQwwRr49oWUTPa/myzAWQtljINuO5IBlydFWQspbU4oYfBQk6oizqU6kLbnrFpMKLVZC2xf7P\nuUaKIwBe2XoxZzn+oVOKBDqZCC9kDN9TtSpHvZBNDVJzwyQgyTDcxX3kdTrhy5wqUm7ZRQUU\na7GJpLl37e56v6nys26A2pOiBK+mK+39aYtnOcEhB8cNXicxa6puZzUzSE0NIzYQZGuKFVJi\nR35wIVoCqUkuAalO9n6R3xwgjYV2Ljnmj2Wp5ajqJWpLedbFFwoHFnomkJoaJu2FjPPJbBpl\nQFLwhZlTzpIrqtR3NPhQlk/RLiAJojh6aAPp/lPNnWmeJtVJ6V897QiSox3nMYstDcPNWSsn\n4Yi6L1+GY4fUMfJjWzGXXg+LDu0GUmZtYl30Gkhyzyezr/NoNvEAMFCUAWlluClZ7jOEmpin\njNIXM1qCdP/R1IpESRQTRlZ7MaJs6bvNwcd+IAXjo33UVoQ+ViAtjUw9mE/Jx2lbro+znK4D\nkssedllsmL+jkdpSSemK8kM77CWt24pNq50Kep0WJNFiDX20ffs755HkiQ0gdbauOUdTg9Ti\nEDLz/loGBVyOPpOP1gEkbYJaQh7079rRHKcnT219q0YwmxqkpnI6P3fwiCWE50hhjZOM1St6\n3dLVc4A0XozYGdUH4tl33ObpcyR+Rh5NShu3chINuavdQGqIXZstNjVQO0iryUymNmlk1l07\nRfixRZ+pFVImA2nzrp1K10iSUbLu8Z7LYDQ/+j1Hc6GPHItPPs2vUezB0Z4gWVlsXllkR252\nlLQV3C6z6Lxs/iQ9IUi7cPRUIJUy7z4ON9Ww2f4per7Qzn7D7q4nB2nvIX5MKTvXv2b++UDa\nSUeApHNm4+/V/BscEkDaYP4JQ7t9dAGPtGVVcwxIF18jASTWcOB3CZA2lnFAOTtsYEjrO9pe\nMf9iod34AurZQdp5iB8jgGSkVU6mBMnsOdLLa45du+uHdjtydI1PNry6AJKJdtr4fmg3kFz2\ncIvF19UcIF1du3IEkK4ggDS/ANIFNAdIlw/t1rTJZZ26RoIa1dn0hhKV+O+02z9G79uytzfp\naB/YW++ryiVNT+itN324BNm2lrWH9VlGO0BCtsPK2sP6LKMdICHbYWXtYX2W0Q6QkO2wsvaw\nPstoB0jIdlhZe1ifZbQDJGQ7rKw9rM8y2gESsh1W1h7WZxntAAnZDitrD+uzjHaAhGyHlbWH\n9VlGO0BCtsPKgiBICiBBkIEAEgQZCCBBkIEAEgQZCCBBkIEAEgQZCCBBkIEAEgQZCCBBkIEA\nEgQZCCBBkIEAEgQZCCBBkIF2AGkxqf/4XelP4WUTF6tVMr09sUgtT66a1olXbjF3+iS13ed6\ntrZ8g/duVcm2bLpaPX8cMs5ro6UCThnX71YS0+l205n0XfUwqLXNLR6kxlbZJVvzvbf1c0u2\npuKU9Y5snMFU7mGSR0/6biUxnbY3XUqsUju/knp74uItHqTGVtklW/O9H1tJXa2ObCqHnVw0\nXKpllRLnWzqfunADtcS50Z40Yjl1PnGhNqXETwCSz7xryrYBpMFsjY5sJpB8NFzWysoldsVq\nZdlYWcjEpgu2XXJQac1M4vz7UuLyLR6iU0Fqvfe4ko2rlrS05qaeFqSGJs8lbgGJG6rYUnlP\n0LHZsApSYqxWDxeF4M8BUuNcL1MOgtRKROL/RjYbngKkSosVUq85O3205r8GTZfSdt7iIToT\npOZ7t6nkM3iklhggTezKiXOmG0Z7U/u49LAFJHV5DVFyTuXEx8gMpH4i2u/dpJJdREwKUgtH\nmcS1L2DKmF4f7U2JLUBadaM0mHq/xcpaViC13YEGqfneAVLOcqmcbOK1MZnOiZXR3pZYzpSN\nIMWXq7c4OJnvJCOQusbnNkf2uiC1cVSqcRWk1HTFxzQmXoa7vl43nUncbLmW/gg13ud6toHS\nmnOaVbK1opGv7eqhvUBiB+7Uu4bElWplU1e3v1sTi8hDvG38iFDDLcaWTwap8T6r2Tri08F7\nN6hkRzY99Z7/ESEIekEBJAgyEECCIAMBJAgyEECCIAMBJAgyEECCIAMBJAgyEECCIAMBJAgy\nEECCIAMBJAgyEECCIAMBJAgyEECCIAMBJAgyEECCIAMBJAgyEECCIAMBJAgyEECCIAMBJAgy\nEECCIAMBJAgyEECCIAMBJAgyEECCIAMBJAgy0HVB4i/aoT/Vn0lUyrxjxaCcnr3Fr3t/Td/X\nApBm0bO3+HXvDyBdSs/e4te9P/VdUuILFemLbW4HTqYNX+qzZOEr9IU6Z34d5ZNLfomX/L4q\nev9xSB3IX1Gk+mdmzV/DktIvZXM+OYhBcvzTJXmdNgtZKuku1Seis1QvOtU3M2v6ChYVfSeg\nlw2uHY7Pdl6a8rptcQE5feCyfRJfzvTkrJq9fmXlPVIdpPuhA0gnqBWk+xsHkI5TASS5J56C\nJCjijpLLq+u2x+RikKKnFlGPZSa69u+qPVGz16+smkfyMUjeJf6q4Iiu2yBzyyUHqk+87rHr\nBQrXqGVOXaHdOkjSd0H2yvCS9kn2LUK7fZUHKTrQiZYXAVKyWXHhBplbaXdpptQ57pYk0phV\n01ewqKhnnHgMsZzm50iU3C0nnTjmLBcIxS8rsdJx+qkEP0eihNwtOsPEmr+G0CvrMuPzMhWF\nXkwXC7SvU1PoxXStQPtCVYWgeQWQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmC\nDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmCDASQIMhAAAmC\nDASQIMhA/we+gVsBTaeHPAAAAABJRU5ErkJggg==",
"text/plain": [
"Plot with title \"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"par(mfrow = c(2, 2))\n",
"plot(fit1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Model 2"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data2 <- read.csv('Model2.csv')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t1009 obs. of 16 variables:\n",
" $ month : Factor w/ 24 levels \"2009-04\",\"2009-05\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ town : Factor w/ 1 level \"YISHUN\": 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ flat_type : Factor w/ 5 levels \"3 ROOM\",\"4 ROOM\",..: 2 2 3 4 1 3 2 2 2 2 ...\n",
" $ block : Factor w/ 159 levels \"201\",\"202\",\"203\",..: 20 20 21 24 135 141 119 123 83 89 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 1 1 1 1 2 2 3 3 4 4 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 3 4 1 2 3 3 2 2 4 ...\n",
" $ floor_area_sqm : int 108 103 121 146 74 122 84 103 84 84 ...\n",
" $ flat_model : Factor w/ 7 levels \"Apartment\",\"Improved\",..: 4 4 2 3 4 2 7 4 7 7 ...\n",
" $ lease_commence_date: int 1988 1988 1988 1988 1987 1987 1985 1986 1987 1987 ...\n",
" $ resale_price : int 275000 260000 302000 399000 230000 373000 272000 315000 248500 270000 ...\n",
" $ Age : int 21 21 21 21 22 22 24 23 22 22 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 1 0 ...\n",
" $ Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Period3 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period3 : int 0 0 0 0 0 0 0 0 0 0 ...\n"
]
}
],
"source": [
"str(data2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"data2 <- data2 %>% mutate(ln_resale_price = log(resale_price))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t1009 obs. of 17 variables:\n",
" $ month : Factor w/ 24 levels \"2009-04\",\"2009-05\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ town : Factor w/ 1 level \"YISHUN\": 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ flat_type : Factor w/ 5 levels \"3 ROOM\",\"4 ROOM\",..: 2 2 3 4 1 3 2 2 2 2 ...\n",
" $ block : Factor w/ 159 levels \"201\",\"202\",\"203\",..: 20 20 21 24 135 141 119 123 83 89 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 1 1 1 1 2 2 3 3 4 4 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 3 4 1 2 3 3 2 2 4 ...\n",
" $ floor_area_sqm : int 108 103 121 146 74 122 84 103 84 84 ...\n",
" $ flat_model : Factor w/ 7 levels \"Apartment\",\"Improved\",..: 4 4 2 3 4 2 7 4 7 7 ...\n",
" $ lease_commence_date: int 1988 1988 1988 1988 1987 1987 1985 1986 1987 1987 ...\n",
" $ resale_price : int 275000 260000 302000 399000 230000 373000 272000 315000 248500 270000 ...\n",
" $ Age : int 21 21 21 21 22 22 24 23 22 22 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 1 0 ...\n",
" $ Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Period3 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period3 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ ln_resale_price : num 12.5 12.5 12.6 12.9 12.3 ...\n"
]
}
],
"source": [
"str(data2)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"t test of coefficients:\n",
"\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 1.2222e+01 2.1487e-01 56.8829 < 2.2e-16 ***\n",
"Treatment -5.3281e-02 6.1576e-02 -0.8653 0.3871347 \n",
"Period2 2.1222e-01 1.4186e-02 14.9604 < 2.2e-16 ***\n",
"Treatment_Period2 -6.1833e-03 7.9621e-03 -0.7766 0.4376306 \n",
"Period3 2.6026e-01 2.1021e-02 12.3807 < 2.2e-16 ***\n",
"Treatment_Period3 3.0522e-03 8.9095e-03 0.3426 0.7320037 \n",
"Age -9.8023e-03 9.9938e-03 -0.9808 0.3269635 \n",
"month2009-05 -8.5751e-03 1.3346e-02 -0.6425 0.5207256 \n",
"month2009-06 7.2273e-03 1.0762e-02 0.6715 0.5020690 \n",
"month2009-07 2.1384e-02 1.1150e-02 1.9178 0.0554830 . \n",
"month2009-08 2.8054e-02 1.0928e-02 2.5672 0.0104300 * \n",
"month2009-09 3.8567e-02 1.2624e-02 3.0550 0.0023238 ** \n",
"month2009-10 6.0320e-02 1.1177e-02 5.3967 8.915e-08 ***\n",
"month2009-11 7.2115e-02 9.8638e-03 7.3111 6.353e-13 ***\n",
"month2009-12 9.2149e-02 1.1879e-02 7.7574 2.600e-14 ***\n",
"month2010-01 1.0696e-01 1.3356e-02 8.0080 4.031e-15 ***\n",
"month2010-02 1.2917e-01 1.3192e-02 9.7918 < 2.2e-16 ***\n",
"month2010-03 1.2652e-01 1.3094e-02 9.6624 < 2.2e-16 ***\n",
"month2010-04 -8.0372e-02 9.7793e-03 -8.2186 8.112e-16 ***\n",
"month2010-05 -5.9222e-02 1.0418e-02 -5.6843 1.831e-08 ***\n",
"month2010-06 -3.6905e-02 1.0172e-02 -3.6282 0.0003033 ***\n",
"month2010-07 -2.5269e-02 9.6879e-03 -2.6083 0.0092655 ** \n",
"month2010-08 9.5192e-05 1.0170e-02 0.0094 0.9925345 \n",
"month2010-10 -9.5137e-03 1.3712e-02 -0.6938 0.4879796 \n",
"month2010-11 -1.9965e-02 1.4785e-02 -1.3503 0.1772944 \n",
"month2010-12 -7.2189e-03 1.5274e-02 -0.4726 0.6366088 \n",
"month2011-01 1.2418e-02 1.0279e-02 1.2080 0.2273936 \n",
"month2011-02 1.6729e-02 1.0771e-02 1.5532 0.1207642 \n",
"flat_type4 ROOM 1.0938e-01 2.0357e-02 5.3732 1.011e-07 ***\n",
"flat_type5 ROOM 2.4533e-01 4.7216e-02 5.1960 2.577e-07 ***\n",
"flat_typeEXECUTIVE 4.0133e-01 6.8848e-02 5.8292 8.031e-09 ***\n",
"flat_typeMULTI-GENERATION 4.1157e-01 6.9778e-02 5.8983 5.388e-09 ***\n",
"block202 1.0634e-02 1.8458e-02 0.5761 0.5646791 \n",
"block203 4.2556e-02 2.0175e-02 2.1093 0.0352215 * \n",
"block204 -3.3757e-03 1.6461e-02 -0.2051 0.8375675 \n",
"block208 -2.2661e-03 3.3738e-02 -0.0672 0.9464658 \n",
"block302 -8.2767e-02 1.8559e-02 -4.4597 9.362e-06 ***\n",
"block303 -9.3313e-02 1.9528e-02 -4.7784 2.097e-06 ***\n",
"block304 -1.0290e-01 2.0725e-02 -4.9649 8.379e-07 ***\n",
"block305 -7.5924e-02 2.9295e-02 -2.5917 0.0097206 ** \n",
"block306 -5.6186e-02 1.7541e-02 -3.2032 0.0014119 ** \n",
"block320 -1.2201e-01 2.8472e-02 -4.2852 2.045e-05 ***\n",
"block321 -1.4083e-01 2.0261e-02 -6.9504 7.482e-12 ***\n",
"block322 -1.2094e-01 2.8277e-02 -4.2771 2.118e-05 ***\n",
"block323 -1.3366e-01 2.3902e-02 -5.5922 3.064e-08 ***\n",
"block324 -2.2100e-01 3.1043e-02 -7.1191 2.392e-12 ***\n",
"block325 -2.1378e-01 3.2559e-02 -6.5661 9.209e-11 ***\n",
"block326 -2.1500e-01 3.1165e-02 -6.8989 1.055e-11 ***\n",
"block327 -1.7103e-01 2.3472e-02 -7.2867 7.533e-13 ***\n",
"block345 -2.0060e-01 2.2307e-02 -8.9926 < 2.2e-16 ***\n",
"block346 -1.8965e-01 2.0248e-02 -9.3662 < 2.2e-16 ***\n",
"block349 -1.9210e-01 3.1591e-02 -6.0810 1.838e-09 ***\n",
"block350 -1.8092e-01 1.9846e-02 -9.1160 < 2.2e-16 ***\n",
"block350A -3.4671e-01 3.1139e-02 -11.1343 < 2.2e-16 ***\n",
"block351 -1.9082e-01 5.9121e-02 -3.2276 0.0012986 ** \n",
"block352 -2.1977e-01 4.2189e-02 -5.2092 2.406e-07 ***\n",
"block353 -2.0714e-01 2.7975e-02 -7.4045 3.297e-13 ***\n",
"block354 -1.8453e-01 2.6450e-02 -6.9764 6.284e-12 ***\n",
"block355 -2.2682e-01 6.2999e-02 -3.6004 0.0003370 ***\n",
"block356 -2.0105e-01 2.9921e-02 -6.7193 3.435e-11 ***\n",
"block415 -2.3606e-01 6.4160e-02 -3.6793 0.0002493 ***\n",
"block416 -2.4599e-01 6.3534e-02 -3.8718 0.0001167 ***\n",
"block602 1.2617e-02 6.2975e-02 0.2004 0.8412555 \n",
"block603 -2.1705e-02 6.1987e-02 -0.3502 0.7263083 \n",
"block604 -6.5108e-02 2.5437e-02 -2.5596 0.0106607 * \n",
"block605 -4.7935e-02 4.9056e-02 -0.9771 0.3287922 \n",
"block607 -7.9091e-02 3.3220e-02 -2.3808 0.0175028 * \n",
"block608 -1.5906e-01 6.8988e-02 -2.3056 0.0213822 * \n",
"block609 -5.5591e-02 2.0551e-02 -2.7051 0.0069724 ** \n",
"block610 -6.0342e-02 2.2947e-02 -2.6296 0.0087095 ** \n",
"block611 -1.0338e-01 3.8052e-02 -2.7169 0.0067304 ** \n",
"block612 -6.2675e-02 2.3715e-02 -2.6429 0.0083795 ** \n",
"block613 -4.1740e-02 2.0400e-02 -2.0461 0.0410714 * \n",
"block614 -1.3863e-01 3.0841e-02 -4.4950 7.969e-06 ***\n",
"block615 -2.6110e-02 2.0950e-02 -1.2463 0.2130103 \n",
"block616 -1.5691e-01 6.0868e-02 -2.5779 0.0101164 * \n",
"block617 -3.1211e-02 2.0577e-02 -1.5168 0.1297185 \n",
"block618 -1.1964e-01 5.9059e-02 -2.0258 0.0431089 * \n",
"block619 -1.7140e-02 1.7469e-02 -0.9812 0.3267903 \n",
"block620 1.4578e-02 1.8348e-02 0.7945 0.4271284 \n",
"block621 -3.8238e-02 2.0930e-02 -1.8269 0.0680752 . \n",
"block622 7.5234e-03 1.8246e-02 0.4123 0.6802117 \n",
"block624 -4.5856e-02 2.2580e-02 -2.0308 0.0425981 * \n",
"block625 -9.6190e-02 6.7830e-02 -1.4181 0.1565414 \n",
"block626 -1.5142e-02 2.4392e-02 -0.6208 0.5349144 \n",
"block627 -1.4259e-02 1.8974e-02 -0.7515 0.4525660 \n",
"block628 -5.3404e-02 3.1112e-02 -1.7165 0.0864572 . \n",
"block629 -4.2388e-02 2.0974e-02 -2.0210 0.0436091 * \n",
"block630 -4.0678e-02 2.3765e-02 -1.7117 0.0873422 . \n",
"block631 -1.2286e-01 6.7481e-02 -1.8206 0.0690381 . \n",
"block632 -8.6339e-02 2.2539e-02 -3.8307 0.0001376 ***\n",
"block633 -1.2590e-01 3.5877e-02 -3.5091 0.0004743 ***\n",
"block633A -7.6960e-02 5.6513e-02 -1.3618 0.1736343 \n",
"block634 -9.0606e-02 2.0985e-02 -4.3176 1.772e-05 ***\n",
"block635 -4.6476e-02 1.8086e-02 -2.5697 0.0103568 * \n",
"block636 -9.3038e-02 3.0243e-02 -3.0763 0.0021659 ** \n",
"block636A -1.3554e-01 5.7805e-02 -2.3448 0.0192755 * \n",
"block637 -8.7274e-02 2.9562e-02 -2.9522 0.0032459 ** \n",
"block638 -8.2453e-02 2.0177e-02 -4.0864 4.816e-05 ***\n",
"block639 -2.5390e-02 6.1282e-02 -0.4143 0.6787563 \n",
"block640 -9.9308e-02 2.3717e-02 -4.1872 3.133e-05 ***\n",
"block641 -2.5312e-02 1.7653e-02 -1.4338 0.1520052 \n",
"block642 -1.8977e-03 7.1064e-02 -0.0267 0.9787029 \n",
"block643 -9.2230e-02 6.6901e-02 -1.3786 0.1683984 \n",
"block644 -3.5087e-02 5.4821e-02 -0.6400 0.5223307 \n",
"block645 -5.9792e-02 1.6407e-02 -3.6443 0.0002852 ***\n",
"block645A -1.0052e-01 1.7214e-02 -5.8394 7.576e-09 ***\n",
"block646 -5.5287e-03 6.1143e-02 -0.0904 0.9279730 \n",
"block647 -1.7019e-02 6.0780e-02 -0.2800 0.7795369 \n",
"block651 -1.8844e-02 6.3903e-02 -0.2949 0.7681542 \n",
"block652 -6.3999e-02 2.8259e-02 -2.2648 0.0237895 * \n",
"block653 -2.1231e-02 6.2196e-02 -0.3414 0.7329185 \n",
"block654 -1.3004e-01 2.6552e-02 -4.8978 1.170e-06 ***\n",
"block655 -2.0866e-02 6.1854e-02 -0.3373 0.7359503 \n",
"block657 -2.2787e-02 6.2353e-02 -0.3655 0.7148680 \n",
"block658 -3.7847e-02 6.4376e-02 -0.5879 0.5567635 \n",
"block659 2.6897e-02 5.9909e-02 0.4490 0.6535714 \n",
"block660 -8.4461e-03 6.1617e-02 -0.1371 0.8910072 \n",
"block661 -6.7275e-02 1.8850e-02 -3.5689 0.0003795 ***\n",
"block662 -9.5317e-02 1.8429e-02 -5.1722 2.917e-07 ***\n",
"block663 -7.7780e-02 2.3387e-02 -3.3258 0.0009213 ***\n",
"block663A -8.0652e-02 6.2455e-02 -1.2913 0.1969511 \n",
"block664 -9.8172e-02 6.3573e-02 -1.5442 0.1229239 \n",
"block664A -1.2287e-01 6.2629e-02 -1.9619 0.0501170 . \n",
"block665 -1.6404e-01 6.0717e-02 -2.7017 0.0070416 ** \n",
"block666 -8.9464e-02 4.4935e-02 -1.9910 0.0468174 * \n",
"block666A -1.3214e-01 5.6785e-02 -2.3270 0.0202079 * \n",
"block744 1.9897e-02 4.0440e-02 0.4920 0.6228373 \n",
"block745 5.9770e-02 2.0863e-02 2.8648 0.0042799 ** \n",
"block746 6.0575e-02 2.1929e-02 2.7623 0.0058687 ** \n",
"block747 -2.8416e-02 2.3241e-02 -1.2226 0.2218191 \n",
"block748 1.0443e-02 2.5539e-02 0.4089 0.6827218 \n",
"block749 7.4317e-02 3.8035e-02 1.9539 0.0510552 . \n",
"block750 1.7093e-02 2.5627e-02 0.6670 0.5049747 \n",
"block751 4.7684e-02 2.4818e-02 1.9214 0.0550346 . \n",
"block752 -8.0489e-03 2.6845e-02 -0.2998 0.7643829 \n",
"block753 3.0775e-02 1.6786e-02 1.8333 0.0671204 . \n",
"block754 -5.8721e-03 2.6767e-02 -0.2194 0.8264124 \n",
"block755 -4.5916e-02 3.3094e-02 -1.3874 0.1656860 \n",
"block756 1.2402e-02 2.6577e-02 0.4666 0.6408933 \n",
"block757 -2.3588e-02 2.2417e-02 -1.0522 0.2929985 \n",
"block758 -4.0946e-02 2.2275e-02 -1.8382 0.0663945 . \n",
"block759 -3.1486e-02 2.9532e-02 -1.0662 0.2866680 \n",
"block760 -1.0341e-02 2.2109e-02 -0.4677 0.6401068 \n",
"block761 -9.5860e-02 4.2055e-02 -2.2794 0.0229033 * \n",
"block762 -3.3645e-02 2.9524e-02 -1.1396 0.2547994 \n",
"block763 -3.7390e-02 1.8017e-02 -2.0753 0.0382748 * \n",
"block764 -2.9837e-02 3.1862e-02 -0.9365 0.3493176 \n",
"block765 -1.7704e-02 2.9257e-02 -0.6051 0.5452733 \n",
"block766 -2.5655e-02 2.8301e-02 -0.9065 0.3649442 \n",
"block767 -7.9244e-02 1.7730e-02 -4.4696 8.950e-06 ***\n",
"block768 -5.3791e-02 2.6937e-02 -1.9969 0.0461646 * \n",
"block769 -8.1768e-02 4.2064e-02 -1.9439 0.0522536 . \n",
"block770 -4.5909e-02 2.5886e-02 -1.7735 0.0765168 . \n",
"block771 -6.6093e-02 2.7801e-02 -2.3774 0.0176665 * \n",
"block772 -4.3465e-02 2.7664e-02 -1.5712 0.1165286 \n",
"block773 -9.6401e-02 2.0276e-02 -4.7544 2.354e-06 ***\n",
"block775 -1.8165e-02 2.3181e-02 -0.7836 0.4335045 \n",
"block776 -3.0811e-02 2.4212e-02 -1.2725 0.2035495 \n",
"block777 -4.5171e-02 4.3209e-02 -1.0454 0.2961429 \n",
"block778 -5.5857e-02 2.6998e-02 -2.0689 0.0388675 * \n",
"block779 -1.7902e-02 1.5952e-02 -1.1222 0.2620886 \n",
"block780 -1.3062e-01 5.1837e-02 -2.5198 0.0119343 * \n",
"block781 -2.8187e-02 2.0590e-02 -1.3690 0.1713878 \n",
"block783 -3.4372e-02 1.8782e-02 -1.8301 0.0676073 . \n",
"block784 -3.5618e-02 1.6200e-02 -2.1986 0.0281904 * \n",
"block785 -5.1497e-02 2.7376e-02 -1.8811 0.0603188 . \n",
"block786 -3.3812e-03 1.9905e-02 -0.1699 0.8651593 \n",
"block787 -1.9683e-02 2.0510e-02 -0.9597 0.3375034 \n",
"block788 -4.6309e-02 1.5767e-02 -2.9370 0.0034072 ** \n",
"block789 -1.0678e-01 5.0116e-02 -2.1307 0.0334165 * \n",
"block790 5.7197e-03 2.0335e-02 0.2813 0.7785723 \n",
"block791 -5.4000e-02 3.5903e-02 -1.5040 0.1329590 \n",
"block792 -9.8542e-02 2.7670e-02 -3.5614 0.0003904 ***\n",
"block796 1.5852e-02 2.2167e-02 0.7151 0.4747487 \n",
"block796A -4.2200e-02 1.5249e-02 -2.7674 0.0057795 ** \n",
"block797 3.1863e-02 5.3371e-02 0.5970 0.5506671 \n",
"block855 -4.5357e-02 2.4497e-02 -1.8515 0.0644543 . \n",
"block858 -2.3704e-02 1.9621e-02 -1.2081 0.2273579 \n",
"block859 -2.4728e-02 2.0723e-02 -1.1932 0.2331225 \n",
"block860 -4.3623e-02 1.9660e-02 -2.2189 0.0267707 * \n",
"block861 -5.0480e-02 2.6224e-02 -1.9249 0.0545852 . \n",
"block862 -3.7620e-02 2.0526e-02 -1.8328 0.0672037 . \n",
"block863 -2.1326e-02 2.1997e-02 -0.9695 0.3325769 \n",
"block926 -1.2399e-01 1.3569e-02 -9.1378 < 2.2e-16 ***\n",
"block927 1.3981e-02 1.5527e-02 0.9005 0.3681454 \n",
"block928 2.6346e-02 4.0270e-02 0.6542 0.5131448 \n",
"block932 2.7457e-02 1.5200e-02 1.8064 0.0712251 . \n",
"storey_range04 TO 06 2.9445e-02 4.2780e-03 6.8828 1.174e-11 ***\n",
"storey_range07 TO 09 4.8893e-02 4.5930e-03 10.6450 < 2.2e-16 ***\n",
"storey_range10 TO 12 6.2505e-02 4.2784e-03 14.6095 < 2.2e-16 ***\n",
"storey_range13 TO 15 4.5504e-02 1.2687e-02 3.5867 0.0003549 ***\n",
"floor_area_sqm 4.3950e-03 6.3936e-04 6.8741 1.244e-11 ***\n",
"flat_modelImproved -2.3141e-02 3.6219e-02 -0.6389 0.5230617 \n",
"flat_modelMaisonette -3.3558e-02 1.4284e-02 -2.3494 0.0190447 * \n",
"flat_modelModel A 5.5961e-02 1.4111e-02 3.9657 7.964e-05 ***\n",
"flat_modelNew Generation 5.3328e-02 1.6206e-02 3.2906 0.0010427 ** \n",
"---\n",
"Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fit2 <- lm(data = data2, ln_resale_price ~ Treatment + Period2 + Treatment_Period2 + Period3 + Treatment_Period3 + Age + month + flat_type + block + storey_range + floor_area_sqm + flat_model )\n",
"## Robust SE\n",
"coeftest(fit2, vcov = vcovHC(fit2, \"HC1\")) "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 164, 168, 177, 355, 512, 648, 699, 813, 818, 973, 988, 1009\"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 164, 168, 177, 355, 512, 648, 699, 813, 818, 973, 988, 1009\"Warning message in sqrt(crit * p * (1 - hh)/hh):\n",
"\"NaNs produced\"Warning message in sqrt(crit * p * (1 - hh)/hh):\n",
"\"NaNs produced\""
]
},
{
"data": {
"image/png": 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vFQqSTnpMGr0tazrTTO939WVWQezrKSVUWwRlVrvYI3umkMpJbk6z2XXTLk9557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\nAwEkCDIQQIIgAwEkCDLQ/wHEYlnFwLW6jAAAAABJRU5ErkJggg==",
"text/plain": [
"Plot with title \"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"par(mfrow = c(2, 2))\n",
"plot(fit2)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Model 3"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data3 <- read.csv('Model3.csv')\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t1194 obs. of 16 variables:\n",
" $ month : Factor w/ 30 levels \"2009-04\",\"2009-05\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ town : Factor w/ 1 level \"YISHUN\": 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ flat_type : Factor w/ 5 levels \"3 ROOM\",\"4 ROOM\",..: 2 2 3 4 1 3 2 2 2 2 ...\n",
" $ block : Factor w/ 162 levels \"201\",\"202\",\"203\",..: 20 20 21 24 137 143 120 124 84 90 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 1 1 1 1 2 2 3 3 4 4 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 3 4 1 2 3 3 2 2 4 ...\n",
" $ floor_area_sqm : int 108 103 121 146 74 122 84 103 84 84 ...\n",
" $ flat_model : Factor w/ 7 levels \"Apartment\",\"Improved\",..: 4 4 2 3 4 2 7 4 7 7 ...\n",
" $ lease_commence_date: int 1988 1988 1988 1988 1987 1987 1985 1986 1987 1987 ...\n",
" $ Age : int 21 21 21 21 22 22 24 23 22 22 ...\n",
" $ resale_price : int 275000 260000 302000 399000 230000 373000 272000 315000 248500 270000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 1 0 ...\n",
" $ Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Period3 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period3 : int 0 0 0 0 0 0 0 0 0 0 ...\n"
]
}
],
"source": [
"str(data3)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data3 <- data3 %>% mutate(ln_resale_price = log(resale_price))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t1194 obs. of 17 variables:\n",
" $ month : Factor w/ 30 levels \"2009-04\",\"2009-05\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ town : Factor w/ 1 level \"YISHUN\": 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ flat_type : Factor w/ 5 levels \"3 ROOM\",\"4 ROOM\",..: 2 2 3 4 1 3 2 2 2 2 ...\n",
" $ block : Factor w/ 162 levels \"201\",\"202\",\"203\",..: 20 20 21 24 137 143 120 124 84 90 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 1 1 1 1 2 2 3 3 4 4 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 3 4 1 2 3 3 2 2 4 ...\n",
" $ floor_area_sqm : int 108 103 121 146 74 122 84 103 84 84 ...\n",
" $ flat_model : Factor w/ 7 levels \"Apartment\",\"Improved\",..: 4 4 2 3 4 2 7 4 7 7 ...\n",
" $ lease_commence_date: int 1988 1988 1988 1988 1987 1987 1985 1986 1987 1987 ...\n",
" $ Age : int 21 21 21 21 22 22 24 23 22 22 ...\n",
" $ resale_price : int 275000 260000 302000 399000 230000 373000 272000 315000 248500 270000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 1 0 ...\n",
" $ Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Period3 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period2 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ Treatment_Period3 : int 0 0 0 0 0 0 0 0 0 0 ...\n",
" $ ln_resale_price : num 12.5 12.5 12.6 12.9 12.3 ...\n"
]
}
],
"source": [
"str(data3)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"t test of coefficients:\n",
"\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 1.1998e+01 1.9575e-01 61.2894 < 2.2e-16 ***\n",
"Treatment 1.1199e-02 5.6368e-02 0.1987 0.8425570 \n",
"Period2 2.4519e-01 1.9431e-02 12.6188 < 2.2e-16 ***\n",
"Treatment_Period2 -2.7847e-03 6.9278e-03 -0.4020 0.6878009 \n",
"Period3 3.3339e-01 2.0558e-02 16.2171 < 2.2e-16 ***\n",
"Treatment_Period3 1.7379e-02 1.0334e-02 1.6817 0.0929488 . \n",
"Age -5.1446e-04 9.1173e-03 -0.0564 0.9550135 \n",
"month2009-05 -8.6618e-03 1.3016e-02 -0.6655 0.5059044 \n",
"month2009-06 7.9094e-03 1.0259e-02 0.7710 0.4408903 \n",
"month2009-07 2.3362e-02 1.0844e-02 2.1545 0.0314455 * \n",
"month2009-08 2.7818e-02 1.0464e-02 2.6584 0.0079786 ** \n",
"month2009-09 3.7981e-02 1.1883e-02 3.1963 0.0014363 ** \n",
"month2009-10 6.0526e-02 1.0806e-02 5.6011 2.761e-08 ***\n",
"month2009-11 7.3534e-02 9.4599e-03 7.7732 1.915e-14 ***\n",
"month2009-12 9.2436e-02 1.1500e-02 8.0377 2.599e-15 ***\n",
"month2010-01 9.7441e-02 1.2772e-02 7.6291 5.550e-14 ***\n",
"month2010-02 1.1954e-01 1.2687e-02 9.4224 < 2.2e-16 ***\n",
"month2010-03 1.1694e-01 1.2280e-02 9.5233 < 2.2e-16 ***\n",
"month2010-04 -1.2095e-01 1.2757e-02 -9.4807 < 2.2e-16 ***\n",
"month2010-05 -1.0011e-01 1.3085e-02 -7.6508 4.734e-14 ***\n",
"month2010-06 -7.8661e-02 1.2997e-02 -6.0522 2.026e-09 ***\n",
"month2010-07 -6.7818e-02 1.2520e-02 -5.4167 7.627e-08 ***\n",
"month2010-08 -4.3744e-02 1.2208e-02 -3.5831 0.0003561 ***\n",
"month2010-09 -4.2770e-02 1.3536e-02 -3.1597 0.0016271 ** \n",
"month2010-10 -1.2631e-03 1.3186e-02 -0.0958 0.9237071 \n",
"month2010-11 -1.2085e-02 1.3982e-02 -0.8643 0.3876201 \n",
"month2010-12 -1.5099e-03 1.4370e-02 -0.1051 0.9163392 \n",
"month2011-01 8.8653e-03 9.6587e-03 0.9179 0.3589206 \n",
"month2011-02 1.7618e-02 1.0595e-02 1.6629 0.0966446 . \n",
"month2011-04 -6.2247e-02 9.8076e-03 -6.3469 3.343e-10 ***\n",
"month2011-05 -5.4180e-02 1.0864e-02 -4.9873 7.229e-07 ***\n",
"month2011-06 -4.5466e-02 1.2596e-02 -3.6096 0.0003221 ***\n",
"month2011-07 -2.0178e-02 1.0985e-02 -1.8367 0.0665472 . \n",
"month2011-08 -2.1691e-02 1.8071e-02 -1.2003 0.2303111 \n",
"flat_type4 ROOM 1.0687e-01 1.8575e-02 5.7535 1.165e-08 ***\n",
"flat_type5 ROOM 2.3450e-01 3.8498e-02 6.0913 1.602e-09 ***\n",
"flat_typeEXECUTIVE 3.7535e-01 6.2827e-02 5.9743 3.221e-09 ***\n",
"flat_typeMULTI-GENERATION 4.0113e-01 6.4131e-02 6.2548 5.917e-10 ***\n",
"block202 1.0415e-02 1.5166e-02 0.6867 0.4924212 \n",
"block203 4.4674e-02 1.7575e-02 2.5419 0.0111763 * \n",
"block204 -6.8437e-03 1.3365e-02 -0.5120 0.6087361 \n",
"block208 -4.1071e-03 2.4512e-02 -0.1676 0.8669656 \n",
"block302 -6.6347e-02 1.7502e-02 -3.7907 0.0001593 ***\n",
"block303 -7.2510e-02 1.7664e-02 -4.1049 4.378e-05 ***\n",
"block304 -7.9327e-02 1.8928e-02 -4.1910 3.027e-05 ***\n",
"block305 -5.8634e-02 2.8620e-02 -2.0487 0.0407558 * \n",
"block306 -4.7068e-02 1.6530e-02 -2.8474 0.0044995 ** \n",
"block320 -1.0242e-01 2.6851e-02 -3.8144 0.0001450 ***\n",
"block321 -1.1496e-01 1.9052e-02 -6.0337 2.263e-09 ***\n",
"block322 -1.0420e-01 2.7259e-02 -3.8228 0.0001402 ***\n",
"block323 -1.2250e-01 2.2379e-02 -5.4738 5.585e-08 ***\n",
"block324 -1.9660e-01 2.8328e-02 -6.9398 7.096e-12 ***\n",
"block325 -1.9725e-01 2.9003e-02 -6.8011 1.796e-11 ***\n",
"block326 -2.0329e-01 3.2371e-02 -6.2799 5.069e-10 ***\n",
"block327 -1.5212e-01 2.1224e-02 -7.1672 1.497e-12 ***\n",
"block345 -1.7799e-01 2.0205e-02 -8.8094 < 2.2e-16 ***\n",
"block346 -1.6990e-01 1.8604e-02 -9.1322 < 2.2e-16 ***\n",
"block349 -1.7801e-01 2.8562e-02 -6.2323 6.795e-10 ***\n",
"block350 -1.7189e-01 1.7974e-02 -9.5633 < 2.2e-16 ***\n",
"block350A -3.2998e-01 2.8381e-02 -11.6266 < 2.2e-16 ***\n",
"block351 -1.7538e-01 5.7067e-02 -3.0732 0.0021758 ** \n",
"block352 -2.0623e-01 4.0075e-02 -5.1460 3.209e-07 ***\n",
"block353 -1.7642e-01 2.5404e-02 -6.9445 6.876e-12 ***\n",
"block354 -1.5560e-01 2.4519e-02 -6.3463 3.354e-10 ***\n",
"block355 -2.2323e-01 4.6667e-02 -4.7834 1.986e-06 ***\n",
"block356 -1.7885e-01 2.8254e-02 -6.3300 3.714e-10 ***\n",
"block415 -1.7289e-01 5.9107e-02 -2.9250 0.0035234 ** \n",
"block416 -1.7652e-01 5.7426e-02 -3.0739 0.0021706 ** \n",
"block602 -4.1501e-02 5.6839e-02 -0.7301 0.4654770 \n",
"block603 -7.6896e-02 5.6186e-02 -1.3686 0.1714370 \n",
"block604 -6.4113e-02 2.2925e-02 -2.7966 0.0052643 ** \n",
"block605 -9.7948e-02 4.4933e-02 -2.1799 0.0295036 * \n",
"block607 -7.1408e-02 3.1953e-02 -2.2348 0.0256531 * \n",
"block608 -1.3634e-01 6.2853e-02 -2.1692 0.0303050 * \n",
"block609 -4.9167e-02 1.8618e-02 -2.6408 0.0084021 ** \n",
"block610 -6.2306e-02 2.1515e-02 -2.8960 0.0038633 ** \n",
"block611 -1.1390e-01 3.3669e-02 -3.3828 0.0007458 ***\n",
"block612 -4.6116e-02 2.2492e-02 -2.0503 0.0405959 * \n",
"block613 -3.6190e-02 1.8564e-02 -1.9495 0.0515168 . \n",
"block614 -1.1836e-01 2.7526e-02 -4.2998 1.879e-05 ***\n",
"block615 -7.1808e-03 1.9048e-02 -0.3770 0.7062717 \n",
"block616 -9.7079e-02 5.3672e-02 -1.8087 0.0707951 . \n",
"block617 -1.3557e-02 1.8029e-02 -0.7519 0.4522680 \n",
"block618 -6.4503e-02 5.4373e-02 -1.1863 0.2357865 \n",
"block619 -1.4940e-02 1.5808e-02 -0.9451 0.3448314 \n",
"block620 1.3279e-02 1.6269e-02 0.8162 0.4145784 \n",
"block621 -1.8723e-02 1.9713e-02 -0.9498 0.3424508 \n",
"block622 1.8167e-02 1.7198e-02 1.0563 0.2910859 \n",
"block624 -2.3390e-02 2.4407e-02 -0.9583 0.3381339 \n",
"block625 -8.8090e-02 6.6330e-02 -1.3281 0.1844637 \n",
"block626 -5.3777e-03 1.9408e-02 -0.2771 0.7817735 \n",
"block627 6.8040e-03 1.7347e-02 0.3922 0.6949806 \n",
"block628 -3.4492e-02 2.3445e-02 -1.4712 0.1415565 \n",
"block629 -1.8514e-02 1.8399e-02 -1.0062 0.3145525 \n",
"block630 -2.0618e-02 2.3166e-02 -0.8900 0.3736701 \n",
"block631 -6.7236e-02 6.3387e-02 -1.0607 0.2890709 \n",
"block632 -7.8158e-02 2.1145e-02 -3.6963 0.0002308 ***\n",
"block633 -1.0861e-01 3.0424e-02 -3.5700 0.0003741 ***\n",
"block633A -8.8554e-03 5.2889e-02 -0.1674 0.8670629 \n",
"block634 -6.8168e-02 1.8378e-02 -3.7092 0.0002195 ***\n",
"block635 -3.4430e-02 1.6872e-02 -2.0407 0.0415471 * \n",
"block636 -8.1292e-02 2.5514e-02 -3.1862 0.0014869 ** \n",
"block636A -6.0129e-02 5.1772e-02 -1.1614 0.2457461 \n",
"block637 -7.8320e-02 2.8781e-02 -2.7212 0.0066184 ** \n",
"block637A -9.0262e-02 5.3561e-02 -1.6852 0.0922624 . \n",
"block638 -7.4671e-02 1.9212e-02 -3.8866 0.0001084 ***\n",
"block639 -8.5158e-02 5.5847e-02 -1.5248 0.1276177 \n",
"block640 -1.0043e-01 2.0684e-02 -4.8551 1.398e-06 ***\n",
"block641 -6.3276e-02 3.1268e-02 -2.0236 0.0432758 * \n",
"block642 -6.8678e-02 6.4895e-02 -1.0583 0.2901823 \n",
"block643 -1.4415e-01 6.1556e-02 -2.3418 0.0193878 * \n",
"block644 -7.7590e-02 4.8846e-02 -1.5885 0.1125007 \n",
"block645 -6.5692e-02 1.5128e-02 -4.3424 1.555e-05 ***\n",
"block645A -1.0986e-01 1.5861e-02 -6.9267 7.753e-12 ***\n",
"block646 -5.9783e-02 5.5796e-02 -1.0715 0.2842232 \n",
"block647 -7.8215e-02 5.5301e-02 -1.4144 0.1575739 \n",
"block651 -6.9044e-02 5.7231e-02 -1.2064 0.2279494 \n",
"block652 -7.3939e-02 2.5612e-02 -2.8868 0.0039763 ** \n",
"block653 -7.9971e-02 5.5967e-02 -1.4289 0.1533490 \n",
"block654 -1.4429e-01 2.2893e-02 -6.3030 4.393e-10 ***\n",
"block655 -7.8971e-02 5.6310e-02 -1.4024 0.1610968 \n",
"block657 -7.9040e-02 5.6182e-02 -1.4068 0.1597883 \n",
"block658 -8.8215e-02 5.8061e-02 -1.5193 0.1289968 \n",
"block659 -2.9221e-02 5.4510e-02 -0.5361 0.5920326 \n",
"block660 -6.6773e-02 5.6249e-02 -1.1871 0.2354743 \n",
"block661 -6.9641e-02 1.8234e-02 -3.8193 0.0001422 ***\n",
"block662 -7.8710e-02 1.7428e-02 -4.5163 7.051e-06 ***\n",
"block663 -7.0525e-02 2.2028e-02 -3.2016 0.0014105 ** \n",
"block663A -1.9636e-02 5.8928e-02 -0.3332 0.7390456 \n",
"block664 -3.7121e-02 5.8311e-02 -0.6366 0.5245308 \n",
"block664A -6.5131e-02 5.6791e-02 -1.1468 0.2517255 \n",
"block665 -1.0512e-01 5.4531e-02 -1.9277 0.0541846 . \n",
"block666 -8.6809e-02 3.7686e-02 -2.3035 0.0214584 * \n",
"block666A -1.2290e-01 5.5258e-02 -2.2241 0.0263701 * \n",
"block744 9.6177e-03 3.8434e-02 0.2502 0.8024569 \n",
"block745 5.2290e-02 1.8122e-02 2.8854 0.0039945 ** \n",
"block746 2.9143e-02 3.1310e-02 0.9308 0.3521888 \n",
"block747 -4.2069e-02 1.9239e-02 -2.1866 0.0290075 * \n",
"block748 -1.8046e-03 2.2269e-02 -0.0810 0.9354311 \n",
"block749 6.1585e-02 3.5446e-02 1.7374 0.0826249 . \n",
"block750 4.5086e-03 2.1855e-02 0.2063 0.8366043 \n",
"block751 2.3611e-02 2.2270e-02 1.0602 0.2893064 \n",
"block752 -2.1555e-02 2.3575e-02 -0.9143 0.3607709 \n",
"block753 2.9267e-02 1.5580e-02 1.8785 0.0606047 . \n",
"block754 -1.6627e-02 2.4653e-02 -0.6744 0.5001921 \n",
"block755 -4.3642e-02 2.8155e-02 -1.5500 0.1214498 \n",
"block756 -5.3805e-03 2.4343e-02 -0.2210 0.8251174 \n",
"block757 -2.4849e-02 2.1364e-02 -1.1631 0.2450576 \n",
"block758 -4.5743e-02 2.0578e-02 -2.2229 0.0264465 * \n",
"block759 -4.3406e-02 2.5688e-02 -1.6898 0.0913887 . \n",
"block760 -1.1397e-02 2.0107e-02 -0.5668 0.5709581 \n",
"block761 -8.7233e-02 3.3739e-02 -2.5855 0.0098655 ** \n",
"block762 -5.0143e-02 2.6683e-02 -1.8792 0.0605149 . \n",
"block763 -5.3039e-02 2.0956e-02 -2.5310 0.0115277 * \n",
"block764 -4.4002e-02 2.7861e-02 -1.5793 0.1145766 \n",
"block765 -2.4496e-02 2.6947e-02 -0.9091 0.3635343 \n",
"block766 -3.6752e-02 2.4805e-02 -1.4816 0.1387572 \n",
"block767 -5.4997e-02 2.2919e-02 -2.3996 0.0165968 * \n",
"block768 -6.0365e-02 2.4414e-02 -2.4725 0.0135831 * \n",
"block769 -8.3016e-02 4.0520e-02 -2.0488 0.0407464 * \n",
"block770 -4.2955e-02 2.2744e-02 -1.8886 0.0592340 . \n",
"block771 -7.6969e-02 2.5119e-02 -3.0642 0.0022419 ** \n",
"block772 -5.5355e-02 2.5030e-02 -2.2115 0.0272280 * \n",
"block773 -8.8443e-02 1.8525e-02 -4.7744 2.075e-06 ***\n",
"block775 -2.0939e-02 2.1833e-02 -0.9591 0.3377638 \n",
"block776 -2.8973e-02 2.2332e-02 -1.2973 0.1948142 \n",
"block777 -4.0283e-02 4.1191e-02 -0.9780 0.3283316 \n",
"block778 -2.8111e-02 2.4136e-02 -1.1647 0.2444143 \n",
"block779 -1.0295e-02 1.4634e-02 -0.7035 0.4819171 \n",
"block780 -1.1495e-01 5.0459e-02 -2.2780 0.0229388 * \n",
"block781 -5.1186e-03 2.0583e-02 -0.2487 0.8036620 \n",
"block782 -3.1945e-02 3.3563e-02 -0.9518 0.3414450 \n",
"block783 -2.3299e-02 1.6146e-02 -1.4430 0.1493313 \n",
"block784 -3.5988e-02 2.0371e-02 -1.7666 0.0775999 . \n",
"block785 -3.4280e-02 2.3113e-02 -1.4832 0.1383509 \n",
"block786 4.0704e-03 1.9096e-02 0.2132 0.8312503 \n",
"block787 -7.3889e-03 2.1223e-02 -0.3482 0.7277989 \n",
"block788 -3.7644e-02 1.4043e-02 -2.6806 0.0074720 ** \n",
"block789 -8.0829e-02 4.7937e-02 -1.6862 0.0920825 . \n",
"block790 1.3549e-02 1.8391e-02 0.7367 0.4614778 \n",
"block791 -4.5526e-02 3.6308e-02 -1.2539 0.2101868 \n",
"block792 -7.9007e-02 2.4770e-02 -3.1896 0.0014694 ** \n",
"block796 1.8143e-02 2.0769e-02 0.8736 0.3825709 \n",
"block796A -4.6700e-02 1.4684e-02 -3.1804 0.0015166 ** \n",
"block797 3.5237e-02 3.5725e-02 0.9863 0.3242053 \n",
"block855 -2.8745e-02 2.3559e-02 -1.2201 0.2227025 \n",
"block858 -5.9671e-03 1.8461e-02 -0.3232 0.7465862 \n",
"block859 -4.8706e-03 1.8647e-02 -0.2612 0.7939915 \n",
"block860 -2.7183e-02 1.8752e-02 -1.4496 0.1474922 \n",
"block861 -2.6796e-02 2.3749e-02 -1.1283 0.2594768 \n",
"block862 -2.0642e-02 1.8032e-02 -1.1447 0.2525941 \n",
"block863 -4.2148e-03 1.9241e-02 -0.2191 0.8266526 \n",
"block926 -1.2034e-01 1.2069e-02 -9.9711 < 2.2e-16 ***\n",
"block927 1.4861e-02 1.4156e-02 1.0499 0.2940388 \n",
"block928 2.5581e-02 3.9602e-02 0.6459 0.5184620 \n",
"block930 9.5607e-05 1.4597e-02 0.0065 0.9947756 \n",
"block932 2.5714e-02 1.4037e-02 1.8318 0.0672753 . \n",
"storey_range04 TO 06 3.0645e-02 3.7588e-03 8.1528 1.071e-15 ***\n",
"storey_range07 TO 09 4.8143e-02 4.0890e-03 11.7739 < 2.2e-16 ***\n",
"storey_range10 TO 12 6.1860e-02 3.9153e-03 15.7996 < 2.2e-16 ***\n",
"storey_range13 TO 15 4.9808e-02 1.1629e-02 4.2830 2.024e-05 ***\n",
"floor_area_sqm 4.5780e-03 5.8861e-04 7.7776 1.853e-14 ***\n",
"flat_modelImproved -2.3869e-02 2.3819e-02 -1.0021 0.3165427 \n",
"flat_modelMaisonette -1.9978e-02 1.4392e-02 -1.3882 0.1653956 \n",
"flat_modelModel A 5.0419e-02 1.2783e-02 3.9442 8.572e-05 ***\n",
"flat_modelNew Generation 4.3819e-02 1.6647e-02 2.6322 0.0086153 ** \n",
"---\n",
"Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fit3 <- lm(data = data3, ln_resale_price ~ Treatment + Period2 + Treatment_Period2 + Period3 + Treatment_Period3 + Age + month + flat_type + block + storey_range + floor_area_sqm + flat_model )\n",
"## Robust SE\n",
"coeftest(fit3, vcov = vcovHC(fit3, \"HC1\")) "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 164, 177, 355, 648, 699, 813, 988, 1188\"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 164, 177, 355, 648, 699, 813, 988, 1188\"Warning message in sqrt(crit * p * (1 - hh)/hh):\n",
"\"NaNs produced\"Warning message in sqrt(crit * p * (1 - hh)/hh):\n",
"\"NaNs produced\""
]
},
{
"data": {
"image/png": 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u/EkYFUr/FBojyHW5krIMvRvZ4ekSxGqtYUINU8R6quOkPROivuZosWNe/adf4r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\nkIFkMino/5zNGZecYni+AAAAAElFTkSuQmCC",
"text/plain": [
"Plot with title \"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"par(mfrow = c(2, 2))\n",
"plot(fit3)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.4.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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