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vincenttzc/gist:f427d8282e3f1684260c31e798cdf20c

Created November 11, 2017 10:01
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RE Proj - Polyclinic with Age Dummy
Raw
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('Model1polyclinic.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t3011 obs. of 14 variables:\n",
" $ month : Factor w/ 24 levels \"1997-10\",\"1997-11\",..: 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\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ block : Factor w/ 165 levels \"201\",\"202\",\"203\",..: 103 103 5 5 134 55 57 46 51 7 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 10 10 8 8 2 9 9 7 7 5 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 4 4 2 4 3 4 1 3 3 ...\n",
" $ floor_area_sqm : int 67 67 67 67 74 73 73 73 73 74 ...\n",
" $ flat_model : Factor w/ 7 levels \"APARTMENT\",\"IMPROVED\",..: 6 6 6 6 4 4 4 4 4 4 ...\n",
" $ Age : int 13 13 12 12 8 9 9 9 10 9 ...\n",
" $ lease_commence_date: int 1984 1984 1985 1985 1989 1988 1988 1988 1987 1988 ...\n",
" $ resale_price : int 180000 182500 196000 185000 190000 196000 195000 177000 225000 193000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 0 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": {
"collapsed": true
},
"outputs": [],
"source": [
"data1$Age <- as.factor(data1$Age)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t3011 obs. of 15 variables:\n",
" $ month : Factor w/ 24 levels \"1997-10\",\"1997-11\",..: 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\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ block : Factor w/ 165 levels \"201\",\"202\",\"203\",..: 103 103 5 5 134 55 57 46 51 7 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 10 10 8 8 2 9 9 7 7 5 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 4 4 2 4 3 4 1 3 3 ...\n",
" $ floor_area_sqm : int 67 67 67 67 74 73 73 73 73 74 ...\n",
" $ flat_model : Factor w/ 7 levels \"APARTMENT\",\"IMPROVED\",..: 6 6 6 6 4 4 4 4 4 4 ...\n",
" $ Age : Factor w/ 12 levels \"4\",\"5\",\"6\",\"7\",..: 10 10 9 9 5 6 6 6 7 6 ...\n",
" $ lease_commence_date: int 1984 1984 1985 1985 1989 1988 1988 1988 1987 1988 ...\n",
" $ resale_price : int 180000 182500 196000 185000 190000 196000 195000 177000 225000 193000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 0 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 12.1 12.1 12.2 12.1 12.2 ...\n"
]
}
],
"source": [
"str(data1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"t test of coefficients:\n",
"\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 11.54998622 0.04159665 277.6662 < 2.2e-16 ***\n",
"Treatment 0.05703091 0.02726056 2.0921 0.0365224 * \n",
"Period2 -0.15726898 0.01565888 -10.0434 < 2.2e-16 ***\n",
"Treatment_Period2 -0.01059183 0.00468785 -2.2594 0.0239336 * \n",
"Age5 0.00880285 0.01956862 0.4498 0.6528569 \n",
"Age6 0.00968379 0.02292650 0.4224 0.6727772 \n",
"Age7 0.00789965 0.03102892 0.2546 0.7990586 \n",
"Age8 0.00815654 0.03467182 0.2352 0.8140321 \n",
"Age9 -0.01684615 0.03086352 -0.5458 0.5852281 \n",
"Age10 -0.01603820 0.03122717 -0.5136 0.6075739 \n",
"Age11 -0.01766787 0.03268624 -0.5405 0.5888751 \n",
"Age12 -0.01314446 0.03507091 -0.3748 0.7078401 \n",
"Age13 -0.01675360 0.03788370 -0.4422 0.6583513 \n",
"Age14 -0.01233628 0.04158967 -0.2966 0.7667796 \n",
"Age15 -0.01062763 0.04676785 -0.2272 0.8202519 \n",
"month1997-11 -0.01776071 0.00808498 -2.1968 0.0281198 * \n",
"month1997-12 -0.03583773 0.00804375 -4.4553 8.704e-06 ***\n",
"month1998-01 -0.05686023 0.00972432 -5.8472 5.577e-09 ***\n",
"month1998-02 -0.07601190 0.00978488 -7.7683 1.108e-14 ***\n",
"month1998-03 -0.09599506 0.01003785 -9.5633 < 2.2e-16 ***\n",
"month1998-04 -0.13570818 0.00950149 -14.2828 < 2.2e-16 ***\n",
"month1998-05 -0.14512858 0.00987755 -14.6928 < 2.2e-16 ***\n",
"month1998-06 -0.15186124 0.00943735 -16.0915 < 2.2e-16 ***\n",
"month1998-07 -0.16601674 0.00954816 -17.3873 < 2.2e-16 ***\n",
"month1998-08 -0.18140839 0.01001056 -18.1217 < 2.2e-16 ***\n",
"month1998-09 -0.18695751 0.00949955 -19.6807 < 2.2e-16 ***\n",
"month1998-10 -0.03998965 0.00998115 -4.0065 6.323e-05 ***\n",
"month1998-11 -0.05789146 0.00970882 -5.9628 2.791e-09 ***\n",
"month1998-12 -0.06826404 0.00996186 -6.8525 8.887e-12 ***\n",
"month1999-01 -0.07610353 0.00758400 -10.0347 < 2.2e-16 ***\n",
"month1999-02 -0.08324927 0.00771268 -10.7938 < 2.2e-16 ***\n",
"month1999-03 -0.09507830 0.00756495 -12.5683 < 2.2e-16 ***\n",
"month1999-04 -0.09411170 0.00765287 -12.2976 < 2.2e-16 ***\n",
"month1999-05 -0.08877614 0.00795413 -11.1610 < 2.2e-16 ***\n",
"month1999-06 -0.07910913 0.00794373 -9.9587 < 2.2e-16 ***\n",
"month1999-07 -0.07224479 0.00856021 -8.4396 < 2.2e-16 ***\n",
"month1999-08 -0.02749655 0.00954501 -2.8807 0.0039978 ** \n",
"flat_type4 ROOM 0.31526277 0.01417762 22.2366 < 2.2e-16 ***\n",
"flat_type5 ROOM 0.50662742 0.03069032 16.5077 < 2.2e-16 ***\n",
"flat_typeEXECUTIVE 0.80008004 0.04939008 16.1992 < 2.2e-16 ***\n",
"flat_typeMULTI GENERATION 0.78824043 0.04759147 16.5626 < 2.2e-16 ***\n",
"block202 0.02065650 0.01212436 1.7037 0.0885447 . \n",
"block203 0.01597460 0.01120556 1.4256 0.1540965 \n",
"block204 0.01544693 0.01825262 0.8463 0.3974657 \n",
"block208 0.00621569 0.01088337 0.5711 0.5679655 \n",
"block302 -0.00522722 0.01292398 -0.4045 0.6859060 \n",
"block303 -0.02891175 0.01214665 -2.3802 0.0173687 * \n",
"block304 -0.03946707 0.00957125 -4.1235 3.840e-05 ***\n",
"block305 -0.04115550 0.01157043 -3.5570 0.0003814 ***\n",
"block306 -0.04299402 0.01209097 -3.5559 0.0003830 ***\n",
"block320 -0.03602275 0.01000831 -3.5993 0.0003246 ***\n",
"block321 -0.04220369 0.01140126 -3.7017 0.0002183 ***\n",
"block322 0.00890249 0.01708440 0.5211 0.6023462 \n",
"block323 -0.02601819 0.01278299 -2.0354 0.0419070 * \n",
"block324 0.01381367 0.02248754 0.6143 0.5390794 \n",
"block325 0.01139368 0.02001124 0.5694 0.5691548 \n",
"block326 -0.00439931 0.02017435 -0.2181 0.8273946 \n",
"block327 -0.04271405 0.01089848 -3.9193 9.095e-05 ***\n",
"block345 -0.06193875 0.01189822 -5.2057 2.072e-07 ***\n",
"block346 -0.04013924 0.01100325 -3.6479 0.0002692 ***\n",
"block349 -0.05082675 0.01106175 -4.5948 4.523e-06 ***\n",
"block350 -0.04689983 0.01004430 -4.6693 3.165e-06 ***\n",
"block350A 0.04496403 0.02702331 1.6639 0.0962447 . \n",
"block351 0.02057237 0.02249507 0.9145 0.3605182 \n",
"block352 0.02393475 0.02245050 1.0661 0.2864649 \n",
"block353 -0.05365232 0.01415741 -3.7897 0.0001540 ***\n",
"block354 -0.07099162 0.01747310 -4.0629 4.980e-05 ***\n",
"block355 0.02420284 0.02621392 0.9233 0.3559399 \n",
"block355A 0.02349936 0.02786653 0.8433 0.3991425 \n",
"block356 0.03240300 0.02050337 1.5804 0.1141340 \n",
"block415 -0.01886359 0.02481503 -0.7602 0.4472180 \n",
"block416 -0.02518187 0.02519074 -0.9996 0.3175673 \n",
"block602 -0.07234941 0.02830785 -2.5558 0.0106466 * \n",
"block603 -0.08278132 0.02939325 -2.8163 0.0048915 ** \n",
"block604 -0.04313609 0.02166638 -1.9909 0.0465865 * \n",
"block605 -0.05883633 0.03022350 -1.9467 0.0516696 . \n",
"block607 -0.01847161 0.01239278 -1.4905 0.1362018 \n",
"block609 -0.02395671 0.01425172 -1.6810 0.0928803 . \n",
"block610 -0.02104622 0.01319255 -1.5953 0.1107556 \n",
"block611 0.02519048 0.02167560 1.1622 0.2452701 \n",
"block612 -0.00697827 0.01295859 -0.5385 0.5902708 \n",
"block613 -0.02207552 0.01269231 -1.7393 0.0820948 . \n",
"block614 0.02983473 0.01855225 1.6081 0.1079158 \n",
"block615 -0.02446439 0.01223103 -2.0002 0.0455761 * \n",
"block616 -0.00127300 0.02595139 -0.0491 0.9608803 \n",
"block617 -0.01676938 0.01032519 -1.6241 0.1044620 \n",
"block618 -0.00181103 0.02913078 -0.0622 0.9504327 \n",
"block619 0.00574145 0.01561334 0.3677 0.7131043 \n",
"block620 0.00096419 0.01194926 0.0807 0.9356943 \n",
"block621 -0.00140865 0.01134219 -0.1242 0.9011691 \n",
"block622 0.00900445 0.01086057 0.8291 0.4071212 \n",
"block624 -0.00934533 0.01100617 -0.8491 0.3958986 \n",
"block625 0.00366369 0.01134291 0.3230 0.7467244 \n",
"block626 -0.00082324 0.01314183 -0.0626 0.9500556 \n",
"block627 -0.01290454 0.01018959 -1.2664 0.2054599 \n",
"block628 -0.00979117 0.00981593 -0.9975 0.3186190 \n",
"block629 -0.00206777 0.01262482 -0.1638 0.8699114 \n",
"block630 -0.02649808 0.01065660 -2.4865 0.0129570 * \n",
"block631 -0.02118376 0.05403515 -0.3920 0.6950610 \n",
"block632 -0.00605639 0.01296850 -0.4670 0.6405305 \n",
"block633 -0.01028093 0.01939409 -0.5301 0.5960803 \n",
"block633A -0.01028523 0.02453329 -0.4192 0.6750760 \n",
"block634 -0.04803403 0.01103631 -4.3524 1.395e-05 ***\n",
"block635 -0.02136600 0.01360622 -1.5703 0.1164557 \n",
"block636 -0.02652492 0.01121784 -2.3645 0.0181209 * \n",
"block636A -0.07575924 0.02487417 -3.0457 0.0023430 ** \n",
"block637 -0.04753683 0.01297612 -3.6634 0.0002535 ***\n",
"block637A 0.01062871 0.03349889 0.3173 0.7510507 \n",
"block638 -0.02775838 0.01294777 -2.1439 0.0321289 * \n",
"block639 -0.09197287 0.02944446 -3.1236 0.0018048 ** \n",
"block640 -0.04686777 0.01879076 -2.4942 0.0126817 * \n",
"block640A -0.07718552 0.01500374 -5.1444 2.869e-07 ***\n",
"block641 -0.06116146 0.01925804 -3.1759 0.0015100 ** \n",
"block642 -0.08599259 0.02974969 -2.8905 0.0038754 ** \n",
"block643 -0.03225291 0.03538814 -0.9114 0.3621607 \n",
"block644 -0.08293792 0.02853418 -2.9066 0.0036823 ** \n",
"block645 -0.06455871 0.01859929 -3.4710 0.0005263 ***\n",
"block645A -0.02652465 0.02886846 -0.9188 0.3582738 \n",
"block646 -0.07394376 0.02877916 -2.5694 0.0102403 * \n",
"block647 -0.06610306 0.02871076 -2.3024 0.0213868 * \n",
"block650 -0.14645307 0.02397103 -6.1096 1.137e-09 ***\n",
"block651 -0.09073605 0.03036058 -2.9886 0.0028268 ** \n",
"block652 -0.05295068 0.02774761 -1.9083 0.0564550 . \n",
"block653 -0.08484875 0.02901995 -2.9238 0.0034855 ** \n",
"block654 -0.07793971 0.01904077 -4.0933 4.373e-05 ***\n",
"block655 -0.09091792 0.02892566 -3.1432 0.0016888 ** \n",
"block656 -0.12796456 0.04414803 -2.8985 0.0037783 ** \n",
"block657 -0.09436829 0.02892441 -3.2626 0.0011173 ** \n",
"block658 -0.09598678 0.02855919 -3.3610 0.0007871 ***\n",
"block659 -0.06849179 0.03085792 -2.2196 0.0265268 * \n",
"block660 -0.09579127 0.02856759 -3.3531 0.0008096 ***\n",
"block661 -0.09480721 0.02381207 -3.9815 7.023e-05 ***\n",
"block662 -0.04276477 0.01336587 -3.1995 0.0013918 ** \n",
"block663 -0.03212452 0.01275344 -2.5189 0.0118278 * \n",
"block663A 0.01859105 0.05328566 0.3489 0.7271951 \n",
"block664 -0.02858710 0.03048088 -0.9379 0.3483921 \n",
"block664A -0.04219442 0.02635587 -1.6009 0.1095009 \n",
"block666 0.03097747 0.02341831 1.3228 0.1860138 \n",
"block666A -0.04986931 0.02538588 -1.9645 0.0495765 * \n",
"block744 0.01859284 0.02229978 0.8338 0.4044829 \n",
"block745 0.03287762 0.01215617 2.7046 0.0068800 ** \n",
"block746 0.01147451 0.01648215 0.6962 0.4863751 \n",
"block747 -0.06236415 0.02661757 -2.3430 0.0192006 * \n",
"block748 0.03118262 0.02433261 1.2815 0.2001187 \n",
"block749 0.04528159 0.01874746 2.4153 0.0157840 * \n",
"block750 0.02034266 0.01417764 1.4348 0.1514439 \n",
"block751 0.02208952 0.01514541 1.4585 0.1448161 \n",
"block752 0.00835030 0.01535264 0.5439 0.5865539 \n",
"block753 -0.00732029 0.02544166 -0.2877 0.7735758 \n",
"block754 -0.01422348 0.01754788 -0.8106 0.4176915 \n",
"block755 -0.01794620 0.01347943 -1.3314 0.1831734 \n",
"block756 -0.02180897 0.01813791 -1.2024 0.2293113 \n",
"block757 -0.02993287 0.01466845 -2.0406 0.0413813 * \n",
"block758 -0.04227146 0.01404245 -3.0103 0.0026335 ** \n",
"block759 -0.01434262 0.01833285 -0.7823 0.4340780 \n",
"block760 -0.02543303 0.01043857 -2.4364 0.0148943 * \n",
"block761 -0.01676046 0.01528083 -1.0968 0.2728104 \n",
"block762 -0.02701083 0.02007536 -1.3455 0.1785820 \n",
"block763 -0.01164646 0.01921471 -0.6061 0.5444829 \n",
"block764 -0.02173115 0.01881926 -1.1547 0.2482998 \n",
"block765 -0.02544035 0.01768269 -1.4387 0.1503429 \n",
"block766 -0.02951406 0.01708938 -1.7270 0.0842706 . \n",
"block767 0.03791880 0.02128644 1.7814 0.0749622 . \n",
"block768 -0.01068513 0.01957618 -0.5458 0.5852312 \n",
"block769 -0.09267256 0.01267458 -7.3117 3.426e-13 ***\n",
"block770 -0.02763333 0.01255794 -2.2005 0.0278552 * \n",
"block771 -0.03469654 0.01715720 -2.0223 0.0432430 * \n",
"block772 -0.03282031 0.01811815 -1.8115 0.0701767 . \n",
"block773 0.00580625 0.01631820 0.3558 0.7220065 \n",
"block775 -0.05161733 0.01329358 -3.8829 0.0001056 ***\n",
"block776 -0.04630274 0.01652588 -2.8018 0.0051163 ** \n",
"block777 -0.00759266 0.04660342 -0.1629 0.8705926 \n",
"block778 -0.03688743 0.01418204 -2.6010 0.0093443 ** \n",
"block780 -0.03248246 0.02067268 -1.5713 0.1162320 \n",
"block781 -0.05102705 0.01033979 -4.9350 8.484e-07 ***\n",
"block782 -0.04199387 0.01749968 -2.3997 0.0164738 * \n",
"block783 -0.03191467 0.01025703 -3.1115 0.0018802 ** \n",
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"block792 0.03456518 0.02294663 1.5063 0.1320953 \n",
"block796 -0.00598592 0.01179771 -0.5074 0.6119282 \n",
"block796A -0.01821275 0.02952557 -0.6168 0.5373862 \n",
"block797 0.08349872 0.03452654 2.4184 0.0156527 * \n",
"block855 0.00217989 0.01652718 0.1319 0.8950751 \n",
"block858 -0.00055826 0.01075410 -0.0519 0.9586031 \n",
"block859 -0.01944130 0.01273420 -1.5267 0.1269486 \n",
"block860 -0.01300354 0.01197397 -1.0860 0.2775799 \n",
"block861 -0.05053746 0.01381972 -3.6569 0.0002600 ***\n",
"block862 -0.03211711 0.01108099 -2.8984 0.0037799 ** \n",
"block863 -0.00943697 0.00997109 -0.9464 0.3440096 \n",
"block926 0.00675423 0.01577497 0.4282 0.6685667 \n",
"block927 -0.00845495 0.03571177 -0.2368 0.8128639 \n",
"block928 0.00989985 0.01944546 0.5091 0.6107164 \n",
"block930 -0.00770774 0.02371937 -0.3250 0.7452389 \n",
"block931 -0.01728141 0.02087135 -0.8280 0.4077428 \n",
"block932 0.02419614 0.02535190 0.9544 0.3399579 \n",
"storey_range04 TO 06 0.02772853 0.00245796 11.2811 < 2.2e-16 ***\n",
"storey_range07 TO 09 0.03944094 0.00246672 15.9892 < 2.2e-16 ***\n",
"storey_range10 TO 12 0.04740083 0.00264238 17.9387 < 2.2e-16 ***\n",
"storey_range13 TO 15 0.05099154 0.00820407 6.2154 5.882e-10 ***\n",
"floor_area_sqm 0.00547992 0.00046066 11.8958 < 2.2e-16 ***\n",
"flat_modelIMPROVED 0.19442652 0.01715687 11.3323 < 2.2e-16 ***\n",
"flat_modelMAISONETTE -0.01416582 0.00795589 -1.7805 0.0750951 . \n",
"flat_modelMODEL A 0.19120535 0.00963958 19.8354 < 2.2e-16 ***\n",
"flat_modelNEW GENERATION 0.13750267 0.01254134 10.9640 < 2.2e-16 ***\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": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"0.986309439062869"
],
"text/latex": [
"0.986309439062869"
],
"text/markdown": [
"0.986309439062869"
],
"text/plain": [
"[1] 0.9863094"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"summary(fit1)$r.squared "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 619, 2713, 2720\"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 619, 2713, 2720\"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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Q4r+IhV4mECawVjiEd9CcMFmDM+UdxZ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\nBf0PeTg5oglVuHwAAAAASUVORK5CYII=",
"text/plain": [
"Plot with title \"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"par(mfrow = c(2, 2))\n",
"plot(fit1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Model 2"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data2 <- read.csv('Model2polyclinic.csv')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t3011 obs. of 16 variables:\n",
" $ month : Factor w/ 24 levels \"1997-10\",\"1997-11\",..: 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\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ block : Factor w/ 165 levels \"201\",\"202\",\"203\",..: 103 103 5 5 134 55 57 46 51 7 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 10 10 8 8 2 9 9 7 7 5 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 4 4 2 4 3 4 1 3 3 ...\n",
" $ floor_area_sqm : int 67 67 67 67 74 73 73 73 73 74 ...\n",
" $ flat_model : Factor w/ 7 levels \"APARTMENT\",\"IMPROVED\",..: 6 6 6 6 4 4 4 4 4 4 ...\n",
" $ Age : int 13 13 12 12 8 9 9 9 10 9 ...\n",
" $ lease_commence_date: int 1984 1984 1985 1985 1989 1988 1988 1988 1987 1988 ...\n",
" $ resale_price : int 180000 182500 196000 185000 190000 196000 195000 177000 225000 193000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 0 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",
" $ Period3 : 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": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data2 <- data2 %>% mutate(ln_resale_price = log(resale_price))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data2$Age <- as.factor(data2$Age)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t3011 obs. of 17 variables:\n",
" $ month : Factor w/ 24 levels \"1997-10\",\"1997-11\",..: 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\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ block : Factor w/ 165 levels \"201\",\"202\",\"203\",..: 103 103 5 5 134 55 57 46 51 7 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 10 10 8 8 2 9 9 7 7 5 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 4 4 2 4 3 4 1 3 3 ...\n",
" $ floor_area_sqm : int 67 67 67 67 74 73 73 73 73 74 ...\n",
" $ flat_model : Factor w/ 7 levels \"APARTMENT\",\"IMPROVED\",..: 6 6 6 6 4 4 4 4 4 4 ...\n",
" $ Age : Factor w/ 12 levels \"4\",\"5\",\"6\",\"7\",..: 10 10 9 9 5 6 6 6 7 6 ...\n",
" $ lease_commence_date: int 1984 1984 1985 1985 1989 1988 1988 1988 1987 1988 ...\n",
" $ resale_price : int 180000 182500 196000 185000 190000 196000 195000 177000 225000 193000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 0 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",
" $ Period3 : 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 12.1 12.1 12.2 12.1 12.2 ...\n"
]
}
],
"source": [
"str(data2)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"t test of coefficients:\n",
"\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 11.55076566 0.04162219 277.5146 < 2.2e-16 ***\n",
"Treatment 0.05755502 0.02732737 2.1061 0.0352818 * \n",
"Period2 -0.25350825 0.01438175 -17.6271 < 2.2e-16 ***\n",
"Treatment_Period2 -0.00576517 0.00509308 -1.1320 0.2577472 \n",
"Period3 -0.15607503 0.01572392 -9.9260 < 2.2e-16 ***\n",
"Treatment_Period3 -0.01910425 0.00631235 -3.0265 0.0024967 ** \n",
"Age5 0.00850180 0.01959173 0.4339 0.6643595 \n",
"Age6 0.01077272 0.02294796 0.4694 0.6387910 \n",
"Age7 0.01095679 0.03107422 0.3526 0.7244144 \n",
"Age8 0.00909300 0.03470051 0.2620 0.7933081 \n",
"Age9 -0.01596490 0.03084601 -0.5176 0.6048007 \n",
"Age10 -0.01520958 0.03117535 -0.4879 0.6256789 \n",
"Age11 -0.01714853 0.03260922 -0.5259 0.5990134 \n",
"Age12 -0.01200214 0.03499568 -0.3430 0.7316539 \n",
"Age13 -0.01582815 0.03780341 -0.4187 0.6754702 \n",
"Age14 -0.01178992 0.04151715 -0.2840 0.7764489 \n",
"Age15 -0.01028183 0.04670707 -0.2201 0.8257826 \n",
"month1997-11 -0.01785095 0.00808884 -2.2069 0.0274047 * \n",
"month1997-12 -0.03596063 0.00804540 -4.4697 8.144e-06 ***\n",
"month1998-01 -0.05687114 0.00974401 -5.8365 5.943e-09 ***\n",
"month1998-02 -0.07600031 0.00980288 -7.7529 1.248e-14 ***\n",
"month1998-03 -0.09593568 0.01005149 -9.5444 < 2.2e-16 ***\n",
"month1998-04 -0.13573297 0.00951824 -14.2603 < 2.2e-16 ***\n",
"month1998-05 -0.14518810 0.00989411 -14.6742 < 2.2e-16 ***\n",
"month1998-06 -0.15184555 0.00945815 -16.0545 < 2.2e-16 ***\n",
"month1998-07 -0.16606065 0.00956740 -17.3569 < 2.2e-16 ***\n",
"month1998-08 -0.18141325 0.01002686 -18.0927 < 2.2e-16 ***\n",
"month1998-09 -0.18686156 0.00951708 -19.6343 < 2.2e-16 ***\n",
"month1998-10 0.05545954 0.00795000 6.9760 3.774e-12 ***\n",
"month1998-11 0.03794017 0.00744770 5.0942 3.735e-07 ***\n",
"month1998-12 0.02734459 0.00787421 3.4727 0.0005231 ***\n",
"month1999-01 0.01944786 0.00422289 4.6053 4.302e-06 ***\n",
"month1999-02 0.01200973 0.00447811 2.6819 0.0073640 ** \n",
"month1999-04 -0.09407394 0.00763814 -12.3163 < 2.2e-16 ***\n",
"month1999-05 -0.08908722 0.00793684 -11.2245 < 2.2e-16 ***\n",
"month1999-06 -0.07931332 0.00792392 -10.0094 < 2.2e-16 ***\n",
"month1999-07 -0.07225500 0.00854972 -8.4512 < 2.2e-16 ***\n",
"month1999-08 -0.02756908 0.00950748 -2.8997 0.0037640 ** \n",
"flat_type4 ROOM 0.31625799 0.01420007 22.2716 < 2.2e-16 ***\n",
"flat_type5 ROOM 0.50844174 0.03074991 16.5347 < 2.2e-16 ***\n",
"flat_typeEXECUTIVE 0.80369566 0.04938695 16.2734 < 2.2e-16 ***\n",
"flat_typeMULTI GENERATION 0.78926237 0.04762874 16.5711 < 2.2e-16 ***\n",
"block202 0.02050243 0.01211307 1.6926 0.0906454 . \n",
"block203 0.01610871 0.01117904 1.4410 0.1497039 \n",
"block204 0.01556212 0.01823099 0.8536 0.3933951 \n",
"block208 0.00613094 0.01088619 0.5632 0.5733539 \n",
"block302 -0.00547504 0.01297084 -0.4221 0.6729817 \n",
"block303 -0.02894168 0.01213176 -2.3856 0.0171168 * \n",
"block304 -0.03942049 0.00959675 -4.1077 4.111e-05 ***\n",
"block305 -0.04111421 0.01162860 -3.5356 0.0004134 ***\n",
"block306 -0.04312210 0.01211981 -3.5580 0.0003799 ***\n",
"block320 -0.03587559 0.01003627 -3.5746 0.0003567 ***\n",
"block321 -0.04227514 0.01143621 -3.6966 0.0002227 ***\n",
"block322 0.00941721 0.01706161 0.5520 0.5810245 \n",
"block323 -0.02575069 0.01282109 -2.0085 0.0446900 * \n",
"block324 0.01293058 0.02256916 0.5729 0.5667373 \n",
"block325 0.01023149 0.02008279 0.5095 0.6104660 \n",
"block326 -0.00558839 0.02024830 -0.2760 0.7825738 \n",
"block327 -0.04266910 0.01093369 -3.9025 9.743e-05 ***\n",
"block345 -0.06200113 0.01188822 -5.2153 1.969e-07 ***\n",
"block346 -0.04030444 0.01105723 -3.6451 0.0002722 ***\n",
"block349 -0.05088825 0.01105687 -4.6024 4.362e-06 ***\n",
"block350 -0.04689958 0.01007225 -4.6563 3.369e-06 ***\n",
"block350A 0.04413596 0.02701886 1.6335 0.1024713 \n",
"block351 0.01962029 0.02260446 0.8680 0.3854778 \n",
"block352 0.02303368 0.02253074 1.0223 0.3067167 \n",
"block353 -0.05372165 0.01420503 -3.7819 0.0001589 ***\n",
"block354 -0.07118026 0.01752331 -4.0620 4.998e-05 ***\n",
"block355 0.02310857 0.02626290 0.8799 0.3789922 \n",
"block355A 0.02275570 0.02804581 0.8114 0.4172188 \n",
"block356 0.03142200 0.02057243 1.5274 0.1267785 \n",
"block415 -0.01830802 0.02474621 -0.7398 0.4594645 \n",
"block416 -0.02435486 0.02514147 -0.9687 0.3327723 \n",
"block602 -0.07317415 0.02834478 -2.5816 0.0098855 ** \n",
"block603 -0.08447780 0.02934651 -2.8786 0.0040243 ** \n",
"block604 -0.04518615 0.02154106 -2.0977 0.0360233 * \n",
"block605 -0.05829325 0.03037622 -1.9190 0.0550806 . \n",
"block607 -0.01884459 0.01239598 -1.5202 0.1285692 \n",
"block609 -0.02417652 0.01427853 -1.6932 0.0905271 . \n",
"block610 -0.02133800 0.01322227 -1.6138 0.1066852 \n",
"block611 0.02406618 0.02175365 1.1063 0.2686894 \n",
"block612 -0.00646204 0.01301643 -0.4965 0.6196139 \n",
"block613 -0.02233378 0.01269915 -1.7587 0.0787405 . \n",
"block614 0.02889058 0.01856332 1.5563 0.1197436 \n",
"block615 -0.02439434 0.01227243 -1.9877 0.0469383 * \n",
"block616 -0.00314143 0.02569720 -0.1222 0.9027114 \n",
"block617 -0.01660487 0.01036185 -1.6025 0.1091577 \n",
"block618 -0.00358493 0.02921589 -0.1227 0.9023498 \n",
"block619 0.00582001 0.01555939 0.3741 0.7083946 \n",
"block620 0.00083946 0.01199341 0.0700 0.9442038 \n",
"block621 -0.00144280 0.01135990 -0.1270 0.8989430 \n",
"block622 0.00897314 0.01090648 0.8227 0.4107290 \n",
"block624 -0.00946646 0.01101345 -0.8595 0.3901184 \n",
"block625 0.00340092 0.01130696 0.3008 0.7636041 \n",
"block626 -0.00099150 0.01313327 -0.0755 0.9398260 \n",
"block627 -0.01289154 0.01022996 -1.2602 0.2077113 \n",
"block628 -0.00978731 0.00985249 -0.9934 0.3206084 \n",
"block629 -0.00207868 0.01262348 -0.1647 0.8692171 \n",
"block630 -0.02647004 0.01066577 -2.4818 0.0131313 * \n",
"block631 -0.02291145 0.05478435 -0.4182 0.6758245 \n",
"block632 -0.00571411 0.01287491 -0.4438 0.6572091 \n",
"block633 -0.00817028 0.01937139 -0.4218 0.6732250 \n",
"block633A -0.00916438 0.02446910 -0.3745 0.7080393 \n",
"block634 -0.04824860 0.01103396 -4.3727 1.272e-05 ***\n",
"block635 -0.02136353 0.01360187 -1.5706 0.1163811 \n",
"block636 -0.02691241 0.01116657 -2.4101 0.0160128 * \n",
"block636A -0.07616227 0.02457561 -3.0991 0.0019604 ** \n",
"block637 -0.04758425 0.01297400 -3.6677 0.0002493 ***\n",
"block637A 0.00929359 0.03344205 0.2779 0.7811086 \n",
"block638 -0.02776255 0.01294510 -2.1446 0.0320677 * \n",
"block639 -0.09169056 0.02930517 -3.1288 0.0017732 ** \n",
"block640 -0.04878872 0.01906987 -2.5584 0.0105671 * \n",
"block640A -0.07762567 0.01518359 -5.1125 3.394e-07 ***\n",
"block641 -0.06215560 0.01944306 -3.1968 0.0014051 ** \n",
"block642 -0.08747504 0.02978520 -2.9369 0.0033426 ** \n",
"block643 -0.03536806 0.03548780 -0.9966 0.3190324 \n",
"block644 -0.08232697 0.02859846 -2.8787 0.0040232 ** \n",
"block645 -0.06719649 0.01879025 -3.5761 0.0003546 ***\n",
"block645A -0.02977464 0.03039179 -0.9797 0.3273220 \n",
"block646 -0.07386056 0.02883764 -2.5613 0.0104814 * \n",
"block647 -0.06645830 0.02874539 -2.3120 0.0208519 * \n",
"block650 -0.14202297 0.02410085 -5.8929 4.249e-09 ***\n",
"block651 -0.09167440 0.03051226 -3.0045 0.0026837 ** \n",
"block652 -0.05318956 0.02820631 -1.8857 0.0594345 . \n",
"block653 -0.08445382 0.02909442 -2.9027 0.0037279 ** \n",
"block654 -0.07860631 0.01961416 -4.0076 6.293e-05 ***\n",
"block655 -0.09115404 0.02895868 -3.1477 0.0016628 ** \n",
"block656 -0.12588213 0.04427928 -2.8429 0.0045027 ** \n",
"block657 -0.09391497 0.02885892 -3.2543 0.0011503 ** \n",
"block658 -0.09572618 0.02845049 -3.3647 0.0007767 ***\n",
"block659 -0.06517256 0.03097827 -2.1038 0.0354836 * \n",
"block660 -0.09598587 0.02862112 -3.3537 0.0008081 ***\n",
"block661 -0.09677322 0.02385695 -4.0564 5.120e-05 ***\n",
"block662 -0.04278002 0.01342004 -3.1878 0.0014495 ** \n",
"block663 -0.03194133 0.01284512 -2.4867 0.0129530 * \n",
"block663A 0.01518096 0.05347935 0.2839 0.7765342 \n",
"block664 -0.03057723 0.03045063 -1.0042 0.3153895 \n",
"block664A -0.04575262 0.02638209 -1.7342 0.0829873 . \n",
"block666 0.03337595 0.02350928 1.4197 0.1558086 \n",
"block666A -0.05016515 0.02576688 -1.9469 0.0516484 . \n",
"block744 0.01944370 0.02227271 0.8730 0.3827473 \n",
"block745 0.03298401 0.01215459 2.7137 0.0066942 ** \n",
"block746 0.01182864 0.01647969 0.7178 0.4729586 \n",
"block747 -0.06200071 0.02677779 -2.3154 0.0206642 * \n",
"block748 0.03208811 0.02431324 1.3198 0.1870166 \n",
"block749 0.04549095 0.01874047 2.4274 0.0152694 * \n",
"block750 0.02053834 0.01418438 1.4480 0.1477416 \n",
"block751 0.02217823 0.01515446 1.4635 0.1434488 \n",
"block752 0.00864744 0.01539209 0.5618 0.5742902 \n",
"block753 -0.00691482 0.02535814 -0.2727 0.7851144 \n",
"block754 -0.01429807 0.01759826 -0.8125 0.4165906 \n",
"block755 -0.01824440 0.01349813 -1.3516 0.1766047 \n",
"block756 -0.02198748 0.01818415 -1.2092 0.2267050 \n",
"block757 -0.03021578 0.01468104 -2.0582 0.0396681 * \n",
"block758 -0.04228222 0.01404629 -3.0102 0.0026340 ** \n",
"block759 -0.01423897 0.01836652 -0.7753 0.4382472 \n",
"block760 -0.02533986 0.01047484 -2.4191 0.0156216 * \n",
"block761 -0.01686783 0.01535588 -1.0985 0.2720979 \n",
"block762 -0.02723447 0.02011146 -1.3542 0.1757893 \n",
"block763 -0.01146421 0.01928891 -0.5943 0.5523314 \n",
"block764 -0.02197348 0.01885435 -1.1654 0.2439431 \n",
"block765 -0.02567026 0.01773397 -1.4475 0.1478636 \n",
"block766 -0.02968911 0.01713624 -1.7325 0.0832887 . \n",
"block767 0.03895329 0.02125525 1.8326 0.0669618 . \n",
"block768 -0.01061624 0.01963264 -0.5407 0.5887269 \n",
"block769 -0.09249550 0.01258119 -7.3519 2.553e-13 ***\n",
"block770 -0.02782796 0.01258475 -2.2112 0.0270994 * \n",
"block771 -0.03469962 0.01720432 -2.0169 0.0437999 * \n",
"block772 -0.03282535 0.01814480 -1.8091 0.0705462 . \n",
"block773 0.00595211 0.01633066 0.3645 0.7155314 \n",
"block775 -0.05176279 0.01334011 -3.8802 0.0001068 ***\n",
"block776 -0.04638738 0.01657836 -2.7981 0.0051761 ** \n",
"block777 -0.00778241 0.04653637 -0.1672 0.8671989 \n",
"block778 -0.03694861 0.01425148 -2.5926 0.0095745 ** \n",
"block780 -0.03214872 0.02058816 -1.5615 0.1185154 \n",
"block781 -0.05091172 0.01037068 -4.9092 9.670e-07 ***\n",
"block782 -0.04175772 0.01753448 -2.3815 0.0173105 * \n",
"block783 -0.03197184 0.01028077 -3.1099 0.0018905 ** \n",
"block784 -0.03592961 0.01384068 -2.5959 0.0094825 ** \n",
"block785 -0.02590401 0.01420227 -1.8239 0.0682686 . \n",
"block786 -0.00319951 0.01402635 -0.2281 0.8195798 \n",
"block787 -0.00346544 0.01239103 -0.2797 0.7797492 \n",
"block788 -0.01344023 0.01446273 -0.9293 0.3528135 \n",
"block789 -0.00327579 0.03404708 -0.0962 0.9233579 \n",
"block790 -0.01338006 0.01299220 -1.0299 0.3031677 \n",
"block791 -0.01184947 0.02142679 -0.5530 0.5802929 \n",
"block792 0.03396123 0.02296492 1.4788 0.1392981 \n",
"block796 -0.00552326 0.01178086 -0.4688 0.6392254 \n",
"block796A -0.01838277 0.02929781 -0.6274 0.5304185 \n",
"block797 0.08270245 0.03473339 2.3811 0.0173292 * \n",
"block855 0.00240535 0.01654590 0.1454 0.8844255 \n",
"block858 -0.00026112 0.01084911 -0.0241 0.9807999 \n",
"block859 -0.01936985 0.01281411 -1.5116 0.1307477 \n",
"block860 -0.01321122 0.01200356 -1.1006 0.2711619 \n",
"block861 -0.05050409 0.01385319 -3.6457 0.0002716 ***\n",
"block862 -0.03216112 0.01111889 -2.8925 0.0038517 ** \n",
"block863 -0.00915430 0.00999579 -0.9158 0.3598425 \n",
"block926 0.00734794 0.01600722 0.4590 0.6462416 \n",
"block927 -0.00778331 0.03581579 -0.2173 0.8279786 \n",
"block928 0.01271754 0.02033802 0.6253 0.5318196 \n",
"block930 -0.00438140 0.02383830 -0.1838 0.8541863 \n",
"block931 -0.01836187 0.02084938 -0.8807 0.3785603 \n",
"block932 0.02705680 0.02532962 1.0682 0.2855277 \n",
"storey_range04 TO 06 0.02772433 0.00245468 11.2945 < 2.2e-16 ***\n",
"storey_range07 TO 09 0.03952002 0.00246347 16.0424 < 2.2e-16 ***\n",
"storey_range10 TO 12 0.04738757 0.00264077 17.9446 < 2.2e-16 ***\n",
"storey_range13 TO 15 0.05006341 0.00825439 6.0651 1.497e-09 ***\n",
"floor_area_sqm 0.00545017 0.00046124 11.8164 < 2.2e-16 ***\n",
"flat_modelIMPROVED 0.19459073 0.01721770 11.3018 < 2.2e-16 ***\n",
"flat_modelMAISONETTE -0.01380596 0.00796307 -1.7337 0.0830731 . \n",
"flat_modelMODEL A 0.19179460 0.00966224 19.8499 < 2.2e-16 ***\n",
"flat_modelNEW GENERATION 0.13783543 0.01257348 10.9624 < 2.2e-16 ***\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": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"0.986329413972326"
],
"text/latex": [
"0.986329413972326"
],
"text/markdown": [
"0.986329413972326"
],
"text/plain": [
"[1] 0.9863294"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"summary(fit2)$r.squared "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 619, 2713\"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 619, 2713\"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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DN3RjYegMQzNy/DEjvg5B4374lR6jH9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\n",
"text/plain": [
"Plot with title \"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"par(mfrow = c(2, 2))\n",
"plot(fit2)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Model 3"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data3 <- read.csv('Model3polyclinicNEW.csv')\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t3428 obs. of 16 variables:\n",
" $ month : Factor w/ 30 levels \"1997-10\",\"1997-11\",..: 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/ 6 levels \"3 ROOM\",\"4 ROOM\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ block : Factor w/ 166 levels \"201\",\"202\",\"203\",..: 103 103 5 5 134 55 57 46 51 7 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 10 10 8 8 2 9 9 7 7 5 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 4 4 2 4 3 4 1 3 3 ...\n",
" $ floor_area_sqm : int 67 67 67 67 74 73 73 73 73 74 ...\n",
" $ flat_model : Factor w/ 14 levels \"Apartment\",\"APARTMENT\",..: 12 12 12 12 8 8 8 8 8 8 ...\n",
" $ Age : int 13 13 12 12 8 9 9 9 10 9 ...\n",
" $ lease_commence_date: int 1984 1984 1985 1985 1989 1988 1988 1988 1987 1988 ...\n",
" $ resale_price : int 180000 182500 196000 185000 190000 196000 195000 177000 225000 193000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 0 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",
" $ Period3 : 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": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data3 <- data3 %>% mutate(ln_resale_price = log(resale_price))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data3$Age <- as.factor(data3$Age)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t3428 obs. of 17 variables:\n",
" $ month : Factor w/ 30 levels \"1997-10\",\"1997-11\",..: 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/ 6 levels \"3 ROOM\",\"4 ROOM\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ block : Factor w/ 166 levels \"201\",\"202\",\"203\",..: 103 103 5 5 134 55 57 46 51 7 ...\n",
" $ street_name : Factor w/ 10 levels \"YISHUN AVE 11\",..: 10 10 8 8 2 9 9 7 7 5 ...\n",
" $ storey_range : Factor w/ 5 levels \"01 TO 03\",\"04 TO 06\",..: 4 4 4 2 4 3 4 1 3 3 ...\n",
" $ floor_area_sqm : int 67 67 67 67 74 73 73 73 73 74 ...\n",
" $ flat_model : Factor w/ 14 levels \"Apartment\",\"APARTMENT\",..: 12 12 12 12 8 8 8 8 8 8 ...\n",
" $ Age : Factor w/ 13 levels \"4\",\"5\",\"6\",\"7\",..: 10 10 9 9 5 6 6 6 7 6 ...\n",
" $ lease_commence_date: int 1984 1984 1985 1985 1989 1988 1988 1988 1987 1988 ...\n",
" $ resale_price : int 180000 182500 196000 185000 190000 196000 195000 177000 225000 193000 ...\n",
" $ Treatment : int 0 0 0 0 0 0 0 0 0 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",
" $ Period3 : 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 12.1 12.1 12.2 12.1 12.2 ...\n"
]
}
],
"source": [
"str(data3)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"t test of coefficients:\n",
"\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 11.54924207 0.04764048 242.4250 < 2.2e-16 ***\n",
"Treatment 0.05239454 0.04359355 1.2019 0.2294961 \n",
"Period2 -0.15671189 0.01541553 -10.1658 < 2.2e-16 ***\n",
"Treatment_Period2 -0.01063418 0.00466950 -2.2774 0.0228299 * \n",
"Period3 -0.10368197 0.02116457 -4.8988 1.012e-06 ***\n",
"Treatment_Period3 -0.00574489 0.00766620 -0.7494 0.4536836 \n",
"Age5 0.00356046 0.01953631 0.1822 0.8553995 \n",
"Age6 0.01146217 0.02212567 0.5180 0.6044603 \n",
"Age7 0.00771605 0.02811387 0.2745 0.7837512 \n",
"Age8 -0.00276917 0.03578093 -0.0774 0.9383163 \n",
"Age9 -0.02885926 0.04244549 -0.6799 0.4966084 \n",
"Age10 -0.02875732 0.04706422 -0.6110 0.5412277 \n",
"Age11 -0.03109968 0.05150824 -0.6038 0.5460323 \n",
"Age12 -0.02661033 0.05620514 -0.4735 0.6359244 \n",
"Age13 -0.03256239 0.06093162 -0.5344 0.5930959 \n",
"Age14 -0.02936555 0.06578871 -0.4464 0.6553663 \n",
"Age15 -0.02687314 0.07138654 -0.3764 0.7066107 \n",
"Age16 -0.00874000 0.08197553 -0.1066 0.9150993 \n",
"month1997-11 -0.01888361 0.00810898 -2.3287 0.0199354 * \n",
"month1997-12 -0.03808051 0.00800159 -4.7591 2.031e-06 ***\n",
"month1998-01 -0.05676502 0.00962220 -5.8994 4.029e-09 ***\n",
"month1998-02 -0.07680656 0.00961274 -7.9901 1.864e-15 ***\n",
"month1998-03 -0.09623546 0.00995462 -9.6674 < 2.2e-16 ***\n",
"month1998-04 -0.13595268 0.00942846 -14.4194 < 2.2e-16 ***\n",
"month1998-05 -0.14475068 0.00983941 -14.7113 < 2.2e-16 ***\n",
"month1998-06 -0.15184522 0.00935044 -16.2394 < 2.2e-16 ***\n",
"month1998-07 -0.16617353 0.00941966 -17.6411 < 2.2e-16 ***\n",
"month1998-08 -0.18102363 0.00994162 -18.2087 < 2.2e-16 ***\n",
"month1998-09 -0.18657742 0.00947637 -19.6887 < 2.2e-16 ***\n",
"month1998-10 -0.04036585 0.00984086 -4.1019 4.200e-05 ***\n",
"month1998-11 -0.05824393 0.00954236 -6.1037 1.161e-09 ***\n",
"month1998-12 -0.06850399 0.00984264 -6.9599 4.112e-12 ***\n",
"month1999-01 -0.07692513 0.00755654 -10.1799 < 2.2e-16 ***\n",
"month1999-02 -0.08344602 0.00765971 -10.8941 < 2.2e-16 ***\n",
"month1999-03 -0.09469482 0.00752327 -12.5869 < 2.2e-16 ***\n",
"month1999-04 -0.09381332 0.00756337 -12.4036 < 2.2e-16 ***\n",
"month1999-05 -0.08865221 0.00789847 -11.2240 < 2.2e-16 ***\n",
"month1999-06 -0.07935758 0.00788043 -10.0702 < 2.2e-16 ***\n",
"month1999-07 -0.07182063 0.00850120 -8.4483 < 2.2e-16 ***\n",
"month1999-08 -0.02758700 0.00947175 -2.9126 0.0036096 ** \n",
"month1999-10 -0.03113622 0.01247865 -2.4952 0.0126401 * \n",
"month1999-11 -0.03557470 0.01302245 -2.7318 0.0063335 ** \n",
"month1999-12 -0.03279280 0.01331018 -2.4637 0.0138019 * \n",
"month2000-01 -0.01423186 0.00896868 -1.5868 0.1126474 \n",
"month2000-02 -0.01950325 0.00955742 -2.0406 0.0413685 * \n",
"flat_type4 ROOM 0.30698477 0.01343287 22.8533 < 2.2e-16 ***\n",
"flat_type5 ROOM 0.49103994 0.02892295 16.9775 < 2.2e-16 ***\n",
"flat_typeEXECUTIVE 0.70212311 0.05246658 13.3823 < 2.2e-16 ***\n",
"flat_typeMULTI-GENERATION 0.57149177 0.06006645 9.5143 < 2.2e-16 ***\n",
"flat_typeMULTI GENERATION 0.75849172 0.04544883 16.6889 < 2.2e-16 ***\n",
"block202 0.02192360 0.01195906 1.8332 0.0668626 . \n",
"block203 0.01448833 0.01065988 1.3591 0.1741963 \n",
"block204 0.02608716 0.01560209 1.6720 0.0946163 . \n",
"block208 0.00117852 0.01152703 0.1022 0.9185730 \n",
"block302 -0.00066063 0.01240572 -0.0533 0.9575344 \n",
"block303 -0.01907616 0.01265781 -1.5071 0.1318925 \n",
"block304 -0.03676595 0.00945404 -3.8889 0.0001027 ***\n",
"block305 -0.03868118 0.01121758 -3.4483 0.0005715 ***\n",
"block306 -0.03873193 0.01153818 -3.3568 0.0007976 ***\n",
"block320 -0.03056275 0.00978569 -3.1232 0.0018049 ** \n",
"block321 -0.03530715 0.01076618 -3.2795 0.0010512 ** \n",
"block322 0.00944330 0.01554198 0.6076 0.5434964 \n",
"block323 -0.01741242 0.01258539 -1.3835 0.1665951 \n",
"block324 0.01602594 0.02221194 0.7215 0.4706538 \n",
"block325 0.01861298 0.02049606 0.9081 0.3638806 \n",
"block326 0.00281700 0.02031024 0.1387 0.8896972 \n",
"block327 -0.05022203 0.01233349 -4.0720 4.774e-05 ***\n",
"block345 -0.05294021 0.01237992 -4.2763 1.956e-05 ***\n",
"block346 -0.03947739 0.01070322 -3.6884 0.0002295 ***\n",
"block349 -0.04686522 0.01041558 -4.4995 7.053e-06 ***\n",
"block350 -0.04929965 0.01045473 -4.7155 2.514e-06 ***\n",
"block350A 0.05123248 0.02728690 1.8775 0.0605338 . \n",
"block351 0.01945513 0.02275976 0.8548 0.3927236 \n",
"block352 0.03002845 0.02272987 1.3211 0.1865623 \n",
"block353 -0.05296373 0.01347415 -3.9308 8.647e-05 ***\n",
"block354 -0.06707542 0.01640863 -4.0878 4.462e-05 ***\n",
"block355 0.02718382 0.02596680 1.0469 0.2952395 \n",
"block355A 0.02999695 0.02827061 1.0611 0.2887405 \n",
"block356 0.03688686 0.02100446 1.7561 0.0791594 . \n",
"block415 -0.02560929 0.04235522 -0.6046 0.5454669 \n",
"block416 -0.02654271 0.04350628 -0.6101 0.5418460 \n",
"block602 -0.06242506 0.04499356 -1.3874 0.1654096 \n",
"block603 -0.06935330 0.04522360 -1.5336 0.1252357 \n",
"block604 -0.05290616 0.02063446 -2.5640 0.0103935 * \n",
"block605 -0.04028745 0.04094506 -0.9839 0.3252198 \n",
"block607 -0.01475113 0.01229310 -1.2000 0.2302469 \n",
"block609 -0.02095864 0.01335177 -1.5697 0.1165775 \n",
"block610 -0.01796498 0.01292580 -1.3899 0.1646697 \n",
"block611 0.02533421 0.02257329 1.1223 0.2618151 \n",
"block612 -0.00287856 0.01203450 -0.2392 0.8109720 \n",
"block613 -0.01402624 0.01206282 -1.1628 0.2450111 \n",
"block614 0.03529351 0.01940108 1.8192 0.0689817 . \n",
"block615 -0.01036764 0.01264521 -0.8199 0.4123416 \n",
"block616 -0.01709796 0.04331585 -0.3947 0.6930703 \n",
"block617 -0.00636250 0.01051115 -0.6053 0.5450161 \n",
"block618 -0.01413627 0.04657027 -0.3035 0.7614927 \n",
"block619 0.01161143 0.01526233 0.7608 0.4468384 \n",
"block620 0.00720316 0.01168439 0.6165 0.5376235 \n",
"block621 0.00612684 0.01099333 0.5573 0.5773453 \n",
"block622 0.01408646 0.01065693 1.3218 0.1863250 \n",
"block624 -0.00222785 0.01053752 -0.2114 0.8325726 \n",
"block625 0.01315619 0.01153572 1.1405 0.2541740 \n",
"block626 0.00084217 0.01300029 0.0648 0.9483527 \n",
"block627 -0.00964886 0.01025705 -0.9407 0.3469270 \n",
"block628 -0.00740069 0.00980802 -0.7546 0.4505717 \n",
"block629 0.00247909 0.01150209 0.2155 0.8293646 \n",
"block630 -0.02295911 0.01040440 -2.2067 0.0274076 * \n",
"block631 -0.07357807 0.06849649 -1.0742 0.2828196 \n",
"block632 -0.00416797 0.01261012 -0.3305 0.7410245 \n",
"block633 -0.00194013 0.01843980 -0.1052 0.9162125 \n",
"block633A -0.01550360 0.04172914 -0.3715 0.7102680 \n",
"block634 -0.04657095 0.01048444 -4.4419 9.218e-06 ***\n",
"block635 -0.01913860 0.01291762 -1.4816 0.1385480 \n",
"block636 -0.02795977 0.01075343 -2.6001 0.0093630 ** \n",
"block636A -0.09086611 0.04193404 -2.1669 0.0303174 * \n",
"block637 -0.03526909 0.01250575 -2.8202 0.0048284 ** \n",
"block637A -0.00483822 0.04783511 -0.1011 0.9194427 \n",
"block638 -0.03536677 0.01265463 -2.7948 0.0052246 ** \n",
"block639 -0.08208629 0.04555735 -1.8018 0.0716673 . \n",
"block640 -0.05985102 0.01746603 -3.4267 0.0006186 ***\n",
"block640A -0.08768313 0.01466543 -5.9789 2.494e-09 ***\n",
"block641 -0.07082179 0.01919093 -3.6904 0.0002276 ***\n",
"block642 -0.07750267 0.04947661 -1.5665 0.1173419 \n",
"block643 -0.02691425 0.04681227 -0.5749 0.5653722 \n",
"block644 -0.07511748 0.04117425 -1.8244 0.0681878 . \n",
"block645 -0.07250541 0.01790261 -4.0500 5.243e-05 ***\n",
"block645A -0.03760506 0.02926818 -1.2848 0.1989395 \n",
"block646 -0.06736137 0.04520887 -1.4900 0.1363219 \n",
"block647 -0.06137391 0.04522785 -1.3570 0.1748789 \n",
"block650 -0.14892249 0.02262792 -6.5814 5.426e-11 ***\n",
"block651 -0.08380781 0.04598412 -1.8225 0.0684666 . \n",
"block652 -0.06214085 0.02649856 -2.3451 0.0190845 * \n",
"block653 -0.08172748 0.04528111 -1.8049 0.0711856 . \n",
"block654 -0.08576973 0.01873263 -4.5786 4.859e-06 ***\n",
"block655 -0.08416981 0.04479863 -1.8788 0.0603560 . \n",
"block656 -0.11565588 0.05206886 -2.2212 0.0264064 * \n",
"block657 -0.07975562 0.04516064 -1.7660 0.0774840 . \n",
"block658 -0.08805024 0.04531305 -1.9432 0.0520851 . \n",
"block659 -0.06490147 0.04487350 -1.4463 0.1481852 \n",
"block660 -0.08997182 0.04496271 -2.0010 0.0454732 * \n",
"block661 -0.09718598 0.02281981 -4.2588 2.114e-05 ***\n",
"block662 -0.04226133 0.01232073 -3.4301 0.0006110 ***\n",
"block663 -0.02862464 0.01220786 -2.3448 0.0190995 * \n",
"block663A 0.00348855 0.06597383 0.0529 0.9578325 \n",
"block664 -0.03505723 0.04521417 -0.7754 0.4381846 \n",
"block664A -0.04934256 0.04386364 -1.1249 0.2607124 \n",
"block665 -0.02088125 0.04032237 -0.5179 0.6045934 \n",
"block666 0.02495923 0.02329571 1.0714 0.2840665 \n",
"block666A -0.04482011 0.02475171 -1.8108 0.0702673 . \n",
"block744 0.01875119 0.02170896 0.8638 0.3877880 \n",
"block745 0.03552767 0.01171814 3.0319 0.0024501 ** \n",
"block746 0.02972669 0.01649785 1.8019 0.0716627 . \n",
"block747 -0.06263300 0.02664963 -2.3502 0.0188218 * \n",
"block748 0.03008672 0.02364733 1.2723 0.2033556 \n",
"block749 0.04710581 0.01708497 2.7571 0.0058639 ** \n",
"block750 0.02114334 0.01364223 1.5498 0.1212776 \n",
"block751 0.02726859 0.01478074 1.8449 0.0651484 . \n",
"block752 0.00856345 0.01483407 0.5773 0.5637894 \n",
"block753 0.01339620 0.02553462 0.5246 0.5998774 \n",
"block754 -0.00742999 0.01701344 -0.4367 0.6623491 \n",
"block755 -0.01328939 0.01316541 -1.0094 0.3128509 \n",
"block756 -0.01714010 0.01732523 -0.9893 0.3225840 \n",
"block757 -0.02766717 0.01427805 -1.9377 0.0527426 . \n",
"block758 -0.03536087 0.01366308 -2.5881 0.0096955 ** \n",
"block759 -0.01148519 0.01767124 -0.6499 0.5157795 \n",
"block760 -0.02251589 0.01030904 -2.1841 0.0290278 * \n",
"block761 -0.02415403 0.01650622 -1.4633 0.1434757 \n",
"block762 -0.01659926 0.01862623 -0.8912 0.3729013 \n",
"block763 -0.01854381 0.01811548 -1.0236 0.3060806 \n",
"block764 -0.02049420 0.01778471 -1.1523 0.2492635 \n",
"block765 -0.01959876 0.01712088 -1.1447 0.2524070 \n",
"block766 -0.02284274 0.01666843 -1.3704 0.1706521 \n",
"block767 0.03091927 0.01870118 1.6533 0.0983613 . \n",
"block768 -0.00728453 0.01896982 -0.3840 0.7009994 \n",
"block769 -0.07543230 0.01856800 -4.0625 4.972e-05 ***\n",
"block770 -0.02596225 0.01228833 -2.1128 0.0346989 * \n",
"block771 -0.03667816 0.01687918 -2.1730 0.0298547 * \n",
"block772 -0.02656292 0.01761032 -1.5084 0.1315579 \n",
"block773 0.00998365 0.01481914 0.6737 0.5005509 \n",
"block775 -0.04700229 0.01307148 -3.5958 0.0003283 ***\n",
"block776 -0.03679748 0.01632215 -2.2545 0.0242351 * \n",
"block777 -0.01004821 0.04628659 -0.2171 0.8281545 \n",
"block778 -0.03754048 0.01414797 -2.6534 0.0080075 ** \n",
"block779 -0.10775481 0.01186320 -9.0831 < 2.2e-16 ***\n",
"block780 -0.03295302 0.02066740 -1.5944 0.1109352 \n",
"block781 -0.05031938 0.01135894 -4.4299 9.742e-06 ***\n",
"block782 -0.04198802 0.01748582 -2.4013 0.0163954 * \n",
"block783 -0.02734166 0.00964496 -2.8348 0.0046140 ** \n",
"block784 -0.02684573 0.01193244 -2.2498 0.0245287 * \n",
"block785 -0.01860434 0.01342579 -1.3857 0.1659301 \n",
"block786 0.00352455 0.01303342 0.2704 0.7868513 \n",
"block787 0.00433936 0.01213547 0.3576 0.7206841 \n",
"block788 0.00225876 0.01462948 0.1544 0.8773060 \n",
"block789 -0.00701488 0.03338402 -0.2101 0.8335820 \n",
"block790 -0.00425578 0.01221827 -0.3483 0.7276279 \n",
"block791 0.00102306 0.02104353 0.0486 0.9612281 \n",
"block792 0.02980863 0.02201749 1.3539 0.1758762 \n",
"block796 0.00470323 0.01158832 0.4059 0.6848731 \n",
"block796A -0.01723692 0.02990709 -0.5763 0.5644198 \n",
"block797 0.05686302 0.02964181 1.9183 0.0551570 . \n",
"block855 0.01108285 0.01556425 0.7121 0.4764725 \n",
"block858 0.00229937 0.01067217 0.2155 0.8294265 \n",
"block859 -0.01187497 0.01270270 -0.9348 0.3499420 \n",
"block860 -0.01054753 0.01193113 -0.8840 0.3767440 \n",
"block861 -0.04833205 0.01340551 -3.6054 0.0003164 ***\n",
"block862 -0.03137905 0.01093960 -2.8684 0.0041526 ** \n",
"block863 -0.00562095 0.01006016 -0.5587 0.5763827 \n",
"block926 0.01977934 0.01555157 1.2719 0.2035171 \n",
"block927 0.01400251 0.02670850 0.5243 0.6001260 \n",
"block928 0.01285330 0.01624602 0.7912 0.4289055 \n",
"block930 -0.01364554 0.02009340 -0.6791 0.4971200 \n",
"block931 0.00537619 0.02662392 0.2019 0.8399836 \n",
"block932 0.02221754 0.02581411 0.8607 0.3894821 \n",
"storey_range04 TO 06 0.02682528 0.00235829 11.3749 < 2.2e-16 ***\n",
"storey_range07 TO 09 0.03888528 0.00234489 16.5830 < 2.2e-16 ***\n",
"storey_range10 TO 12 0.04760704 0.00253758 18.7608 < 2.2e-16 ***\n",
"storey_range13 TO 15 0.05041349 0.00819854 6.1491 8.757e-10 ***\n",
"floor_area_sqm 0.00571680 0.00043844 13.0390 < 2.2e-16 ***\n",
"flat_modelAPARTMENT 0.06778048 0.01812096 3.7404 0.0001869 ***\n",
"flat_modelImproved 0.13547235 0.01952692 6.9377 4.802e-12 ***\n",
"flat_modelIMPROVED 0.18838237 0.01601731 11.7612 < 2.2e-16 ***\n",
"flat_modelMaisonette -0.06068111 0.02336266 -2.5974 0.0094375 ** \n",
"flat_modelMAISONETTE 0.05672357 0.01869636 3.0339 0.0024332 ** \n",
"flat_modelModel A 0.17214388 0.01266358 13.5936 < 2.2e-16 ***\n",
"flat_modelMODEL A 0.18504960 0.00916966 20.1806 < 2.2e-16 ***\n",
"flat_modelNew Generation 0.13929361 0.02420069 5.7558 9.439e-09 ***\n",
"flat_modelNEW GENERATION 0.13939860 0.01243932 11.2063 < 2.2e-16 ***\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": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"0.985072014143272"
],
"text/latex": [
"0.985072014143272"
],
"text/markdown": [
"0.985072014143272"
],
"text/plain": [
"[1] 0.985072"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"summary(fit3)$r.squared "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 1012, 2713, 3067\"Warning message:\n",
"\"not plotting observations with leverage one:\n",
" 1012, 2713, 3067\"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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ADocKgcUNpYBXeKt4t3xvsrG6rStqzBfg336ztF0uhLah6QGDYu/E4lZMwgf6bW\ngldFxIl4CkhLhy+kxyPiQQ1xDheUoKpyl8T2SEEkVgJJJSQN9mwnORrb2VBNAxLlcMzdABtW\nBLcc0ktZa9y/KUWMyIK0jWJUAGeUnG2DA/48b5AdMvAIxmnEe9puDL+z4buk818UpICilTGa\nHiTpDBQd+LEYW+IfpQL/ppRwS7Qw2kFpEcX4D04NbtmggICNhhvsdSCJo2ArE8HAAGLvlDRU\nvXHnBsldiqO5QEIq8IqTjgX+y/dKeJ0ld4mYQHTxa1CZIUEIIQSsEONFtMq2OIiNWByIbElx\ncHP7hqpVBsr2VsbIiX/5ujpGE4GEHo7zXzAvCx2Pqo75jcjQsKwnDCmcsQhDOyZEiL57ggKe\nBZTy9Z/HJ5ld0iihryqznuhlg7oOlrflMZoJJLHh3ktMKNxgYbad8ZwLii7YK3qxyBYp7cLs\nDcII+jwxwoGkS3gjEitHOzO2K2NRrMlQz9SYroNodAGOZgJJXGVQpUtJVxQsUSaFCPHzZwYY\nBQ0kiQMFPRAHEiSiCZvCL3QPIhIhx8vFW9wzVKG8no+O8PHrhSO/JEgUhoQH47kDbaF8EiRM\n6LAuVuaRAyo5rMSeOydO+QiQBB4AFiZ4BG/eChWGqqlzeNIGzPr1otFN84KU8zJc9ClfY08x\nHFHWxsMDb4NTAZUp04MS5JIO8zzPzw2gNtAZDXwLSZQ30grg6szaYXoX/OyV9qw7AdFVKPIz\ngoR7pJSXsQWfzwOkY2HI4Elb3I5ojEDCrgOQRPfYMHTkMC6KOEb1ZUIplos9XQckd8VgdNeE\nIGE0ii3N8qHAW6lCIocrDx0TM2idDizYMIkT7xFRAhar42M5CsINGipZIL7tVlWChLbJzkV7\n13nJaHQpjk4AqcaSGTcjH+TLe9B4RTtRqzQcSuswO8RiRK9ACIMbYhKOCwImyz5TC0FphM2q\nvfWdnZqet7vLZnV3zRiRiiDBbiPniWKKqr2J8cISPtkapmVBYsljEA836ZHxqFYeVzjCRlV4\nK4tGw0G6cjS6aU6QMnPLYgft1osVq9b9qFCOURwEeyqfJRI7Vt1hCbpY41Oj3I7dwWCQ3NU5\nmhWkjIMRQpm1PwpBdY6azMUS5dgXj2FejCoPEtuFIXc1hh0NUjaI6nT9JaDoghjNC1KmBnle\neu2vyuX65QTFfFQ8PBWcUm694Ihit9u+oe47LZE0DiQX6nCLU2oxkILv0TtER0Gqned8flZq\ngXZZnjZL+WhA9arG1FPFRQ8ez9TcPopGF+VoYZDokCwsQSt/67RVbakOayKQxrYTQXRZjFYD\nKXWWEJIEGdoIeosAACAASURBVF/2XG9vQCMmW7gQgYSbpppxtXXYXkW9nVcJRnctBlKYU0VX\nWMPtWAwDKcG/CJ1V42ruUaNstzG+vMrmaNNqIEUtpNb02UCK2hWndrX1e/o8XLTXGHFW19nQ\nKlobpMy5Vy9Io/ZIh49AOqo0+K86SBiN6s4OL6HFQUp8vg5a7tgjdRxP1LXKvvfXHyZtkF5q\nbwRaHqR0S52ndqN0MNItBNKXaG90ZGQLaXWQij46D07HxtB5KqPhx+31X5Oj9UEq+OhkCV6/\n+k7tVDZ8LQ18RCMvQTra+0JaH6Sd5kcdOTzTTfpBOmzj5vovypGBVK5XLPQ0R+l8H0ljjLX1\nH9HIc5AO9ryYDKRivSNl9DQ7SACR52feBzteTRcGie+RWub1EiBpbZJq6kuOXPy7Zl5BVwZJ\nnNo1klSR2c0N0p2k4w6928AXesji0dFu19OlQRL9tJC07wmz75Ge1TVx5F6aIwOpu9G5T+2e\n0fUXFo1enSMDaQV1HjZ0Vu3qmv/i2YNdrqlXAOm5edgATQiSiEb+cajzwvHoRUBa/RipdfCK\nb4rWRyT/mqd1oNcAaXEdiEgjug6jkfdan+xbWAbSAprpsCGGyHv4rbPjhzOvDKQF1G6oew0N\n1w5bSHOE/72uBoKE85ir+cp2b1K76eGb7huyKYowp3MGknZJKv+sX9B+cfWYntGk1XWSI49/\nG8BA0i7Ji5eWxSfZff3kfQKQEgcM95dhd/Tqmd1wkODDkwotdmr1N5F8J0g6b0OX6+Pvnnnx\nIzv/BJAKK9VTLL/8xxr86SBlotE2tY7+HPUra+we6fHAQBJq97pZI9IWjwpJx+to5KndXs3X\nBKkj1ewxvdJ9Z6MRtP7qb8SCrv8+0mR7pB4H73gfydH3Q9rZI+2WeBldH6TJTu2eA5Kadrqe\ny7Zn6gVAmkvXAskEegZI9oYs1zP2SI0qtP+ac9Qhi0hP1/hTu1YZSMdlIC2gUYaq+HdLNkeV\nej5IL/qbOI9omKGc+PHUrq+mke8j7eFycJJeh8Rx91n+VPHQri+mZ3yyQavFuPaLTPPI29z5\nXMKLWPi4xn/WbtCp3XSfWBiosXeZ+DBkVfr9OhlBjQykBTT4Lou0FF95DfNX6QIgOfg8DN86\nX2q5nPEN2VdayGq0/h5p+8IF0vEXrqHREannNQNJ6hqndgbSqOYNpEpd4w1ZBpLzBpJi88nX\nnHNXM/FhXQIkvitCkC60SZoMJDTwdSx8XKNB6kobejqhrRIkeNeZ5rlAsqQupSuA5OCb8/y3\ncFxnquc6tTOQUroASOwgXDy6zlQbSPNrfZAgBtn7SM/q+lqrlJLWB+kFNBlI11qllHSJU7ur\nazaQTLEMpAU0BUgWhooykBbQDCDZxqgsA2kBTQCSHdXtyEBaQAbS/DKQFpCBNL8MpAU0AUi2\nR9qRgbSAZgDJTu3KMpAW0BQgmYoykBaQgTS/DKQFZCDNLwNpARlI88tAWkAG0vw6FSRTpdRN\nb3OkrnqTjpimaRubeWhLStEEmtY8YWYMpEkaW1MG0sguJ/bWiYe2pAykkV1O7K0TD21JGUgj\nu5zYWyce2pIykEZ2ObG3Tjy0JWUgjexyYm+deGhLykAa2eXE3jrx0JaUgTSyy4m9deKhLSkD\naWSXE3vrxENbUgbSiV2aTNeTgWQyKchAMpkUZCCZTAoykEwmBRlIJpOCDCSTSUEGksmkIAPJ\nZFKQgWQyKchAMpkUZCCZTAoykEwmBRlIJpOC1EBy0QP4PYT9jcnafU1lWlMYGr/4sn8cpdOK\nqXY0moG2nj8dWj3i0IXrH2vMiSbkM43Wzh/a8tK6c00LnjMbSj06XKn9cZC2Nshl42dHW9MY\nWtDWK5KkdeeaFjxpNtQG7+SDA22LNg6DVGrtWGMG0qa5QOItPlEj90i92xBVkDKtHR9aSJSB\ndLQFA0k0FDjYQdfXWPZzrR1pTBxcBL28mBRuXN2Cy+6RCruPA0djcbNaICWf9jZmIOk0YSCJ\nhlRBUln2BzGukXWuKpYaax3aaVrwhLmYG6TIaw+BFFU2kI5L5baVLXjGVAzfIx3xVhddOrSt\nUWmN7a4MJK271rXgKTMxNCK53g5Shu1uTLe1B0iyev/QVpem7y/N0TCQtu9Hzpjpr3gea0y3\nteDU7uDQFlfrH1ottqTQytbSGZ8Rek0HMJmUZSCZTAoykEwmBRlIJpOCDCSTSUEGksmkIAPJ\nZFKQgWQyKchAMpkUZCCZTAoykEwmBRlIJpOCDCSTSUEGksmkIAPJZFKQgWQyKchAMpkUZCCZ\nTAoykEwmBRlIJpOCDCSTSUEGksmkIAPJZFKQgWQyKchAMpkUZCCZTAoykEwmBRlIJpOC1gXJ\n4W9Lh9+Mn7iX3O2te9tTyeEk1Bs0/4cn+N8xqfg1+IVpP0FzjKJHVX+mxUAarta/bZQvGfxl\nrb0mXfDzXM0xih4ZSHNoAEiJvwxXKD7HXM4xih6J9YvyPPpLOyzjoMmmv5HEHAD/2NGr/p2j\nIwI7OmZFzx84zyeGJYK8IF8IOVBylhzrCRuaYwrXdRyZCJBFxYMQJEc/XVS3KqEwBQIHhsfR\nTLiCpcnkjkyfBglL8eKpr3OmcF2/kX+CT+485VJGL/FX45Lr2uJMOfm98ICepmeqCFL6QWJe\nT9K6zpOOSGWQ7g+dgaSpYyBBI87JyUpV5qUMJDVlQOJn4jFIjCIyPt9erWuPsxRyEk3A9iD/\nZoVc2nIgJRdAAOn8KVzXcUoRyXsxv/cHYbzKrGLrGuQkJSNSfEVcT89UEaT0A+dnmcJ1/aYE\nUmr6dkCKZtFUpyRIOftGESm5oj1Ci0/FtRJIp07hun6TBil4IAtt3xhI0WHFwgY5SQEn8Uw4\nH70Wv873SOHc0Iu5PdIEU7iu3wQgOfl2BVwKisObD449piq2R+pQCFLifST5NHofiU8KlXVe\nvu8kSzlqaI4pNMcxmRRkIJlMCjKQTCYFGUgmk4IMJJNJQQaSyaQgA8lkUpCBZDIpyEAymRRk\nIJlMCjKQTCYFGUgmk4IMJJNJQQaSyaQgA8lkUpCBZDIpyEAymRRkIJlMCjKQTCYFGUgmk4IM\nJJNJQQaSyaQgA8lkUpCBZDIpyEAymRRkIJlMCjKQTCYFGUgmk4IMJJNJQQaSyaQgA8lkUpCB\nZDIpyEAymRRkIJlMCjKQTCYFGUgmk4IMJJNJQQaSyaQgA8lkUpCBZDIpyEAymRRkIJlMCjKQ\nTCYFGUgmk4IMJJNJQQaSyaQgA8lkUpCBZDIpyEAymRRkIJlMCjKQTCYFGUgmk4IMJJNJQQaS\nyaQgA8lkUpCBZDIpyEAymRRkIJlMCjKQTCYFGUgmk4IMJJNJQQaSyaQgA8lkUpCBZDIpyEAy\nmRRkIJlMCjKQTCYFGUgmk4IMJJNJQQaSyaQgA8lkUpCBZDIpyEAymRRkIJlMCjKQTCYFGUgm\nk4IMJJNJQQaSyaQgA8lkUpCBZDIpyEAymRRkIJlMCjKQTCYFrQLS329vzn3+kX3dpW8kczml\nn43lX0zuoc+/CiVSD7NlqvpsKX2uFhnq30+Pefz0N1PgMEhvrq38q8mBsiQZSAvoH/f5j/d/\nPrtvmQKHQVpp0s7QZp9v7nN94YYXFEqfq0WG6tw9FP1tnSEDSUtgnyo7GUizSpr026d7gPrY\n13z9yPa+UYEfb+7Tj1y9jxfffuQauGctrJlHSef+fHWfvg+5pcUUgESW/vn5Y+f0E1/5MO03\nT6a8fw+mCWvc9Ne93X++fSyV4gUfzd6tQ16cBvGxzr65r7wjNpCEWwzQIiB9c//8wSefYbf0\n/ZG1P0D4+Pb1sR9m9dhUfKYXEw1wkKjkR6nbQyMpTO3I0j8eJvzBbfdVghRME9W467O7zeyf\nj8aCF8TsYYdUnA3i3uU33tFjIP9k3GKEfcY2r6YPu7x9e+xz/3Of/35smu7e/9/t6e0ebt9+\n3l74+9kl17T/3Kff/venR41MA4/vrKS7lfyxLYKvLTxs+O2FpT/dLvx3MxG3nQApsDLVuOu/\n+zr1/aOt4AU+e9QhFWeDuM+T6OgnDSThFiPsM7R1Rf385xZFbsb4ejs4+us+wSs4Q1/vG6m/\ntxgvXrvr692QPx8rWaYBaAZLPs6oVkrVhwmOv28ccUs7dNCH7W4G+xmkdvjyxpV06Ts5b4kX\nxOxRh1BcDOJXUAsmMe0WA7SSj/z6/ulmMO7Xf35+/8xmaBO9HswjlMs0IF5OOcML626Et08/\ntydo6W8fadXv31AiYzthZarx0D8fydqfW34QviBmDzvE4uwaFgymM+cWA7SWj/yGFGLTZ7SQ\ntJi4/FAapM9BSQMpp7sRfrn7DkX45vfbNvLTn5LtAitjjYd+fSRr3+4hJXghDRIWT4AUTqeB\nFAiNIDn4x739+PmHgUTl60AKGjCQ8noY4esjQZIW+fntDRa4pO0iK0ONTZ/ebv8nXohmTxRn\n17aHcUdhAjJOa/jI1+0o576x+YxbnLuJyHBf4/1kvEf6WmhA7pG+GkhMDyP8fhw2RJYGh328\n8Av9lx4J/xaPPuLLD3YwGvMRdAjF2TWGzdaR2CONPWbYhvCEPo7rYz5+fOwYf32+AfXjdgrz\n7ZEl//K/KSe+Hxl9vJw8bGBncZkG/vBm4NRONvLC2ozwCEnM0m+Pk7ItIrHDsrePufr7+QGS\nmCaqsenD9e/nAdELwextUwvF2TUECTtiA0m4xQj7DG1dTd/g0Oj2BN8GgqtwAvFIkVmS7Vl6\nnHofiTXw5jBE8feRvDeQ7tqM8PcRksjS/8kpuL9nc3/75v6u0NftdIGXoRqgt8e0RC9Es/eY\n2q04u7YNjnUE26W0W4ywz9DW9fT7n4/V5fN/jye34527Wf65fRyZJWE/PnD4hxuM7zN/fKJP\nNsQN/HpDkKikgYQCI3x7rOxk6fvHEehdgu/4gYKPR/88HgXThDVA/23JV/iCmD2aWihO12Bw\n1NHj0yu/Mm4xQOYjpgtr9OcZWE/P6shkeqLuH3L4+zX7rwX0O3xWRybTE7V97O7TfkklGUim\nS+rH/dOZz+vPQDKZFGQgmUwKMpBMJgUZSCaTgvRBcqZKqZu+Z47+Pe32n6X3I5XrTao/Seot\nXlRngkQP/z1vFM/Se39VA2kBzQHS+trlxEC6tgwkJQ0kyUBaQHOAdIXU7kDI2dFFQdp2fw4+\nlL32x7INJDUNI+mqIOF351lHiwI1B0imkq4Jkkv9CKBaSAbS89Qbsi4K0pbZeUjwHPW3oGvM\nAdIlUju/i0onSRcF6fF1/zUAYYZnIHV2fRWQxpB0CZBECkcHDDyVM5CW63qcRpB0BZD4r1jC\nYBTkdfy15WQgKWvA2d0FQHJACcHCN0iY4Xk7tTvU9WVSuyG6AEgytQOGHP5Ylh+UgTS/rggS\npHr00tocTQLSC6kj9bscSH47ZDCQahrm7w48t+uTVUbFQGIPouO6dTUOJC+NVez6WqmdNkmX\nA0m+aeTX3yD5cSAFbw/sdH0tkMqsXA4kpIAfGkRoJN5HavtHi5Pr6SD1/MPP5aRL0qQgOfGm\nEB5oe/5BhdfRHBHJVNKcIHFWDCTbIy2gKUESR23bG0PeQBrR8l4ClwDpQvme4iccpgdpWzDZ\n5ojeKXoVTfU+0qXWsQJJjZAtARL7vNzjKVxVH9KcmgmkS7wxR1IjaUqQ2KqHJ3Ayp3utDG8O\nkP5lF65jeC2S5gQJo404thN7pKtNaEkG0im6AkhQAnfCwe8yeakJnQQkduVFsuoWkuYGiRUL\nCxtIZ3XN/oHXNaRydrcASJnd0MVms6Q5QPpXXruQ7TVIagBJ3XLVDaYziRfJL7yBNFwKJC0B\n0qtrDpCCa5eavRxJ9YQ1gtT5lyyO9v3imgykV8qqq0nqikhKBzcvMheh2i03B0jss3ZNfw6o\n6gP8s2ooSPxTBke0iC2V1WG56UCqqnmND/DXkjQQpJYPRL6OejYYc4DUXtGxx6uCVKtxILno\nQX/fF9KLgCRu8+Ess4N07OhuGEgu+bCz7wvpWSDp72ObUjsXfl/gA/yHSOo7bKh4b9tAyug5\ne6QB+1gO0i6iIUhODgl/1dNUSpNUx1cXSI0NnwzSbCdETzm1G3sgVLND9oKm5Af4p1OSmZNB\nmmaPNGU+3qbZQKpKT/kH+MMhrTUnVSQ1g1T/huwcp3Y9e5LZNAdI/wYX65omF2B7pOmyhKLG\ngKSoYbbk0/SiIGl9RvswSKspRU0NSVcESXjQFSb9xNF375GW1fi/2CdO7Sb+rB1ta8O/Nraq\npgNprdSsTU8EqXW3KAsO/y2ej7HRJmH9SZ8DpFf5vXZ9yd0BkAYdrR4V/dP0K2R1dzUflw/5\nhP6rgJTSMJAGvkdxVC4Y3wuCdI2up9IuSVcESfzd2Cu4goH0ZMXYDAGpcpMk/yHKob6bJBm6\ngid0Hn9batetdpL6jr9dxbmNwzpPBokNrdWRJj2X6HtDdtz7SC+gkX/WpdEn6cezQdqA6PtM\n24wk9YM0afq9gFpJGg0SzuihvhuFwbBnyDV1nhy5+kCqXxcKhfIvTRq9z1LXYUNN/s2OJp6e\n2t2/tS/I1SA9O3LNAZL8nQ2zRu9xKseoAxGpYo+Urtjed5tGg/T008Cuw4YKkCreb0qC5B7m\nfbGYNAykepIO951rID2TMUiVU57yvETVJUCqPhBKtV8G7GEm55rT/bUUgnMaSPstKtTPksRz\nD/jI3X6LybCbDsWzg1TX8l7cLoCE36+qJpJmB6mwqBb8WZ7aHZjydB8L7JHq2y6cBsmu5a8s\n3updGaSQnJVBKrisq90Fsb8L06wMrM27g2PbiZ7Dhvo3ZItrTHqPRG1fGqSWM/AekLQ+vH1o\nW+987b+Q0AEpvN+m+z/ocv1VKxPaxgZehaMWHYlI4/tOgUQz6MqLKTVyYMpd8MVHUd3k0U3V\nKQzm60Pe/GKndmUtB9Lm0VuUqVtxW6dc/FP15Odf27Zd1wCJ75HuKL1ePCqkes0gjfm3LqUi\nEUfkw5WZSwdHQYWgu8Zs8WogPc5yCvd/1VilCRJVesIeKbE1ge816ZpzlQWjLkKSjoF03h5p\nUGr3WJuK7+JqbKKn0Hv2iVDXYUNz3aN9h3VSu/902fCXq1X0kIREotC87Trt1O5Ar7munQeQ\nSnV2jtUX0nvmsdRqIDV4MAWvthO2dLSRbTx3p32iQyZSO57Zw3sQgXGKSw1tEfSHO0RVJC0C\nErN69QTARLcGj/qs8VmaDCRanDZLBQYrgxTmCAsANez32j1xj0Qlm7tzWzjy8ZZnp95sc9s6\nmrEHQvCxEWg8zp7B7JnEULxycPs4jbpAaj8IO9Z3J7hwttSarmfu7Ty8jm0mtbt2Hk4aciCV\n/5mFYyW0hnm++kB6ct9Fa5c+jUefUNbB/qQJ7zls6K+a61q8jwQ7JJcEyfv8vFBVlgLOvmN6\nTz7kWh6kkn+7oMSR2Tpz6ZwMJKLAeYxMtR1tH0eBKhi6HDZ9cMRj9J54JLQESIWZivPz8FMJ\ntOU5FFOOgnQc4q4qA/ZI3m9nOI4R1bYHZXMhQZx2x6QPEm40n/LJBuo8l9gFL8L6Bp/yZ6d2\nMP6+gR8ESQHinjoj9khwM64zaZYRKfEZrCVJWiMiFdtwbGyQwnm8xvhnr/X1VFsvRvWYh3TV\nUz8QYh8RYotpex/irSeXQGhKkIifa4AUfWQIQg9vUnwPQcp3veMX5d0zvxB3kDnFqnTDEz0r\nAxJ8uiHzOaHyfbEUITktk4OUVhdIzWnx0b5lz566d4GPyvnAY1oqU/jIkKOEvWtI/EJ4SdDO\nR1/V3RwgsWuOR6TYG8r3xW/dObnEHMqAT1UPSGxFeVLfsgal2FuWzeYxTOqADqAvn9g7RK9i\nHNRf3F4MksNxsEE2rL+zgUQIsRWCojlfx3ItOvEoWmLW05ogiZ9whLQVwWMgwIK/I1uIOg5z\nlqph0BBCUyRBEh9yYqOvcpyOIDngQIg+IiR3SLCWBEtDZjMaxbDcwjafirukdUCiYBH9jOcs\nVRi/ZT64UB2RyA8c+z94PbwADsiehoGq2OFJikGC6IMxCW4Kb03g9MSxjtY1QBKTRZ5JA4ta\ny4GU69fxT0LsDIVFtsQ/eMc12lEN4CgRHKvIPUmJkCICElqMR+bSrKys9+AnVw9IuTOoVrU0\ngFQE23QeetjY4CFkEWJac/3CGrs/FgFQ9jSPdQU5EJHEqq4GkosF7oEpQkPiuo5KIakLpKri\nW6XSeXJ7c2w0Tr7ooocJkHYCKX//qTgYkdJlA9z2HSkm16oDO2iqTUp+HKd27G0kRIqfi1L8\nTQXrtVUISWNBYqtUrki2N5d45jJF5M41+C48uuhb6T1Sogq5Tb49vpJs4Ye+80IV4O6WSFbR\nSBsSIIURCReVcHXAzdO14lJafSBVZEBBlGjsW/pA0iuSbpIBiXKN6kHv9CMMwD0lZJpHroRP\nVTnZHCDRtQCkYIECezMLXCsuJdUFEjlHueFukGT8wbWd+106K0qD5Gj4eyRFfi2q0aLrpDnE\nWB1/yL7TEVejJgMp3iSJ9xTiIyGFcUyvHpCqJkkfpKBRxycv1a2oIQ/vGrw59Abn4+FRfAqx\nE79r3mXGXDmG5irKIKVTOxaPpN0hDl8MpHf2nWscSOFxWqJIvrJ4HZ9FTp0AIpFmiUTDQ+6R\nqRNeiHpz0fAifxHBypHppL/Vq8cLe/vKdx398pNgp8QMgm8NlJxgza3T00Hy+3NZqC8rJiKB\nw/+C7sJGaCpZK47fTnwv4sK2vjr2IYZcwEzESQFt7+J8osOlAQgQYjDh66EBky1fiKQekMAV\njw6p1ACfGR4M4AlNIm8tgQP3f2gFcw82DOn8/AIwJM8NMNjhsTYEpuBcLjqnb1+Ix/kbeX99\n16lohEfhj4fBXafuuHdROV2aIPVumiv7pkgWlsDrm3MDSJCshwkbuH7gzUmQWB4YhhUKYuAk\n25K7sYMO5agddifsCQvScQDdOUlvlIgKhYb5GlXsOrlH2qzryChoov2ulwQpQ1IfSDrKNMjd\nPCKJ7XrI33Ghl36bC51OXoZYIpsT3s6BdKwK1HfyAt0Jth3dViaAthiqqJqsgUWjBpCCaBRS\nBGvF7nt2QfOLqwek1ruX5cVyli2dPexhGZX46DWlWlguPE3iQ2Acsdl3jBXojKOFQWjzHWgA\n+abmvQx9HDoGVOpQI2eSJvGRFwttP+vnKP6IkGf/kWkyG2Rscnd0a+kZILX1zUGKJ1J4PMUL\n7+k6FcxwJBp25DYY1vApDoiFoYcrcWdjvQv/lXTRbbFP6Qln3jFJk5pAQrBrug4xgrkAo3gW\nqOL6bEzFgDW1ND/9raEiSA8Pj5ZKD/t5dmQHzMFkwmjJ29lNREuvJ0/gfk7tS6QlSLgx2waF\nnVEU4nGLQiiLVfK26w1VVBVIjKQKkOCfUSQwkssKhKkEoKW7XEfv+I3UA5KWITLNyAggSpFP\n02RRaYwODjGip6Lp7RkyGYYXFmaiYfITB+oVhiPp8x4TP2yRk8fvIbV+lw1VlENed0qVu4hA\nSmR2tGWldYnFddFc6TbX0btXAcmljdSs7OTJbCiqwfyQ12DBgadiLtFg4AX8OX4FEUMMj1xF\neJEPQKL8krUKL0a05g3aZWiNGUrdfTIkkSnwEs8ncCTJMLWelEDSUt1yyd2BgxSkRZSV0bIP\n4cWD32+lya0jp+AhI1gt2FPhQWIrBr2yOixaAjss6eNhVxUkHdWAJEzB7I/2Qotklqb1FJM0\nDCTpnOkiUY1EgSAb8z6iiW9iOArku+J4j0JBsI96PEU0OB5bL7TGhhyJkcobgQoPmllm56mq\nPkhazsraye2RaIngUHm44XwesbjSIO3/CZjW+98v7qJnEUnRNgWnATkgpugnRhM+s567L8+2\nGO9yfeVdO/a/R+SwPIwovRogQx7tSExSLyuAlIxIlOYGNCVA8sy4F9J2Q+/6IO3PpYufJDKJ\nTFM8f4BwIY/TKIfbwg1bTT2toZhdAR2e0VUAiTgNIhLGLHYLjq4wQNnakVpG0oaq0wCQtgtp\n5XZO1AZNAN89HR7fLEg+hvE+ICLt2yoHEgYDuTsP6nIkWELG0y5aInm8ktMNd8YdgLZDAgcB\nEsU+LzlCanjsZAi5xJ3Kh82G1KpT00wJIc++hIGxISinNUC1hpqV+4jQCJB2W4yfcCuDt2YC\nkiAB4gjHSKTuEHB49sHI8Wz2PeLo6TEEvQQr4D2evciSOLwNB5uj3C1lLdwTkbgjHxCr/69s\nOEMU75lFe7BMcVI7R3cKSWHsmQakODNiuYAYBmcHXBlyO7EiejpF8uwatSG2UGLHJNwB/J97\npnCd4I4cFqeKLJPkeCfuf9dQT1UtSDwPwC8P0LDWCC610a0JkrDeMQX10cser4UJExVhTg2u\nTgOSEYfFuAgkCQzDRjQtZkuAjt9S+yHsR/oTtYKICmOkTToHSNuFOtE6EoDEFhit0Z1jnneF\niKQ0/niSsJ8txgSLF7olOyZCAFgWQSvkVpvmNnjEasuABC0JBIghVlb6hXP8JnAQbCzsLmtt\nuiBIEIKDFmgdjJtlP5qGd5J1gpDUA5ILfvYqDZJY1SkKeg9oUQLBJo27MLtO7TknX0rThcGK\nv7wNgSEgq8GouRdA/zByDhLli0uBtLdH4gSh/TJpR3gNp7fqZlkjreRpKgXSOwaq9/fMUfgT\nQEKfR+cVaGEe5TyLB4wdGGc0t8Kv2WxzdHhZD4Swn+gYVITxyzNRqOCFPzl8BcDiOWHRieYH\niVmEnle6OSwrfLHZHdOJJknrMSD2iYdsaHoGSGR555hnbqU3ZsBJ+STS6sfjVZCBEW0U6KgF\noo28giDzHmMaPAyahJYpdmJcDSIRC3fcCNL3QsZOUpw17Ajt3dIDZuzJLpNDmo2kEKR8ileZ\nhjT3nXmJORTPnSJ80DO3wuFUby4tyAhBYvPvydEJDoeTzUcVd0JWYhAyxuA7DjM2ggu+9gw1\nWs0gLadiAgAAIABJREFUcTNX97AkSKnDBgZSLrPjIPk2U+WUb4HZiiBB9+T+i8+Z+7mYOIoB\ntOrzpngGBhc8izyeJpsiIBZ0QDNPUkSq4+RN+fB5eOPytVZTdzl0pil6WLVHQmu39UAGrMvs\npgApsUeSEWlvj6SlGpAiM2+uy7YozGXIf+XMyoMGbMczJrxszYt9EsYV8JSgAyCHpXOCOJbE\nBUw5MK7E7BhI0oSH1ApSaPqtyl4Xrq4oq9B5P4oqg+T96SCBUfmyTe7HnDqIJ+Dv8WQHexpo\nnBGT8gGY4OSxBjaL4YeNUrbkMZrhq3SnXt6pGkhHGEy1w67skAOGo/v1O4Aw5ioHpe+LPVIA\nCRzkmNItwDR49GaxvEpyMERwz6bUDcFh80xbHg5N0i08cSCqhzmhZ44jPcqxweUdgN9e8FUw\nVJVtnwoSZdl45ziVrKXADA6tvJiOp3aUaR1SsgEMQWwDyr2Cw0H0pNya+Xs63fPhRe+DorQx\n2ip4Xjws49H/Zan8wrO5lFgtnPyZNVSdbRVBangfCeyCSS/3lwAalnccHOuzlX8f6R0fJUQT\nG62YnSqB5DHiC49KMwFzJvcvsGkBSmh6aREUyMTuwJCknDLsma23FDOhEC+dvNcaP+oxtJJv\nhiDtIuSjZxtFYAAHl1gHfD1ZVPWDfzZIMAPgw/g6TdRjWOiu3nP3z845oYUVxMxjeiZwRpAC\nP6G+PXlF0JfDvRSYMkhbd6zZZWg2rAMKWyiBJDNuT8mxWG0g1eMdIEgaQz5H04GE2RH9h/1F\nGZsX7u1DZw8pIUBk0pas53jUYnCJhjEW8juSqzL1BctyIhCVHehE52oCKW15TAVwYYycyeGk\nrxSXkp+125fM2Z3CHWcaILNihu29dMZg8cvPY5jYwTXvU2iEDUCXUYPygeckxW1QW3jNsxBV\nYcY5QNpN7WLzb6HYk8lxHlkPYoVZiCSxCeoCaVDaEL0ITse6F3OU+MEhiK9G4GACkmgoZjcd\n7DBN3MbHAGYFHNyDaBqGecBQ2UosUvarDaQo+vNlyDvxTAwU57shvastN0wKIOmo3CBZl3ef\niT/xdZ/y/KicnPO4OPSMU5zomg8OnspGEWriixynIp3psfyorCFpg7zgxiFuO1xFgi5k/l4z\nMpyg07QGSJSFYS7k5arGpytO+MTzxNzKcCSKUlzZxkElthGJUl6AgjVkPEKHgo5lvD1iqFwV\njR1HN0iBPeG+ExEYLdCU71KIP09HQXLBz15V1yeXi5GR8+bl07gg5zDBiKwW73l82pMwdPKQ\n51mrzJU4gZX7gjlAaviIkDCsD1eRcFBsrFuB4rB5GyeHJKYukFxr3aa+XW6dCZ094CaMVsU5\nF06ezva8dHsHXuBSWZ7nlzAmYSOpEfBtzDIglWxaEq0xiUE5Rs8uSIBcOLyT1ReRauNvT9/F\n6cc8Koo54e6nYm4LFWiPweKSSM2ooMj3yGkSTcoB4/5pP8ntM27lZqOp6zYTiz0SrXqlzpKk\nRQNyU6R2XL2p3ZD8my7mYtI2kgI0+bnObpiKHhATkCrrGU0896QtEWU49J1R2G6oXeu6/YZ5\npK3rOmeuwDwYkgkfXJjyHIGxiokdfK+6v7HiA+gFCdbSQ+PIX8yCxN2WvGDz1ZyrxzMsPTpT\nRCZmbFENE8jAvyggidiE+yQ22rpFdZi/uOhBoevWf0bBEIK24L9sV/t3Sv7hTufI11gwrhbW\nOXwftSCRxTCehyhQJrM/vzIpLAcwLzqIK/DQ4nB6WR3PCkp8aCx9hlKQSz7MFakGKUy9qTFm\nsExPdcaYIqOTAz4A0m5mHxkyOZLUVf6Kc5TrU2IczR0LS8UphnStHMO2cfNThp10EvI3Rh5C\ni1epRTaSigRsr0CnsiDRrf37gY/8ul27fb07epz7wjqsbtheWGb3q9BGz9f7+4EvaqcHpMZZ\nqkkbZEeO/YScaqtRYqRwfBAkcuT/qWJR1KHsvbADQ2jCMj5VjMLYmD1S1WLXFpEez2+6/T6C\n+7eECubxMJvJG35cy7Q6TBWmLFvvSERq6KbYRzrCb2bfIpPDn9uLPGWQrpqJGZFfM1fOgZfg\nLi4rd0ow9lSk43EJXoPiLuNXu4baqQIxvK7h6j3SbcxZWpzL4aXo2JixuLX3SGKPsdPLTh/p\ndWmDB8gJQIJzn6xPN+yGCyTxthgWPnhJNrbZxidex8QOrS/a3zP9zutpO+JyVCpGg9/tGkAq\ncSTXELAa7Ay9JzukB1NzY9v3CfZKfMCTRSTiBslh2/xyYrfLBS8fRJuKQmVGaS/nRNDjtQP3\ndhhEG2xZK+d1XC2Z2rWJUl4P68fhAWFoP9CSrubaIznnApAwFMATvpaLuaqcZHYUELcTF0w/\ny1zHUYq0jb3IQXLgDft27d0jKXhaB0jpMrhHokIHBrQZ8uzkjqk9tRPGKVfYKRW9IrZElCuB\nhzrho+HcpTYn0cwGu6wdd5DxpFCYpy88YUNuKaZCOb5g7JmyFyQFT0vtkYo2K1uI7OF9fOt7\ndsARUep/8Pb01BWRlG4iWu1gO842k7Q3ouU9NV+YfbfOb3HG8ZvPNM36CLNL2i7xQcucyznx\ntNZQDbZ9KkhkjOgxy+y4HcStV6aijoLRyhHJD5mkrQeMQPicZdaeu2sUMXBa6ie6JU/ZbZiF\nrPTeCozoKCJx3ykZ80R3ibOGalEwdvJ+MbFz4tZZMl81MLf8HmkQSJu55QIFCzcHySen04sX\nSqmY5w92dkk+VYxSNrltC1yHjcs5uBO6NbDAVUEiU2M7YBdc9HgfuNTU3XBtuYF69r9HkuW4\nmWUpsDKvJXYaGEoiTAC2cAprpzp1lahNh5joMZDOEsK4GN2beKIKEh9wY9VC182/shgGQMPg\naw2kEFFECrrND+30xO7wv5Ctv9lyi8EzGa1ZpHdsXeezE/jzToBJT3PiEe+FiCo04SFf2UKq\neCkcY2y53bV1jojUBRLbBvoNnfB6IgU5O9TU6fhvEeIbmQMKQfJOxD2KBp42F3mfTpwHxGVx\nFuv2UuKso9iopwpsQIlQmVpIE5cKhnqqulM7luaydmCPS8tl4FZai/QzpACSjuKFmZmVju8w\nvXY0N+kAUnjWK9l3pkOxylJMCjZL/NDhkKH2y4c30K8+kFhu4AVIMsPN9rgER8dB0rrNsB1u\nX0fPHaZO6TU+duyaea6Vr12EGVWegxQUYTuGbkPV19FMvytSO5EEU/SndiC7SPVEG6k1ONI7\nbDiq/f31NgfbFLHNap1vJ2a6nP3txp7C65Twip1A0CPPX1QMtVNFMSLV7pEgKWd2iza+6Y5W\nAQj0FJCk96SLlKoH33GbnvPu2pyj0hPY2KvaxtzfecxI2ek4TwI9eVqNIWsNnq7y/NSO2GGr\nHjXn5DoCry20NUKp/MriivLHSoQ7UjwcS3l78kLwomczvOME4cV0hTiYEU38ZRGqnOPeVGn9\nVUCCiaJ7jbdIPvIkVqDWHnMkf/k/67I92vv7SGS4onZvtlwgbD/YI9UgsFNM5ziCt+d9gHui\nN0hR0ZSJew/+ac4yeyRxtx5+eg6S47fMMo6G4c6SBZb/Yl/2n1a1rwJ7FSrNxjahruj9+3kb\nO03KtpULdYnCPngEg/RJYLd7wYUbTMDMkPnnbV1eA30eUyNI3FY+uM/HT7rmBT71dMySBe78\nVfNsPf1wKj0Iu3HiUWDgWlyqFWy6UnV3Dzgwi8FWwI3pVVHM04uP2yv/+9ATnaZ9j+TkFAU5\nOn2n1jE4twxpepCy08kzWzDSMcn6j55lmA8XtGjnr52dpZyiqiMvQBKVWbbnBXTsNxS0GKrD\ntv3qAckzq8nAGO0Me7K0RUCKSmBJvo6opKlxA8yvYJkKSlbMY7Xq0pT4aWYPhLT4sAzL+O7/\nIT+VRw5zgFT9zyjkssE3ggFXvu/cYI09UliASoYg6Z8IIT4IFAfJiXU/O4s7/l73alxwu+98\nTY+vygIUgtiv+OBnWvtmXAckGXk3u7CbLDDg6pFiRRtqqUsDJJVVIQ/S9kULN12sJkVHPvM4\nLEb5W5Thvb/z+3Bbekq7vS5DDalT0UyD4Tyd9kNDwVlD2NFawUkltRsFktgj4WRQxufSU1k5\nvx3k0SkB2xrGpQAQxhKPP1gMG4LdXpUVeyISG9oRNe6R2GGQR7NxkJJtFl/ZH90pJL0nQdpO\nzd49Pz8LxA8bxoGUPLXbCtPKHrn6YHkBUuJ1iJWex5/H4suO7VhzRNWY1E5LYWq3a20vbhfW\nlxcCabcmX9wcd/hu7TQgrbQ9E77q3DiO+Nkbxpl8Z5wfdmpFEYrtryB0xYdYfYYaqUaQaIl4\nPPCQvPI9kku4zmogRbxAatcGko6aQKKEMuG9FWCEs72DYBhFMh7E8jdyEliIWVvQHKQ+HsJ6\nVW7XtUfCgR9S5R6JH0vSaR3GXHE4kF48FtsjZUAqvRGLJZ8HEthflmHuHMzn+zGi2gtHHcL9\nYMbmwtECZB7KO3C8A4YqVlFxskqQ8EaD9SO1yUq225vi9NU6ruMgCcMdU64+7U2DLlwYkiK9\nNzDl20AqNg4merS7gUIpDv7weOKNS/ZQkHajPg59v+vH8XfJZol7jheKJEhVZphMyT1SW0RS\nSk0zDRR2pA2u/150ezHX0hVyjUA76WPDbXy8GR8/ht63cJRZs2sNtVdlv3EAvh6kMkeMM4i8\nESGp6T0tQzui4yC54GevmkGiXXqT3kPVFyavyPZJUUWe+9LGwW3weLlx8lSvz1B7VXbd08my\ndV0XbCfnhqWxyaGFnSxGUkDM5CAF8+BcM0WJyY7A2qNsv0s5ukQGSvtu2JHTOcMIkKo2SU6W\nreq6ZFja2botGNHxpWwi1clCICXebZ0QpGCtZkSlfbsNr1YO+eFAoRSMMdPLxs4WmMR2yYVu\nVW+ovUp7TbMZPZTaiY7YUYvH1wrjAANU2GEWpWCpH/zT9kiefBeLbcm8TB4yYBXcvaZQshqO\nKfv6Y6SOkSJ75M4Fz8Hr8K5bDXVURFItSDkj8hrs7uC9gMJce7LCKiSpgZTOfZtViEi8C0qB\nGEmJzCkHwe6VHaHrFw4nyCq8oNwnwWvgjVDF0/laMdkdoSjL2J6xOwwrpG0kKnvPbVCxB4Mm\n1iApmb31gaSjnNuQK0IxBCkdEYJ1f1d+J1GL2mLnUWmcAtcL2vCMKd5GGHNzJmm1fHCvR1QJ\nUkAS2QD8ZgekpbZJ7/iNaT6QpPPJfVIrMRoKc7K9opsTJUIhT/m2dXsMSFRHM/0u7pESXQuD\n7A1zJZDuUgGJucYRJevjYiazJg/pQs6f9z29iz3I8/1OD8mN23YFElJcqz24Dkt7lEFywc+a\nsjuv/btdSVqIKsCmiN1fyVNc8LWIsp9s2BdZQ+t+syBts+EidvOenN0FZz2+VhAaeX6XKxqM\ngkcox07ueMKzldDeIw0Aia6lTMTK033XpDF4//opz0CtA1KYMDjxWkRFdgOVcvag7m6VinDH\n2vQhTJwvfncOtn97njQXSMkDHFEc1o4R+4HTlfklQdOBBHmBcxFHAEvu4KjC22uJCKqUA6KX\nr4VHD6xqsGC7yq3M4D1SY2rng3UiDEiUuzo1V5lHx3/T0zNACuYnKps6+RaHE43aydTASRyL\nJOV2wkLyKbbBkr0ai3YZHHqsKFn3WhIkujNWnNa75lFPL02QtEiKmsFVLHAvnCvhjTUslNy6\noRI7iCqXjSJeMFiPyZ9YskeApKPEYsfuyGOQ5eUd5g7XAyn7a1S7IhLziyMK69NJlnQvPHgA\nzMKFfgeD+Mnu2UXczjaQ3XKp8zvM/Tykc3CIVzxjyBrqiUrtkdiOD29avMpm8mpSBamu+Fap\nUC0PknAvyuc2N4ydN+PTCvIQRgq7M9aZZyhRBX54sj1EiGoSoK49EgzsmOLUjgxBeWqq80ue\nNGxK5HdjQaJv2SLiOa1jzAVgUmDOcn5cebCWiBhZOGh7VOgANz7oXrkw6KMtVIMt2zRgH8tB\nkrcXnQxdNbEDaYFU4wcspuTKpfZIcbN8ww9xoe9YwYsAIR8V62KWwmpxIwQeJRsmCPnhRU1G\nlzfUkCr17YibowWQ1bl0PFIDqSqz7wEJcwVRiHls3aa/GrPagtGuTLxIcUhuw/lDBhfDzlcl\ndtOBJFcGFrVZnar7upIOgFSu2wdSfJ02trxA5MjVRLhafkTZciciVD0eBFEKQ6qTXlYXmOYA\niVI7cXPoGU7WuR5IxX+z1wnSvgc4PMyp3iOxKXC03FFiRwVSXlwpH/ysqkAb7KDXndb4BgK+\nwy2J+90x/V4BpTrlZgRILA67EKTK5WE1lf/t6zCQPLpOQ98YcDwkEN6zpU+A1i0WPkrtoJNA\nz/mSMWdOXsCbYS2mPLDaULvigzui3B5JxNnIG473O6HeowdcPSDVbZIqWkxfoh0sOakXrugD\nL20QBbvGWqUTDi++e34ZdwzUjqMtxcCIpKR01+x28MLzxnSOdv7IRBdI3qmYjpHpxIyA73mK\nQ+B+gZ+GzryPREthcBeguNQhy/XC1BPt58TCACaQW8KUWecA6V92Nby5F1A5IHWCpKMoV2TP\nyTk9UsS3TCkUMpfjUrVFyVn2S0vMYIso8uGtGEHJ1oeMJQJDtRiXjf6AkiCxVPUFpQeSC37u\n1gzaccEkxwkOwoL5HBSJtvltaj5qAH8P40+4A/JB4c3b4qMs+dZLYMKtgbIBa22ukX0nG8C3\nxo42vqbOAynbovgZZTjoop6v54fUzmAukcy0wbYPsiaBtG30kn6epOsISCP2sYiRQnK/TFjb\n/XV1zSBJtzmkEkhbV7SyIk4+cNmE05e5qKaR7amDS+kWPG3nZCs0bE8gsW0fWJJeSxuq0bYa\nISmV2sFdqTjAGiRV/NrH6rbiiHRUmT2Sk+cN8BBXwsjR009LaNSVzsCAQLCWKF6mdl9eBCpP\n6Dh59+kwNSFIENk12l6BpH2ORh42MDfa65uX4pkc21IwRyVnFhf20eDRoiFLpHvhpIjGKNuR\n46DVAEYMgRa93Dl6yMJU2lAN1lcHiV2Dm9ZoewGQ3jOPuTpAYtNfLL/bRfIFsi4t3Q7/59sc\nn2JhBw9GXiVInvUoKnpBjaeyPioMN+IEOrRbYluopFW6fM2pbEGSIPGgerjttUDKxqZ2kIJ0\npKLhXMEKkHiOIvxY7tUyj4t81JbCEMkA9Cy182GNKFJB8MJN0HZjzrGXwcjpEHCaUqld7kyk\nq/G1OFIEKUju88X3+2gCKTomSABVk931nnwzGDa2guxQFEvsk+gw3JE9KUZW2vLZSoPkIE09\n2vrxsPZc5TdL04GUYgiTqRinlr1OgpJ0ZZmZ0QA2HqjPMN9jgYp3Q6NmtyjarbZlrVzws1d7\nc/Ri0gapJrvt3CPxZYo8DzwS4gHzUEzAEvuYAkLVSaDc5/BrOBAeoGi4xBEPnp7dBW6z6hel\nWo0Fab1Q0q+KE7ubxoHkuSf19e1En9xHMVHC7RPtOAICuEfL62HxAB9GadCtaMQ7vEjJG7tx\n3hq/abgph1T1Gyq2m+D4gJKp3SupkqORIB3um7ZHOADhv6m0Lh9mmPvT6dpuYshxCZgIi7Ct\njsPmGbP8yA5vz4lLnYY6WqVQ+NVBei88E2oGyTstjnYjGn5nyzykU3SIxlKuAhU+gCbEIAOR\nd9zZiYnNCqwNViYYOhu0Z0cOiNv++dfoNKoOpFdUNUcdIOFK2zqo1r4JH765FTsjiBCUcGWR\nwMIirpRJYrWcY51QmPGU+3lAxiO1NFIkjy1EdDsHDZWr4XYjnbhFpa4vLGWQ1NQQkWimeT7H\nUr0cO4lQVX3IwMpDZKGtmGiM9mmbqSh7xHL0n7DkrrfvGypToYJRJ37sdP2SqR2X+u9s0FL1\nHskHnkcuDGu+iFLkyNzj80ild1rUJpHEOhY1PYWsx3AFQJjxMSTlDR00VLo8217mS+4VemWQ\nQnA0QQqd85D2UxroI3Y83MHwZT5FBfl67P8uvCG+dfIUZgijMCBtaRs1tw03alGMvTW5GweS\n987VgiRrXT/nqz2ve2jmiERlQpACQnDfEwNBXOQiT+4ZXaFI4xLpJF5GQih/o9SPEHKSnMQN\ndhkqKl/T8FYkKiRuL9PB5Um6FkjRnJGHe88CRjqf4zxI92CUcIRisiAgPcaSQMxBATpjYDQ5\nQmiL6X42kIozm0zt6pteWBFHer+OSxuluuZktwwI7v3pU4cUWF4QlNVjfPyZj/JAPg7PV3ba\nuuEPuuEgl5oApMquDaSSGkDyGA6U1NGQkxTB6h5yFWPh5EYn2utEcNx7w7xt671QiS/sGI3Q\nXA6q0w+e5SnvkWgXVh30m17DthWdYXrtZHptIG2PlHDqA+mx89gWdsyaEl7uhZNHDNApxY6Q\nEFE46A99ysldG0vlHL5Gt4+7J2VDAbJ1hZtfA5PvVL6UBoC0PT/MUg9ISBJmU9x/k5Ei3iFl\nisZHFJiaIYpxDkmHHnBPEmHYHgnc8O4PhwwFVYLEj7/ZkvAiJO0dPfSCdByjvimgtT50TB+E\nDIfHbdL3t4VakJLECl4HXEvFxS4oPBChLIjOIxYHib32IiDtqQ8kndS4CyRaC4NdO9/FhSwI\ntiArLOd1kEAyIvjOJ2gV9z8ec0xM5YgnGrPeJkZB7akdf+2KILWdfN/VCVJ7R/t9uyo8ySnD\nXXsCJBlBKD/zLg5fKZCgFh4WAFFRiudxCEHnDscdAlS/wzjRV4tdX3WP1MFRb2o3ICLVzgp5\np6MrfOcegcSI8vDDhyXCJ1ASf0JHUQmxsQ9LQEDCltg6TpQ1GeqpKqV2Ogn+fEpwpPp77fqq\ntbVYHZNYWUi7ZDwgZ8ctC2ZZuHuJ6BFbJ0CW73IwqklcKNllffFELhx2kwGnBemS6uKo+7BB\nQ40g0dJOT/ARz6RYBAoCBPbCQBI7GqAIm6VwR4EpqInboO2l6HAD93QsGPUb6rnKdX3NWJTT\npUDKrOgOEzDvQxfGgEEBZ6sc7J6EBKsyoAhsONdynBDVMrclnLDGI+cDqWkxwPUk33z6ZSdf\nOM0MNXumaUDamxv0yGB/BP/FW5Qg//IiIkHhILjwYzYvEzoWnpDa1JCxO0celL8vBlueqDlA\n+je8WjmsUuFdkApPh6jnnOGueUDaWZtlWsQDEqVfmHPxAOURI8zS6AudPgpLUeCLdz9EUzRS\n2UKZo8RdJUudouMgFVmZDaRujmYCqaYwbTYoKrB0LAhGjBWKSRi7aFdEoYYdNsghIjvwMg84\n4VCDzVx7nE2WOkUlD68aViarlTaCxSdIefm0UqYACYPjUykf9Kqfo3VAClMtv9mfwg/CATR5\noghPGkSfCe4kSDh1nl6grvcmrhyMmAkWAun9po/L208f/iw3gBZx4RPxEF50spDjxVy6nUOm\nSnNUR9c6IOHmnydyPB45drAAaMg4FfaJzJA/Y/oGk8UP1gk4VzX4ihVSeMLcIInj7+rF30WP\nXfDAxQWTBaSdCg90pf8LIjsHotoioXR/5vDK9qoIMZ5yuXjTA/W5T8gAFWcfdD3rRs3ZBU8i\ns422NampHEg9DaSd3yUK5kG6/3QuaovX0dX5IHFv1GmR4gN5t8jYIJxgNOK5WsJV+XYIWsRB\ni20ShS7MJX0CmwPZRcFSc4B0vIEUSHTX3FdkST55jlZP3pb3fs/ddpQBZsBvWq0uGZTX9A95\naoYYoKUZBlu6x08TIiuz/I+ueBm9WMDgfeHF+I7U/f7CILlUiUy8cekmdIaaA+Z0kFzy4ZEW\nH3VwCyOO6jwtbriT2p7g/7kh0CtAG8scecRz7DuNP+kpupoDpM6PCDE2MhQkXKUZJHfU+AcO\n7Br7nQOk5JbH0StRiMKoJJugbRbjSDQRP9/iYDB+A6mqCURGPHCJAvgsdWrH18pUO2dxtCBI\nLFcLQWKRyHsqEWTOwvjyOu2C0sOTLbG5RNR6b6uoOUDqb8OxpUg+CBZCJypRyGILmuMv4MtB\n6+06uEMaCFJF7toJ0v17uOX0j/DDz8CggovdP0mK81t6KNCIByBzuwDLbL0Dk7w6SOuqIU6N\nA2nIqZ0XQT/a9btkMJFXSiD5MAvMdI9PorBYVa1Vc4D0Kv+MgmkOkEa1iLTw7UrmMeULQRjJ\n5HZVHKVALd+NSGTaZSCN1dETu5sWBImq7jeQChi5PVJV+tUFUvLzSQ2aA6TLKsvLC4BU6Zfb\nXjToLP2eUn2/maSwVIfv6Ho7PUUvANLhA7u7ngFSsH6Tulv0lZt3cns1h0g2Vh4MJJO2R5pR\nOhwtG5FaejjKbNBmc2Our1rQwAiFH9Uodn1RkJR0bZB0g9FpoxgHEjSe7eF8452mYX/WpXUg\nJ7SY6EMzGHXraBqrNY5Eu67cxQzWG6gCLI0p30CQBr2P9IKaA6QLpnYlWKYByUUPjrb4sjKQ\nxkiRo3EgueTDIy2+rmyPNERFVgykC2qcoTLpt9JbFK8kA2kB2ftI8+vUPZKpUo2mVxQbxL+n\n3f4IvWs3WG/S3jnQbLPYn2ZjMw9NW5Wj67wJq6bT1/PaNJA6ZSAZSMMam3lo2jKQDKRhjc08\nNG0ZSAbSsMZmHpq2DCQDaVhjMw/tJC3go0tUM5AmaewsLeCjS1QzkCZp7Cwt4KNLVDOQJmns\nLC3go0tUM5AmaewsLeCjS1S7hDOYTGfLQDKZFGQgmUwKMpBMJgUZSCaTggwkk0lBBpLJpCAD\nyWRSkIFkMinIQDKZFGQgmUwKMpBMJgUZSCaTggwkk0lBaiC56IH3jb9jL2pM1u5rKtOawtD4\nxeV+CXDn+KNqdfXCcr29Da0mh9U6pVoOgN0K1z/WmBNNyGcarZ0/tNPUOX6VatJH2nqrqdg9\nN6HrNk2p0vzjn1IVf1O1O4I4qo2A9raYbE1jaEFbK5HUaVqVaoGPDO6tYW6cGGJ1NVHjqBzr\n+vCqL9o4DFKptWONvTpIPvGsqtoBkDqrVQay80HykVvdH3VuQ1RByrR2fGhHrH62TgXJVdqV\nKUodAAADRUlEQVQqHGTljCVSj9qpmRak7g50l/1ca0caEwcXQS/zSw+kyrXeS9v3gFRLRBT/\neg4b5gKpv4fhICWf9jZmILX21p3adcW/5SNSfw+6+dMgxjWyzpOkBlI7EfW5gMogm+bmuiBF\nXnsIpKiygZR4Vl2trk5Yrf4veBlI/aMJ2ojXviMg6bTGVtSXB6nJP48FspcGqSk/TTTmomt9\no1Vt7QGSrN4/tLPUOf64Wkdv1TXVBlk70COuOwqk7fuRM2bKAY41ptsaZSjs6bofEWocP6/W\n8FdWZW/1EeL4IJvm5siUruYAJtOUMpBMJgUZSCaTggwkk0lBBpLJpCADyWRSkIFkMinIQDKZ\nFGQgmUwKMpBMJgUZSCaTggwkk0lBBpLJpCADyWRSkIFkMinIQDKZFGQgmUwKMpBMJgUZSCaT\nggwkk0lBBpLJpCADyWRSkIFkMinIQDKZFGQgmUwKMpBMJgUZSCaTggwkk0lB64JEf2gHfjN+\n4l5yt7fuba+qq1t83fur+jMtBtIsurrF170/A2kpXd3i696f+FtS7A8q4h+2uT1wvCz8UZ+t\nCr2Cf1Bnvb9ztIz4H/Hif6YKn388xAmkP1Ek5mdmzT/CnOI/yuZ89CAEydFPF9V1slmTpqLp\nEnPCJkvMohNzM7OmH2BW8k/wSYPLgOOTkxeXXNcWC8jJBy45J+HLiZmcVbOPL690RCqDdH/o\nDKQTVAvS/YkzkJ6nDEj8TDwGiVFEE8W3V+vaY3IRSMG7FsGMJRa6+r9Ve6JmH19epYjkQ5C8\ni+JVJhCta5C55aIHYk68nLH1EoU1RplSU2q3DxKPXSZ9JXiJ5yT51FK7sUqDFDyQhbZvDKTo\nsGJhg8yteLokU+IaTUuUacyq6QeYVTAzjr0NsV2m95GwuNsuOvaYqiyQii8rttNx8l0Jeh8J\nC9K0yAoTa/4Rml5Zy/jnMgM1vZgWS7TXGanpxbRWor3QUE2meWUgmUwKMpBMJgUZSCaTggwk\nk0lBBpLJpCADyWRSkIFkMinIQDKZFGQgmUwKMpBMJgUZSCaTggwkk0lBBpLJpCADyWRSkIFk\nMinIQDKZFGQgmUwKMpBMJgUZSCaTggwkk0lBBpLJpKD/ATuDeU6/oNS+AAAAAElFTkSuQmCC\n",
"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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