*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sat, 18 Dec 2010 14:02:17 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd.htm/, Retrieved Sat, 18 Dec 2010 15:01:23 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

15 2 0 0 9 12 1 1 2 9 9 1 2 1 9 10 0 0 0 9 13 0 0 0 9 16 1 0 0 9 14 0 0 0 9 16 1 1 0 9 10 0 0 0 9 8 2 0 1 10 12 1 0 0 10 15 0 0 0 10 14 0 1 0 10 14 1 1 2 10 12 1 2 1 10 12 0 0 0 10 10 0 0 0 10 4 0 0 0 10 14 0 1 0 10 15 0 0 0 10 16 0 0 0 10 12 0 1 0 10 12 0 0 0 10 12 0 0 1 10 12 1 0 1 9 12 0 0 0 9 11 3 2 1 9 11 1 0 0 9 11 1 1 0 9 11 1 1 0 9 11 3 1 1 9 11 0 0 0 9 15 0 0 0 9 15 0 0 0 9 9 0 0 0 9 16 0 0 0 9 13 0 2 1 9 9 1 0 0 9 16 0 0 0 9 12 0 0 0 9 15 0 0 2 9 5 0 0 0 9 11 2 2 0 9 17 2 2 0 9 9 0 1 1 9 13 0 0 0 9 16 0 0 0 10 16 0 0 0 10 14 2 0 2 10 16 1 0 0 10 11 0 0 0 10 11 0 0 0 10 11 0 0 0 10 12 0 0 0 10 12 1 1 1 10 12 0 0 0 10 14 0 0 0 10 10 2 0 0 10 9 0 2 0 10 12 0 0 1 10 10 0 0 0 10 14 0 0 0 10 8 0 0 0 10 16 1 0 0 10 14 1 0 0 10 14 0 0 0 10 12 0 0 0 10 14 1 0 0 10 7 1 1 1 10 19 0 0 0 10 15 0 0 0 10 8 0 0 0 10 10 0 0 0 10 13 0 0 0 10 13 0 0 0 9 10 0 0 0 9 12 0 0 0 9 15 0 1 1 9 7 1 0 0 9 14 0 0 0 9 10 0 0 0 9 6 0 3 0 9 11 2 0 0 9 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 18 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Popularity[t] = + 11.3072637357884 + 0.102867075172941B[t] -0.120473088007881`2B`[t] + 0.193536916246142`3B`[t] + 0.0624951815495425Month[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 11.3072637357884 1.949911 5.7989 0 0 B 0.102867075172941 0.21303 0.4829 0.629528 0.314764 `2B` -0.120473088007881 0.22302 -0.5402 0.589458 0.294729 `3B` 0.193536916246142 0.224721 0.8612 0.389782 0.194891 Month 0.0624951815495425 0.18697 0.3343 0.738417 0.369209 Multiple Linear Regression - Regression Statistics Multiple R 0.0633220517929961 R-squared 0.00400968224327489 Adjusted R-squared -0.00896739030078675 F-TEST (value) 0.308982031938301 F-TEST (DF numerator) 4 F-TEST (DF denominator) 307 p-value 0.871893926998178 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.94501866374813 Sum Squared Residuals 2662.65242345623 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15 12.0754545200800 2.92454547992003 2 12 12.2391881893916 -0.239188189391642 3 9 11.9251781851376 -2.92517818513762 4 10 11.8697203697343 -1.86972036973428 5 13 11.8697203697343 1.13027963026570 6 16 11.9725874449072 4.02741255509277 7 14 11.8697203697343 2.13027963026571 8 16 11.8521143568994 4.14788564310065 9 10 11.8697203697343 -1.86972036973429 10 8 12.3314866178759 -4.33148661787586 11 12 12.0350826264568 -0.0350826264567775 12 15 11.9322155512838 3.06778444871616 13 14 11.8117424632760 2.18825753672404 14 14 12.3016833709412 1.69831662905882 15 12 11.9876733666872 0.0123266333128416 16 12 11.9322155512838 0.0677844487161635 17 10 11.9322155512838 -1.93221555128384 18 4 11.9322155512838 -7.93221555128384 19 14 11.8117424632760 2.18825753672404 20 15 11.9322155512838 3.06778444871616 21 16 11.9322155512838 4.06778444871616 22 12 11.8117424632760 0.188257536724044 23 12 11.9322155512838 0.0677844487161635 24 12 12.1257524675300 -0.125752467529979 25 12 12.1661243611534 -0.166124361153378 26 12 11.8697203697343 0.130279630265706 27 11 12.1309123354835 -1.13091233548350 28 11 11.9725874449072 -0.972587444907235 29 11 11.8521143568994 -0.852114356899354 30 11 11.8521143568994 -0.852114356899354 31 11 12.2513854234914 -1.25138542349138 32 11 11.8697203697343 -0.869720369734294 33 15 11.8697203697343 3.13027963026571 34 15 11.8697203697343 3.13027963026571 35 9 11.8697203697343 -2.86972036973429 36 16 11.8697203697343 4.13027963026571 37 13 11.8223111099647 1.17768889003532 38 9 11.9725874449072 -2.97258744490723 39 16 11.8697203697343 4.13027963026571 40 12 11.8697203697343 0.130279630265706 41 15 12.2567942022266 2.74320579777342 42 5 11.8697203697343 -6.8697203697343 43 11 11.8345083440644 -0.834508344064414 44 17 11.8345083440644 5.16549165593559 45 9 11.9427841979726 -2.94278419797256 46 13 11.8697203697343 1.13027963026571 47 16 11.9322155512838 4.06778444871616 48 16 11.9322155512838 4.06778444871616 49 14 12.525023534122 1.47497646587800 50 16 12.0350826264568 3.96491737354322 51 11 11.9322155512838 -0.932215551283836 52 11 11.9322155512838 -0.932215551283836 53 11 11.9322155512838 -0.932215551283836 54 12 11.9322155512838 0.0677844487161635 55 12 12.1081464546950 -0.108146454695039 56 12 11.9322155512838 0.0677844487161635 57 14 11.9322155512838 2.06778444871616 58 10 12.1379497016297 -2.13794970162972 59 9 11.6912693752681 -2.69126937526808 60 12 12.1257524675300 -0.125752467529979 61 10 11.9322155512838 -1.93221555128384 62 14 11.9322155512838 2.06778444871616 63 8 11.9322155512838 -3.93221555128384 64 16 12.0350826264568 3.96491737354322 65 14 12.0350826264568 1.96491737354322 66 14 11.9322155512838 2.06778444871616 67 12 11.9322155512838 0.0677844487161635 68 14 12.0350826264568 1.96491737354322 69 7 12.1081464546950 -5.10814645469504 70 19 11.9322155512838 7.06778444871616 71 15 11.9322155512838 3.06778444871616 72 8 11.9322155512838 -3.93221555128384 73 10 11.9322155512838 -1.93221555128384 74 13 11.9322155512838 1.06778444871616 75 13 11.8697203697343 1.13027963026571 76 10 11.8697203697343 -1.86972036973429 77 12 11.8697203697343 0.130279630265706 78 15 11.9427841979726 3.05721580202744 79 7 11.9725874449072 -4.97258744490724 80 14 11.8697203697343 2.13027963026571 81 10 11.8697203697343 -1.86972036973429 82 6 11.5083011057107 -5.50830110571065 83 11 12.0754545200802 -1.07545452008018 84 12 11.8697203697343 0.130279630265706 85 14 12.2567942022266 1.74320579777342 86 12 11.8697203697343 0.130279630265706 87 14 11.8697203697343 2.13027963026571 88 11 11.8345083440644 -0.834508344064414 89 10 12.1661243611534 -2.16612436115338 90 13 12.0632572859804 0.936742714019563 91 8 11.8697203697343 -3.86972036973429 92 9 11.8697203697343 -2.86972036973429 93 6 11.9322155512838 -5.93221555128384 94 12 12.4221564589491 -0.422156458949063 95 14 11.9322155512838 2.06778444871616 96 11 11.9322155512838 -0.932215551283836 97 8 12.2286195427029 -4.22861954270292 98 7 11.9322155512838 -4.93221555128384 99 9 11.8848062915142 -2.88480629151422 100 14 12.0174766136218 1.98252338637816 101 13 11.9322155512838 1.06778444871616 102 15 11.9322155512838 3.06778444871616 103 5 11.9322155512838 -6.93221555128384 104 15 12.1203436887948 2.87965631120522 105 13 11.8117424632760 1.18825753672404 106 12 11.9322155512838 0.0677844487161635 107 6 12.0350826264568 -6.03508262645678 108 7 11.9322155512838 -4.93221555128384 109 13 11.9322155512838 1.06778444871616 110 16 11.9146095384489 4.08539046155110 111 10 11.9322155512838 -1.93221555128384 112 16 11.9322155512838 4.06778444871616 113 15 11.9322155512838 3.06778444871616 114 8 11.9322155512838 -3.93221555128384 115 11 11.9322155512838 -0.932215551283836 116 13 11.7643332035063 1.23566679649366 117 16 12.0350826264568 3.96491737354322 118 11 11.9322155512838 -0.932215551283836 119 14 11.9322155512838 2.06778444871616 120 9 12.0174766136218 -3.01747661362184 121 8 11.9322155512838 -3.93221555128384 122 8 12.2286195427029 -4.22861954270292 123 11 12.1379497016297 -1.13794970162972 124 12 11.9322155512838 0.0677844487161635 125 11 12.3436838519756 -1.3436838519756 126 14 12.1988162957682 1.80118370423176 127 11 12.0174766136218 -1.01747661362184 128 14 11.9322155512838 2.06778444871616 129 13 12.4045504461141 0.595449553885878 130 12 11.9322155512838 0.0677844487161635 131 4 11.9322155512838 -7.93221555128384 132 15 12.2110135298680 2.78898647013202 133 10 11.9322155512838 -1.93221555128384 134 13 11.9876733666872 1.01232663331284 135 15 12.3016833709412 2.69831662905882 136 12 11.9876733666872 0.0123266333128416 137 13 11.9322155512838 1.06778444871616 138 8 11.9322155512838 -3.93221555128384 139 10 12.1379497016297 -2.13794970162972 140 15 11.9322155512838 3.06778444871616 141 16 11.9322155512838 4.06778444871616 142 16 11.9322155512838 4.06778444871616 143 14 11.9322155512838 2.06778444871616 144 14 12.1081464546950 1.89185354530496 145 12 12.1081464546950 -0.108146454695039 146 15 12.1363211142187 2.8636788857813 147 13 12.0456512731455 0.954348726854503 148 16 11.9322155512838 4.06778444871616 149 14 11.9322155512838 2.06778444871616 150 8 11.9322155512838 -3.93221555128384 151 16 11.8117424632760 4.18825753672404 152 16 12.1081464546950 3.89185354530496 153 12 11.9322155512838 0.0677844487161635 154 11 11.7643332035063 -0.764333203506336 155 16 12.1081464546950 3.89185354530496 156 9 11.9322155512838 -2.93221555128384 157 15 12.5577154687369 2.44228453126313 158 12 12.0975778080063 -0.0975778080063198 159 9 12.6781885567447 -3.67818855674475 160 10 11.7361585439827 -1.73615854398268 161 13 12.5577154687369 0.442284531263133 162 16 11.9771047199984 4.02289528000156 163 14 11.8566316319906 2.14336836800944 164 16 12.3939817994254 3.60601820057460 165 10 12.1706416362446 -2.17064163624458 166 8 11.9947107328334 -3.99471073283338 167 12 12.0799717951714 -0.0799717951713801 168 15 12.4846516404986 2.51534835950140 169 14 12.0975778080063 1.90242219199368 170 14 12.0975778080063 1.90242219199368 171 12 12.2911147242525 -0.291114724252462 172 12 11.9473014730638 0.0526985269362402 173 10 11.8268283850559 -1.82682838505588 174 4 11.7537645568176 -7.75376455681762 175 14 11.9473014730638 2.05269852693624 176 15 11.9947107328334 3.00528926716662 177 16 12.0975778080063 3.90242219199368 178 12 12.2004448831793 -0.200444883179261 179 12 11.9947107328334 0.00528926716662115 180 12 11.8742376448255 0.125762355174502 181 12 11.7537645568176 0.246235443182383 182 12 12.0975778080063 -0.0975778080063198 183 11 11.9771047199984 -0.97710471999844 184 11 12.3033119583522 -1.30331195835220 185 11 12.4548483935639 -1.45484839356393 186 11 12.2613114773178 -1.26131147731778 187 11 12.0975778080063 -1.09757780800632 188 11 12.3939817994254 -1.39398179942540 189 15 12.0975778080063 2.90242219199368 190 15 12.2911147242525 2.70888527574754 191 9 12.1408383893099 -3.1408383893099 192 16 11.9473014730638 4.05269852693624 193 13 12.1882476490795 0.811752350920479 194 9 11.9594987071635 -2.9594987071635 195 16 11.8566316319906 4.14336836800944 196 12 12.0975778080063 -0.0975778080063198 197 15 12.0799717951714 2.92002820482862 198 5 11.6332914688097 -6.63329146880974 199 11 11.8566316319906 -0.856631631990558 200 17 12.2004448831793 4.79955511682074 201 9 11.9473014730638 -2.94730147306376 202 13 12.2004448831793 0.79955511682074 203 16 12.0677745610716 3.93222543892836 204 16 11.8742376448255 4.1257623551745 205 14 11.9771047199984 2.02289528000156 206 16 12.0677745610716 3.93222543892836 207 11 12.0975778080063 -1.09757780800632 208 11 12.2613114773178 -1.26131147731778 209 11 12.0501685482367 -1.0501685482367 210 12 12.0975778080063 -0.0975778080063198 211 12 12.1706416362446 -0.170641636244582 212 12 11.9771047199984 0.0228952800015609 213 14 12.1706416362446 1.82935836375542 214 10 12.4846516404986 -2.48465164049860 215 9 11.8742376448255 -2.8742376448255 216 12 11.8742376448255 0.125762355174502 217 10 12.0975778080063 -2.09757780800632 218 14 11.9594987071635 2.0405012928365 219 8 12.0975778080063 -4.09757780800632 220 16 11.9473014730638 4.05269852693624 221 14 12.4548483935639 1.54515160643607 222 14 12.1882476490795 1.81175235092048 223 12 11.8742376448255 0.125762355174502 224 14 11.8742376448255 2.1257623551745 225 7 11.8742376448255 -4.8742376448255 226 19 12.0799717951714 6.92002820482862 227 15 12.3817845653257 2.61821543467434 228 8 11.9947107328334 -3.99471073283338 229 10 12.0975778080063 -2.09757780800632 230 13 11.9771047199984 1.02289528000156 231 13 12.6781885567447 0.321811443255253 232 10 12.2911147242525 -2.29111472425246 233 12 12.0975778080063 -0.0975778080063198 234 15 12.1882476490795 2.81175235092048 235 7 12.1882476490795 -5.18824764907952 236 14 12.1882476490795 1.81175235092048 237 10 12.1706416362446 -2.17064163624458 238 6 12.2004448831793 -6.20044488317926 239 11 11.9771047199984 -0.97710471999844 240 12 11.9594987071635 0.0405012928365007 241 14 12.1828388703443 1.81716112965568 242 12 11.8566316319906 0.143368368009442 243 14 12.2613114773178 1.73868852268222 244 11 12.2735087114175 -1.27350871141752 245 10 12.0975778080063 -2.09757780800632 246 13 12.2613114773178 0.738688522682217 247 8 12.1882476490795 -4.18824764907952 248 9 12.9745925481638 -3.97459254816383 249 6 11.9771047199984 -5.97710471999844 250 12 12.0975778080063 -0.0975778080063198 251 14 11.9947107328334 2.00528926716662 252 11 11.9594987071635 -0.9594987071635 253 8 12.1882476490795 -4.18824764907952 254 7 12.0975778080063 -5.09757780800632 255 9 11.7537645568176 -2.75376455681762 256 14 12.1828388703443 1.81716112965568 257 13 11.9947107328334 1.00528926716662 258 15 12.2613114773178 2.73868852268222 259 5 12.3641785524907 -7.36417855249072 260 15 12.1408383893099 2.8591616106901 261 13 11.8742376448255 1.12576235517450 262 12 12.1882476490795 -0.188247649079521 263 6 12.0975778080063 -6.09757780800632 264 7 12.0975778080063 -5.09757780800632 265 13 12.3033119583522 0.696688041647798 266 16 12.2004448831793 3.79955511682074 267 10 11.8742376448255 -1.87423764482550 268 16 12.0975778080063 3.90242219199368 269 15 12.0975778080063 2.90242219199368 270 8 12.2911147242525 -4.29111472425246 271 11 12.0975778080063 -1.09757780800632 272 13 12.3641785524907 0.635821447509276 273 16 11.9473014730638 4.05269852693624 274 11 12.4548483935639 -1.45484839356393 275 14 12.0677745610716 1.93222543892836 276 9 12.3817845653257 -3.38178456532566 277 8 11.9367328263750 -3.93673282637504 278 8 12.2629400647288 -4.2629400647288 279 11 11.7986537255322 -0.79865372553222 280 12 11.8162597383672 0.183740261632840 281 11 12.1424669767209 -1.14246697672092 282 14 12.1600729895559 1.83992701044414 283 11 12.1600729895559 -1.16007298955586 284 14 12.0395999015480 1.96040009845202 285 13 12.2507428306291 0.749257169370936 286 12 12.0395999015480 -0.0395999015479815 287 4 12.1600729895559 -8.16007298955586 288 15 12.0572059143829 2.94279408561708 289 10 12.2507428306291 -2.25074283062906 290 13 12.0572059143829 0.942794085617079 291 15 12.4442797468752 2.55572025312479 292 12 12.0572059143829 -0.0572059143829212 293 13 12.1600729895559 0.839927010444138 294 8 12.0572059143829 -4.05720591438292 295 10 12.2507428306291 -2.25074283062906 296 15 12.1600729895559 2.83992701044414 297 16 11.9367328263750 4.06326717362496 298 16 12.1600729895559 3.83992701044414 299 14 12.0572059143829 1.94279408561708 300 14 12.1600729895559 1.83992701044414 301 12 12.0572059143829 -0.0572059143829212 302 15 12.0572059143829 2.94279408561708 303 13 12.2507428306291 0.749257169370936 304 16 12.1600729895559 3.83992701044414 305 14 12.2507428306291 1.74925716937094 306 8 12.0572059143829 -4.05720591438292 307 16 12.0572059143829 3.94279408561708 308 16 12.353609905802 3.64639009419800 309 12 11.9367328263750 0.0632671736249595 310 11 12.0572059143829 -1.05720591438292 311 16 12.0572059143829 3.94279408561708 312 9 12.0572059143829 -3.05720591438292 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.5852358720493 0.8295282559014 0.4147641279507 9 0.531038364751206 0.93792327049759 0.468961635248794 10 0.3883849516069 0.7767699032138 0.6116150483931 11 0.420485707209835 0.84097141441967 0.579514292790165 12 0.590804314812756 0.818391370374489 0.409195685187244 13 0.513622399033511 0.972755201932979 0.486377600966489 14 0.586305998994954 0.827388002010092 0.413694001005046 15 0.493379954709781 0.986759909419563 0.506620045290219 16 0.410388011946512 0.820776023893023 0.589611988053488 17 0.38902717270544 0.77805434541088 0.61097282729456 18 0.789157601676903 0.421684796646193 0.210842398323097 19 0.758239703889275 0.483520592221449 0.241760296110725 20 0.773359246768608 0.453281506462783 0.226640753231392 21 0.807809394798213 0.384381210403574 0.192190605201787 22 0.758624571741721 0.482750856516557 0.241375428258279 23 0.701841425360833 0.596317149278334 0.298158574639167 24 0.64602115790423 0.70795768419154 0.35397884209577 25 0.582475870142428 0.835048259715144 0.417524129857572 26 0.520732102839023 0.958535794321954 0.479267897160977 27 0.480691773612021 0.961383547224042 0.519308226387979 28 0.43621211257141 0.87242422514282 0.56378788742859 29 0.394489582543338 0.788979165086676 0.605510417456662 30 0.350647941162227 0.701295882324455 0.649352058837773 31 0.299018088276581 0.598036176553162 0.700981911723419 32 0.259798871309876 0.519597742619751 0.740201128690124 33 0.252667572156336 0.505335144312673 0.747332427843664 34 0.240153614630445 0.480307229260889 0.759846385369555 35 0.265103317960517 0.530206635921034 0.734896682039483 36 0.287558885008712 0.575117770017424 0.712441114991288 37 0.244844422595438 0.489688845190876 0.755155577404562 38 0.257098225016283 0.514196450032566 0.742901774983717 39 0.275005611129293 0.550011222258586 0.724994388870707 40 0.235696496525665 0.47139299305133 0.764303503474335 41 0.223935639242715 0.44787127848543 0.776064360757285 42 0.491609144938366 0.983218289876732 0.508390855061634 43 0.443575632054258 0.887151264108516 0.556424367945742 44 0.538028234038819 0.923943531922363 0.461971765961181 45 0.553345383833952 0.893309232332095 0.446654616166048 46 0.508879815866079 0.982240368267841 0.491120184133921 47 0.536651201523719 0.926697596952562 0.463348798476281 48 0.556088021586858 0.887823956826284 0.443911978413142 49 0.531720221649453 0.936559556701093 0.468279778350547 50 0.544033150233878 0.911933699532244 0.455966849766122 51 0.514694316735597 0.970611366528805 0.485305683264403 52 0.483800247410935 0.96760049482187 0.516199752589065 53 0.451817310974699 0.903634621949399 0.548182689025301 54 0.409583191754583 0.819166383509167 0.590416808245417 55 0.367624803718746 0.735249607437491 0.632375196281254 56 0.328169297291552 0.656338594583103 0.671830702708448 57 0.300482540921755 0.60096508184351 0.699517459078245 58 0.28878896406215 0.5775779281243 0.71121103593785 59 0.296099679355357 0.592199358710714 0.703900320644643 60 0.260531531706090 0.521063063412179 0.73946846829391 61 0.247634279501497 0.495268559002994 0.752365720498503 62 0.226839207880335 0.453678415760671 0.773160792119665 63 0.264279708077684 0.528559416155368 0.735720291922316 64 0.287075987908225 0.57415197581645 0.712924012091775 65 0.263350791622962 0.526701583245925 0.736649208377038 66 0.242082505245302 0.484165010490604 0.757917494754698 67 0.211711624058597 0.423423248117194 0.788288375941403 68 0.191407523408604 0.382815046817209 0.808592476591396 69 0.252779586069446 0.505559172138892 0.747220413930554 70 0.405836274790807 0.811672549581614 0.594163725209193 71 0.39827540023402 0.79655080046804 0.60172459976598 72 0.446474842903148 0.892949685806297 0.553525157096852 73 0.432001943593905 0.864003887187811 0.567998056406094 74 0.396774375794003 0.793548751588007 0.603225624205997 75 0.363283533815317 0.726567067630634 0.636716466184683 76 0.348178983761345 0.69635796752269 0.651821016238655 77 0.313974397278589 0.627948794557178 0.686025602721411 78 0.321792683707955 0.643585367415911 0.678207316292045 79 0.397794696789336 0.795589393578672 0.602205303210664 80 0.377783208869066 0.755566417738132 0.622216791130934 81 0.360443157618503 0.720886315237005 0.639556842381497 82 0.427226808132905 0.85445361626581 0.572773191867095 83 0.397760803791332 0.795521607582664 0.602239196208668 84 0.362610267264287 0.725220534528573 0.637389732735713 85 0.338570240529455 0.67714048105891 0.661429759470545 86 0.305814792475218 0.611629584950437 0.694185207524782 87 0.289438039792286 0.578876079584572 0.710561960207714 88 0.259245852762858 0.518491705525717 0.740754147237142 89 0.248307423924197 0.496614847848394 0.751692576075803 90 0.222798007143489 0.445596014286978 0.777201992856511 91 0.247007077807956 0.494014155615912 0.752992922192044 92 0.246938437750822 0.493876875501643 0.753061562249178 93 0.354711048704807 0.709422097409613 0.645288951295193 94 0.323761225692835 0.64752245138567 0.676238774307165 95 0.305119301702115 0.61023860340423 0.694880698297885 96 0.279066690703238 0.558133381406477 0.720933309296762 97 0.316720070808665 0.63344014161733 0.683279929191335 98 0.380205188817949 0.760410377635899 0.619794811182051 99 0.369541851510407 0.739083703020813 0.630458148489593 100 0.353432844767322 0.706865689534644 0.646567155232678 101 0.324764357240199 0.649528714480397 0.675235642759801 102 0.325269995352653 0.650539990705305 0.674730004647347 103 0.481142400013459 0.962284800026918 0.518857599986541 104 0.476363461658267 0.952726923316533 0.523636538341733 105 0.449390968028496 0.898781936056991 0.550609031971504 106 0.415300341002785 0.83060068200557 0.584699658997215 107 0.530618874886362 0.938762250227277 0.469381125113638 108 0.590772944141367 0.818454111717266 0.409227055858633 109 0.561391798175916 0.877216403648168 0.438608201824084 110 0.592889917858155 0.81422016428369 0.407110082141845 111 0.574099327415108 0.851801345169783 0.425900672584892 112 0.601586266574625 0.79682746685075 0.398413733425375 113 0.602430283595201 0.795139432809599 0.397569716404799 114 0.627805252738909 0.744389494522183 0.372194747261091 115 0.598882185535719 0.802235628928562 0.401117814464281 116 0.576878433693439 0.846243132613123 0.423121566306561 117 0.598584517024093 0.802830965951813 0.401415482975907 118 0.56928521482788 0.86142957034424 0.43071478517212 119 0.551350025283257 0.897299949433485 0.448649974716743 120 0.554411178646073 0.891177642707854 0.445588821353927 121 0.580486174416301 0.839027651167398 0.419513825583699 122 0.613538816173225 0.77292236765355 0.386461183826775 123 0.586996425244602 0.826007149510796 0.413003574755398 124 0.554306796691203 0.891386406617593 0.445693203308797 125 0.528949546390217 0.942100907219566 0.471050453609783 126 0.510139751814805 0.97972049637039 0.489860248185195 127 0.481099180383221 0.962198360766443 0.518900819616779 128 0.462844491446734 0.925688982893468 0.537155508553266 129 0.432224968750877 0.864449937501755 0.567775031249123 130 0.399901133851301 0.799802267702601 0.600098866148699 131 0.607016616830254 0.785966766339493 0.392983383169746 132 0.602933160930626 0.794133678138749 0.397066839069374 133 0.587190991205302 0.825618017589396 0.412809008794698 134 0.558669874174406 0.882660251651188 0.441330125825594 135 0.550438712168201 0.899122575663598 0.449561287831799 136 0.517846180833165 0.96430763833367 0.482153819166835 137 0.488491251700668 0.976982503401337 0.511508748299332 138 0.519691113615377 0.960617772769245 0.480308886384622 139 0.50720827681831 0.98558344636338 0.49279172318169 140 0.504903366649545 0.99019326670091 0.495096633350455 141 0.52764606956297 0.94470786087406 0.47235393043703 142 0.551114638051489 0.897770723897023 0.448885361948511 143 0.532407433075811 0.935185133848377 0.467592566924189 144 0.511459980779699 0.977080038440602 0.488540019220301 145 0.479023677248121 0.958047354496241 0.520976322751879 146 0.472057977072733 0.944115954145466 0.527942022927267 147 0.442520073272042 0.885040146544083 0.557479926727958 148 0.472571344702311 0.945142689404622 0.527428655297689 149 0.458278621181085 0.91655724236217 0.541721378818915 150 0.47909467050521 0.95818934101042 0.52090532949479 151 0.515169128013297 0.969661743973406 0.484830871986703 152 0.541743542745071 0.916512914509859 0.458256457254929 153 0.510486100998608 0.979027798002784 0.489513899001392 154 0.480650588910028 0.961301177820056 0.519349411089972 155 0.511292169081086 0.977415661837828 0.488707830918914 156 0.502703487165224 0.994593025669552 0.497296512834776 157 0.487120133908848 0.974240267817696 0.512879866091152 158 0.454744403368793 0.909488806737587 0.545255596631207 159 0.480330327017455 0.96066065403491 0.519669672982545 160 0.459232643438618 0.918465286877235 0.540767356561382 161 0.427708431584773 0.855416863169546 0.572291568415227 162 0.456569231670162 0.913138463340323 0.543430768329838 163 0.440563115564483 0.881126231128966 0.559436884435517 164 0.454427733140328 0.908855466280656 0.545572266859672 165 0.441329965970214 0.882659931940429 0.558670034029786 166 0.465914732675543 0.931829465351086 0.534085267324457 167 0.433609982942172 0.867219965884343 0.566390017057828 168 0.424525652077143 0.849051304154287 0.575474347922857 169 0.406508884818603 0.813017769637207 0.593491115181397 170 0.388623717043469 0.777247434086938 0.611376282956531 171 0.358384645632524 0.716769291265047 0.641615354367477 172 0.328646384834729 0.657292769669459 0.671353615165271 173 0.311527248575382 0.623054497150763 0.688472751424618 174 0.491826704405774 0.983653408811548 0.508173295594226 175 0.475075965216735 0.95015193043347 0.524924034783265 176 0.47896244511953 0.95792489023906 0.52103755488047 177 0.507090541641975 0.98581891671605 0.492909458358025 178 0.474839417866749 0.949678835733497 0.525160582133251 179 0.442538918125806 0.885077836251613 0.557461081874194 180 0.410374429880646 0.820748859761292 0.589625570119354 181 0.378896133011735 0.757792266023471 0.621103866988264 182 0.348312225153295 0.69662445030659 0.651687774846705 183 0.321010920745195 0.642021841490389 0.678989079254805 184 0.296845744907600 0.593691489815201 0.7031542550924 185 0.276651215785425 0.55330243157085 0.723348784214575 186 0.254353441175914 0.508706882351828 0.745646558824086 187 0.230997633560678 0.461995267121355 0.769002366439323 188 0.211147964301447 0.422295928602895 0.788852035698553 189 0.213615364933455 0.427230729866911 0.786384635066545 190 0.212480661314599 0.424961322629198 0.787519338685401 191 0.217091374784575 0.434182749569151 0.782908625215425 192 0.237505389723619 0.475010779447237 0.762494610276381 193 0.215900423538329 0.431800847076659 0.78409957646167 194 0.216824802126998 0.433649604253995 0.783175197873002 195 0.240063456667168 0.480126913334336 0.759936543332832 196 0.215235299174145 0.43047059834829 0.784764700825855 197 0.215840178591608 0.431680357183216 0.784159821408392 198 0.338249595421151 0.676499190842302 0.661750404578849 199 0.311981152112083 0.623962304224166 0.688018847887917 200 0.379693529821840 0.759387059643679 0.62030647017816 201 0.389417971388288 0.778835942776577 0.610582028611712 202 0.366525810843050 0.733051621686101 0.63347418915695 203 0.390669592312977 0.781339184625955 0.609330407687023 204 0.425136396850281 0.850272793700562 0.574863603149719 205 0.410319063521869 0.820638127043737 0.589680936478131 206 0.438702699627588 0.877405399255175 0.561297300372412 207 0.408236468839799 0.816472937679598 0.591763531160201 208 0.381470283858197 0.762940567716393 0.618529716141803 209 0.355814126494288 0.711628252988576 0.644185873505712 210 0.327071540101394 0.654143080202788 0.672928459898606 211 0.296533671024115 0.59306734204823 0.703466328975885 212 0.267506773043445 0.535013546086889 0.732493226956555 213 0.251511019126073 0.503022038252147 0.748488980873927 214 0.239238165135342 0.478476330270683 0.760761834864658 215 0.234147058643836 0.468294117287672 0.765852941356164 216 0.208316745319915 0.416633490639829 0.791683254680085 217 0.190943879591623 0.381887759183246 0.809056120408377 218 0.177294636693729 0.354589273387457 0.822705363306271 219 0.186673843398325 0.373347686796650 0.813326156601675 220 0.202742329941533 0.405484659883065 0.797257670058467 221 0.185211419727321 0.370422839454643 0.814788580272679 222 0.176742456496647 0.353484912993293 0.823257543503353 223 0.154985147721726 0.309970295443452 0.845014852278274 224 0.148530557200481 0.297061114400963 0.851469442799519 225 0.177319217461679 0.354638434923359 0.82268078253832 226 0.322851826369303 0.645703652738606 0.677148173630697 227 0.32894892729723 0.65789785459446 0.67105107270277 228 0.339891438347289 0.679782876694578 0.660108561652711 229 0.3153772612842 0.6307545225684 0.6846227387158 230 0.291037433510983 0.582074867021965 0.708962566489017 231 0.267652172873784 0.535304345747568 0.732347827126216 232 0.247142649108951 0.494285298217902 0.752857350891049 233 0.220896414849117 0.441792829698235 0.779103585150883 234 0.232669548594527 0.465339097189054 0.767330451405473 235 0.271594253756957 0.543188507513913 0.728405746243043 236 0.263403277677836 0.526806555355672 0.736596722322164 237 0.242775423677846 0.485550847355692 0.757224576322154 238 0.314737324626176 0.629474649252352 0.685262675373824 239 0.282691966145641 0.565383932291282 0.717308033854359 240 0.250945660999867 0.501891321999733 0.749054339000133 241 0.242174010847667 0.484348021695333 0.757825989152333 242 0.213083630102407 0.426167260204815 0.786916369897593 243 0.201341163690415 0.402682327380831 0.798658836309585 244 0.176413609721289 0.352827219442578 0.823586390278711 245 0.157194295240419 0.314388590480838 0.842805704759581 246 0.139517019256179 0.279034038512358 0.86048298074382 247 0.147437853994974 0.294875707989948 0.852562146005026 248 0.146383638602237 0.292767277204474 0.853616361397763 249 0.207351460465715 0.41470292093143 0.792648539534285 250 0.180048321293812 0.360096642587624 0.819951678706188 251 0.172559004289310 0.345118008578620 0.82744099571069 252 0.149151914540629 0.298303829081258 0.85084808545937 253 0.158763138040578 0.317526276081157 0.841236861959422 254 0.195169034878450 0.390338069756900 0.80483096512155 255 0.195671128812777 0.391342257625554 0.804328871187223 256 0.182163059452981 0.364326118905962 0.817836940547019 257 0.158248747265350 0.316497494530700 0.84175125273465 258 0.157708141980140 0.315416283960279 0.84229185801986 259 0.300633021119001 0.601266042238003 0.699366978880999 260 0.289342862823067 0.578685725646134 0.710657137176933 261 0.258785553558405 0.517571107116811 0.741214446441595 262 0.224011210038657 0.448022420077314 0.775988789961343 263 0.329175463373501 0.658350926747002 0.670824536626499 264 0.430827994795946 0.86165598959189 0.569172005204055 265 0.385958147371881 0.771916294743761 0.614041852628119 266 0.403709481586020 0.807418963172041 0.59629051841398 267 0.395352616313208 0.790705232626416 0.604647383686792 268 0.42013749527254 0.84027499054508 0.57986250472746 269 0.437355430705427 0.874710861410853 0.562644569294573 270 0.459447408001671 0.918894816003342 0.540552591998329 271 0.415813750566345 0.83162750113269 0.584186249433655 272 0.370351503684915 0.74070300736983 0.629648496315085 273 0.416480178261444 0.832960356522889 0.583519821738556 274 0.370210648611687 0.740421297223374 0.629789351388313 275 0.403260718497613 0.806521436995227 0.596739281502387 276 0.358862133478333 0.717724266956666 0.641137866521667 277 0.410654149109241 0.821308298218481 0.589345850890759 278 0.470902967371008 0.941805934742015 0.529097032628992 279 0.426036763028383 0.852073526056765 0.573963236971617 280 0.375578204191331 0.751156408382663 0.624421795808669 281 0.364291140378126 0.728582280756253 0.635708859621874 282 0.320097689561454 0.640195379122908 0.679902310438546 283 0.287731830082319 0.575463660164638 0.712268169917681 284 0.241307673467167 0.482615346934334 0.758692326532833 285 0.196994573479166 0.393989146958331 0.803005426520834 286 0.178214005747162 0.356428011494324 0.821785994252838 287 0.775681495124575 0.44863700975085 0.224318504875425 288 0.781915112115254 0.436169775769491 0.218084887884746 289 0.784442691279104 0.431114617441791 0.215557308720896 290 0.734500264036956 0.530999471926087 0.265499735963044 291 0.688101302845652 0.623797394308696 0.311898697154348 292 0.616946903271447 0.766106193457107 0.383053096728553 293 0.580746655767734 0.838506688464532 0.419253344232266 294 0.679495246315541 0.641009507368917 0.320504753684459 295 0.687629899858118 0.624740200283763 0.312370100141882 296 0.608415044623792 0.783169910752417 0.391584955376208 297 0.610832656511974 0.778334686976052 0.389167343488026 298 0.527539903005558 0.944920193988884 0.472460096994442 299 0.451356260605888 0.902712521211776 0.548643739394112 300 0.36525214297778 0.73050428595556 0.63474785702222 301 0.267009588057952 0.534019176115904 0.732990411942048 302 0.235196324689280 0.470392649378559 0.76480367531072 303 0.146313445215414 0.292626890430827 0.853686554784586 304 0.0843557529104864 0.168711505820973 0.915644247089514 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/10rkg11292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/10rkg11292680918.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/12jjp1292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/12jjp1292680918.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/22jjp1292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/22jjp1292680918.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/32jjp1292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/32jjp1292680918.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/4daia1292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/4daia1292680918.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/5daia1292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/5daia1292680918.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/6daia1292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/6daia1292680918.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/7n2zd1292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/7n2zd1292680918.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/8gtzy1292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/8gtzy1292680918.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/9gtzy1292680918.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926808701cmfw7vnstvy3nd/9gtzy1292680918.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by