Multiple regression

*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: Wed, 22 Dec 2010 17:19:07 +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/22/t12930382988wb82in3b78d6iu.htm/, Retrieved Wed, 22 Dec 2010 18:18:18 +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/22/t12930382988wb82in3b78d6iu.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 «

41 38 13 12 14 12 39 32 16 11 18 11 30 35 19 15 11 14 31 33 15 6 12 12 34 37 14 13 16 21 35 29 13 10 18 12 39 31 19 12 14 22 34 36 15 14 14 11 36 35 14 12 15 10 37 38 15 6 15 13 38 31 16 10 17 10 36 34 16 12 19 8 38 35 16 12 10 15 39 38 16 11 16 14 33 37 17 15 18 10 32 33 15 12 14 14 36 32 15 10 14 14 38 38 20 12 17 11 39 38 18 11 14 10 32 32 16 12 16 13 32 33 16 11 18 7 31 31 16 12 11 14 39 38 19 13 14 12 37 39 16 11 12 14 39 32 17 9 17 11 41 32 17 13 9 9 36 35 16 10 16 11 33 37 15 14 14 15 33 33 16 12 15 14 34 33 14 10 11 13 31 28 15 12 16 9 27 32 12 8 13 15 37 31 14 10 17 10 34 37 16 12 15 11 34 30 14 12 14 13 32 33 7 7 16 8 29 31 10 6 9 20 36 33 14 12 15 12 29 31 16 10 17 10 35 33 16 10 13 10 37 32 16 10 15 9 34 33 14 12 16 14 38 32 20 15 16 8 35 33 14 10 12 14 38 28 14 10 12 11 37 35 11 12 11 13 38 39 14 13 15 9 33 34 15 11 15 11 36 38 16 11 17 15 38 32 14 12 13 11 32 38 16 14 16 10 32 30 14 10 14 14 32 33 12 12 11 18 34 38 16 13 12 14 32 32 9 5 12 11 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Happiness[t] = + 15.4893520503043 + 0.0216155702168623Connected[t] + 0.0649005494285598Separate[t] + 0.0731669569957836Learning[t] -0.0475755057203567Software[t] -0.385554294834322Depression[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) 15.4893520503043 2.277804 6.8001 0 0 Connected 0.0216155702168623 0.050678 0.4265 0.670312 0.335156 Separate 0.0649005494285598 0.04706 1.3791 0.16984 0.08492 Learning 0.0731669569957836 0.085303 0.8577 0.392358 0.196179 Software -0.0475755057203567 0.086735 -0.5485 0.584121 0.29206 Depression -0.385554294834322 0.050539 -7.6288 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.559914901167637 R-squared 0.313504696549565 Adjusted R-squared 0.291501641951794 F-TEST (value) 14.2482351782799 F-TEST (DF numerator) 5 F-TEST (DF denominator) 156 p-value 1.75743863906064e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.96762793629027 Sum Squared Residuals 603.963312524505 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14 14.5954241417700 -0.595424141769973 2 18 14.8154203763069 3.18457962369312 3 11 13.6881178562438 -2.68811785624375 4 12 14.4865526407722 -2.48655264077222 5 16 10.9348173985899 5.06518260141014 6 18 13.9767767870524 4.02322321294757 7 14 10.6813479489678 3.31865205103223 8 14 14.7510512487800 -0.751051248779952 9 15 15.1369201890644 -0.136920189064369 10 15 14.5551945143819 0.444805485618133 11 17 15.1620340572161 1.83796594278386 12 19 15.9894621432960 3.01053785670398 13 10 13.3987137693181 -3.39871376931805 14 16 14.0481607883753 1.95183921162473 15 18 15.2786489310972 2.72135106890282 16 14 13.4516065869983 0.548393413001706 17 14 13.5683193298779 0.431680670122103 18 17 15.4283004249242 1.57169957507585 19 14 15.7367118817041 -1.73671188170413 20 16 13.8454272893998 2.15457271060016 21 18 16.2712291135547 1.72877088644531 22 11 13.3733568749201 -2.37335687492010 23 14 14.9436192375906 -0.943619237590552 24 12 14.0698301973701 -2.06983019737010 25 17 14.9837383447434 2.01626165525662 26 9 15.6077760519643 -6.60777605196432 27 16 14.9928508196623 1.00714918033767 28 14 13.2521190486544 0.74788095134564 29 15 13.5463891142109 1.45361088578906 30 11 13.9023760767113 -2.90237607671127 31 16 15.0332597438102 0.966740256189755 32 13 12.8638750435452 0.136124956454824 33 17 14.9940845730077 2.00591542699229 34 15 14.984269766645 0.015730233354991 35 14 13.6125234169849 0.387476583015122 36 16 15.4174742286397 0.582525771360257 37 9 10.8632512578279 -1.86325125782788 38 15 14.2360105005386 0.763989499461395 39 17 14.9674939252644 2.03250607473563 40 13 15.2269884454227 -2.22698844542267 41 15 15.5908733312622 -0.590873331262156 42 16 13.4216707704362 2.57832922956377 43 16 16.0528334956947 -0.0528334956946907 44 12 13.5384373520938 -1.53843735209381 45 12 14.4354442001046 -2.43544420010457 46 11 13.7823720037909 -2.78237200379091 47 15 15.7777323163263 -0.777732316326299 48 15 14.7423610968670 0.257638903132960 49 17 13.5977597828904 3.40224021710964 50 13 14.5998953863781 -1.59989538637809 51 16 15.2963424590335 0.703657540966547 52 14 13.2788889931575 0.721111006842455 53 11 11.6898885366737 -0.689888536673653 54 12 13.8449319258502 -1.84493192585024 55 12 14.4373957201405 -2.43739572014050 56 15 14.4781785556490 0.521821444350952 57 16 14.126537379775 1.87346262022500 58 15 15.4525988569437 -0.452598856943663 59 12 15.3441787714259 -3.34417877142593 60 12 13.3641175206796 -1.36411752067962 61 8 10.7889235880303 -2.78892358803027 62 13 14.4904761043925 -1.49047610439246 63 11 14.3737633615129 -3.37376336151285 64 14 13.5342006643631 0.465799335636875 65 15 13.823757880405 1.17624211959499 66 10 15.0314767881373 -5.0314767881373 67 11 12.6355569417665 -1.63555694176653 68 12 14.0571571163696 -2.05715711636956 69 15 14.2232728483632 0.77672715163678 70 15 13.8492224523589 1.15077754764110 71 14 13.9860231893220 0.0139768106780217 72 16 13.3433480178145 2.65665198218552 73 15 14.3941451783843 0.605854821615662 74 15 15.7670714426119 -0.767071442611874 75 13 14.3352642723594 -1.33526427235941 76 12 11.7695028872117 0.230497112788324 77 17 14.2304323819066 2.76956761809341 78 13 12.3291162067881 0.670883793211928 79 15 13.4745635881165 1.52543641188352 80 13 15.1712195726786 -2.17121957267864 81 15 14.8977536469996 0.102246353000413 82 16 15.7922391495416 0.207760850458387 83 15 15.7769939265305 -0.776993926530516 84 16 14.3307930277513 1.66920697224875 85 15 14.3351912318159 0.664808768184144 86 14 13.8972933216289 0.102706678371143 87 15 14.4258171598704 0.574182840129564 88 14 14.3823982533081 -0.382398253308145 89 13 13.0094719890806 -0.00947198908060875 90 7 10.3887877985406 -3.38878779854058 91 17 13.9334726060752 3.06652739392485 92 13 13.0912214204334 -0.0912214204333582 93 15 14.4943789449075 0.505621055092534 94 14 12.7415495450212 1.25845045497883 95 13 14.5879414934078 -1.58794149340775 96 16 15.1225980797415 0.877401920258526 97 12 12.9439683985465 -0.943968398546507 98 14 14.7592085574516 -0.759208557451583 99 17 14.8544148290099 2.14558517099008 100 15 15.2105094690662 -0.210509469066193 101 17 15.2962347814775 1.70376521852249 102 12 13.0391194099872 -1.03911940998722 103 16 14.7918879230665 1.20811207693352 104 11 14.3313422300788 -3.33134223007882 105 15 13.212259326834 1.78774067316599 106 9 11.4855252111415 -2.48552521114145 107 16 15.0140979056511 0.985902094348878 108 15 13.3376083828486 1.66239161715145 109 10 13.4030581346047 -3.40305813460468 110 10 10.4363165135120 -0.436316513512027 111 15 13.5464429529889 1.45355704701109 112 11 13.0224526675022 -2.02245266750218 113 13 15.3213832866417 -2.32138328664166 114 14 12.9672938838928 1.03270611610724 115 18 14.2713437176332 3.72865628236683 116 16 15.2089264332583 0.79107356674167 117 14 13.8067835404989 0.193216459501065 118 14 14.4518309341519 -0.451830934151903 119 14 15.4320363219727 -1.43203632197268 120 14 13.4636143187466 0.536385681253391 121 12 13.2485116518239 -1.24851165182386 122 14 13.0661791714855 0.9338208285145 123 15 14.9810322753822 0.0189677246177731 124 15 16.1755288997864 -1.17552889978641 125 15 14.4525084371406 0.547491562859352 126 13 14.5170943411191 -1.51709434111911 127 17 15.9464918095344 1.05350819046558 128 17 15.8365233252212 1.16347667477879 129 19 14.9673132071649 4.03268679283513 130 15 13.7300822272162 1.26991777278379 131 13 14.7765874399378 -1.77658743993776 132 9 10.6686587084378 -1.66865870843779 133 15 15.6898195056619 -0.689819505661932 134 15 12.5913474275270 2.40865257247301 135 15 14.5821288178983 0.417871182101734 136 16 13.1920043892841 2.80799561071594 137 11 9.17764221207729 1.82235778792271 138 14 13.6722143320095 0.327785667990505 139 11 12.5695511392106 -1.56955113921062 140 15 14.7088178834745 0.291182116525516 141 13 13.9614585234978 -0.96145852349778 142 15 15.3221932956414 -0.322193295641354 143 16 13.6160025131542 2.3839974868458 144 14 14.3128925319208 -0.312892531920831 145 15 13.6728365748806 1.32716342511938 146 16 14.4758601722815 1.52413982771845 147 16 14.0052372453626 1.99476275463743 148 11 13.9719908969942 -2.97199089699418 149 12 14.5516353296399 -2.55163532963993 150 9 11.5201738772182 -2.52017387721821 151 16 14.6138546381623 1.38614536183766 152 13 12.7231260966578 0.276873903342167 153 16 15.9630054229033 0.0369945770967159 154 12 15.0355851752068 -3.03558517520681 155 9 11.7707174387915 -2.77071743879147 156 13 11.6256711169233 1.37432888307674 157 13 13.0912214204334 -0.0912214204333582 158 14 13.8503279050431 0.149672094956893 159 19 14.9673132071649 4.03268679283513 160 13 15.4784511148912 -2.47845111489121 161 12 12.0099916584122 -0.0099916584121995 162 13 12.9623988949389 0.0376011050610872 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.610384536324888 0.779230927350224 0.389615463675112 10 0.697694753005909 0.604610493988182 0.302305246994091 11 0.60133736106553 0.797325277868941 0.398662638934470 12 0.837510118435455 0.324979763129089 0.162489881564545 13 0.971178285658212 0.0576434286835757 0.0288217143417878 14 0.969003983338667 0.0619920333226657 0.0309960166613329 15 0.986461702507744 0.027076594984512 0.013538297492256 16 0.97796589466562 0.0440682106687614 0.0220341053343807 17 0.968789972929166 0.062420054141668 0.031210027070834 18 0.964863491588869 0.070273016822263 0.0351365084111315 19 0.95899647331651 0.0820070533669787 0.0410035266834893 20 0.948293502106552 0.103412995786896 0.0517064978934478 21 0.94022719959051 0.119545600818981 0.0597728004094907 22 0.96430397169394 0.0713920566121196 0.0356960283060598 23 0.953712524635104 0.0925749507297923 0.0462874753648962 24 0.950067273582667 0.099865452834667 0.0499327264173335 25 0.936994885726581 0.126010228546837 0.0630051142734187 26 0.999304202358273 0.00139159528345385 0.000695797641726925 27 0.99892492081625 0.00215015836749926 0.00107507918374963 28 0.998270873708441 0.00345825258311753 0.00172912629155877 29 0.997476553497488 0.00504689300502335 0.00252344650251167 30 0.998858671557668 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0.113119394449714 116 0.861596080480604 0.276807839038792 0.138403919519396 117 0.842351711970607 0.315296576058786 0.157648288029393 118 0.808826438387924 0.382347123224151 0.191173561612076 119 0.795390577831738 0.409218844336524 0.204609422168262 120 0.764476658820438 0.471046682359124 0.235523341179562 121 0.727886711758426 0.544226576483148 0.272113288241574 122 0.68450082862825 0.6309983427435 0.31549917137175 123 0.635651177598462 0.728697644803076 0.364348822401538 124 0.593058167704078 0.813883664591844 0.406941832295922 125 0.538003162864335 0.92399367427133 0.461996837135665 126 0.515376074484044 0.969247851031911 0.484623925515956 127 0.461249030640761 0.922498061281523 0.538750969359239 128 0.437303974997241 0.874607949994482 0.562696025002759 129 0.634963287482515 0.730073425034971 0.365036712517486 130 0.593460485661681 0.813079028676637 0.406539514338319 131 0.591438694513771 0.817122610972459 0.408561305486229 132 0.58029271418835 0.8394145716233 0.41970728581165 133 0.518440510065116 0.963118979869769 0.481559489934884 134 0.507838317885237 0.984323364229526 0.492161682114763 135 0.459078276327237 0.918156552654474 0.540921723672763 136 0.495988723710652 0.991977447421303 0.504011276289348 137 0.454180068633283 0.908360137266565 0.545819931366717 138 0.39537266574439 0.79074533148878 0.60462733425561 139 0.353036592314713 0.706073184629426 0.646963407685287 140 0.287369836863365 0.574739673726729 0.712630163136635 141 0.283893370426812 0.567786740853623 0.716106629573188 142 0.223670079669777 0.447340159339554 0.776329920330223 143 0.252724079476739 0.505448158953478 0.747275920523261 144 0.192040993644147 0.384081987288294 0.807959006355853 145 0.147848800993597 0.295697601987193 0.852151199006403 146 0.108484152267255 0.216968304534509 0.891515847732745 147 0.197810955969729 0.395621911939459 0.80218904403027 148 0.631913912458567 0.736172175082866 0.368086087541433 149 0.676265062495184 0.647469875009632 0.323734937504816 150 0.570899813553849 0.858200372892301 0.429100186446151 151 0.504722831749453 0.990554336501095 0.495277168250547 152 0.363077299053851 0.726154598107702 0.636922700946149 153 0.266137821380696 0.532275642761392 0.733862178619304 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.0620689655172414 NOK 5% type I error level 42 0.289655172413793 NOK 10% type I error level 68 0.468965517241379 NOK

Charts produced by software:

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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') }

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Software written by Ed van Stee & Patrick Wessa

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