Multiple lineair 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: Sun, 07 Dec 2008 08:26:14 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy.htm/, Retrieved Sun, 07 Dec 2008 15:27:45 +0000

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/2008/Dec/07/t1228663655md5ybse96v886gy.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

15107 0 15024 0 12083 0 15761 0 16943 0 15070 0 13660 0 14769 0 14725 0 15998 0 15371 0 14957 0 15470 0 15102 0 11704 0 16284 0 16727 0 14969 0 14861 0 14583 0 15306 0 17904 0 16379 0 15420 0 17871 0 15913 0 13867 0 17823 0 17872 0 17422 0 16705 0 15991 0 16584 0 19124 0 17839 0 17209 0 18587 0 16258 0 15142 0 19202 0 17747 0 19090 0 18040 0 17516 0 17752 0 21073 0 17170 0 19440 0 19795 0 17575 0 16165 0 19465 1 19932 1 19961 1 17343 1 18924 1 18574 1 21351 1 18595 1 19823 1 20844 1 19640 1 17735 1 19814 1 22239 1 20682 1 17819 1 21872 1 22117 1 21866 1

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 3 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation y[t] = + 14007.0238848242 + 38.1340571158834D[t] + 1043.13076075839M1[t] -410.401164345842M2[t] -2639.59975611675M3[t] + 869.679309259697M4[t] + 1294.98071748879M5[t] + 490.782125717882M6[t] -1063.41646605303M7[t] -285.448391157266M8[t] -144.813649594839M9[t] + 1804.98775863425M10[t] -205.801408229093M11[t] + 93.1985917709071t + 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) 14007.0238848242 404.734427 34.6079 0 0 D 38.1340571158834 330.097704 0.1155 0.908443 0.454222 M1 1043.13076075839 466.734848 2.235 0.029427 0.014714 M2 -410.401164345842 466.363474 -0.88 0.382619 0.191309 M3 -2639.59975611675 466.103655 -5.6631 1e-06 0 M4 869.679309259697 469.208817 1.8535 0.069081 0.034541 M5 1294.98071748879 468.51898 2.764 0.007715 0.003857 M6 490.782125717882 467.939537 1.0488 0.298769 0.149384 M7 -1063.41646605303 467.470901 -2.2748 0.026766 0.013383 M8 -285.448391157266 467.113404 -0.6111 0.543613 0.271806 M9 -144.813649594839 466.867301 -0.3102 0.757574 0.378787 M10 1804.98775863425 466.73277 3.8673 0.000289 0.000145 M11 -205.801408229093 486.63376 -0.4229 0.673982 0.336991 t 93.1985917709071 7.219978 12.9084 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.955996621165197 R-squared 0.913929539679274 Adjusted R-squared 0.89394889710482 F-TEST (value) 45.7407481402904 F-TEST (DF numerator) 13 F-TEST (DF denominator) 56 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 769.350843574968 Sum Squared Residuals 33146440.3485328 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15107 15143.3532373535 -36.3532373534897 2 15024 13783.0199040201 1240.98009597986 3 12083 11647.0199040201 435.980095979866 4 15761 15249.4975611675 511.50243883251 5 16943 15767.9975611675 1175.00243883251 6 15070 15056.9975611675 13.0024388325070 7 13660 13595.9975611675 64.0024388325062 8 14769 14467.1642278342 301.835772165840 9 14725 14700.9975611675 24.0024388325080 10 15998 16743.9975611675 -745.997561167492 11 15371 14826.4069860751 544.593013924947 12 14957 15125.4069860751 -168.406986075054 13 15470 16261.7363386044 -791.736338604356 14 15102 14901.4030052710 200.596994728978 15 11704 12765.4030052710 -1061.40300527103 16 16284 16367.8806624184 -83.8806624183785 17 16727 16886.3806624184 -159.380662418378 18 14969 16175.3806624184 -1206.38066241838 19 14861 14714.3806624184 146.619337581622 20 14583 15585.5473290850 -1002.54732908504 21 15306 15819.3806624184 -513.380662418378 22 17904 17862.3806624184 41.6193375816217 23 16379 15944.7900873259 434.209912674062 24 15420 16243.7900873259 -823.790087325937 25 17871 17380.1194398552 490.88056014476 26 15913 16019.7861065219 -106.786106521911 27 13867 13883.7861065219 -16.7861065219117 28 17823 17486.2637636693 336.736236330737 29 17872 18004.7637636693 -132.763763669263 30 17422 17293.7637636693 128.236236330737 31 16705 15832.7637636693 872.236236330737 32 15991 16703.9304303359 -712.930430335929 33 16584 16937.7637636693 -353.763763669263 34 19124 18980.7637636693 143.236236330737 35 17839 17063.1731885768 775.826811423177 36 17209 17362.1731885768 -153.173188576822 37 18587 18498.5025411061 88.4974588938752 38 16258 17138.1692077728 -880.169207772796 39 15142 15002.1692077728 139.830792227203 40 19202 18604.6468649201 597.353135079851 41 17747 19123.1468649201 -1376.14686492015 42 19090 18412.1468649201 677.853135079853 43 18040 16951.1468649201 1088.85313507985 44 17516 17822.3135315868 -306.313531586814 45 17752 18056.1468649201 -304.146864920148 46 21073 20099.1468649201 973.85313507985 47 17170 18181.5562898277 -1011.55628982771 48 19440 18480.5562898277 959.443710172292 49 19795 19616.885642357 178.11435764299 50 17575 18256.5523090237 -681.552309023682 51 16165 16120.5523090237 44.4476909763180 52 19465 19761.1640232869 -296.164023286917 53 19932 20279.6640232869 -347.664023286917 54 19961 19568.6640232869 392.335976713083 55 17343 18107.6640232869 -764.664023286917 56 18924 18978.8306899536 -54.8306899535836 57 18574 19212.6640232869 -638.664023286917 58 21351 21255.6640232869 95.3359767130826 59 18595 19338.0734481945 -743.073448194477 60 19823 19637.0734481945 185.926551805523 61 20844 20773.4028007238 70.5971992762211 62 19640 19413.0694673905 226.930532609549 63 17735 17277.0694673905 457.930532609549 64 19814 20879.5471245378 -1065.54712453780 65 22239 21398.0471245378 840.952875462197 66 20682 20687.0471245378 -5.04712453780199 67 17819 19226.0471245378 -1407.04712453780 68 21872 20097.2137912045 1774.78620879553 69 22117 20331.0471245378 1785.95287546220 70 21866 22374.0471245378 -508.047124537802 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0724158864531486 0.144831772906297 0.927584113546851 18 0.0263860950938242 0.0527721901876484 0.973613904906176 19 0.0784051164160536 0.156810232832107 0.921594883583946 20 0.0448252843219021 0.0896505686438042 0.955174715678098 21 0.0246909101530613 0.0493818203061226 0.97530908984694 22 0.114215374428350 0.228430748856701 0.88578462557165 23 0.0877503518240498 0.175500703648100 0.91224964817595 24 0.0558167813212944 0.111633562642589 0.944183218678706 25 0.195967234134561 0.391934468269123 0.804032765865439 26 0.137076720578578 0.274153441157155 0.862923279421422 27 0.127251407575435 0.25450281515087 0.872748592424565 28 0.103691139171922 0.207382278343845 0.896308860828078 29 0.0680129670328927 0.136025934065785 0.931987032967107 30 0.0762115799924737 0.152423159984947 0.923788420007526 31 0.105133126208788 0.210266252417577 0.894866873791212 32 0.0797173224234982 0.159434644846996 0.920282677576502 33 0.0533699819576004 0.106739963915201 0.9466300180424 34 0.0412100571177306 0.0824201142354611 0.95878994288227 35 0.0528926647281619 0.105785329456324 0.947107335271838 36 0.0380216367629562 0.0760432735259125 0.961978363237044 37 0.0246031268598364 0.0492062537196728 0.975396873140164 38 0.0279872781290633 0.0559745562581266 0.972012721870937 39 0.0192120550429600 0.0384241100859199 0.98078794495704 40 0.019429375717499 0.038858751434998 0.9805706242825 41 0.0518035951684044 0.103607190336809 0.948196404831596 42 0.0550666635679439 0.110133327135888 0.944933336432056 43 0.173917574555137 0.347835149110274 0.826082425444863 44 0.154619406808278 0.309238813616556 0.845380593191722 45 0.127090306518022 0.254180613036044 0.872909693481978 46 0.199451404657738 0.398902809315476 0.800548595342262 47 0.196028774712844 0.392057549425688 0.803971225287156 48 0.218706643564231 0.437413287128461 0.78129335643577 49 0.161764429769934 0.323528859539869 0.838235570230065 50 0.112125538390629 0.224251076781257 0.887874461609371 51 0.06392157393117 0.12784314786234 0.93607842606883 52 0.0584892602663925 0.116978520532785 0.941510739733608 53 0.0311686628847024 0.0623373257694049 0.968831337115298 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 4 0.108108108108108 NOK 10% type I error level 10 0.270270270270270 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/1022y51228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/1022y51228663569.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/1fttj1228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/1fttj1228663569.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/2kgne1228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/2kgne1228663569.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/3ueom1228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/3ueom1228663569.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/426ef1228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/426ef1228663569.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/5sk9n1228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/5sk9n1228663569.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/62ykj1228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/62ykj1228663569.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/773cz1228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/773cz1228663569.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/8fzk01228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/8fzk01228663569.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/9y6251228663569.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663655md5ybse96v886gy/9y6251228663569.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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