*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: Fri, 20 Nov 2009 07:02:22 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n.htm/, Retrieved Fri, 20 Nov 2009 15:04:16 +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/2009/Nov/20/t1258725844nkekb5jrworxx9n.htm/}, year = {2009}, } @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 = {2009}, 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 «

1 2 1,2 1,4 1,7 2 1 1,2 2,4 2 1,7 1 2 2 2,4 1,7 2,1 2 2 2,4 2 2 2,1 2 1,8 2 2 2,1 2,7 2 1,8 2 2,3 2 2,7 1,8 1,9 2 2,3 2,7 2 2 1,9 2,3 2,3 2 2 1,9 2,8 2 2,3 2 2,4 2 2,8 2,3 2,3 2 2,4 2,8 2,7 2 2,3 2,4 2,7 2 2,7 2,3 2,9 2 2,7 2,7 3 2 2,9 2,7 2,2 2 3 2,9 2,3 2 2,2 3 2,8 2,21 2,3 2,2 2,8 2,25 2,8 2,3 2,8 2,25 2,8 2,8 2,2 2,45 2,8 2,8 2,6 2,5 2,2 2,8 2,8 2,5 2,6 2,2 2,5 2,64 2,8 2,6 2,4 2,75 2,5 2,8 2,3 2,93 2,4 2,5 1,9 3 2,3 2,4 1,7 3,17 1,9 2,3 2 3,25 1,7 1,9 2,1 3,39 2 1,7 1,7 3,5 2,1 2 1,8 3,5 1,7 2,1 1,8 3,65 1,8 1,7 1,8 3,75 1,8 1,8 1,3 3,75 1,8 1,8 1,3 3,9 1,3 1,8 1,3 4 1,3 1,3 1,2 4 1,3 1,3 1,4 4 1,2 1,3 2,2 4 1,4 1,2 2,9 4 2,2 1,4 3,1 4 2,9 2,2 3,5 4 3,1 2,9 3,6 4 3,5 3,1 4,4 4 3,6 3,5 4,1 4 4,4 3,6 5,1 4 4,1 4,4 5,8 4 5,1 4,1 5,9 4,18 5,8 5,1 5,4 4,25 5,9 5,8 5,5 4,25 5,4 5,9 4,8 3,97 5,5 5,4 3,2 3,42 4,8 5,5 2,7 2,75 3,2 4,8

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -0.0734302746307986 + 0.240213449888453X[t] + 1.06847062139874Y1[t] -0.151877506173371Y2[t] -0.0658484772738318M1[t] -0.0807067438688125M2[t] + 0.119085905707768M3[t] -0.0704160867844202M4[t] -0.105059685311399M5[t] -0.236207021068785M6[t] -0.138233436004764M7[t] -0.0983526673209309M8[t] -0.251765154607209M9[t] -0.0539842332914323M10[t] -0.123558533753511M11[t] -0.0100825486711427t + 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) -0.0734302746307986 0.516944 -0.142 0.887722 0.443861 X 0.240213449888453 0.245619 0.978 0.333677 0.166839 Y1 1.06847062139874 0.155586 6.8674 0 0 Y2 -0.151877506173371 0.176414 -0.8609 0.394173 0.197087 M1 -0.0658484772738318 0.333842 -0.1972 0.844588 0.422294 M2 -0.0807067438688125 0.333377 -0.2421 0.80989 0.404945 M3 0.119085905707768 0.332721 0.3579 0.7222 0.3611 M4 -0.0704160867844202 0.334438 -0.2106 0.834256 0.417128 M5 -0.105059685311399 0.335323 -0.3133 0.755597 0.377798 M6 -0.236207021068785 0.335886 -0.7032 0.485789 0.242894 M7 -0.138233436004764 0.335708 -0.4118 0.682604 0.341302 M8 -0.0983526673209309 0.332598 -0.2957 0.768908 0.384454 M9 -0.251765154607209 0.333541 -0.7548 0.454566 0.227283 M10 -0.0539842332914323 0.337482 -0.16 0.873678 0.436839 M11 -0.123558533753511 0.350141 -0.3529 0.725942 0.362971 t -0.0100825486711427 0.014672 -0.6872 0.495748 0.247874 Multiple Linear Regression - Regression Statistics Multiple R 0.93060263788341 R-squared 0.866021269635562 Adjusted R-squared 0.818171723076834 F-TEST (value) 18.0988396321093 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 1.14797060746241e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.494837629499835 Sum Squared Residuals 10.2842997418987 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1 1.40060183623691 -0.400601836236909 2 1.7 1.19234239792570 0.507657602074297 3 2.4 2.16035743504493 0.239642564955067 4 2 2.60238807453935 -0.602388074539354 5 2.1 2.02395942446038 0.0760405755396217 6 2 2.05032760464107 -0.0503276046410725 7 1.8 2.01618382827674 -0.216183828276739 8 2.7 1.84747567462702 0.85252432537298 9 2.3 2.67597969916314 -0.375979699163136 10 1.9 2.29960006769224 -0.399600067692242 11 2 1.85330597246887 0.146694027531126 12 2.3 2.13438002216046 0.165619977839535 13 2.8 2.36380243201777 0.436197567982226 14 2.4 2.82753367559901 -0.427533675599007 15 2.3 2.51391677485826 -0.213916774858265 16 2.7 2.26823617402441 0.431763825975592 17 2.7 2.66608602600312 0.0339139739968816 18 2.9 2.46410513910524 0.435894860894758 19 3 2.76569029977787 0.234309700222133 20 2.2 2.87196008069576 -0.671960080695757 21 2.3 1.83850079700201 0.461499202997989 22 2.8 2.30499306120179 0.495006938798211 23 2.8 2.75399231014614 0.0460076898538632 24 2.8 2.79152954214182 0.00847045785817974 25 2.2 2.76364120617454 -0.563641206174536 26 2.6 2.10962869056359 0.490371309436406 27 2.8 2.81785354373255 -0.0178535437325490 28 2.5 2.804842007364 -0.304842007364 29 2.4 2.43562265199931 -0.035622651999313 30 2.3 2.27634737826284 0.0236526217371558 31 1.9 2.28939404462538 -0.389394044625376 32 1.7 1.94782805317695 -0.247828053176947 33 2 1.65060697140020 0.349393028599796 34 2.1 2.22285191468352 -0.122851914683516 35 1.7 2.23090235532589 -0.530902355325887 36 1.8 1.90180234123142 -0.101802341231424 37 1.8 2.02950139737894 -0.229501397378939 38 1.8 2.01339417648432 -0.213394176484324 39 1.3 2.20310427738976 -0.903104277389762 40 1.3 1.50531644301033 -0.205316443010331 41 1.3 1.56055039388774 -0.260550393887739 42 1.2 1.41932050945921 -0.219320509459211 43 1.4 1.40036448371222 -0.000364483712215286 44 2.2 1.65904457862199 0.54095542137801 45 2.9 2.31995053854888 0.580049461451115 46 3.1 3.13407634123394 -0.0340763412339369 47 3.5 3.1617993620591 0.338200637940897 48 3.6 3.67228809446629 -0.0722880944662918 49 4.4 3.64245312819184 0.757546871808157 50 4.1 4.45710105942737 -0.357101059427372 51 5.1 4.20476796897449 0.895232031025509 52 5.8 5.11921730106191 0.680782698938093 53 5.9 5.71378150364945 0.186218496350549 54 5.4 5.58989936853163 -0.189899368531630 55 5.5 5.1283673436078 0.371632656392198 56 4.8 5.27369161287829 -0.473691612878285 57 3.2 4.21496199388576 -1.01496199388576 58 2.7 2.63847861518852 0.0615213848114844 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.459441795118545 0.91888359023709 0.540558204881455 20 0.655118093845668 0.689763812308664 0.344881906154332 21 0.531802581282074 0.936394837435852 0.468197418717926 22 0.410558377005022 0.821116754010043 0.589441622994978 23 0.288902370838456 0.577804741676912 0.711097629161544 24 0.19822987805666 0.39645975611332 0.80177012194334 25 0.139448752202932 0.278897504405865 0.860551247797068 26 0.132048983907634 0.264097967815269 0.867951016092366 27 0.0892095606444316 0.178419121288863 0.910790439355568 28 0.0543232348449099 0.108646469689820 0.94567676515509 29 0.0321969583426167 0.0643939166852334 0.967803041657383 30 0.0221094872279176 0.0442189744558351 0.977890512772082 31 0.0125918719360459 0.0251837438720917 0.987408128063954 32 0.00652494975561953 0.0130498995112391 0.99347505024438 33 0.0107959423839636 0.0215918847679272 0.989204057616036 34 0.00670560119209043 0.0134112023841809 0.99329439880791 35 0.00286879147266795 0.0057375829453359 0.997131208527332 36 0.00480771940822882 0.00961543881645763 0.995192280591771 37 0.00237267514447376 0.00474535028894751 0.997627324855526 38 0.0814361482648349 0.162872296529670 0.918563851735165 39 0.197217967825072 0.394435935650144 0.802782032174928 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.142857142857143 NOK 5% type I error level 8 0.380952380952381 NOK 10% type I error level 9 0.428571428571429 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/10g4bm1258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/10g4bm1258725737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/15npo1258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/15npo1258725737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/2g5zl1258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/2g5zl1258725737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/3wuqu1258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/3wuqu1258725737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/4t0061258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/4t0061258725737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/5it2t1258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/5it2t1258725737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/6izax1258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/6izax1258725737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/7exsu1258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/7exsu1258725737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/8arzl1258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/8arzl1258725737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/9r1df1258725737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725844nkekb5jrworxx9n/9r1df1258725737.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