*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Wed, 26 Nov 2008 11:19:41 -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/Nov/26/t1227723650ebu64qh8kxxdj9u.htm/, Retrieved Wed, 26 Nov 2008 18:20:59 +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/Nov/26/t1227723650ebu64qh8kxxdj9u.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:

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

147768 0 137507 0 136919 0 136151 1 133001 1 125554 1 119647 0 114158 0 116193 0 152803 0 161761 0 160942 0 149470 0 139208 0 134588 0 130322 1 126611 1 122401 1 117352 0 112135 0 112879 0 148729 0 157230 0 157221 0 146681 0 136524 0 132111 0 125326 1 122716 1 116615 1 113719 0 110737 0 112093 0 143565 0 149946 0 149147 0 134339 0 122683 0 115614 0 116566 1 111272 1 104609 1 101802 0 94542 0 93051 0 124129 0 130374 0 123946 0 114971 0 105531 0 104919 0 104782 1 101281 1 94545 1 93248 0 84031 0 87486 0 115867 0 120327 0 117008 0 108811 0

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 jonger_dan_25[t] = + 125298.086956522 -7181.28695652175winter[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) 125298.086956522 2803.344134 44.6959 0 0 winter -7181.28695652175 5653.2176 -1.2703 0.208964 0.104482 Multiple Linear Regression - Regression Statistics Multiple R 0.163162860071836 R-squared 0.0266221189068215 Adjusted R-squared 0.0101241887188016 F-TEST (value) 1.61366417504623 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.208964207845363 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 19013.2049750854 Sum Squared Residuals 21328615842.0522 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 147768 125298.086956521 22469.9130434787 2 137507 125298.086956522 12208.9130434782 3 136919 125298.086956522 11620.9130434782 4 136151 118116.8 18034.2 5 133001 118116.8 14884.2 6 125554 118116.8 7437.2 7 119647 125298.086956522 -5651.08695652175 8 114158 125298.086956522 -11140.0869565218 9 116193 125298.086956522 -9105.08695652175 10 152803 125298.086956522 27504.9130434783 11 161761 125298.086956522 36462.9130434782 12 160942 125298.086956522 35643.9130434782 13 149470 125298.086956522 24171.9130434783 14 139208 125298.086956522 13909.9130434782 15 134588 125298.086956522 9289.91304347825 16 130322 118116.8 12205.2 17 126611 118116.8 8494.2 18 122401 118116.8 4284.2 19 117352 125298.086956522 -7946.08695652175 20 112135 125298.086956522 -13163.0869565218 21 112879 125298.086956522 -12419.0869565218 22 148729 125298.086956522 23430.9130434783 23 157230 125298.086956522 31931.9130434783 24 157221 125298.086956522 31922.9130434783 25 146681 125298.086956522 21382.9130434783 26 136524 125298.086956522 11225.9130434782 27 132111 125298.086956522 6812.91304347825 28 125326 118116.8 7209.2 29 122716 118116.8 4599.2 30 116615 118116.8 -1501.80000000000 31 113719 125298.086956522 -11579.0869565218 32 110737 125298.086956522 -14561.0869565217 33 112093 125298.086956522 -13205.0869565218 34 143565 125298.086956522 18266.9130434782 35 149946 125298.086956522 24647.9130434783 36 149147 125298.086956522 23848.9130434783 37 134339 125298.086956522 9040.91304347825 38 122683 125298.086956522 -2615.08695652175 39 115614 125298.086956522 -9684.08695652175 40 116566 118116.8 -1550.80000000000 41 111272 118116.8 -6844.8 42 104609 118116.8 -13507.8 43 101802 125298.086956522 -23496.0869565217 44 94542 125298.086956522 -30756.0869565218 45 93051 125298.086956522 -32247.0869565218 46 124129 125298.086956522 -1169.08695652175 47 130374 125298.086956522 5075.91304347825 48 123946 125298.086956522 -1352.08695652175 49 114971 125298.086956522 -10327.0869565218 50 105531 125298.086956522 -19767.0869565217 51 104919 125298.086956522 -20379.0869565217 52 104782 118116.8 -13334.8 53 101281 118116.8 -16835.8 54 94545 118116.8 -23571.8 55 93248 125298.086956522 -32050.0869565218 56 84031 125298.086956522 -41267.0869565218 57 87486 125298.086956522 -37812.0869565218 58 115867 125298.086956522 -9431.08695652175 59 120327 125298.086956522 -4971.08695652175 60 117008 125298.086956522 -8290.08695652175 61 108811 125298.086956522 -16487.0869565217 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0252774868245957 0.0505549736491914 0.974722513175404 6 0.0146942261505928 0.0293884523011856 0.985305773849407 7 0.0601377091270239 0.120275418254048 0.939862290872976 8 0.0990237452866827 0.198047490573365 0.900976254713317 9 0.0855650653002379 0.171130130600476 0.914434934699762 10 0.146301922183852 0.292603844367704 0.853698077816148 11 0.298245936871677 0.596491873743354 0.701754063128323 12 0.411035962451586 0.822071924903173 0.588964037548414 13 0.379036752137094 0.758073504274188 0.620963247862906 14 0.3055306031229 0.6110612062458 0.6944693968771 15 0.240745426918922 0.481490853837845 0.759254573081078 16 0.184409602648946 0.368819205297892 0.815590397351054 17 0.138599073278913 0.277198146557826 0.861400926721087 18 0.10458212318089 0.20916424636178 0.89541787681911 19 0.119676130115015 0.239352260230030 0.880323869884985 20 0.154153124885371 0.308306249770741 0.84584687511463 21 0.170057618056408 0.340115236112817 0.829942381943592 22 0.179607513238382 0.359215026476765 0.820392486761618 23 0.271655956751795 0.543311913503591 0.728344043248205 24 0.397988442928198 0.795976885856396 0.602011557071802 25 0.428435394280229 0.856870788560459 0.571564605719771 26 0.40120862374756 0.80241724749512 0.59879137625244 27 0.366417804315649 0.732835608631298 0.633582195684351 28 0.324928237237658 0.649856474475315 0.675071762762342 29 0.286771743179940 0.573543486359881 0.71322825682006 30 0.251772067249733 0.503544134499467 0.748227932750267 31 0.266201747243848 0.532403494487696 0.733798252756152 32 0.288333399602041 0.576666799204083 0.711666600397959 33 0.288882592413286 0.577765184826572 0.711117407586714 34 0.336751651207266 0.673503302414533 0.663248348792734 35 0.511313736324411 0.977372527351179 0.488686263675589 36 0.743488161083763 0.513023677832474 0.256511838916237 37 0.804548186163147 0.390903627673707 0.195451813836853 38 0.803556248308095 0.39288750338381 0.196443751691905 39 0.79024156042931 0.419516879141382 0.209758439570691 40 0.769195523678464 0.461608952643072 0.230804476321536 41 0.738531481352353 0.522937037295295 0.261468518647647 42 0.70210854221772 0.595782915564561 0.297891457782280 43 0.722305582452293 0.555388835095413 0.277694417547707 44 0.791709936332908 0.416580127334184 0.208290063667092 45 0.852761771397939 0.294476457204122 0.147238228602061 46 0.840418339385905 0.319163321228190 0.159581660614095 47 0.88789518233487 0.224209635330260 0.112104817665130 48 0.904456875975893 0.191086248048215 0.0955431240241075 49 0.88371983409409 0.232560331811821 0.116280165905910 50 0.8389297083051 0.3221405833898 0.1610702916949 51 0.778724491909742 0.442551016180515 0.221275508090258 52 0.701509831604984 0.596980336790032 0.298490168395016 53 0.60579655959426 0.78840688081148 0.39420344040574 54 0.492213540308983 0.984427080617967 0.507786459691017 55 0.438809874308041 0.877619748616082 0.561190125691959 56 0.585544667039853 0.828910665920295 0.414455332960148 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 1 0.0192307692307692 OK 10% type I error level 2 0.0384615384615385 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/10f6191227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/10f6191227723576.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/1rs7y1227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/1rs7y1227723576.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/2oeka1227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/2oeka1227723576.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/3ld2s1227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/3ld2s1227723576.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/4fthv1227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/4fthv1227723576.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/54lsp1227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/54lsp1227723576.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/6dftk1227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/6dftk1227723576.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/7bswm1227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/7bswm1227723576.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/8txmh1227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/8txmh1227723576.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/99hu21227723576.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227723650ebu64qh8kxxdj9u/99hu21227723576.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