*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, 18 Nov 2009 15:32:40 -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/18/t1258583731dmw2akrhg7ri92a.htm/, Retrieved Wed, 18 Nov 2009 23:35:43 +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/18/t1258583731dmw2akrhg7ri92a.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 «

7.3 20.9 7.4 8.1 8.2 7.7 20.9 7.3 7.4 8.3 8 22.3 7.7 7.3 8.1 8 22.3 8 7.7 7.4 7.7 22.3 8 8 7.3 6.9 19.9 7.7 8 7.7 6.6 19.9 6.9 7.7 8 6.9 19.9 6.6 6.9 8 7.5 24.1 6.9 6.6 7.7 7.9 24.1 7.5 6.9 6.9 7.7 24.1 7.9 7.5 6.6 6.5 13.8 7.7 7.9 6.9 6.1 13.8 6.5 7.7 7.5 6.4 13.8 6.1 6.5 7.9 6.8 16.2 6.4 6.1 7.7 7.1 16.2 6.8 6.4 6.5 7.3 16.2 7.1 6.8 6.1 7.2 18.6 7.3 7.1 6.4 7 18.6 7.2 7.3 6.8 7 18.6 7 7.2 7.1 7 22.4 7 7 7.3 7.3 22.4 7 7 7.2 7.5 22.4 7.3 7 7 7.2 22.6 7.5 7.3 7 7.7 22.6 7.2 7.5 7 8 22.6 7.7 7.2 7.3 7.9 20 8 7.7 7.5 8 20 7.9 8 7.2 8 20 8 7.9 7.7 7.9 21.8 8 8 8 7.9 21.8 7.9 8 7.9 8 21.8 7.9 7.9 8 8.1 28.7 8 7.9 8 8.1 28.7 8.1 8 7.9 8.2 28.7 8.1 8.1 7.9 8 19.5 8.2 8.1 8 8.3 19.5 8 8.2 8.1 8.5 19.5 8.3 8 8.1 8.6 19.4 8.5 8.3 8.2 8.7 19.4 8.6 8.5 8 8.7 19.4 8.7 8.6 8.3 8.5 21.7 8.7 8.7 8.5 8.4 21.7 8.5 8.7 8.6 8.5 21.7 8.4 8.5 8.7 8.7 26.2 8.5 8.4 8.7 8.7 26.2 8.7 8.5 8.5 8.6 26.2 8.7 8.7 8.4 7.9 19.1 8.6 8.7 8.5 8.1 19.1 7.9 8.6 8.7 8.2 19.1 8.1 7.9 8.7 8.5 21.3 8 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 0.547157098944361 + 0.0321674963811641X[t] + 1.33488233954830V3[t] -0.795642086510696V4[t] + 0.227809531092081V5[t] + 0.868738676572356M1[t] + 0.458688561229227M2[t] + 0.372250932452915M3[t] + 0.566569053536188M4[t] + 0.495916374206385M5[t] + 0.254352832302715M6[t] + 0.403918034920457M7[t] + 0.533870371726237M8[t] + 0.275340055011409M9[t] + 0.310975385977565M10[t] + 0.283747612908392M11[t] + 0.00681426277693805t + 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) 0.547157098944361 0.394221 1.3879 0.173033 0.086516 X 0.0321674963811641 0.011624 2.7673 0.008598 0.004299 V3 1.33488233954830 0.123865 10.7769 0 0 V4 -0.795642086510696 0.128564 -6.1887 0 0 V5 0.227809531092081 0.063004 3.6158 0.000847 0.000424 M1 0.868738676572356 0.12258 7.0871 0 0 M2 0.458688561229227 0.118164 3.8818 0.000389 0.000195 M3 0.372250932452915 0.118381 3.1445 0.003177 0.001589 M4 0.566569053536188 0.102459 5.5297 2e-06 1e-06 M5 0.495916374206385 0.101036 4.9083 1.7e-05 8e-06 M6 0.254352832302715 0.103875 2.4487 0.018938 0.009469 M7 0.403918034920457 0.112205 3.5998 0.000887 0.000444 M8 0.533870371726237 0.114775 4.6515 3.7e-05 1.9e-05 M9 0.275340055011409 0.14052 1.9594 0.057238 0.028619 M10 0.310975385977565 0.132602 2.3452 0.0242 0.0121 M11 0.283747612908392 0.128757 2.2037 0.033514 0.016757 t 0.00681426277693805 0.00201 3.3894 0.001614 0.000807 Multiple Linear Regression - Regression Statistics Multiple R 0.982536898781787 R-squared 0.965378757467732 Adjusted R-squared 0.951175170787828 F-TEST (value) 67.967252161284 F-TEST (DF numerator) 16 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.149118148521042 Sum Squared Residuals 0.867212666515399 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.3 7.39647727953587 -0.0964772795358743 2 7.7 7.43948360668153 0.260516393318473 3 8 7.97284997386776 0.0271500261322408 4 8 8.09672355322372 -0.0967235532237242 5 7.7 7.77141155760844 -0.0714115576084428 6 6.9 7.15011939773926 -0.250119397739259 7 6.6 6.54562847677613 0.0543715232238701 8 6.9 6.91844404370291 -0.0184440437029134 9 7.5 7.37264594305599 0.127354056944011 10 7.9 7.79508468970119 0.104915310298811 11 7.7 7.76289600399423 -0.0628960039942347 12 6.5 6.63774699795048 -0.137746997950475 13 6.1 6.2072552657992 -0.107255265799194 14 6.4 6.31596079366335 0.08403920633665 15 6.8 6.98669904922912 -0.186699049229124 16 7.1 7.20972030564495 -0.109720305644949 17 7.3 7.13696594391546 0.163034056084537 18 7.2 7.0760453573876 0.123954642612399 19 7 7.03093198396214 -0.0309319839621446 20 7 7.0486291836139 -0.0486291836138961 21 7 7.12383993944499 -0.123839939444986 22 7.3 7.14350858007887 0.156491419921128 23 7.5 7.47799786543271 0.0220021345672888 24 7.2 7.23578185653394 -0.0357818565339417 25 7.7 7.5517416767166 0.148258323283394 26 8 8.1229824792054 -0.122982479205400 27 7.9 8.00792918744256 -0.107929187442557 28 8 7.76853785206711 0.231462147932895 29 8 8.03165664366618 -0.0316566436661801 30 7.9 7.8435875087021 0.0564124912979019 31 7.9 7.84369778703274 0.0563022129672599 32 8 8.08280954837574 -0.0828095483757363 33 8.1 8.1865374534227 -0.0865374534227094 34 8.1 8.26013011936035 -0.160130119360355 35 8.2 8.16015240041705 0.039847599582948 36 8 7.74354727064292 0.256452729357076 37 8.3 8.2953404865407 0.00465951345930334 38 8.5 8.45169775314114 0.0483022468588625 39 8.6 8.4199224325693 0.180077567430695 40 8.7 8.54985272686379 0.150147273136209 41 8.7 8.60828119494231 0.0917188050576886 42 8.5 8.4135148550596 0.086485144940397 43 8.4 8.32569880565383 0.0743011943461689 44 8.5 8.51088654169307 -0.0108865416930670 45 8.7 8.61697666407632 0.0830233359236845 46 8.7 8.80127661085958 -0.101276610859583 47 8.6 8.598953730156 0.00104626984399785 48 7.9 7.98292387487266 -0.0829238748726598 49 8.1 8.04918529140763 0.0508147085923708 50 8.2 8.46987536730859 -0.269875367308585 51 8.5 8.41259935689125 0.0874006431087455 52 8.6 8.77516556220043 -0.175165562200431 53 8.5 8.6516846598676 -0.151684659867602 54 8.3 8.31673288111144 -0.0167328811114385 55 8.2 8.35404294657515 -0.154042946575154 56 8.7 8.53923068261439 0.160769317385613 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.105658987273038 0.211317974546076 0.894341012726962 21 0.044579093698518 0.089158187397036 0.955420906301482 22 0.335807629034397 0.671615258068793 0.664192370965603 23 0.433546707116921 0.867093414233843 0.566453292883079 24 0.513054911014209 0.973890177971582 0.486945088985791 25 0.439007546959346 0.878015093918691 0.560992453040654 26 0.48956934187762 0.97913868375524 0.51043065812238 27 0.582399907958401 0.835200184083197 0.417600092041598 28 0.752451810507922 0.495096378984156 0.247548189492078 29 0.645625461695885 0.70874907660823 0.354374538304115 30 0.54906745191894 0.901865096162121 0.450932548081061 31 0.462443992556908 0.924887985113815 0.537556007443092 32 0.426386885307179 0.852773770614357 0.573613114692821 33 0.411324260986009 0.822648521972018 0.588675739013991 34 0.476019837553374 0.952039675106747 0.523980162446626 35 0.681572961120581 0.636854077758837 0.318427038879419 36 0.582830157478647 0.834339685042706 0.417169842521353 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 1 0.0588235294117647 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/10e9he1258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/10e9he1258583556.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/18hy01258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/18hy01258583556.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/2mj391258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/2mj391258583556.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/3we2z1258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/3we2z1258583556.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/4waz51258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/4waz51258583556.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/5rsw11258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/5rsw11258583556.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/6x9fa1258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/6x9fa1258583556.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/7jqyn1258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/7jqyn1258583556.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/8t2dl1258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/8t2dl1258583556.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/9ssfj1258583556.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258583731dmw2akrhg7ri92a/9ssfj1258583556.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