shw7: Multiple lineair regression software (4)

*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: Thu, 19 Nov 2009 11:19:23 -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/19/t1258654819tpa9b6af0y2m642.htm/, Retrieved Thu, 19 Nov 2009 19:20:31 +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/19/t1258654819tpa9b6af0y2m642.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:

Multiple lineair regression software (4)

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0.7905 0.313 0.7744 0.779 0.7775 0.7461 0.7719 0.364 0.7905 0.7744 0.779 0.7775 0.7811 0.363 0.7719 0.7905 0.7744 0.779 0.7557 -0.155 0.7811 0.7719 0.7905 0.7744 0.7637 0.052 0.7557 0.7811 0.7719 0.7905 0.7595 0.568 0.7637 0.7557 0.7811 0.7719 0.7471 0.668 0.7595 0.7637 0.7557 0.7811 0.7615 1.378 0.7471 0.7595 0.7637 0.7557 0.7487 0.252 0.7615 0.7471 0.7595 0.7637 0.7389 -0.402 0.7487 0.7615 0.7471 0.7595 0.7337 -0.05 0.7389 0.7487 0.7615 0.7471 0.751 0.555 0.7337 0.7389 0.7487 0.7615 0.7382 0.05 0.751 0.7337 0.7389 0.7487 0.7159 0.15 0.7382 0.751 0.7337 0.7389 0.7542 0.45 0.7159 0.7382 0.751 0.7337 0.7636 0.299 0.7542 0.7159 0.7382 0.751 0.7433 0.199 0.7636 0.7542 0.7159 0.7382 0.7658 0.496 0.7433 0.7636 0.7542 0.7159 0.7627 0.444 0.7658 0.7433 0.7636 0.7542 0.748 -0.393 0.7627 0.7658 0.7433 0.7636 0.7692 -0.444 0.748 0.7627 0.7658 0.7433 0.785 0.198 0.7692 0.748 0.7627 0.7658 0.7913 0.494 0.785 0.7692 0.748 0.7627 0.772 0.133 0.7913 0.785 0.7692 0.748 0.788 0.388 0.772 0.7913 0.785 0 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 USDOLLAR[t] = + 0.105597037179138 + 0.00941197428820186Amerikaanse_inflatie[t] + 1.20596116767540`Y[t-1]`[t] -0.527201466526525`Y[t-2]`[t] + 0.547813323376132`Y[t-3]`[t] -0.388942635999796`Y[t-4]`[t] + 0.0293145636322456M1[t] -0.00111634037128101M2[t] + 0.0413735451661571M3[t] + 0.0121472277427398M4[t] + 0.0212072164688286M5[t] + 0.0097722538432314M6[t] -0.0136301901705398M7[t] + 0.0219708478339727M8[t] -0.00996368572382476M9[t] + 0.0239631461363912M10[t] + 0.0182904283986546M11[t] + 0.000324299601614479t + 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) 0.105597037179138 0.049654 2.1267 0.040181 0.020091 Amerikaanse_inflatie 0.00941197428820186 0.01381 0.6815 0.499782 0.249891 `Y[t-1]` 1.20596116767540 0.183309 6.5788 0 0 `Y[t-2]` -0.527201466526525 0.247678 -2.1286 0.040012 0.020006 `Y[t-3]` 0.547813323376132 0.238998 2.2921 0.027685 0.013843 `Y[t-4]` -0.388942635999796 0.151709 -2.5637 0.014552 0.007276 M1 0.0293145636322456 0.021012 1.3952 0.171286 0.085643 M2 -0.00111634037128101 0.021164 -0.0527 0.958217 0.479109 M3 0.0413735451661571 0.021036 1.9668 0.056745 0.028372 M4 0.0121472277427398 0.021818 0.5568 0.581042 0.290521 M5 0.0212072164688286 0.02116 1.0022 0.322739 0.16137 M6 0.0097722538432314 0.021568 0.4531 0.653129 0.326565 M7 -0.0136301901705398 0.021231 -0.642 0.524832 0.262416 M8 0.0219708478339727 0.022859 0.9611 0.342726 0.171363 M9 -0.00996368572382476 0.023219 -0.4291 0.670327 0.335163 M10 0.0239631461363912 0.022911 1.0459 0.302391 0.151196 M11 0.0182904283986546 0.022231 0.8227 0.415924 0.207962 t 0.000324299601614479 0.000282 1.1515 0.256916 0.128458 Multiple Linear Regression - Regression Statistics Multiple R 0.94880027255588 R-squared 0.900221957202111 Adjusted R-squared 0.85437799159227 F-TEST (value) 19.6366510886849 F-TEST (DF numerator) 17 F-TEST (DF denominator) 37 p-value 1.49324996812084e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0305246824388129 Sum Squared Residuals 0.0344749808056438 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 0.7905 0.79712299239437 -0.00662299239437012 2 0.7719 0.777946421441421 -0.00604642144142124 3 0.7811 0.786729018034817 -0.00562901803481656 4 0.7557 0.784461318183687 -0.0287613181836867 5 0.7637 0.743860913783656 0.0198390862163442 6 0.7595 0.772919651688219 -0.0134196516882189 7 0.7471 0.724007325403481 0.0230926745965185 8 0.7615 0.768137141975872 -0.00663714197587215 9 0.7487 0.74441980692445 0.00428019307554969 10 0.7389 0.744328176998905 -0.00542817699890496 11 0.7337 0.749933933683564 -0.0162339336835641 12 0.751 0.723944841133322 0.0270551588666775 13 0.7382 0.772045128350074 -0.0338451283500743 14 0.7159 0.71928584161107 -0.00338584161107040 15 0.7542 0.756278535970568 -0.00207853597056763 16 0.7636 0.770159497314745 -0.00655949731474485 17 0.7433 0.76250903565132 -0.0192090356513203 18 0.7658 0.754411694569875 0.0113883054301251 19 0.7627 0.75893338581886 0.00376661418113985 20 0.748 0.756603717086188 -0.00860371708618804 21 0.7692 0.728641503109469 0.0405584968905306 22 0.785 0.791801929764523 -0.00680192976452251 23 0.7913 0.790269837694587 0.00103016230541267 24 0.772 0.785504857569513 -0.0135048575695132 25 0.788 0.79135722109776 -0.00335722109775968 26 0.807 0.788930463502756 0.0180695364972439 27 0.8268 0.831260684911889 -0.00446068491188936 28 0.8244 0.833347966055096 -0.00894796605509596 29 0.8487 0.83166468675651 0.0170353132434903 30 0.8572 0.854486837602672 0.00271316239732783 31 0.8214 0.819192537058665 0.00220746294133468 32 0.8827 0.82478629407322 0.057913705926781 33 0.9216 0.880672152779527 0.0409278472204730 34 0.8865 0.912388358570235 -0.0258883585702349 35 0.8816 0.885072227619392 -0.0034722276193915 36 0.8884 0.878007080980017 0.0103929190199829 37 0.9466 0.882283376034397 0.0643166239656029 38 0.918 0.931741681406962 -0.0137416814069616 39 0.9337 0.912876683150589 0.0208233168494111 40 0.9559 0.950348918563324 0.00555108143667627 41 0.9626 0.94405641603773 0.0185435839622705 42 0.9434 0.947192089912107 -0.00379208991210688 43 0.8639 0.896498612726762 -0.0325986127267623 44 0.7996 0.842272846864721 -0.0426728468647208 45 0.668 0.753766537186553 -0.0857665371865532 46 0.6572 0.619081534666338 0.0381184653336624 47 0.6928 0.674124001002457 0.0186759989975429 48 0.6438 0.667743220317147 -0.0239432203171471 49 0.6454 0.665891282123399 -0.0204912821233989 50 0.6873 0.68219559203779 0.00510440796220938 51 0.7265 0.735155077932138 -0.0086550779321376 52 0.7912 0.752482299883149 0.0387177001168513 53 0.8114 0.847608947770785 -0.0362089477707847 54 0.8281 0.824989726227127 0.00311027377287283 55 0.8393 0.83576813899223 0.0035318610077692 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.145730276264427 0.291460552528853 0.854269723735573 22 0.0666558641426012 0.133311728285202 0.933344135857399 23 0.0236722145265902 0.0473444290531805 0.97632778547341 24 0.0232310826196132 0.0464621652392265 0.976768917380387 25 0.0158342423218833 0.0316684846437665 0.984165757678117 26 0.00744278463504582 0.0148855692700916 0.992557215364954 27 0.0028250229230559 0.0056500458461118 0.997174977076944 28 0.00122212352569661 0.00244424705139322 0.998777876474303 29 0.000403525439058238 0.000807050878116476 0.999596474560942 30 0.000234483543839377 0.000468967087678754 0.99976551645616 31 0.000697188006406072 0.00139437601281214 0.999302811993594 32 0.000490859058266538 0.000981718116533076 0.999509140941733 33 0.0026897377436839 0.0053794754873678 0.997310262256316 34 0.00126699205251235 0.00253398410502471 0.998733007947488 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.571428571428571 NOK 5% type I error level 12 0.857142857142857 NOK 10% type I error level 12 0.857142857142857 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/10tnpv1258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/10tnpv1258654759.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/1zfto1258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/1zfto1258654759.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/2x8n21258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/2x8n21258654759.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/3q14p1258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/3q14p1258654759.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/49gsf1258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/49gsf1258654759.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/5mezc1258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/5mezc1258654759.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/6gr2s1258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/6gr2s1258654759.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/7n8a91258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/7n8a91258654759.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/89ylu1258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/89ylu1258654759.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/97vbi1258654759.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654819tpa9b6af0y2m642/97vbi1258654759.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