*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: Mon, 21 Dec 2009 02:45:41 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc.htm/, Retrieved Mon, 21 Dec 2009 13:52:14 +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/Dec/21/t1261399921uvvrxgk2vo3ihgc.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 «

3.2 27.6 2.7 2.6 2.8 24.9 3.2 2.4 2.8 23.8 2.8 2.5 3 24.3 2.8 2.7 3.1 23.6 3 3.2 3.1 24.2 3.1 2.8 3 28.1 3.1 2.8 2.4 30.1 3 3 2.7 31.1 2.4 3.1 3 32 2.7 3.1 2.7 32.4 3 3 2.7 34 2.7 2.4 2 35.1 2.7 2.7 2.4 37.1 2 3 2.6 37.3 2.4 2.7 2.4 38.1 2.6 2.7 2.3 39.5 2.4 2 2.4 38.3 2.3 2.4 2.5 37.3 2.4 2.6 2.6 38.7 2.5 2.4 2.6 37.5 2.6 2.3 2.6 38.7 2.6 2.4 2.7 37.9 2.6 2.5 2.8 36.6 2.7 2.6 2.6 35.5 2.8 2.6 2.6 37.6 2.6 2.6 2 38.6 2.6 2.7 2 40.3 2 2.8 2.1 39 2 2.6 1.9 36.8 2.1 2.6 2 36.5 1.9 2 2.5 34.1 2 2 2.9 34.2 2.5 2.1 3.3 31.9 2.9 1.9 3.5 33.7 3.3 2 3.8 33.5 3.5 2.5 4.6 33.8 3.8 2.9 4.4 29.9 4.6 3.3 5.3 32.3 4.4 3.5 5.8 30.5 5.3 3.8 5.9 28.5 5.8 4.6 5.6 29 5.9 4.4 5.8 23.8 5.6 5.3 5.5 17.9 5.8 5.8 4.6 9.9 5.5 5.9 4.2 3 4.6 5.6 4 4.2 4.2 5.8 3.5 0.4 4 5.5 2.3 0 3.5 4.6 2.2 2.4 2.3 4.2 1.4 4.2 2.2 4 0.6 8.2 1.4 3.5 0 9 0.6 2.3 0.5 13.6 0 2.2 0.1 14 0.5 1.4 0.1 17.6 0.1 0.6

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] = -0.292849249941967 + 0.0134244026370332X[t] + 1.08350167270624Y1[t] -0.133309614940154Y2[t] -0.137399955322172M1[t] -0.0238083435085413M2[t] -0.0357127578837311M3[t] -0.044701180135979M4[t] -0.0788911693856083M5[t] + 0.0109118261505348M6[t] -0.0355533832106162M7[t] -0.0810949181381833M8[t] -0.0331665972168241M9[t] + 0.103802503434295M10[t] -0.0288910987831445M11[t] + 0.00270333617091151t + 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) -0.292849249941967 0.45343 -0.6459 0.522063 0.261031 X 0.0134244026370332 0.008012 1.6756 0.101622 0.050811 Y1 1.08350167270624 0.081233 13.3382 0 0 Y2 -0.133309614940154 0.112632 -1.1836 0.243564 0.121782 M1 -0.137399955322172 0.275986 -0.4979 0.621315 0.310658 M2 -0.0238083435085413 0.275796 -0.0863 0.931638 0.465819 M3 -0.0357127578837311 0.275814 -0.1295 0.897626 0.448813 M4 -0.044701180135979 0.276692 -0.1616 0.872469 0.436235 M5 -0.0788911693856083 0.275954 -0.2859 0.776441 0.388221 M6 0.0109118261505348 0.276434 0.0395 0.968709 0.484355 M7 -0.0355533832106162 0.276285 -0.1287 0.898253 0.449126 M8 -0.0810949181381833 0.276625 -0.2932 0.770916 0.385458 M9 -0.0331665972168241 0.290904 -0.114 0.909799 0.454899 M10 0.103802503434295 0.290052 0.3579 0.722318 0.361159 M11 -0.0288910987831445 0.290153 -0.0996 0.921181 0.460591 t 0.00270333617091151 0.004263 0.6341 0.529645 0.264823 Multiple Linear Regression - Regression Statistics Multiple R 0.967121489718825 R-squared 0.935323975875959 Adjusted R-squared 0.911070466829444 F-TEST (value) 38.5644804668107 F-TEST (DF numerator) 15 F-TEST (DF denominator) 40 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.410025249215748 Sum Squared Residuals 6.72482819977743 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.2 2.52181716115134 0.678182838848662 2 2.8 3.17027898135704 -0.370278981357045 3 2.8 2.69957942967552 0.100420570324478 4 3 2.67334462192467 0.326655378075332 5 3.1 2.7825064140712 0.317493585928802 6 3.1 3.04474140060716 0.0552585993928412 7 3 3.05333469770135 -0.0533346977013491 8 2.4 2.90233321396010 -0.502333213960105 9 2.7 2.30295730857165 0.397042691428352 10 3 2.77976220957888 0.220237790421119 11 2.7 2.99352316789305 -0.293523167893054 12 2.7 2.80153191421858 -0.101531914218584 13 2 2.64160925348601 -0.641609253486014 14 2.4 1.98630895136821 0.413691048631793 15 2.6 2.45318630725588 0.146813692744122 16 2.4 2.67434107782542 -0.274341077825417 17 2.3 2.53826498435541 -0.238264984355405 18 2.4 2.45298801965133 -0.0529880196513338 19 2.5 2.47748998810665 0.0225100118933455 20 2.6 2.5884580433005 0.0115419566994995 21 2.6 2.74466154599297 -0.144661545992971 22 2.6 2.88711230448543 -0.287112304485426 23 2.7 2.73305155483526 -0.033051554835256 24 2.8 2.84221347213778 -0.0422134721377779 25 2.6 2.80110017735640 -0.201100177356405 26 2.6 2.72888603633747 -0.128886036337468 27 2 2.71977839927621 -0.719778399276208 28 2 2.07288283256007 -0.0728828325600675 29 2.1 2.05060637904124 0.0493936209587626 30 1.9 2.22192919221744 -0.321929192217443 31 2 2.03742543265894 -0.0374254326589376 32 2.5 2.07071883484403 0.429281165155973 33 2.9 2.65111280705911 0.248887192940894 34 3.3 3.21997170988649 0.0800282901135123 35 3.5 3.5342150761751 -0.0342150761751004 36 3.8 3.71317015767292 0.0868298423270787 37 4.6 3.85422751514858 0.745772484851418 38 4.4 4.73164478503763 -0.331644785037625 39 5.3 4.51130001563295 0.788699984367051 40 5.8 5.41600962575852 0.383990374241477 41 5.9 5.79277731180674 0.107222688193265 42 5.6 6.02700793509096 -0.427007935090962 43 5.8 5.46841001293014 0.331589987069861 44 5.5 5.49641336568616 0.00358663431384228 45 4.6 5.10126833837627 -0.501268338376275 46 4.2 4.21315377604920 -0.0131537760492046 47 4 3.63921020109659 0.36078979890341 48 3.5 3.44308445597072 0.0569155440292828 49 2.3 2.88124589285766 -0.581245892857662 50 2.2 1.78288124589966 0.417118754100345 51 1.4 1.71615584815944 -0.316155848159444 52 0.6 0.963421841931324 -0.363421841931324 53 0 0.235844910725424 -0.235844910725424 54 0.5 -0.246666547566898 0.746666547566898 55 0.1 0.36333986860292 -0.26333986860292 56 0.1 0.0420765422092107 0.0579234577907893 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.159726064873817 0.319452129747634 0.840273935126183 20 0.466394852519498 0.932789705038995 0.533605147480502 21 0.404551097718805 0.809102195437609 0.595448902281196 22 0.278577470349728 0.557154940699455 0.721422529650272 23 0.181884435691012 0.363768871382024 0.818115564308988 24 0.119796319490527 0.239592638981055 0.880203680509473 25 0.0678598887717862 0.135719777543572 0.932140111228214 26 0.0391944554640345 0.078388910928069 0.960805544535966 27 0.060765165905267 0.121530331810534 0.939234834094733 28 0.0726587429840925 0.145317485968185 0.927341257015908 29 0.062739085672566 0.125478171345132 0.937260914327434 30 0.103109844610781 0.206219689221562 0.896890155389219 31 0.0848180143884671 0.169636028776934 0.915181985611533 32 0.0604213967120436 0.120842793424087 0.939578603287956 33 0.0580151615352357 0.116030323070471 0.941984838464764 34 0.0358185585759848 0.0716371171519696 0.964181441424015 35 0.0297551638814811 0.0595103277629621 0.97024483611852 36 0.0239739836268635 0.047947967253727 0.976026016373136 37 0.0377367601647321 0.0754735203294642 0.962263239835268 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 0.0526315789473684 NOK 10% type I error level 5 0.263157894736842 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/10wo581261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/10wo581261388735.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/1pazo1261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/1pazo1261388735.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/2lg3y1261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/2lg3y1261388735.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/3z5if1261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/3z5if1261388735.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/4wjx71261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/4wjx71261388735.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/5klc71261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/5klc71261388735.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/6xclx1261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/6xclx1261388735.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/7afpa1261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/7afpa1261388735.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/8kmyu1261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/8kmyu1261388735.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/9beyl1261388735.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261399921uvvrxgk2vo3ihgc/9beyl1261388735.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