*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: Sat, 19 Dec 2009 08:44:35 -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/19/t1261237593dnq081ufioijep5.htm/, Retrieved Sat, 19 Dec 2009 16:46:45 +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/19/t1261237593dnq081ufioijep5.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 «

101.09 0 102.71 0 102.11 0 101.68 0 101.7 0 101.53 0 101.76 0 101.15 0 100.92 0 100.73 0 100.55 0 102.15 0 100.79 0 99.93 0 100.03 0 100.25 0 99.6 0 100.16 0 100.49 0 99.72 0 100.14 0 98.48 0 100.38 0 101.45 0 98.42 0 98.6 0 100.06 0 98.62 0 100.84 0 100.02 0 97.95 0 98.32 0 98.27 0 97.22 0 99.28 0 100.38 0 99.02 0 100.32 0 99.81 0 100.6 0 101.19 0 100.47 0 101.77 0 102.32 0 102.39 0 101.16 0 100.63 0 101.48 0 101.44 1 100.09 1 100.7 1 100.78 1 99.81 1 98.45 1 98.49 1 97.48 1 97.91 1 96.94 1 98.53 1 96.82 1 95.76 1

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 11 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 100.388333333333 -1.68064102564103X[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) 100.388333333333 0.200241 501.3368 0 0 X -1.68064102564103 0.433757 -3.8746 0.00027 0.000135 Multiple Linear Regression - Regression Statistics Multiple R 0.450375989886427 R-squared 0.202838532266179 Adjusted R-squared 0.189327320948657 F-TEST (value) 15.0126089733437 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.000270485574901058 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.38731234758470 Sum Squared Residuals 113.553497435898 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.09 100.388333333333 0.701666666666981 2 102.71 100.388333333333 2.32166666666665 3 102.11 100.388333333333 1.72166666666666 4 101.68 100.388333333333 1.29166666666667 5 101.7 100.388333333333 1.31166666666666 6 101.53 100.388333333333 1.14166666666666 7 101.76 100.388333333333 1.37166666666667 8 101.15 100.388333333333 0.761666666666666 9 100.92 100.388333333333 0.531666666666662 10 100.73 100.388333333333 0.341666666666664 11 100.55 100.388333333333 0.161666666666657 12 102.15 100.388333333333 1.76166666666667 13 100.79 100.388333333333 0.401666666666667 14 99.93 100.388333333333 -0.458333333333333 15 100.03 100.388333333333 -0.358333333333338 16 100.25 100.388333333333 -0.138333333333340 17 99.6 100.388333333333 -0.788333333333345 18 100.16 100.388333333333 -0.228333333333343 19 100.49 100.388333333333 0.101666666666655 20 99.72 100.388333333333 -0.66833333333334 21 100.14 100.388333333333 -0.248333333333339 22 98.48 100.388333333333 -1.90833333333334 23 100.38 100.388333333333 -0.00833333333334421 24 101.45 100.388333333333 1.06166666666666 25 98.42 100.388333333333 -1.96833333333334 26 98.6 100.388333333333 -1.78833333333335 27 100.06 100.388333333333 -0.328333333333337 28 98.62 100.388333333333 -1.76833333333334 29 100.84 100.388333333333 0.451666666666664 30 100.02 100.388333333333 -0.368333333333344 31 97.95 100.388333333333 -2.43833333333334 32 98.32 100.388333333333 -2.06833333333335 33 98.27 100.388333333333 -2.11833333333334 34 97.22 100.388333333333 -3.16833333333334 35 99.28 100.388333333333 -1.10833333333334 36 100.38 100.388333333333 -0.00833333333334421 37 99.02 100.388333333333 -1.36833333333334 38 100.32 100.388333333333 -0.0683333333333465 39 99.81 100.388333333333 -0.578333333333337 40 100.6 100.388333333333 0.211666666666655 41 101.19 100.388333333333 0.801666666666658 42 100.47 100.388333333333 0.0816666666666592 43 101.77 100.388333333333 1.38166666666666 44 102.32 100.388333333333 1.93166666666665 45 102.39 100.388333333333 2.00166666666666 46 101.16 100.388333333333 0.771666666666657 47 100.63 100.388333333333 0.241666666666656 48 101.48 100.388333333333 1.09166666666666 49 101.44 98.7076923076923 2.73230769230769 50 100.09 98.7076923076923 1.38230769230770 51 100.7 98.7076923076923 1.99230769230769 52 100.78 98.7076923076923 2.07230769230769 53 99.81 98.7076923076923 1.10230769230769 54 98.45 98.7076923076923 -0.257692307692305 55 98.49 98.7076923076923 -0.217692307692313 56 97.48 98.7076923076923 -1.22769230769230 57 97.91 98.7076923076923 -0.797692307692311 58 96.94 98.7076923076923 -1.76769230769231 59 98.53 98.7076923076923 -0.177692307692307 60 96.82 98.7076923076923 -1.88769230769231 61 95.76 98.7076923076923 -2.9476923076923 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.144908018266437 0.289816036532874 0.855091981733563 6 0.0658171720093947 0.131634344018789 0.934182827990605 7 0.0260041847002618 0.0520083694005237 0.973995815299738 8 0.0162893054500249 0.0325786109000497 0.983710694549975 9 0.0130187688182039 0.0260375376364079 0.986981231181796 10 0.0119235155610133 0.0238470311220266 0.988076484438987 11 0.0119491683088611 0.0238983366177222 0.98805083169114 12 0.00944853283178785 0.0188970656635757 0.990551467168212 13 0.0065652469568167 0.0131304939136334 0.993434753043183 14 0.0144088813457019 0.0288177626914038 0.985591118654298 15 0.0180086311972183 0.0360172623944366 0.981991368802782 16 0.0153857353438151 0.0307714706876302 0.984614264656185 17 0.0237643424563702 0.0475286849127404 0.97623565754363 18 0.0186236051376477 0.0372472102752953 0.981376394862352 19 0.0118927704968941 0.0237855409937883 0.988107229503106 20 0.0123024340698937 0.0246048681397874 0.987697565930106 21 0.00869087017273396 0.0173817403454679 0.991309129827266 22 0.0324028014960795 0.0648056029921591 0.96759719850392 23 0.0210157114250330 0.0420314228500660 0.978984288574967 24 0.0163512115302237 0.0327024230604475 0.983648788469776 25 0.0417335956758906 0.0834671913517812 0.95826640432411 26 0.0664435514754025 0.132887102950805 0.933556448524598 27 0.0470583281653682 0.0941166563307364 0.952941671834632 28 0.0658102453702674 0.131620490740535 0.934189754629733 29 0.0465410777818822 0.0930821555637643 0.953458922218118 30 0.0320091258244527 0.0640182516489054 0.967990874175547 31 0.0753134700693726 0.150626940138745 0.924686529930627 32 0.112132837169321 0.224265674338643 0.887867162830679 33 0.164875802480063 0.329751604960126 0.835124197519937 34 0.434420922630691 0.868841845261381 0.565579077369309 35 0.424575307790421 0.849150615580842 0.575424692209579 36 0.357103477735894 0.714206955471788 0.642896522264106 37 0.391972221407747 0.783944442815493 0.608027778592253 38 0.334263184407355 0.668526368814709 0.665736815592645 39 0.311876050865013 0.623752101730025 0.688123949134987 40 0.25995365820836 0.51990731641672 0.74004634179164 41 0.2117636275466 0.4235272550932 0.7882363724534 42 0.17695433910183 0.35390867820366 0.82304566089817 43 0.149218059312931 0.298436118625861 0.85078194068707 44 0.145809259746941 0.291618519493881 0.85419074025306 45 0.148915052100319 0.297830104200639 0.85108494789968 46 0.108113973732769 0.216227947465539 0.89188602626723 47 0.0761014289452382 0.152202857890476 0.923898571054762 48 0.0526491067244698 0.105298213448940 0.94735089327553 49 0.100649136082101 0.201298272164202 0.899350863917899 50 0.102894366627818 0.205788733255636 0.897105633372182 51 0.172007660030672 0.344015320061345 0.827992339969328 52 0.409841615586704 0.819683231173407 0.590158384413296 53 0.61579161527955 0.768416769440901 0.384208384720450 54 0.587543936541866 0.82491212691627 0.412456063458135 55 0.580779860587393 0.838440278825213 0.419220139412607 56 0.443159490020202 0.886318980040403 0.556840509979798 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 16 0.307692307692308 NOK 10% type I error level 22 0.423076923076923 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/105v0m1261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/105v0m1261237463.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/190di1261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/190di1261237463.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/277we1261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/277we1261237463.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/38a811261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/38a811261237463.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/4199b1261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/4199b1261237463.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/5rdr01261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/5rdr01261237463.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/6hljn1261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/6hljn1261237463.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/7okmn1261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/7okmn1261237463.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/8pfsd1261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/8pfsd1261237463.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/9256z1261237463.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261237593dnq081ufioijep5/9256z1261237463.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