Model 3

*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 14:10:22 -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/t12585786670nn88cokyorltzd.htm/, Retrieved Wed, 18 Nov 2009 22:11:19 +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/t12585786670nn88cokyorltzd.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 «

115.6 0 111.3 0 114.6 0 137.5 0 83.7 0 106.0 0 123.4 0 126.5 0 120.0 0 141.6 0 90.5 0 96.5 0 113.5 0 120.1 0 123.9 0 144.4 0 90.8 0 114.2 0 138.1 0 135.0 0 131.3 0 144.6 0 101.7 0 108.7 0 135.3 0 124.3 0 138.3 0 158.2 0 93.5 0 124.8 0 154.4 0 152.8 0 148.9 0 170.3 0 124.8 0 134.4 0 154.0 0 147.9 0 168.1 0 175.7 0 116.7 0 140.8 0 164.2 0 173.8 0 167.8 0 166.6 0 135.1 1 158.1 1 151.8 1 166.7 1 165.3 1 187.0 1 125.2 1 144.4 1 181.7 1 175.9 1 166.3 1 181.5 1 121.8 1 134.8 1 162.9 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 89.1350491803279 -3.90549180327873X[t] + 17.4961821493625M1[t] + 17.5919981785064M2[t] + 24.4906885245902M3[t] + 41.9293788706739M4[t] -17.7319307832422M5[t] + 5.24675956284152M6[t] + 30.4854499089253M7[t] + 29.8441402550091M8[t] + 22.8228306010929M9[t] + 35.8015209471767M10[t] -10.6386903460838M11[t] + 1.08130965391621t + 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) 89.1350491803279 3.918951 22.7446 0 0 X -3.90549180327873 3.358537 -1.1629 0.250759 0.125379 M1 17.4961821493625 4.457349 3.9252 0.000282 0.000141 M2 17.5919981785064 4.677988 3.7606 0.000469 0.000234 M3 24.4906885245902 4.67317 5.2407 4e-06 2e-06 M4 41.9293788706739 4.669786 8.9789 0 0 M5 -17.7319307832422 4.667838 -3.7987 0.000417 0.000208 M6 5.24675956284152 4.667329 1.1241 0.26666 0.13333 M7 30.4854499089253 4.668259 6.5304 0 0 M8 29.8441402550091 4.670628 6.3897 0 0 M9 22.8228306010929 4.674432 4.8825 1.3e-05 6e-06 M10 35.8015209471767 4.679669 7.6504 0 0 M11 -10.6386903460838 4.646838 -2.2894 0.02659 0.013295 t 1.08130965391621 0.081958 13.1935 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.967564799200113 R-squared 0.936181640651156 Adjusted R-squared 0.918529754022752 F-TEST (value) 53.0357836733860 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.34615257901844 Sum Squared Residuals 2536.40001256831 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 115.6 107.712540983606 7.8874590163935 2 111.3 108.889666666667 2.41033333333336 3 114.6 116.869666666667 -2.26966666666668 4 137.5 135.389666666667 2.11033333333327 5 83.7 76.8096666666667 6.89033333333334 6 106 100.869666666667 5.1303333333333 7 123.4 127.189666666667 -3.7896666666667 8 126.5 127.629666666667 -1.12966666666669 9 120 121.689666666667 -1.68966666666666 10 141.6 135.749666666667 5.85033333333333 11 90.5 90.3907650273224 0.109234972677633 12 96.5 102.110765027322 -5.61076502732237 13 113.5 120.688256830601 -7.1882568306011 14 120.1 121.865382513661 -1.76538251366123 15 123.9 129.845382513661 -5.9453825136612 16 144.4 148.365382513661 -3.96538251366118 17 90.8 89.7853825136612 1.01461748633879 18 114.2 113.845382513661 0.354617486338808 19 138.1 140.165382513661 -2.06538251366119 20 135 140.605382513661 -5.6053825136612 21 131.3 134.665382513661 -3.3653825136612 22 144.6 148.725382513661 -4.12538251366120 23 101.7 103.366480874317 -1.66648087431694 24 108.7 115.086480874317 -6.38648087431694 25 135.3 133.663972677596 1.63602732240437 26 124.3 134.841098360656 -10.5410983606557 27 138.3 142.821098360656 -4.52109836065573 28 158.2 161.341098360656 -3.14109836065574 29 93.5 102.761098360656 -9.26109836065575 30 124.8 126.821098360656 -2.02109836065573 31 154.4 153.141098360656 1.25890163934428 32 152.8 153.581098360656 -0.781098360655725 33 148.9 147.641098360656 1.25890163934425 34 170.3 161.701098360656 8.59890163934428 35 124.8 116.342196721311 8.4578032786885 36 134.4 128.062196721311 6.33780327868851 37 154 146.639688524590 7.36031147540982 38 147.9 147.816814207650 0.0831857923497378 39 168.1 155.796814207650 12.3031857923497 40 175.7 174.316814207650 1.38318579234973 41 116.7 115.736814207650 0.963185792349718 42 140.8 139.796814207650 1.00318579234973 43 164.2 166.116814207650 -1.91681420765027 44 173.8 166.556814207650 7.24318579234974 45 167.8 160.616814207650 7.18318579234973 46 166.6 174.676814207650 -8.07681420765028 47 135.1 125.412420765027 9.68757923497267 48 158.1 137.132420765027 20.9675792349727 49 151.8 155.709912568306 -3.90991256830602 50 166.7 156.887038251366 9.81296174863387 51 165.3 164.867038251366 0.432961748633893 52 187 183.387038251366 3.61296174863391 53 125.2 124.807038251366 0.392961748633878 54 144.4 148.867038251366 -4.46703825136611 55 181.7 175.187038251366 6.51296174863388 56 175.9 175.627038251366 0.272961748633879 57 166.3 169.687038251366 -3.38703825136612 58 181.5 183.747038251366 -2.24703825136613 59 121.8 138.388136612022 -16.5881366120219 60 134.8 150.108136612022 -15.3081366120219 61 162.9 168.685628415301 -5.78562841530056 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.108033345248084 0.216066690496168 0.891966654751916 18 0.041532079421493 0.083064158842986 0.958467920578507 19 0.0406147033803018 0.0812294067606036 0.959385296619698 20 0.0160532538069980 0.0321065076139961 0.983946746193002 21 0.00716379134511868 0.0143275826902374 0.992836208654881 22 0.00387940739645103 0.00775881479290207 0.99612059260355 23 0.00166788042955542 0.00333576085911084 0.998332119570445 24 0.000985035210247753 0.00197007042049551 0.999014964789752 25 0.00126656900247207 0.00253313800494415 0.998733430997528 26 0.00141051820075442 0.00282103640150884 0.998589481799246 27 0.00158095672955218 0.00316191345910436 0.998419043270448 28 0.00101615902731497 0.00203231805462994 0.998983840972685 29 0.00264769978421726 0.00529539956843453 0.997352300215783 30 0.00153385023447074 0.00306770046894147 0.99846614976553 31 0.00403645135959667 0.00807290271919334 0.995963548640403 32 0.0114573151356277 0.0229146302712554 0.988542684864372 33 0.039129102648306 0.078258205296612 0.960870897351694 34 0.0616888056445666 0.123377611289133 0.938311194355433 35 0.0777238752572971 0.155447750514594 0.922276124742703 36 0.130893538583196 0.261787077166392 0.869106461416804 37 0.103762420064814 0.207524840129629 0.896237579935186 38 0.102193468132375 0.204386936264751 0.897806531867624 39 0.182654410971252 0.365308821942504 0.817345589028748 40 0.121249802479414 0.242499604958828 0.878750197520586 41 0.0708575388892716 0.141715077778543 0.929142461110728 42 0.0401345948331742 0.0802691896663483 0.959865405166826 43 0.0321948030684856 0.0643896061369711 0.967805196931514 44 0.0178383598053350 0.0356767196106699 0.982161640194665 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.357142857142857 NOK 5% type I error level 14 0.5 NOK 10% type I error level 19 0.678571428571429 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/104apj1258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/104apj1258578617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/1xjr01258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/1xjr01258578617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/2te1g1258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/2te1g1258578617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/3jm281258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/3jm281258578617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/43ynv1258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/43ynv1258578617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/5r31u1258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/5r31u1258578617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/6a3oe1258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/6a3oe1258578617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/77c5x1258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/77c5x1258578617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/8u7ei1258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/8u7ei1258578617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/9v67d1258578617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585786670nn88cokyorltzd/9v67d1258578617.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