*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: Tue, 21 Dec 2010 20:36:54 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56.htm/, Retrieved Tue, 21 Dec 2010 21:35:46 +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/2010/Dec/21/t1292963746suge1ziuyxe1x56.htm/}, year = {2010}, } @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 = {2010}, 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 «

112,3 1 117,2 96,8 80 126,1 117,3 1 112,3 117,2 96,8 80 111,1 1 117,3 112,3 117,2 96,8 102,2 1 111,1 117,3 112,3 117,2 104,3 1 102,2 111,1 117,3 112,3 122,9 0 104,3 102,2 111,1 117,3 107,6 0 122,9 104,3 102,2 111,1 121,3 0 107,6 122,9 104,3 102,2 131,5 0 121,3 107,6 122,9 104,3 89 0 131,5 121,3 107,6 122,9 104,4 0 89 131,5 121,3 107,6 128,9 0 104,4 89 131,5 121,3 135,9 0 128,9 104,4 89 131,5 133,3 0 135,9 128,9 104,4 89 121,3 0 133,3 135,9 128,9 104,4 120,5 0 121,3 133,3 135,9 128,9 120,4 0 120,5 121,3 133,3 135,9 137,9 0 120,4 120,5 121,3 133,3 126,1 0 137,9 120,4 120,5 121,3 133,2 0 126,1 137,9 120,4 120,5 151,1 0 133,2 126,1 137,9 120,4 105 0 151,1 133,2 126,1 137,9 119 0 105 151,1 133,2 126,1 140,4 0 119 105 151,1 133,2 156,6 1 140,4 119 105 151,1 137,1 1 156,6 140,4 119 105 122,7 1 137,1 156,6 140,4 119 125,8 1 122,7 137,1 156,6 140,4 139,3 1 125,8 122,7 137,1 156,6 134,9 1 139,3 125,8 122,7 137,1 149,2 1 134,9 139,3 125,8 122,7 132,3 1 149,2 134,9 139,3 125,8 149 1 132,3 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Y[t] = + 36.8237007045577 -0.824448773739979X[t] + 0.376099529769240Y1[t] + 0.352836564802845Y2[t] + 0.284327425700985Y3[t] -0.124456499746576Y4[t] + 3.23567226815469M1[t] -23.9939024131591M2[t] -39.1093984489867M3[t] -34.921357299371M4[t] -19.7510994500274M5[t] -7.68983270275417M6[t] -17.4063077063867M7[t] -23.5023389425598M8[t] -7.22853404842681M9[t] -44.4354552559275M10[t] -30.5172429008307M11[t] -0.0939055582967211t + 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) 36.8237007045577 10.666289 3.4523 0.001281 0.00064 X -0.824448773739979 3.016003 -0.2734 0.785918 0.392959 Y1 0.376099529769240 0.151984 2.4746 0.01746 0.00873 Y2 0.352836564802845 0.157022 2.247 0.02995 0.014975 Y3 0.284327425700985 0.153399 1.8535 0.070841 0.035421 Y4 -0.124456499746576 0.148062 -0.8406 0.405347 0.202674 M1 3.23567226815469 9.78102 0.3308 0.742432 0.371216 M2 -23.9939024131591 10.617577 -2.2598 0.029079 0.014539 M3 -39.1093984489867 8.156355 -4.795 2.1e-05 1e-05 M4 -34.921357299371 5.996148 -5.824 1e-06 0 M5 -19.7510994500274 6.168581 -3.2019 0.002603 0.001302 M6 -7.68983270275417 6.507724 -1.1816 0.243993 0.121997 M7 -17.4063077063867 7.738292 -2.2494 0.02979 0.014895 M8 -23.5023389425598 7.866716 -2.9876 0.00468 0.00234 M9 -7.22853404842681 6.460081 -1.119 0.269519 0.134759 M10 -44.4354552559275 7.415133 -5.9925 0 0 M11 -30.5172429008307 8.378217 -3.6425 0.000736 0.000368 t -0.0939055582967211 0.08445 -1.112 0.272479 0.13624 Multiple Linear Regression - Regression Statistics Multiple R 0.938042895159454 R-squared 0.87992447315913 Adjusted R-squared 0.83132247419973 F-TEST (value) 18.1046971729329 F-TEST (DF numerator) 17 F-TEST (DF denominator) 42 p-value 4.27435864480685e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.79061769767403 Sum Squared Residuals 2549.1364126751 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 112.3 124.426692440582 -12.1266924405816 2 117.3 112.972335817173 4.32766418282654 3 111.1 101.623942992919 9.47605700708099 4 102.2 101.218327342918 0.98167265708199 5 104.3 112.791281094504 -8.49128109450415 6 122.9 120.847542104912 2.05245789508827 7 107.6 117.014685792466 -9.4146857924663 8 121.3 113.337936739577 7.96206326042334 9 131.5 134.299131660338 -2.79913166033848 10 89 99.0032805274749 -10.0032805274749 11 104.4 106.241760448297 -1.84176044829746 12 128.9 128.656562240779 0.243437759221261 13 135.9 133.09307863824 2.80692136176006 14 133.3 126.714834539708 6.58516546029159 15 121.3 118.046821955381 3.25317804461913 16 120.5 115.651495857097 4.84850414290258 17 120.4 124.582482941646 -4.18248294164614 18 137.9 133.141622716733 4.75837728326727 19 126.1 131.143716325683 -5.043716325683 20 133.2 126.761577421213 6.43842257878684 21 151.1 146.436487553479 4.6635124465207 22 105 112.839929611815 -7.83992961181475 23 119 119.129134015710 -0.129134015710474 24 140.4 142.757918909450 -2.35791890944956 25 156.6 142.72821301959 13.8717869804100 26 137.1 138.766276247153 -1.66627624715293 27 122.7 126.281102085884 -3.58110208588355 28 125.8 120.021826636649 5.7781733633507 29 139.3 123.622660839756 15.6773391602439 30 134.9 140.093745846470 -5.19374584647019 31 149.2 136.065409594418 13.1345904055815 32 132.3 137.153820288265 -4.85382028826516 33 149 149.091997028019 -0.0919970280188442 34 117.2 116.722585250609 0.477414749391365 35 119.6 117.894436192232 1.70556380776819 36 152 144.851792500405 7.14820749959497 37 149.4 149.905956047254 -0.505956047254019 38 127.3 137.676624243479 -10.3766242434791 39 114.1 122.151560966287 -8.05156096628736 40 102.1 108.711852783898 -6.61185278389793 41 107.7 108.657518853666 -0.957518853665662 42 104.4 117.494365256862 -13.0943652568621 43 102.1 106.649637697833 -4.54963769783328 44 96 101.516022901929 -5.51602290192929 45 109.3 112.954954103733 -3.65495410373255 46 90 78.2607014086201 11.7392985913799 47 83.9 88.0708662453928 -4.17086624539284 48 112 113.930990165917 -1.93099016591675 49 114.3 118.346059854334 -4.0460598543345 50 103.6 102.469929152486 1.13007084751393 51 91.7 92.7965719995292 -1.09657199952921 52 80.8 85.7964973794373 -4.99649737943734 53 87.2 89.246056270428 -2.04605627042797 54 109.2 97.7227240750233 11.4772759249768 55 102.7 96.826550589599 5.87344941040104 56 95.1 99.1306426490157 -4.03064264901573 57 117.5 115.617429654431 1.88257034556918 58 85.1 79.4735032014817 5.62649679851834 59 92.1 87.6638030983674 4.43619690163258 60 113.5 116.60273618345 -3.10273618344991 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0220619366409806 0.0441238732819613 0.97793806335902 22 0.0053055116763784 0.0106110233527568 0.994694488323622 23 0.00100901046456700 0.00201802092913400 0.998990989535433 24 0.00141240416785208 0.00282480833570415 0.998587595832148 25 0.000433292952341720 0.000866585904683439 0.999566707047658 26 0.00199038660744966 0.00398077321489932 0.99800961339255 27 0.0513014202963381 0.102602840592676 0.948698579703662 28 0.111789302102594 0.223578604205188 0.888210697897406 29 0.121591050095089 0.243182100190178 0.87840894990491 30 0.106941087790061 0.213882175580123 0.893058912209939 31 0.357401546247182 0.714803092494365 0.642598453752818 32 0.262114198724485 0.524228397448969 0.737885801275516 33 0.276417945587917 0.552835891175835 0.723582054412083 34 0.185352803635223 0.370705607270445 0.814647196364777 35 0.137778582045089 0.275557164090177 0.862221417954911 36 0.219370869347072 0.438741738694143 0.780629130652928 37 0.275175095168437 0.550350190336873 0.724824904831563 38 0.361861509692425 0.72372301938485 0.638138490307575 39 0.319077404242617 0.638154808485234 0.680922595757383 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.210526315789474 NOK 5% type I error level 6 0.315789473684211 NOK 10% type I error level 6 0.315789473684211 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/10i7361292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/10i7361292963806.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/1u66u1292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/1u66u1292963806.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/2u66u1292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/2u66u1292963806.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/3mfnx1292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/3mfnx1292963806.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/4mfnx1292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/4mfnx1292963806.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/5mfnx1292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/5mfnx1292963806.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/6f7m01292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/6f7m01292963806.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/7qy3l1292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/7qy3l1292963806.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/8qy3l1292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/8qy3l1292963806.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/9qy3l1292963806.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963746suge1ziuyxe1x56/9qy3l1292963806.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