model 5

*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: Fri, 20 Nov 2009 06:25:42 -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/20/t1258723702z7b81op08gp9uxk.htm/, Retrieved Fri, 20 Nov 2009 14:28:34 +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/20/t1258723702z7b81op08gp9uxk.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 «

98.2 137.7 98.54 98.71 96.92 148.3 98.2 98.54 99.06 152.2 96.92 98.2 99.65 169.4 99.06 96.92 99.82 168.6 99.65 99.06 99.99 161.1 99.82 99.65 100.33 174.1 99.99 99.82 99.31 179 100.33 99.99 101.1 190.6 99.31 100.33 101.1 190 101.1 99.31 100.93 181.6 101.1 101.1 100.85 174.8 100.93 101.1 100.93 180.5 100.85 100.93 99.6 196.8 100.93 100.85 101.88 193.8 99.6 100.93 101.81 197 101.88 99.6 102.38 216.3 101.81 101.88 102.74 221.4 102.38 101.81 102.82 217.9 102.74 102.38 101.72 229.7 102.82 102.74 103.47 227.4 101.72 102.82 102.98 204.2 103.47 101.72 102.68 196.6 102.98 103.47 102.9 198.8 102.68 102.98 103.03 207.5 102.9 102.68 101.29 190.7 103.03 102.9 103.69 201.6 101.29 103.03 103.68 210.5 103.69 101.29 104.2 223.5 103.68 103.69 104.08 223.8 104.2 103.68 104.16 231.2 104.08 104.2 103.05 244 104.16 104.08 104.66 234.7 103.05 104.16 104.46 250.2 104.66 103.05 104.95 265.7 104.46 104.66 105.85 287.6 104.95 104.46 106.23 283.3 105.85 104.95 104.86 295.4 106.23 105.85 107.44 312.3 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 16.11068832504 + 0.00550259812927356X[t] + 0.563481661967873Y1[t] + 0.264939208226435Y2[t] -0.0089156783423854M1[t] -1.63469029339611M2[t] + 1.55987630003095M3[t] + 0.681932272652926M4[t] + 0.249103850212106M5[t] + 0.170577036072006M6[t] + 0.0643935082957647M7[t] -1.38645209180325M8[t] + 0.9533266763827M9[t] + 0.0969898423860808M10[t] -0.171226582904688M11[t] + 0.0245832256744107t + 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) 16.11068832504 4.349466 3.7041 0.000613 0.000307 X 0.00550259812927356 0.000869 6.3345 0 0 Y1 0.563481661967873 0.13938 4.0428 0.000221 0.00011 Y2 0.264939208226435 0.130438 2.0311 0.048601 0.0243 M1 -0.0089156783423854 0.184012 -0.0485 0.961586 0.480793 M2 -1.63469029339611 0.183923 -8.8879 0 0 M3 1.55987630003095 0.285358 5.4664 2e-06 1e-06 M4 0.681932272652926 0.361867 1.8845 0.066433 0.033216 M5 0.249103850212106 0.184143 1.3528 0.183365 0.091683 M6 0.170577036072006 0.188406 0.9054 0.370436 0.185218 M7 0.0643935082957647 0.185928 0.3463 0.73082 0.36541 M8 -1.38645209180325 0.185907 -7.4578 0 0 M9 0.9533266763827 0.257504 3.7022 0.000617 0.000308 M10 0.0969898423860808 0.28776 0.3371 0.737757 0.368878 M11 -0.171226582904688 0.19489 -0.8786 0.384628 0.192314 t 0.0245832256744107 0.009308 2.6412 0.011553 0.005776 Multiple Linear Regression - Regression Statistics Multiple R 0.99806437715787 R-squared 0.996132500951525 Adjusted R-squared 0.994751251291355 F-TEST (value) 721.182078574541 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.272965625676694 Sum Squared Residuals 3.12942977764490 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.2 98.5616958491187 -0.361695849118701 2 96.92 96.782208569442 0.137791430558001 3 99.06 99.2114826631318 -0.151482663131774 4 99.65 99.3194951193331 0.330504880666927 5 99.82 99.8062719302289 0.0137280697711273 6 99.99 99.9631648711817 0.0268351288182420 7 100.33 100.093929892694 0.236070107306488 8 99.31 98.93125367957 0.378746320430079 9 101.1 100.874773847320 0.225226152680380 10 101.1 100.778112862651 0.321887137348628 11 100.93 100.962499021474 -0.0324990214744211 12 100.85 101.02509928024 -0.175099280239939 13 100.93 100.982013438553 -0.0520134385528849 14 99.6 99.49439779498 0.105602205019939 15 101.88 101.968804345935 -0.0888043459345502 16 101.81 102.065420900590 -0.255420900590195 17 102.38 102.327993526137 0.0520064738627035 18 102.74 102.604751990877 0.135248009123261 19 102.82 102.85776134232 -0.037761342319952 20 101.72 101.636886273740 0.0831137262602805 21 103.47 103.389957600396 0.0800423996037959 22 102.98 103.125203494870 -0.145203494869545 23 102.68 103.027288149503 -0.347288149502712 24 102.9 102.936338963345 -0.0363389633449008 25 103.03 103.044363317567 -0.0143633175666123 26 101.29 101.482267521481 -0.192267521481133 27 103.69 103.815379665437 -0.125379665437035 28 103.68 103.902353753493 -0.222353753492841 29 104.2 104.195861615531 0.00413838446924012 30 104.08 104.433929878645 -0.353929878644888 31 104.16 104.463199391541 -0.303199391541281 32 103.05 103.120656101142 -0.070656101141637 33 104.66 104.829574424274 -0.169574424273526 34 104.46 104.696234041592 -0.236234041591993 35 104.95 104.851746905830 0.0982530941696496 36 105.85 105.391181786160 0.458818213840477 37 106.23 106.020141869338 0.209858130662301 38 104.86 104.938100236274 -0.0781002362741863 39 107.44 107.578950985990 -0.138950985990441 40 108.23 107.934712016673 0.295287983326902 41 108.45 108.731646604082 -0.281646604082416 42 109.39 109.206313189338 0.183686810662242 43 110.15 109.844184370585 0.305815629414825 44 109.13 109.130977802829 -0.000977802829298137 45 110.28 110.841860054954 -0.56186005495448 46 110.17 110.16336636183 0.00663363816999925 47 109.99 109.708465923193 0.281534076807483 48 109.26 109.507379970256 -0.247379970255636 49 109.11 108.891785525424 0.218214474575898 50 107.06 107.033025877823 0.0269741221773794 51 109.53 109.025382339506 0.5046176604938 52 108.92 109.068018209911 -0.148018209910793 53 109.24 109.028226324021 0.211773675979345 54 109.12 109.111840069959 0.00815993004114356 55 109 109.20092500286 -0.20092500286008 56 107.23 107.620226142719 -0.390226142719424 57 109.49 109.063834073056 0.426165926943829 58 109.04 108.987083239057 0.0529167609429108 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.177249920439821 0.354499840879642 0.82275007956018 20 0.130818922608943 0.261637845217886 0.869181077391057 21 0.0891291954435935 0.178258390887187 0.910870804556406 22 0.067685756484473 0.135371512968946 0.932314243515527 23 0.040701040771256 0.081402081542512 0.959298959228744 24 0.0178522223541326 0.0357044447082651 0.982147777645867 25 0.00742439256367792 0.0148487851273558 0.992575607436322 26 0.00380783079774177 0.00761566159548354 0.996192169202258 27 0.00269038716992946 0.00538077433985893 0.99730961283007 28 0.00146488056690036 0.00292976113380072 0.9985351194331 29 0.000588153779758906 0.00117630755951781 0.99941184622024 30 0.00226356279643795 0.00452712559287589 0.997736437203562 31 0.00510380251430023 0.0102076050286005 0.9948961974857 32 0.00675101936927038 0.0135020387385408 0.99324898063073 33 0.00561057573759687 0.0112211514751937 0.994389424262403 34 0.00740574269229877 0.0148114853845975 0.9925942573077 35 0.00374581452602675 0.0074916290520535 0.996254185473973 36 0.00835298141339484 0.0167059628267897 0.991647018586605 37 0.0194753229634417 0.0389506459268833 0.980524677036558 38 0.0268924205842183 0.0537848411684366 0.973107579415782 39 0.0198125921173112 0.0396251842346225 0.980187407882689 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.285714285714286 NOK 5% type I error level 15 0.714285714285714 NOK 10% type I error level 17 0.80952380952381 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/100hhq1258723538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/100hhq1258723538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/1fr9l1258723537.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/1fr9l1258723537.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/20g5t1258723538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/20g5t1258723538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/3v3xp1258723538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/3v3xp1258723538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/4a9c41258723538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/4a9c41258723538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/5ztlw1258723538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/5ztlw1258723538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/6py111258723538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/6py111258723538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/7nx7v1258723538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/7nx7v1258723538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/8vi9g1258723538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/8vi9g1258723538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/9nfyb1258723538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723702z7b81op08gp9uxk/9nfyb1258723538.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