*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 20:19:59 +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/t1292962658m2c97wojk74zxeb.htm/, Retrieved Tue, 21 Dec 2010 21:17:41 +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/t1292962658m2c97wojk74zxeb.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 1 117,3 1 111,1 1 102,2 1 104,3 1 122,9 0 107,6 0 121,3 0 131,5 0 89 0 104,4 0 128,9 0 135,9 0 133,3 0 121,3 0 120,5 0 120,4 0 137,9 0 126,1 0 133,2 0 151,1 0 105 0 119 0 140,4 0 156,6 1 137,1 1 122,7 1 125,8 1 139,3 1 134,9 1 149,2 1 132,3 1 149 1 117,2 1 119,6 1 152 1 149,4 1 127,3 1 114,1 1 102,1 1 107,7 1 104,4 1 102,1 1 96 1 109,3 1 90 1 83,9 1 112 1 114,3 1 103,6 1 91,7 1 80,8 1 87,2 1 109,2 1 102,7 1 95,1 1 117,5 1 85,1 1 92,1 1 113,5 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 5 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk 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 Promet[t] = + 135.547741935484 -10.3129032258065Dummy[t] + 6.40258064516126M1[t] -3.57741935483871M2[t] -15.1174193548387M3[t] -21.0174193548387M4[t] -15.5174193548387M5[t] -7.5M6[t] -11.82M7[t] -13.78M8[t] + 2.32M9[t] -32.1M10[t] -25.56M11[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) 135.547741935484 7.858516 17.2485 0 0 Dummy -10.3129032258065 4.663171 -2.2116 0.031896 0.015948 M1 6.40258064516126 10.427168 0.614 0.542157 0.271079 M2 -3.57741935483871 10.427168 -0.3431 0.733063 0.366532 M3 -15.1174193548387 10.427168 -1.4498 0.153752 0.076876 M4 -21.0174193548387 10.427168 -2.0156 0.049575 0.024788 M5 -15.5174193548387 10.427168 -1.4882 0.143385 0.071692 M6 -7.5 10.385375 -0.7222 0.473768 0.236884 M7 -11.82 10.385375 -1.1381 0.260831 0.130416 M8 -13.78 10.385375 -1.3269 0.190964 0.095482 M9 2.32 10.385375 0.2234 0.824199 0.4121 M10 -32.1 10.385375 -3.0909 0.00335 0.001675 M11 -25.56 10.385375 -2.4612 0.017577 0.008788 Multiple Linear Regression - Regression Statistics Multiple R 0.634856007965505 R-squared 0.403042150849898 Adjusted R-squared 0.250627380854127 F-TEST (value) 2.64437725334022 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.00865003239583761 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.4207198740039 Sum Squared Residuals 12673.0819354839 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 112.3 131.637419354839 -19.3374193548388 2 117.3 121.657419354839 -4.35741935483872 3 111.1 110.117419354839 0.982580645161287 4 102.2 104.217419354839 -2.0174193548387 5 104.3 109.717419354839 -5.41741935483872 6 122.9 128.047741935484 -5.14774193548387 7 107.6 123.727741935484 -16.1277419354839 8 121.3 121.767741935484 -0.46774193548388 9 131.5 137.867741935484 -6.36774193548387 10 89 103.447741935484 -14.4477419354839 11 104.4 109.987741935484 -5.58774193548387 12 128.9 135.547741935484 -6.64774193548386 13 135.9 141.950322580645 -6.05032258064512 14 133.3 131.970322580645 1.32967741935485 15 121.3 120.430322580645 0.869677419354837 16 120.5 114.530322580645 5.96967741935484 17 120.4 120.030322580645 0.369677419354839 18 137.9 128.047741935484 9.85225806451613 19 126.1 123.727741935484 2.37225806451613 20 133.2 121.767741935484 11.4322580645161 21 151.1 137.867741935484 13.2322580645161 22 105 103.447741935484 1.55225806451613 23 119 109.987741935484 9.01225806451613 24 140.4 135.547741935484 4.85225806451612 25 156.6 131.637419354839 24.9625806451613 26 137.1 121.657419354839 15.4425806451613 27 122.7 110.117419354839 12.5825806451613 28 125.8 104.217419354839 21.5825806451613 29 139.3 109.717419354839 29.5825806451613 30 134.9 117.734838709677 17.1651612903226 31 149.2 113.414838709677 35.7851612903226 32 132.3 111.454838709677 20.8451612903226 33 149 127.554838709677 21.4451612903226 34 117.2 93.1348387096774 24.0651612903226 35 119.6 99.6748387096774 19.9251612903226 36 152 125.234838709677 26.7651612903226 37 149.4 131.637419354839 17.7625806451613 38 127.3 121.657419354839 5.64258064516129 39 114.1 110.117419354839 3.98258064516129 40 102.1 104.217419354839 -2.11741935483871 41 107.7 109.717419354839 -2.01741935483871 42 104.4 117.734838709677 -13.3348387096774 43 102.1 113.414838709677 -11.3148387096774 44 96 111.454838709677 -15.4548387096774 45 109.3 127.554838709677 -18.2548387096774 46 90 93.1348387096774 -3.13483870967742 47 83.9 99.6748387096774 -15.7748387096774 48 112 125.234838709677 -13.2348387096774 49 114.3 131.637419354839 -17.3374193548387 50 103.6 121.657419354839 -18.0574193548387 51 91.7 110.117419354839 -18.4174193548387 52 80.8 104.217419354839 -23.4174193548387 53 87.2 109.717419354839 -22.5174193548387 54 109.2 117.734838709677 -8.53483870967742 55 102.7 113.414838709677 -10.7148387096774 56 95.1 111.454838709677 -16.3548387096774 57 117.5 127.554838709677 -10.0548387096774 58 85.1 93.1348387096774 -8.03483870967742 59 92.1 99.6748387096774 -7.57483870967743 60 113.5 125.234838709677 -11.7348387096774 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0123775074571677 0.0247550149143355 0.987622492542832 17 0.00209263050263874 0.00418526100527749 0.997907369497361 18 0.00592170369632168 0.0118434073926434 0.994078296303678 19 0.0109333153940734 0.0218666307881468 0.989066684605927 20 0.00650250934785755 0.0130050186957151 0.993497490652142 21 0.00835133264266978 0.0167026652853396 0.99164866735733 22 0.00651563529626179 0.0130312705925236 0.993484364703738 23 0.00437864105284081 0.00875728210568161 0.99562135894716 24 0.00236097616521164 0.00472195233042327 0.997639023834788 25 0.0311916651484147 0.0623833302968294 0.968808334851585 26 0.027237518062028 0.0544750361240559 0.972762481937972 27 0.018720506715896 0.0374410134317919 0.981279493284104 28 0.0215603818513693 0.0431207637027387 0.97843961814863 29 0.0529473069710567 0.105894613942113 0.947052693028943 30 0.0435590254503805 0.087118050900761 0.95644097454962 31 0.133352791661252 0.266705583322504 0.866647208338748 32 0.156020848031787 0.312041696063573 0.843979151968213 33 0.194503947101161 0.389007894202322 0.805496052898839 34 0.24134090677489 0.48268181354978 0.75865909322511 35 0.302966818691548 0.605933637383097 0.697033181308452 36 0.573073933503826 0.853852132992348 0.426926066496174 37 0.782072418868534 0.435855162262933 0.217927581131466 38 0.841749055123154 0.316501889753692 0.158250944876846 39 0.901800683013761 0.196398633972478 0.098199316986239 40 0.96097711182647 0.0780457763470589 0.0390228881735294 41 0.996312997555765 0.0073740048884705 0.00368700244423525 42 0.993260428681014 0.0134791426379711 0.00673957131898556 43 0.980101495174523 0.0397970096509546 0.0198985048254773 44 0.944822715403772 0.110354569192456 0.0551772845962278 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.137931034482759 NOK 5% type I error level 14 0.482758620689655 NOK 10% type I error level 18 0.620689655172414 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/10xg741292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/10xg741292962790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/1yob81292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/1yob81292962790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/2qfsa1292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/2qfsa1292962790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/3qfsa1292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/3qfsa1292962790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/4qfsa1292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/4qfsa1292962790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/5169v1292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/5169v1292962790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/6169v1292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/6169v1292962790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/7uxqg1292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/7uxqg1292962790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/8uxqg1292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/8uxqg1292962790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/946p11292962790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292962658m2c97wojk74zxeb/946p11292962790.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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