Paper - Multiple Linear Regression

*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, 28 Dec 2010 17:01:21 +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/28/t1293555577a2qugbxqxvzsch9.htm/, Retrieved Tue, 28 Dec 2010 17:59:39 +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/28/t1293555577a2qugbxqxvzsch9.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 «

141 9.3 16 6 7 136 14.2 20 20 0 246 17.3 7 12 0 309 23 8 15 0 95 16.3 21 25 0 161 18.4 7 4 0 108 14.2 17 6 0 79 9.1 20 2 0 40 5.9 18 1 1 35 7.2 26 4 2 49 6.8 18 4 2 145 8 20 8 2 284 14.3 0 3 0 164 14.6 22 14 0 130 17.5 19 17 0 178 17.2 18 14 0 150 17.2 13 10 0 104 14.1 16 7 0 111 10.4 11 4 0 51 6.8 22 1 1 70 4.1 19 6 0 42 6.5 23 2 1 126 6.1 11 2 0 68 6.3 24 8 7 135 9.3 14 10 0 231 16.4 11 13 0 185 16.1 17 10 0 181 18 20 14 0 138 17.6 19 13 0 158 14 12 6 0 122 10.5 19 6 2 40 6.9 26 9 3 62 2.8 13 2 5 89 0.7 12 4 5 33 3.6 20 3 7 150 6.7 15 4 2 196 12.5 15 10 0 196 14.4 17 15 0 225 16.5 11 14 0 213 18.7 20 18 0 258 19.4 9 10 0 156 15.8 10 5 0 90 11.3 17 5 0 48 9.7 25 7 0 46 2.9 19 2 7 49 0.1 18 0 14 29 2.5 24 4 10 118 6.7 13 7 2 223 10.3 6 8 0 172 11.2 14 6 0 259 17.4 9 3 0 252 20.5 13 12 0 136 17 23 15 0 143 14.2 18 8 0 119 10.6 16 6 0 24 6.1 21 1 6

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 8 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation GemiddeldeTemperatuur[t] = + 1.89334475185611 + 0.0456508505260396UrenZonneschijn[t] + 0.13932506715621Neerslagdagen[t] + 0.26861894142388Onweersdagen[t] -0.578107831724152Sneeuwdagen[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) 1.89334475185611 2.6699 0.7091 0.481464 0.240732 UrenZonneschijn 0.0456508505260396 0.010017 4.5573 3.3e-05 1.6e-05 Neerslagdagen 0.13932506715621 0.115008 1.2114 0.231313 0.115656 Onweersdagen 0.26861894142388 0.096456 2.7849 0.007496 0.003748 Sneeuwdagen -0.578107831724152 0.143342 -4.0331 0.000184 9.2e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.893898479690697 R-squared 0.79905449199334 Adjusted R-squared 0.783294059992818 F-TEST (value) 50.7000374080391 F-TEST (DF numerator) 4 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.61024325365338 Sum Squared Residuals 347.481862005391 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.3 8.12427457700125 1.17572542299875 2 14.2 16.2607405949993 -2.0607405949993 3 17.3 17.3221567484419 -0.0221567484418873 4 23 21.1433422230102 1.85665777698977 5 16.3 15.8714754977073 0.428524502292716 6 18.4 11.2928829223375 7.10711707766252 7 14.2 10.8038763988672 3.39612360113277 8 9.1 8.8235011693852 0.276498830614809 9 5.9 5.9177410914092 -0.0177410914091945 10 7.2 7.03183636857616 0.168163631423839 11 6.8 6.55634773869104 0.243652261308959 12 8 12.2919552891988 -4.29195528919878 13 14.3 15.664043125523 -1.364043125523 14 14.6 16.2059008954975 -1.60590089549754 15 17.5 15.0416536004152 2.45834639958479 16 17.2 16.2877125342373 0.91228746576274 17 17.2 13.2383876180316 3.96161238196842 18 14.1 10.7505668710307 3.34943312896925 19 10.4 9.56764066466033 0.832359335339666 20 6.8 6.97720071582047 -0.177200715820468 21 4.1 9.34779421319015 -5.24779421319015 22 6.5 6.9742870696662 -0.474287069666202 23 6.1 9.71516553970317 -3.61516553970317 24 6.3 6.44360090869781 -0.14360090869781 25 9.3 12.6929499272972 -3.3929499272972 26 16.4 17.4633132006 -1.06331320060001 27 16.1 15.3934676550678 0.706532344932196 28 18 16.7033152201278 1.2966847798722 29 17.6 14.332384638928 3.267615361072 30 14 12.3897935893882 1.61020641061183 31 10.5 10.5654227770959 -0.065422777095902 32 6.9 8.02507749660161 -1.12507749660161 33 2.8 4.18162208172829 -1.38162208172829 34 0.7 5.81210786162291 -5.11210786162291 35 3.6 2.94542616454218 0.654573835457818 36 6.7 10.7491084403524 -4.04910844035241 37 12.5 15.6169768765418 -3.11697687654182 38 14.4 17.2387217179736 -2.83872171797364 39 16.5 17.4580270388677 -0.958027038867656 40 18.7 19.2386182026566 -0.538618202656589 41 19.4 17.611379206219 1.78862079378098 42 15.8 11.7512228125998 4.04877718740021 43 11.3 9.71354214797464 1.58645785202536 44 9.7 9.44804484597841 0.251955154021586 45 2.9 3.13094321280061 -0.230943212800607 46 0.1 -1.45542200769431 1.55542200769431 47 2.5 1.85441847731428 0.645581522685715 48 6.7 9.81548791347837 -3.11548791347837 49 10.3 15.0583863534912 -4.75838635349124 50 11.2 13.3075556310651 -2.10755563106514 51 17.4 15.7766974667779 1.6233025332221 52 20.5 18.4320122545354 2.06798774546462 53 17 15.3356210893485 1.66437891065148 54 14.2 13.0782191172826 1.12178088271741 55 10.6 11.1667106874975 -0.56671068749746 56 6.1 2.71476352584043 3.38523647415957 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.573515480297434 0.852969039405133 0.426484519702566 9 0.605566529340638 0.788866941318724 0.394433470659362 10 0.496395662161856 0.992791324323712 0.503604337838144 11 0.418671002891678 0.837342005783356 0.581328997108322 12 0.513849797875447 0.972300404249107 0.486150202124553 13 0.708311230714714 0.583377538570572 0.291688769285286 14 0.6301647919096 0.7396704161808 0.3698352080904 15 0.609826279163602 0.780347441672796 0.390173720836398 16 0.540121238117582 0.919757523764835 0.459878761882418 17 0.596555212763035 0.80688957447393 0.403444787236965 18 0.616920427837161 0.766159144325678 0.383079572162839 19 0.62065507498922 0.75868985002156 0.37934492501078 20 0.534259557788596 0.931480884422808 0.465740442211404 21 0.824568791404964 0.350862417190073 0.175431208595036 22 0.772449126287602 0.455101747424795 0.227550873712398 23 0.856856910589365 0.286286178821269 0.143143089410635 24 0.80618045835612 0.387639083287758 0.193819541643879 25 0.834826517784207 0.330346964431587 0.165173482215793 26 0.783817359329857 0.432365281340287 0.216182640670144 27 0.72686954948381 0.54626090103238 0.27313045051619 28 0.677385442534153 0.645229114931695 0.322614557465847 29 0.74826781515028 0.503464369699441 0.251732184849721 30 0.720143228983261 0.559713542033477 0.279856771016739 31 0.648098604690518 0.703802790618964 0.351901395309482 32 0.587903777373583 0.824192445252834 0.412096222626417 33 0.522200703586818 0.955598592826365 0.477799296413182 34 0.637786273136492 0.724427453727017 0.362213726863508 35 0.569276610987664 0.861446778024672 0.430723389012336 36 0.740584803939037 0.518830392121925 0.259415196060963 37 0.79077402182861 0.418451956342781 0.20922597817139 38 0.786464778024942 0.427070443950117 0.213535221975058 39 0.714141995299141 0.571716009401718 0.285858004700859 40 0.632578976362716 0.734842047274568 0.367421023637284 41 0.59256398359146 0.81487203281708 0.40743601640854 42 0.923067376264908 0.153865247470184 0.076932623735092 43 0.941980461975206 0.116039076049588 0.058019538024794 44 0.921901555223179 0.156196889553642 0.078098444776821 45 0.86305119226397 0.27389761547206 0.13694880773603 46 0.795479955124688 0.409040089750623 0.204520044875312 47 0.938622320549194 0.122755358901612 0.061377679450806 48 0.856132872331073 0.287734255337853 0.143867127668927 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/10q9r41293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/10q9r41293555669.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/118us1293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/118us1293555669.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/2uztd1293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/2uztd1293555669.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/3uztd1293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/3uztd1293555669.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/4uztd1293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/4uztd1293555669.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/5uztd1293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/5uztd1293555669.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/6nrag1293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/6nrag1293555669.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/7xhrj1293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/7xhrj1293555669.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/8xhrj1293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/8xhrj1293555669.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/9xhrj1293555669.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293555577a2qugbxqxvzsch9/9xhrj1293555669.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by