*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: Mon, 21 Dec 2009 14:53:47 -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/Dec/21/t1261432461mj0jttg90zz8f7k.htm/, Retrieved Mon, 21 Dec 2009 22:54:34 +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/Dec/21/t1261432461mj0jttg90zz8f7k.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 «

120,9 0 0 119,6 0 0 125,9 0 0 116,1 0 0 107,5 0 0 116,7 0 0 112,5 0 0 113 0 0 126,4 0 0 114,1 0 0 112,5 0 0 112,4 0 0 113,1 0 0 116,3 0 0 111,7 0 0 118,8 0 0 116,5 0 0 125,1 0 0 113,1 0 0 119,6 0 0 114,4 0 0 114 0 0 117,8 0 0 117 0 0 120,9 0 0 115 0 0 117,3 0 0 119,4 0 0 114,9 0 0 125,8 0 0 117,6 0 0 117,6 0 0 114,9 0 0 121,9 0 0 117 0 1 106,4 0 1 110,5 0 1 113,6 0 1 114,2 0 1 125,4 0 1 124,6 0 1 120,2 0 1 120,8 0 1 111,4 0 1 124,1 0 1 120,2 0 1 125,5 0 1 116 1 0 117 1 0 105,7 1 0 102 1 0 106,4 1 0 96,9 1 0 107,6 1 0 98,8 1 0 101,1 1 0 105,7 1 0 104,6 1 0 103,2 1 0 101,6 1 0

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation ChemischeNijverheid[t] = + 114.045891608392 -14.2594696969697Dummy_1[t] -0.643036130536138Dummy_2[t] + 3.70175990675989M1[t] + 1.19324592074591M2[t] + 1.30473193473193M3[t] + 4.23621794871794M4[t] -0.972296037296041M5[t] + 5.95918997668996M6[t] -0.629324009324017M7[t] -0.717837995338005M8[t] + 3.77364801864801M9[t] + 1.56513403263402M10[t] + 1.86522727272727M11[t] + 0.0685139860139864t + 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) 114.045891608392 2.967911 38.4263 0 0 Dummy_1 -14.2594696969697 3.839393 -3.714 0.000561 0.00028 Dummy_2 -0.643036130536138 2.802759 -0.2294 0.819575 0.409787 M1 3.70175990675989 3.390255 1.0919 0.280696 0.140348 M2 1.19324592074591 3.382025 0.3528 0.725871 0.362935 M3 1.30473193473193 3.376365 0.3864 0.700999 0.350499 M4 4.23621794871794 3.373288 1.2558 0.215665 0.107833 M5 -0.972296037296041 3.372802 -0.2883 0.77446 0.38723 M6 5.95918997668996 3.374908 1.7657 0.084225 0.042112 M7 -0.629324009324017 3.3796 -0.1862 0.853115 0.426557 M8 -0.717837995338005 3.386869 -0.2119 0.833105 0.416553 M9 3.77364801864801 3.396697 1.111 0.27248 0.13624 M10 1.56513403263402 3.409062 0.4591 0.648365 0.324183 M11 1.86522727272727 3.384044 0.5512 0.584235 0.292117 t 0.0685139860139864 0.093506 0.7327 0.467529 0.233765 Multiple Linear Regression - Regression Statistics Multiple R 0.766919546586582 R-squared 0.588165590936568 Adjusted R-squared 0.460039330339056 F-TEST (value) 4.59051554453926 F-TEST (DF numerator) 14 F-TEST (DF denominator) 45 p-value 4.53898964412058e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.30396995837526 Sum Squared Residuals 1265.94437937063 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 120.9 117.816165501166 3.08383449883446 2 119.6 115.376165501166 4.22383449883448 3 125.9 115.556165501165 10.3438344988345 4 116.1 118.556165501165 -2.4561655011655 5 107.5 113.416165501165 -5.9161655011655 6 116.7 120.416165501165 -3.7161655011655 7 112.5 113.896165501165 -1.39616550116549 8 113 113.876165501165 -0.876165501165494 9 126.4 118.436165501165 7.96383449883452 10 114.1 116.296165501166 -2.19616550116550 11 112.5 116.664772727273 -4.16477272727273 12 112.4 114.868059440559 -2.46805944055944 13 113.1 118.638333333333 -5.53833333333334 14 116.3 116.198333333333 0.101666666666667 15 111.7 116.378333333333 -4.67833333333333 16 118.8 119.378333333333 -0.578333333333339 17 116.5 114.238333333333 2.26166666666666 18 125.1 121.238333333333 3.86166666666667 19 113.1 114.718333333333 -1.61833333333334 20 119.6 114.698333333333 4.90166666666666 21 114.4 119.258333333333 -4.85833333333333 22 114 117.118333333333 -3.11833333333333 23 117.8 117.486940559441 0.313059440559436 24 117 115.690227272727 1.30977272727272 25 120.9 119.460501165501 1.43949883449885 26 115 117.020501165501 -2.02050116550116 27 117.3 117.200501165501 0.099498834498827 28 119.4 120.200501165501 -0.800501165501166 29 114.9 115.060501165501 -0.160501165501166 30 125.8 122.060501165501 3.73949883449884 31 117.6 115.540501165501 2.05949883449883 32 117.6 115.520501165501 2.07949883449883 33 114.9 120.080501165501 -5.18050116550117 34 121.9 117.940501165501 3.95949883449884 35 117 117.666072261072 -0.66607226107226 36 106.4 115.869358974359 -9.46935897435897 37 110.5 119.639632867133 -9.13963286713286 38 113.6 117.199632867133 -3.59963286713287 39 114.2 117.379632867133 -3.17963286713287 40 125.4 120.379632867133 5.02036713286714 41 124.6 115.239632867133 9.36036713286713 42 120.2 122.239632867133 -2.03963286713286 43 120.8 115.719632867133 5.08036713286713 44 111.4 115.699632867133 -4.29963286713286 45 124.1 120.259632867133 3.84036713286713 46 120.2 118.119632867133 2.08036713286714 47 125.5 118.488240093240 7.0117599067599 48 116 103.075093240093 12.9249067599068 49 117 106.845367132867 10.1546328671329 50 105.7 104.405367132867 1.29463286713287 51 102 104.585367132867 -2.58536713286713 52 106.4 107.585367132867 -1.18536713286713 53 96.9 102.445367132867 -5.54536713286713 54 107.6 109.445367132867 -1.84536713286713 55 98.8 102.925367132867 -4.12536713286713 56 101.1 102.905367132867 -1.80536713286714 57 105.7 107.465367132867 -1.76536713286713 58 104.6 105.325367132867 -0.725367132867139 59 103.2 105.693974358974 -2.49397435897436 60 101.6 103.897261072261 -2.29726107226108 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.894969661594563 0.210060676810874 0.105030338405437 19 0.80587886051146 0.388242278977078 0.194121139488539 20 0.763635736408602 0.472728527182797 0.236364263591398 21 0.782103087358743 0.435793825282514 0.217896912641257 22 0.689183217644121 0.621633564711757 0.310816782355879 23 0.614276663233658 0.771446673532683 0.385723336766342 24 0.522066724468244 0.955866551063512 0.477933275531756 25 0.424404151703132 0.848808303406264 0.575595848296868 26 0.333762829127885 0.667525658255769 0.666237170872116 27 0.244357021684049 0.488714043368098 0.755642978315951 28 0.175318235426063 0.350636470852126 0.824681764573937 29 0.122758938440850 0.245517876881701 0.87724106155915 30 0.0898019499237692 0.179603899847538 0.91019805007623 31 0.0589675822827187 0.117935164565437 0.941032417717281 32 0.0373509704854906 0.0747019409709813 0.96264902951451 33 0.0330846715765257 0.0661693431530515 0.966915328423474 34 0.0246099302578340 0.0492198605156679 0.975390069742166 35 0.0141292110519984 0.0282584221039967 0.985870788948002 36 0.082135044142416 0.164270088284832 0.917864955857584 37 0.478664800687971 0.957329601375943 0.521335199312029 38 0.545320311901681 0.909359376196637 0.454679688098319 39 0.502600119686117 0.994799760627766 0.497399880313883 40 0.452668738090829 0.905337476181658 0.547331261909171 41 0.68744436992592 0.625111260148159 0.312555630074080 42 0.594110159647831 0.811779680704338 0.405889840352169 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 2 0.08 NOK 10% type I error level 4 0.16 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/10gt9u1261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/10gt9u1261432421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/1tf2g1261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/1tf2g1261432421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/2dgil1261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/2dgil1261432421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/38oct1261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/38oct1261432421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/4gixx1261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/4gixx1261432421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/5dgzt1261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/5dgzt1261432421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/6guhx1261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/6guhx1261432421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/7ey8r1261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/7ey8r1261432421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/8of6i1261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/8of6i1261432421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/92ip91261432421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261432461mj0jttg90zz8f7k/92ip91261432421.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