*Unverified author*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 18 Nov 2009 13:17:23 -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/18/t1258575549i7m5bsbf3jfxtgn.htm/, Retrieved Wed, 18 Nov 2009 21:19:21 +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/18/t1258575549i7m5bsbf3jfxtgn.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 «

944.2 0 930.9 874 891.9 902.2 935.9 0 944.2 930.9 874 891.9 937.1 0 935.9 944.2 930.9 874 885.1 0 937.1 935.9 944.2 930.9 892.4 0 885.1 937.1 935.9 944.2 987.3 0 892.4 885.1 937.1 935.9 946.3 0 987.3 892.4 885.1 937.1 799.6 0 946.3 987.3 892.4 885.1 875.4 0 799.6 946.3 987.3 892.4 846.2 0 875.4 799.6 946.3 987.3 880.6 0 846.2 875.4 799.6 946.3 885.7 0 880.6 846.2 875.4 799.6 868.9 0 885.7 880.6 846.2 875.4 882.5 0 868.9 885.7 880.6 846.2 789.6 0 882.5 868.9 885.7 880.6 773.3 0 789.6 882.5 868.9 885.7 804.3 0 773.3 789.6 882.5 868.9 817.8 0 804.3 773.3 789.6 882.5 836.7 0 817.8 804.3 773.3 789.6 721.8 0 836.7 817.8 804.3 773.3 760.8 0 721.8 836.7 817.8 804.3 841.4 0 760.8 721.8 836.7 817.8 1045.6 0 841.4 760.8 721.8 836.7 949.2 0 1045.6 841.4 760.8 721.8 850.1 0 949.2 1045.6 841.4 760.8 957.4 0 850.1 949.2 1045.6 841.4 851.8 0 957.4 850.1 949.2 1045.6 913.9 0 851.8 957.4 850.1 949.2 888 0 913.9 851.8 957.4 850.1 973.8 0 888 913.9 851.8 957.4 927.6 1 973.8 888 913.9 851.8 833 1 92 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] = + 216.239700723775 -49.088920369534X[t] + 0.371323296188282Y1[t] + 0.185849885726791Y2[t] + 0.235814478394152Y3[t] -0.129975533253905Y4[t] + 71.868552684524M1[t] + 90.2999304272343M2[t] + 1.57747955706059M3[t] + 52.3107421420012M4[t] + 28.1986325605593M5[t] + 146.712766884783M6[t] + 88.1853992603089M7[t] + 6.55743999523928M8[t] + 34.2452911491028M9[t] + 41.726930355377M10[t] + 173.295912043324M11[t] + 1.22679408189955t + 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) 216.239700723775 184.801971 1.1701 0.249241 0.12462 X -49.088920369534 34.986374 -1.4031 0.168709 0.084355 Y1 0.371323296188282 0.159155 2.3331 0.025037 0.012518 Y2 0.185849885726791 0.157421 1.1806 0.245101 0.12255 Y3 0.235814478394152 0.164549 1.4331 0.160008 0.080004 Y4 -0.129975533253905 0.164839 -0.7885 0.435295 0.217648 M1 71.868552684524 48.513974 1.4814 0.146746 0.073373 M2 90.2999304272343 45.624021 1.9792 0.05507 0.027535 M3 1.57747955706059 46.590416 0.0339 0.973167 0.486584 M4 52.3107421420012 50.545056 1.0349 0.307242 0.153621 M5 28.1986325605593 47.732837 0.5908 0.558179 0.27909 M6 146.712766884783 50.613612 2.8987 0.006192 0.003096 M7 88.1853992603089 41.652951 2.1171 0.040851 0.020425 M8 6.55743999523928 45.61648 0.1438 0.886456 0.443228 M9 34.2452911491028 52.866488 0.6478 0.521029 0.260514 M10 41.726930355377 51.346385 0.8127 0.421478 0.210739 M11 173.295912043324 51.427596 3.3697 0.001737 0.000869 t 1.22679408189955 1.159387 1.0581 0.296672 0.148336 Multiple Linear Regression - Regression Statistics Multiple R 0.755754198583511 R-squared 0.571164408676605 Adjusted R-squared 0.379316907295086 F-TEST (value) 2.97717929378061 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0.00257148750707459 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 61.6353408099398 Sum Squared Residuals 144358.778996782 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 944.2 890.491711215155 53.7082887848453 2 935.9 922.781010206185 13.1189897938154 3 937.1 850.419579405586 86.6804205944141 4 885.1 897.023394696815 -11.9233946968148 5 892.4 851.366352895405 41.0336471045945 6 987.3 965.51552160599 21.7844783940093 7 946.3 932.391909521089 13.9080904789111 8 799.6 862.883816771152 -63.283816771152 9 875.4 851.135461748147 24.2645382518527 10 846.2 838.722950931317 7.47704906868335 11 880.6 945.498520673544 -64.8985206735445 12 885.7 817.718255628399 67.981744371601 13 868.9 882.362659084629 -13.4626590846292 14 882.5 908.637737578255 -26.1377375782554 15 789.6 819.801295033808 -30.2012950338076 16 773.3 835.168417474024 -61.868417474024 17 804.3 794.35574372742 9.94425627258015 18 817.8 898.903508882963 -81.1035088829636 19 836.7 860.608097337924 -23.9080973379241 20 721.8 799.162765932282 -77.3627659322818 21 760.8 788.079181203698 -27.2791812036976 22 841.4 792.617295115928 48.7827048840723 23 1045.6 933.038252955908 112.561747044092 24 949.2 875.903806293955 73.2961937060446 25 850.1 965.091735134906 -114.991735134906 26 957.4 967.713127831015 -10.3131278310154 27 851.8 852.369217440575 -0.569217440575073 28 913.9 874.219653365233 39.6803466347666 29 888 892.951235503389 -4.95123550338862 30 973.8 975.767784805303 -1.96778480530282 31 927.6 924.793813085714 2.80618691428599 32 833 829.004356208528 3.99564379147196 33 879.5 837.804801461796 41.6951985382037 34 797.3 824.151839177976 -26.8518391779758 35 834.5 918.763679667685 -84.2636796676853 36 735.1 768.49198640887 -33.3919864088699 37 835 786.16360086291 48.8363991370910 38 892.8 843.899778765219 48.9002212347813 39 697.2 768.15796309131 -70.9579630913096 40 821.1 794.706740815744 26.3932591842557 41 732.7 782.121665144887 -49.4216651448865 42 797.6 838.426517213543 -40.8265172135434 43 866.3 843.43632387284 22.8636761271608 44 826.3 763.65675826127 62.6432417387291 45 778.6 817.280555586359 -38.6805555863589 46 779.2 808.60791477478 -29.4079147747798 47 951 914.399546702862 36.6004532971378 48 692.3 800.185951668776 -107.885951668776 49 841.4 815.490293702401 25.9097062975988 50 857.3 882.868345619326 -25.568345619326 51 760.7 745.651945028722 15.0480549712781 52 841.2 833.481793648183 7.71820635181659 53 810.3 806.9050027289 3.39499727110049 54 1007.4 905.2866674922 102.113332507801 55 931.3 946.969856182434 -15.6698561824337 56 931.2 857.192302826767 74.0076971732328 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0479435520905402 0.0958871041810805 0.95205644790946 22 0.0176147321937711 0.0352294643875422 0.982385267806229 23 0.136697586989548 0.273395173979096 0.863302413010452 24 0.464068892525801 0.928137785051602 0.535931107474199 25 0.445085648166293 0.890171296332586 0.554914351833707 26 0.346265807708944 0.692531615417888 0.653734192291056 27 0.344852964676808 0.689705929353615 0.655147035323193 28 0.712364498664475 0.575271002671051 0.287635501335525 29 0.64873118100073 0.702537637998542 0.351268818999271 30 0.562100910422811 0.875798179154377 0.437899089577189 31 0.447659525649263 0.895319051298526 0.552340474350737 32 0.360456269060378 0.720912538120755 0.639543730939622 33 0.250776420027299 0.501552840054597 0.749223579972701 34 0.177199228404627 0.354398456809253 0.822800771595373 35 0.180507301917374 0.361014603834748 0.819492698082626 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 1 0.0666666666666667 NOK 10% type I error level 2 0.133333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/10fpwj1258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/10fpwj1258575439.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/1et441258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/1et441258575439.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/2e1mw1258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/2e1mw1258575439.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/3f3wr1258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/3f3wr1258575439.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/4sqfk1258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/4sqfk1258575439.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/5r55x1258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/5r55x1258575439.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/6cowb1258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/6cowb1258575439.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/7ue981258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/7ue981258575439.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/8q1mv1258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/8q1mv1258575439.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/91e6v1258575439.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258575549i7m5bsbf3jfxtgn/91e6v1258575439.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