*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: Wed, 18 Nov 2009 13:49:00 -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/t1258578962urfs7ngk6nori5g.htm/, Retrieved Wed, 18 Nov 2009 22:16:14 +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/t1258578962urfs7ngk6nori5g.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 «

1.7 0 2.4 0 2.0 0 2.1 0 2.0 0 1.8 0 2.7 0 2.3 0 1.9 0 2.0 0 2.3 0 2.8 0 2.4 0 2.3 0 2.7 0 2.7 0 2.9 0 3.0 0 2.2 0 2.3 0 2.8 0 2.8 0 2.8 0 2.2 0 2.6 0 2.8 0 2.5 0 2.4 0 2.3 0 1.9 0 1.7 0 2.0 0 2.1 0 1.7 0 1.8 0 1.8 0 1.8 0 1.3 0 1.3 0 1.3 0 1.2 0 1.4 0 2.2 1 2.9 1 3.1 1 3.5 1 3.6 1 4.4 1 4.1 1 5.1 1 5.8 1 5.9 1 5.4 1 5.5 1 4.8 1 3.2 1 2.7 1 2.1 1 1.9 1 0.6 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 2.19333333333333 + 2.31666666666667X[t] + 0.391111111111108M1[t] + 0.672222222222222M2[t] + 0.773333333333333M3[t] + 0.814444444444445M4[t] + 0.715555555555555M5[t] + 0.696666666666665M6[t] + 0.254444444444444M7[t] + 0.0955555555555554M8[t] + 0.0966666666666665M9[t] + 0.0177777777777775M10[t] + 0.0988888888888882M11[t] -0.0211111111111111t + 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) 2.19333333333333 0.515713 4.253 0.000102 5.1e-05 X 2.31666666666667 0.44222 5.2387 4e-06 2e-06 M1 0.391111111111108 0.59861 0.6534 0.516774 0.258387 M2 0.672222222222222 0.597589 1.1249 0.266471 0.133235 M3 0.773333333333333 0.596793 1.2958 0.201503 0.100752 M4 0.814444444444445 0.596223 1.366 0.178579 0.089289 M5 0.715555555555555 0.595882 1.2008 0.235962 0.117981 M6 0.696666666666665 0.595768 1.1694 0.248283 0.124141 M7 0.254444444444444 0.596155 0.4268 0.671509 0.335755 M8 0.0955555555555554 0.595129 0.1606 0.873141 0.43657 M9 0.0966666666666665 0.59433 0.1626 0.871508 0.435754 M10 0.0177777777777775 0.593758 0.0299 0.976244 0.488122 M11 0.0988888888888882 0.593415 0.1666 0.868381 0.43419 t -0.0211111111111111 0.011654 -1.8116 0.076585 0.038293 Multiple Linear Regression - Regression Statistics Multiple R 0.698672819377352 R-squared 0.488143708536698 Adjusted R-squared 0.343488669644896 F-TEST (value) 3.37453649922153 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.00112376002416636 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.938090876596518 Sum Squared Residuals 40.4806666666667 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.7 2.56333333333334 -0.86333333333334 2 2.4 2.82333333333333 -0.423333333333331 3 2 2.90333333333333 -0.903333333333332 4 2.1 2.92333333333333 -0.823333333333334 5 2 2.80333333333333 -0.803333333333332 6 1.8 2.76333333333333 -0.963333333333334 7 2.7 2.3 0.400000000000002 8 2.3 2.12 0.180000000000000 9 1.9 2.1 -0.2 10 2 2 1.66533453693773e-16 11 2.3 2.06 0.24 12 2.8 1.94 0.86 13 2.4 2.31 0.0900000000000015 14 2.3 2.57 -0.270000000000001 15 2.7 2.65 0.0499999999999999 16 2.7 2.67 0.0300000000000005 17 2.9 2.55 0.35 18 3 2.51 0.490000000000001 19 2.2 2.04666666666667 0.153333333333334 20 2.3 1.86666666666667 0.433333333333333 21 2.8 1.84666666666667 0.953333333333333 22 2.8 1.74666666666667 1.05333333333333 23 2.8 1.80666666666667 0.993333333333333 24 2.2 1.68666666666667 0.513333333333333 25 2.6 2.05666666666666 0.543333333333335 26 2.8 2.31666666666667 0.483333333333333 27 2.5 2.39666666666667 0.103333333333333 28 2.4 2.41666666666667 -0.0166666666666666 29 2.3 2.29666666666667 0.00333333333333302 30 1.9 2.25666666666667 -0.356666666666666 31 1.7 1.79333333333333 -0.0933333333333333 32 2 1.61333333333333 0.386666666666666 33 2.1 1.59333333333333 0.506666666666666 34 1.7 1.49333333333333 0.206666666666666 35 1.8 1.55333333333333 0.246666666666667 36 1.8 1.43333333333333 0.366666666666666 37 1.8 1.80333333333333 -0.00333333333333195 38 1.3 2.06333333333333 -0.763333333333334 39 1.3 2.14333333333333 -0.843333333333334 40 1.3 2.16333333333333 -0.863333333333333 41 1.2 2.04333333333333 -0.843333333333334 42 1.4 2.00333333333333 -0.603333333333333 43 2.2 3.85666666666667 -1.65666666666667 44 2.9 3.67666666666667 -0.776666666666667 45 3.1 3.65666666666667 -0.556666666666667 46 3.5 3.55666666666667 -0.0566666666666667 47 3.6 3.61666666666667 -0.0166666666666663 48 4.4 3.49666666666667 0.903333333333333 49 4.1 3.86666666666666 0.233333333333335 50 5.1 4.12666666666667 0.973333333333333 51 5.8 4.20666666666667 1.59333333333333 52 5.9 4.22666666666667 1.67333333333333 53 5.4 4.10666666666667 1.29333333333333 54 5.5 4.06666666666667 1.43333333333333 55 4.8 3.60333333333333 1.19666666666667 56 3.2 3.42333333333333 -0.223333333333334 57 2.7 3.40333333333333 -0.703333333333334 58 2.1 3.30333333333333 -1.20333333333333 59 1.9 3.36333333333333 -1.46333333333333 60 0.6 3.24333333333333 -2.64333333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.043037081809525 0.08607416361905 0.956962918190475 18 0.0262032992933039 0.0524065985866079 0.973796700706696 19 0.0451234289975354 0.0902468579950709 0.954876571002465 20 0.0221883398429926 0.0443766796859851 0.977811660157007 21 0.0105063715043277 0.0210127430086554 0.989493628495672 22 0.00440627913126174 0.00881255826252349 0.995593720868738 23 0.00160579744759499 0.00321159489518999 0.998394202552405 24 0.00247881108281684 0.00495762216563369 0.997521188917183 25 0.000891019436875723 0.00178203887375145 0.999108980563124 26 0.00031060863899125 0.0006212172779825 0.99968939136101 27 0.000141560111038614 0.000283120222077228 0.999858439888961 28 7.69962916701483e-05 0.000153992583340297 0.99992300370833 29 4.72658269742324e-05 9.45316539484647e-05 0.999952734173026 30 7.0002883637721e-05 0.000140005767275442 0.999929997116362 31 9.36543726337404e-05 0.000187308745267481 0.999906345627366 32 5.24932600055746e-05 0.000104986520011149 0.999947506739994 33 3.35445007176023e-05 6.70890014352047e-05 0.999966455499282 34 3.80249155590266e-05 7.60498311180533e-05 0.99996197508444 35 6.157440915633e-05 0.00012314881831266 0.999938425590844 36 0.000267590646308785 0.00053518129261757 0.999732409353691 37 0.000281492509836661 0.000562985019673323 0.999718507490163 38 0.000334499759329283 0.000668999518658566 0.99966550024067 39 0.000232552859223209 0.000465105718446418 0.999767447140777 40 0.000137419599191941 0.000274839198383883 0.999862580400808 41 7.34083723223419e-05 0.000146816744644684 0.999926591627678 42 2.32832942647783e-05 4.65665885295566e-05 0.999976716705735 43 0.00222171629940108 0.00444343259880216 0.997778283700599 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 22 0.814814814814815 NOK 5% type I error level 24 0.888888888888889 NOK 10% type I error level 27 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/10usaw1258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/10usaw1258577335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/15aar1258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/15aar1258577335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/2iqwe1258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/2iqwe1258577335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/3bfef1258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/3bfef1258577335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/4fmpi1258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/4fmpi1258577335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/5pvju1258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/5pvju1258577335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/6h7sx1258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/6h7sx1258577335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/7jmum1258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/7jmum1258577335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/8hjy61258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/8hjy61258577335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/9vfbz1258577335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578962urfs7ngk6nori5g/9vfbz1258577335.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