WS 7.4

*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: Fri, 20 Nov 2009 13:16:51 -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/20/t1258748254p069wt8k7dh9c48.htm/, Retrieved Fri, 20 Nov 2009 21:17:47 +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/20/t1258748254p069wt8k7dh9c48.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 «

114,91 93,13 107,18 96,31 96,21 100,00 92,56 93,88 114,91 107,18 96,31 96,21 115,00 92,55 92,56 114,91 107,18 96,31 107,12 94,43 115,00 92,56 114,91 107,18 117,78 96,25 107,12 115,00 92,56 114,91 107,37 100,44 117,78 107,12 115,00 92,56 106,30 101,50 107,37 117,78 107,12 115,00 114,51 99,40 106,30 107,37 117,78 107,12 98,00 99,69 114,51 106,30 107,37 117,78 103,06 101,69 98,00 114,51 106,30 107,37 100,29 103,67 103,06 98,00 114,51 106,30 104,61 103,05 100,29 103,06 98,00 114,51 111,15 100,95 104,61 100,29 103,06 98,00 104,99 102,35 111,15 104,61 100,29 103,06 109,93 101,65 104,99 111,15 104,61 100,29 111,54 99,57 109,93 104,99 111,15 104,61 132,50 95,68 111,54 109,93 104,99 111,15 100,34 96,58 132,50 111,54 109,93 104,99 123,10 96,33 100,34 132,50 111,54 109,93 114,24 95,37 123,10 100,34 132,50 111,54 104,57 96,00 114,24 123,10 100,34 132,50 109,08 96,88 104,57 114,24 123,10 100,34 106,98 94,85 109,08 104,57 114,24 123,10 133,68 92,47 106,98 109,08 104,57 114,24 124,85 93,99 133,68 106 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] = + 106.850129100590 -0.538798718918726X[t] + 0.291815822997867Y1[t] + 0.500730273935326Y2[t] + 0.260686397627196Y3[t] -0.383518985923385Y4[t] -11.1406334334353M1[t] -25.8052401812007M2[t] -25.288184371146M3[t] -20.9695688872197M4[t] -1.12377457251293M5[t] -18.6728896312874M6[t] -16.4695496880641M7[t] -16.1366071199349M8[t] -17.4192741971750M9[t] -30.11444063088M10[t] -8.84376235170137M11[t] -0.0619092178886454t + 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) 106.850129100590 35.621142 2.9996 0.004751 0.002376 X -0.538798718918726 0.283326 -1.9017 0.064813 0.032406 Y1 0.291815822997867 0.148786 1.9613 0.0572 0.0286 Y2 0.500730273935326 0.157535 3.1785 0.00294 0.00147 Y3 0.260686397627196 0.169209 1.5406 0.131697 0.065848 Y4 -0.383518985923385 0.171674 -2.234 0.031446 0.015723 M1 -11.1406334334353 7.707668 -1.4454 0.156544 0.078272 M2 -25.8052401812007 8.277069 -3.1177 0.003467 0.001733 M3 -25.288184371146 7.816939 -3.235 0.00252 0.00126 M4 -20.9695688872197 7.452359 -2.8138 0.007711 0.003855 M5 -1.12377457251293 7.449147 -0.1509 0.880885 0.440442 M6 -18.6728896312874 7.636335 -2.4453 0.01922 0.00961 M7 -16.4695496880641 8.442743 -1.9507 0.058492 0.029246 M8 -16.1366071199349 7.910961 -2.0398 0.048369 0.024184 M9 -17.4192741971750 8.067463 -2.1592 0.037212 0.018606 M10 -30.11444063088 8.312143 -3.6229 0.000849 0.000424 M11 -8.84376235170137 8.077025 -1.0949 0.28044 0.14022 t -0.0619092178886454 0.187738 -0.3298 0.74339 0.371695 Multiple Linear Regression - Regression Statistics Multiple R 0.822066171071495 R-squared 0.675792789620149 Adjusted R-squared 0.530752721818636 F-TEST (value) 4.65935241112113 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 3.97256906923271e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.6850633507316 Sum Squared Residuals 4338.48199474764 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 114.91 111.700154071361 3.20984592863867 2 92.56 105.747819052381 -13.1878190523812 3 115 107.063438557842 7.93656144215788 4 107.12 103.510483154601 3.60951684539874 5 117.78 122.459690496717 -4.67969049671719 6 107.37 116.177553800474 -8.80755380047352 7 106.3 109.387465029074 -3.08746502907399 8 114.51 111.066177214552 3.44382278544845 9 98 104.623318015396 -6.62331801539574 10 103.06 93.7952594352802 9.26474056471982 11 100.29 109.697338914265 -9.40733891426535 12 104.61 113.085989310960 -8.47598931096034 13 111.15 110.539517095504 0.610482904495695 14 104.99 96.4676057989711 8.52239420102888 15 109.93 100.965614845008 8.96438515499232 16 111.54 104.748181145857 6.79181885414297 17 132.5 125.457381910214 7.04261808978643 18 100.34 117.934341935161 -17.5943419351612 19 123.1 119.846103324017 3.25389667598318 20 114.24 116.01914729302 -1.7791472930201 21 104.57 106.724028155335 -2.15402815533472 22 109.08 104.501673392927 4.57832660707274 23 106.98 112.239657863795 -5.25965786379481 24 133.68 124.826473006013 8.8535269939869 25 124.85 124.429229447889 0.420770552110658 26 122.51 118.509317325922 4.00068267407811 27 116.8 122.261665748833 -5.46166574883334 28 116.01 112.14613061372 3.86386938627997 29 129.76 131.600613859887 -1.84061385988741 30 125.2 116.681340123916 8.51865987608409 31 143.79 127.422516594758 16.3674834052421 32 127.95 134.393826793097 -6.44382679309673 33 130.3 132.227021356999 -1.92702135699910 34 108.44 119.918301444609 -11.4783014446093 35 129.37 126.104394851696 3.26560514830363 36 143.68 136.385323976781 7.29467602321861 37 131.88 133.697055028054 -1.8170550280537 38 117.62 136.618661700689 -18.9986617006888 39 118.96 124.948670069839 -5.98867006983904 40 104.82 114.495194217001 -9.67519421700053 41 134.62 131.109283390083 3.51071660991693 42 140.4 120.921569103020 19.4784308969795 43 143.8 136.053338961402 7.74666103859817 44 153.43 151.187717468672 2.24228253132755 45 153.29 142.585632472270 10.7043675277296 46 127.31 129.674765727183 -2.36476572718326 47 153.55 142.148608370243 11.4013916297565 48 136.93 144.602213706245 -7.67221370624518 49 131.77 134.194044357191 -2.42404435719133 50 144.34 124.676596122037 19.663403877963 51 107.42 112.870610778478 -5.45061077847782 52 113.62 118.210010868821 -4.59001086882115 53 124.22 128.253030343099 -4.03303034309875 54 102.06 103.655195037429 -1.59519503742886 55 96.37 120.650576090749 -24.2805760907494 56 111.68 109.143131230659 2.53686876934082 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0245400847239681 0.0490801694479361 0.975459915276032 22 0.00540706971063744 0.0108141394212749 0.994592930289363 23 0.00248134963239024 0.00496269926478049 0.99751865036761 24 0.0120536218700694 0.0241072437401389 0.98794637812993 25 0.0586095109158621 0.117219021831724 0.941390489084138 26 0.0332215486875245 0.066443097375049 0.966778451312476 27 0.0233359739881288 0.0466719479762576 0.976664026011871 28 0.0110968979905668 0.0221937959811337 0.988903102009433 29 0.00486341652313687 0.00972683304627374 0.995136583476863 30 0.00416954354095026 0.00833908708190052 0.99583045645905 31 0.00552771924401556 0.0110554384880311 0.994472280755984 32 0.00287549589434724 0.00575099178869447 0.997124504105653 33 0.00229275271241693 0.00458550542483385 0.997707247287583 34 0.0111032921992575 0.0222065843985149 0.988896707800742 35 0.0110998610493838 0.0221997220987675 0.988900138950616 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.333333333333333 NOK 5% type I error level 13 0.866666666666667 NOK 10% type I error level 14 0.933333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/10s4rg1258748207.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/10s4rg1258748207.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/1ewor1258748206.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/1ewor1258748206.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/228xn1258748206.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/228xn1258748206.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/3xq0c1258748206.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/3xq0c1258748206.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/4j1o61258748206.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/4j1o61258748206.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/5n0qq1258748206.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/5n0qq1258748206.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/6oucz1258748206.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/6oucz1258748206.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/7jafk1258748207.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/7jafk1258748207.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/81q8w1258748207.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/81q8w1258748207.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/955vr1258748207.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748254p069wt8k7dh9c48/955vr1258748207.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