*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, 23 Nov 2009 12:54:10 -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/23/t1259007072iovw3pi8a7h6ngs.htm/, Retrieved Mon, 23 Nov 2009 21:11:24 +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/23/t1259007072iovw3pi8a7h6ngs.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 «

93.3 121.8 97.3 127.6 127 129.9 111.7 128 96.4 123.5 133 124 72.2 127.4 95.8 127.6 124.1 128.4 127.6 131.4 110.7 135.1 104.6 134 112.7 144.5 115.3 147.3 139.4 150.9 119 148.7 97.4 141.4 154 138.9 81.5 139.8 88.8 145.6 127.7 147.9 105.1 148.5 114.9 151.1 106.4 157.5 104.5 167.5 121.6 172.3 141.4 173.5 99 187.5 126.7 205.5 134.1 195.1 81.3 204.5 88.6 204.5 132.7 201.7 132.9 207 134.4 206.6 103.7 210.6 119.7 211.1 115 215 132.9 223.9 108.5 238.2 113.9 238.9 142 229.6 97.7 232.2 92.2 222.1 128.8 221.6 134.9 227.3 128.2 221 114.8 213.6 117.9 243.4 119.1 253.8 120.7 265.3 129.1 268.2 117.6 268.5 129.2 266.9 100 268.4 87 250.8 128 231.2 127.7 192 93.4 171.4 84.1 160 71.7 148.1

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 IPtran[t] = + 76.2878881544539 + 0.259021168205223IGPic[t] -1.42618565989672M1[t] + 3.59316284151128M2[t] + 21.314459070658M3[t] + 1.61647699326119M4[t] -1.29060083467887M5[t] + 28.5023504634329M6[t] -23.8138522411022M7[t] -18.2237877168161M8[t] + 21.1078487635521M9[t] + 20.2881455653972M10[t] + 12.5819471261346M11[t] -0.525912654275461t + 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) 76.2878881544539 7.052157 10.8177 0 0 IGPic 0.259021168205223 0.052842 4.9018 1.2e-05 6e-06 M1 -1.42618565989672 5.87495 -0.2428 0.80925 0.404625 M2 3.59316284151128 6.356785 0.5652 0.574593 0.287297 M3 21.314459070658 6.395024 3.333 0.001682 0.000841 M4 1.61647699326119 6.437086 0.2511 0.802816 0.401408 M5 -1.29060083467887 6.418071 -0.2011 0.841497 0.420749 M6 28.5023504634329 6.313319 4.5146 4.2e-05 2.1e-05 M7 -23.8138522411022 6.326095 -3.7644 0.000463 0.000232 M8 -18.2237877168161 6.242291 -2.9194 0.005369 0.002684 M9 21.1078487635521 6.180928 3.415 0.001324 0.000662 M10 20.2881455653972 6.132565 3.3083 0.001806 0.000903 M11 12.5819471261346 6.109528 2.0594 0.04502 0.02251 t -0.525912654275461 0.13915 -3.7795 0.000442 0.000221 Multiple Linear Regression - Regression Statistics Multiple R 0.88943138357706 R-squared 0.791088186091805 Adjusted R-squared 0.73330406735124 F-TEST (value) 13.690408425948 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 7.45647987798748e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9.65309347975947 Sum Squared Residuals 4379.56404526181 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 93.3 105.884568127678 -12.5845681276778 2 97.3 111.880326750401 -14.5803267504007 3 127 129.671459012144 -2.671459012144 4 111.7 108.955424060882 2.74457593911827 5 96.4 104.356838321743 -7.9568383217427 6 133 133.753387549682 -0.753387549681674 7 72.2 81.7919441627688 -9.59194416276883 8 95.8 86.9079002664205 8.89209973357947 9 124.1 125.920841027077 -1.82084102707739 10 127.6 125.352288679263 2.24771132073725 11 110.7 118.078555908084 -7.37855590808402 12 104.6 104.685772842648 -0.0857728426482145 13 112.7 105.453396794631 7.24660320536917 14 115.3 110.672091912738 4.62790808726197 15 139.4 128.799951693148 10.6000483068519 16 119 108.006210391424 10.9937896085757 17 97.4 102.682365381311 -5.28236538131067 18 154 131.301851104634 22.6981488953661 19 81.5 78.692854797208 2.80714520279194 20 88.8 85.259329442809 3.54067055719098 21 127.7 124.660801955774 3.03919804422629 22 105.1 123.470598804267 -18.3705988042665 23 114.9 115.911942748062 -1.01194274806205 24 106.4 104.461818444165 1.93818155583461 25 104.5 105.099931812045 -0.599931812045432 26 121.6 110.836669266563 10.7633307334369 27 141.4 128.342878243281 13.0571217567194 28 99 111.745279866481 -12.7452798664814 29 126.7 112.97467041196 13.7253295880401 30 134.1 139.547888906462 -5.44788890646194 31 81.3 89.1405725287804 -7.84057252878043 32 88.6 94.2047243987911 -5.60472439879111 33 132.7 132.285188953909 0.414811046090823 34 132.9 132.312385292967 0.587614707033468 35 134.4 123.976665732146 10.4233342678536 36 103.7 111.904890624557 -8.20489062455719 37 119.7 110.082302894488 9.6176971055124 38 115 115.585921297621 -0.585921297620538 39 132.9 135.086593269518 -2.18659326951830 40 108.5 118.566701243181 -10.0667012431807 41 113.9 115.315025578709 -1.41502557870882 42 142 142.173167358237 -0.173167358236589 43 97.7 90.0045070367596 7.69549296324043 44 92.2 92.4525451078975 -0.252545107897483 45 128.8 131.128758349888 -2.32875834988755 46 134.9 131.259563156227 3.64043684377297 47 128.2 121.395618702996 6.80438129700393 48 114.8 106.371002277867 8.42899772213267 49 117.9 112.137734776211 5.76226522378923 50 119.1 119.324990772678 -0.224990772677658 51 120.7 139.499117781909 -18.799117781909 52 129.1 120.026384438032 9.07361556196816 53 117.6 116.671100306278 0.928899693722106 54 129.2 145.523705080986 -16.3237050809859 55 100 93.0701214744831 6.9298785255169 56 87 93.5755007840819 -6.57550078408186 57 128 127.304409713352 0.695590286647829 58 127.7 115.805164067277 11.8948359327229 59 93.4 102.237216908711 -8.83721690871148 60 84.1 86.1765158107619 -2.07651581076187 61 71.7 81.1420655949475 -9.44206559494751 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0932358857654141 0.186471771530828 0.906764114234586 18 0.237839401597638 0.475678803195277 0.762160598402362 19 0.133724017412248 0.267448034824497 0.866275982587752 20 0.288282952413081 0.576565904826163 0.711717047586919 21 0.211049374591728 0.422098749183455 0.788950625408272 22 0.710838119110162 0.578323761779677 0.289161880889838 23 0.617289880916366 0.765420238167269 0.382710119083634 24 0.5297985440162 0.9404029119676 0.4702014559838 25 0.465422707226449 0.930845414452899 0.534577292773551 26 0.411354050875874 0.822708101751748 0.588645949124126 27 0.507797245983628 0.984405508032744 0.492202754016372 28 0.584373029147603 0.831253941704794 0.415626970852397 29 0.824897239676156 0.350205520647687 0.175102760323844 30 0.852541463609329 0.294917072781343 0.147458536390671 31 0.848604775749625 0.30279044850075 0.151395224250375 32 0.801223642703324 0.397552714593353 0.198776357296676 33 0.723206510242237 0.553586979515525 0.276793489757763 34 0.71826517001232 0.563469659975359 0.281734829987680 35 0.692949264000754 0.614101471998492 0.307050735999246 36 0.791821079919688 0.416357840160623 0.208178920080312 37 0.721098473953559 0.557803052092882 0.278901526046441 38 0.626743382466518 0.746513235066964 0.373256617533482 39 0.693658667785434 0.612682664429133 0.306341332214566 40 0.842792346935239 0.314415306129522 0.157207653064761 41 0.771298555083965 0.45740288983207 0.228701444916035 42 0.82938713880078 0.341225722398439 0.170612861199220 43 0.724560375089612 0.550879249820777 0.275439624910388 44 0.699044124732409 0.601911750535182 0.300955875267591 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/10472c1259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/10472c1259006045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/1hejo1259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/1hejo1259006045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/2gaws1259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/2gaws1259006045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/3z5tv1259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/3z5tv1259006045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/4zh661259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/4zh661259006045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/5to7z1259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/5to7z1259006045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/6vc7r1259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/6vc7r1259006045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/7by8o1259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/7by8o1259006045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/8ntm01259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/8ntm01259006045.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/95gps1259006045.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259007072iovw3pi8a7h6ngs/95gps1259006045.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