Workshop7/module3

*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: Tue, 24 Nov 2009 11:27:10 -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/24/t1259087300vg6nmwb7nejh39z.htm/, Retrieved Tue, 24 Nov 2009 19:28:32 +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/24/t1259087300vg6nmwb7nejh39z.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 «

114.08 136.49 112.95 142.62 135.31 141.71 134.31 149.51 133.03 147.39 140.11 131.96 124.69 136.38 131.68 127.34 150.95 133.85 137.26 125.14 130.51 141.25 143.15 149.32 118.01 120.92 122.56 134.85 147.97 131.93 135.74 134.22 151.62 143.07 154.82 145.37 145.59 134.32 147.12 126.31 175.86 162.21 140.66 124.09 152.69 153.91 154.38 154.34 132.45 138.70 136.44 150.98 153.24 146.39 154.11 178.30 155.93 168.23 142.53 162.52 148.73 158.86 147.73 152.17 166.79 171.01 144.30 171.49 156.07 189.62 161.70 177.46 152.10 179.98 140.45 156.96 155.56 167.89 174.53 194.78 167.16 192.78 159.48 165.06 173.22 196.60 176.13 151.64 180.31 187.02 185.84 210.99 169.43 219.08 195.25 235.68 174.99 241.44 156.42 187.46 182.08 229.57 182.00 208.44 153.28 215.09 136.72 217.00 130.19 171.08 132.04 178.41 143.89 196.34 133.38 172.11 127.98 154.93 150.45 182.26 133.55 181.74

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation InvoerEU[t] = + 82.683850593337 + 0.467262809361258InvoerAM[t] -18.0508952623992M1[t] -17.0173631257305M2[t] + 0.0395977543686826M3[t] -2.95888240981108M4[t] -6.85649107502474M5[t] -7.99701999658972M6[t] -7.7807311043623M7[t] + 0.569266808576778M8[t] + 6.64215551133062M9[t] -4.11520638896474M10[t] -10.0454795242434M11[t] -0.158814190838987t + 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) 82.683850593337 14.786529 5.5918 1e-06 1e-06 InvoerAM 0.467262809361258 0.098679 4.7352 2e-05 1e-05 M1 -18.0508952623992 8.131757 -2.2198 0.031293 0.015647 M2 -17.0173631257305 8.589618 -1.9812 0.053442 0.026721 M3 0.0395977543686826 8.507552 0.0047 0.996306 0.498153 M4 -2.95888240981108 8.494898 -0.3483 0.729161 0.36458 M5 -6.85649107502474 8.482331 -0.8083 0.422975 0.211488 M6 -7.99701999658972 8.502859 -0.9405 0.351765 0.175883 M7 -7.7807311043623 8.572446 -0.9076 0.368697 0.184348 M8 0.569266808576778 8.879305 0.0641 0.949153 0.474577 M9 6.64215551133062 8.474143 0.7838 0.437081 0.218541 M10 -4.11520638896474 8.605814 -0.4782 0.634732 0.317366 M11 -10.0454795242434 8.476515 -1.1851 0.241937 0.120969 t -0.158814190838987 0.163431 -0.9718 0.336149 0.168075 Multiple Linear Regression - Regression Statistics Multiple R 0.770106253769868 R-squared 0.59306364209546 Adjusted R-squared 0.480506777143141 F-TEST (value) 5.26901351016386 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 1.07834850648914e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.3604016177259 Sum Squared Residuals 8389.5155751859 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 114.08 128.250841989817 -14.1708419898174 2 112.95 131.989880957031 -19.0398809570312 3 135.31 148.462818489773 -13.1528184897726 4 134.31 148.950174047772 -14.6401740477717 5 133.03 143.903154035873 -10.8731540358732 6 140.11 135.393945775025 4.71605422497503 7 124.69 137.516722093790 -12.8267220937902 8 131.68 141.483850019265 -9.8038500192645 9 150.95 150.439805420121 0.51019457987886 10 137.26 135.453770259450 1.80622974054976 11 130.51 136.892286792142 -6.38228679214242 12 143.15 150.549762997092 -7.3997629970922 13 118.01 119.069789757994 -1.05978975799429 14 122.56 126.453478638226 -3.89347863822638 15 147.97 141.987217924152 5.98278207584834 16 135.74 139.899955402570 -4.15995540257019 17 151.62 139.978808409365 11.6411915906353 18 154.82 139.754169758492 15.0658302415084 19 145.59 134.648390416438 10.9416095835619 20 147.12 139.096799035555 8.02320096444544 21 175.86 161.785608403539 14.0743915964614 22 140.66 133.057374019553 7.60262598044692 23 152.69 140.902063668588 11.7879363314119 24 154.38 150.989652010018 3.39034798998211 25 132.45 125.471952218370 6.97804778163037 26 136.44 132.084657463156 4.35534253684437 27 153.24 146.838067857448 6.40193214255241 28 154.11 158.591129749147 -4.48112974914660 29 155.93 149.829370402826 6.10062959717393 30 142.53 145.861956648969 -3.33195664896934 31 148.73 144.209249468096 4.52075053190441 32 147.73 149.274444995569 -1.54444499556885 33 166.79 163.991750835850 2.7982491641502 34 144.3 153.299860893209 -8.99986089320883 35 156.07 155.682248300811 0.387751699189206 36 161.7 159.886997872382 1.81300212761766 37 152.1 142.854790698734 9.2452093012655 38 140.45 132.973118773068 7.47688122693188 39 155.56 154.978447968647 0.581552031353204 40 174.53 164.385850557352 10.1441494426477 41 167.16 159.394902082577 7.76509791742287 42 159.48 145.143033894679 14.3369661053209 43 173.22 159.937977603322 13.2820223966784 44 176.13 147.121025416540 29.0089745834604 45 180.31 169.566858123656 10.7431418763443 46 185.84 169.850971572911 15.9890284270893 47 169.43 167.542040374526 1.8879596254744 48 195.25 185.185268343327 10.0647316566731 49 174.99 169.666992672010 5.32300732799045 50 156.42 145.318864168519 11.1011358314814 51 182.08 181.893447759981 0.186552240018664 52 182 168.862890243159 13.1371097568408 53 153.28 167.913765069359 -14.6337650693590 54 136.72 167.506893922835 -30.7868939228350 55 130.19 146.107660418354 -15.9176604183545 56 132.04 157.723880533073 -25.6838805330726 57 143.89 172.015977216835 -28.1259772168348 58 133.38 149.778023254877 -16.3980232548771 59 127.98 135.661360863933 -7.68136086393306 60 150.45 158.318318777181 -7.86831877718068 61 133.55 139.865632663075 -6.31563266307461 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00529033439112727 0.0105806687822545 0.994709665608873 18 0.0170618036388247 0.0341236072776495 0.982938196361175 19 0.0121568556843005 0.0243137113686010 0.9878431443157 20 0.00359891049774835 0.0071978209954967 0.996401089502252 21 0.00104163843375873 0.00208327686751746 0.998958361566241 22 0.00118109164654624 0.00236218329309247 0.998818908353454 23 0.000393697706978438 0.000787395413956877 0.999606302293021 24 0.000144422465824639 0.000288844931649278 0.999855577534175 25 8.87137090871153e-05 0.000177427418174231 0.999911286290913 26 3.69589071457293e-05 7.39178142914585e-05 0.999963041092854 27 3.6496548818841e-05 7.2993097637682e-05 0.999963503451181 28 4.59821952321633e-05 9.19643904643266e-05 0.999954017804768 29 2.75024263123857e-05 5.50048526247715e-05 0.999972497573688 30 0.00103139031821666 0.00206278063643332 0.998968609681783 31 0.000450451447721435 0.00090090289544287 0.999549548552279 32 0.000355546243191837 0.000711092486383675 0.999644453756808 33 0.0002845640868886 0.0005691281737772 0.999715435913111 34 0.000410225652435575 0.00082045130487115 0.999589774347564 35 0.000291300058127802 0.000582600116255603 0.999708699941872 36 0.0003329290540057 0.0006658581080114 0.999667070945994 37 0.00079260885666607 0.00158521771333214 0.999207391143334 38 0.00140958056179293 0.00281916112358585 0.998590419438207 39 0.0123060565132756 0.0246121130265513 0.987693943486724 40 0.403438455834657 0.806876911669314 0.596561544165343 41 0.742314336499743 0.515371327000513 0.257685663500257 42 0.895650787760174 0.208698424479652 0.104349212239826 43 0.819344299312323 0.361311401375354 0.180655700687677 44 0.741171778259962 0.517656443480076 0.258828221740038 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.678571428571429 NOK 5% type I error level 23 0.821428571428571 NOK 10% type I error level 23 0.821428571428571 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/103q7m1259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/103q7m1259087225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/111u91259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/111u91259087225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/2ro651259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/2ro651259087225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/3kksh1259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/3kksh1259087225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/4qn0e1259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/4qn0e1259087225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/5cy6x1259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/5cy6x1259087225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/6bffd1259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/6bffd1259087225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/778061259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/778061259087225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/80dsn1259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/80dsn1259087225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/9f31h1259087225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259087300vg6nmwb7nejh39z/9f31h1259087225.ps (open in new window)

Parameters (Session):

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