model 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 06:11:39 -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/t12587227961ok1wjxqu8q5s9n.htm/, Retrieved Fri, 20 Nov 2009 14:13:32 +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/20/t12587227961ok1wjxqu8q5s9n.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 «

99.06 152.2 96.92 98.2 98.54 98.71 99.65 169.4 99.06 96.92 98.2 98.54 99.82 168.6 99.65 99.06 96.92 98.2 99.99 161.1 99.82 99.65 99.06 96.92 100.33 174.1 99.99 99.82 99.65 99.06 99.31 179 100.33 99.99 99.82 99.65 101.1 190.6 99.31 100.33 99.99 99.82 101.1 190 101.1 99.31 100.33 99.99 100.93 181.6 101.1 101.1 99.31 100.33 100.85 174.8 100.93 101.1 101.1 99.31 100.93 180.5 100.85 100.93 101.1 101.1 99.6 196.8 100.93 100.85 100.93 101.1 101.88 193.8 99.6 100.93 100.85 100.93 101.81 197 101.88 99.6 100.93 100.85 102.38 216.3 101.81 101.88 99.6 100.93 102.74 221.4 102.38 101.81 101.88 99.6 102.82 217.9 102.74 102.38 101.81 101.88 101.72 229.7 102.82 102.74 102.38 101.81 103.47 227.4 101.72 102.82 102.74 102.38 102.98 204.2 103.47 101.72 102.82 102.74 102.68 196.6 102.98 103.47 101.72 102.82 102.9 198.8 102.68 102.98 103.47 101.72 103.03 207.5 102.9 102.68 102.98 103.47 101.29 190.7 103.03 102.9 102.68 102.98 103.69 201.6 101.29 103.03 102.9 102.68 103.68 210.5 103.69 101.29 103.03 102.9 1 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 12.0075106155311 + 0.00706906994709151X[t] + 0.492628156335883Y1[t] + 0.141018411173Y2[t] + 0.00778650777325245Y3[t] + 0.210384272455964Y4[t] + 3.10696889836303M1[t] + 2.18939852178247M2[t] + 2.06031177005909M3[t] + 2.32501060991481M4[t] + 1.76821492983135M5[t] + 0.33342314126969M6[t] + 2.55766571757778M7[t] + 1.64749851532543M8[t] + 1.54105844819217M9[t] + 1.91751211161407M10[t] + 1.68745560545471M11[t] + 0.0151867036463499t + 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) 12.0075106155311 4.458237 2.6933 0.010469 0.005234 X 0.00706906994709151 0.001277 5.5351 2e-06 1e-06 Y1 0.492628156335883 0.147233 3.3459 0.001856 0.000928 Y2 0.141018411173 0.168918 0.8348 0.409029 0.204514 Y3 0.00778650777325245 0.183195 0.0425 0.96632 0.48316 Y4 0.210384272455964 0.151534 1.3884 0.173113 0.086557 M1 3.10696889836303 0.30933 10.0442 0 0 M2 2.18939852178247 0.375757 5.8266 1e-06 0 M3 2.06031177005909 0.401601 5.1302 9e-06 4e-06 M4 2.32501060991481 0.394889 5.8878 1e-06 0 M5 1.76821492983135 0.182991 9.6628 0 0 M6 0.33342314126969 0.185596 1.7965 0.080368 0.040184 M7 2.55766571757778 0.274398 9.321 0 0 M8 1.64749851532543 0.30322 5.4333 3e-06 2e-06 M9 1.54105844819217 0.299573 5.1442 8e-06 4e-06 M10 1.91751211161407 0.296605 6.4649 0 0 M11 1.68745560545471 0.192941 8.746 0 0 t 0.0151867036463499 0.010331 1.47 0.149788 0.074894 Multiple Linear Regression - Regression Statistics Multiple R 0.998073681717132 R-squared 0.99615107413639 Adjusted R-squared 0.99442918625004 F-TEST (value) 578.522609998258 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.267843432610432 Sum Squared Residuals 2.72612396691648 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99.06 99.3334215628548 -0.273421562854746 2 99.65 99.3879338423074 0.262066157692614 3 99.82 99.7793111678363 0.0406888321636927 4 99.99 99.9204975937955 0.069502406204525 5 100.33 100.033322825789 0.296677174210958 6 99.31 98.9652733137386 0.344726686261437 7 101.1 100.869258378054 0.230741621945542 8 101.1 100.746414796885 0.353585203114607 9 100.93 100.911792616549 0.0182073834511236 10 100.85 100.970962412409 -0.120962412408868 11 100.93 101.109590773884 -0.179590773884153 12 99.6 99.579352785505 0.0206472144950217 13 101.88 101.999998955701 -0.119998955700819 14 101.81 102.039666195008 -0.229666195008403 15 102.38 102.355711889899 0.0242881101007720 16 102.74 102.680518605817 0.0594813941826108 17 102.82 102.851025600870 -0.031025600870458 18 101.72 101.594723832219 0.125276167781191 19 103.47 103.410006930318 0.0599930696824191 20 102.98 103.134364288943 -0.154364288942553 21 102.68 103.003046000052 -0.323046000051811 22 102.9 102.975554541530 -0.0755545415297692 23 103.03 103.252615406587 -0.222615406587493 24 101.29 101.451227594614 -0.161227594614316 25 103.69 103.750193210448 -0.0601932104483822 26 103.68 103.894956585759 -0.214956585759212 27 104.2 104.22027378414 -0.0202737841400317 28 104.08 104.409655490392 -0.329655490391595 29 104.16 104.439416215429 -0.279416215429192 30 103.05 103.134728410320 -0.0847284103202966 31 104.66 104.881344999872 -0.221344999872108 32 104.46 104.707912788672 -0.247912788671913 33 104.95 104.862931738254 0.0870682617455716 34 105.85 105.401578586623 0.4484214133772 35 106.23 106.005937522614 0.224062477386205 36 104.86 104.695058170946 0.164941829053714 37 107.44 107.425463627626 0.0145363723735566 38 108.23 107.945155096758 0.284844903241532 39 108.45 108.751797373162 -0.301797373161609 40 109.39 109.234280375796 0.155719624203682 41 110.15 109.904659452586 0.245340547414266 42 109.13 109.205874634595 -0.0758746345947503 43 110.28 110.743785384518 -0.463785384518273 44 110.17 110.186749394477 -0.0167493944770209 45 109.99 109.772229645145 0.217770354855116 46 109.26 109.511904459439 -0.251904459438562 47 109.11 108.931856296915 0.17814370308544 48 107.06 107.084361448934 -0.02436144893442 49 109.53 109.090922643370 0.439077356630390 50 108.92 109.022288280167 -0.102288280166531 51 109.24 108.982905784963 0.257094215037176 52 109.12 109.075047934199 0.0449520658007774 53 109 109.231575905326 -0.231575905325573 54 107.23 107.539399809128 -0.309399809127581 55 109.49 109.095604307238 0.39439569276242 56 109.04 108.974558731023 0.0654412689768803 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.331941144208623 0.663882288417245 0.668058855791377 22 0.189451502833616 0.378903005667233 0.810548497166384 23 0.133656320477219 0.267312640954439 0.86634367952278 24 0.0690976365435254 0.138195273087051 0.930902363456475 25 0.0360099717254213 0.0720199434508426 0.963990028274579 26 0.0166258085416427 0.0332516170832853 0.983374191458357 27 0.0066161567463256 0.0132323134926512 0.993383843253674 28 0.0222534850719351 0.0445069701438703 0.977746514928065 29 0.0532621030879311 0.106524206175862 0.946737896912069 30 0.0539614901478575 0.107922980295715 0.946038509852142 31 0.0336306829187975 0.067261365837595 0.966369317081203 32 0.0231597064923158 0.0463194129846316 0.976840293507684 33 0.0247568151579229 0.0495136303158458 0.975243184842077 34 0.0181076461460783 0.0362152922921567 0.981892353853922 35 0.00843561953182531 0.0168712390636506 0.991564380468175 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 7 0.466666666666667 NOK 10% type I error level 9 0.6 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/10lnb81258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/10lnb81258722692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/1zp0x1258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/1zp0x1258722692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/2po611258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/2po611258722692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/32ugf1258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/32ugf1258722692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/4hwxs1258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/4hwxs1258722692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/5zmqz1258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/5zmqz1258722692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/68tyt1258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/68tyt1258722692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/78b3d1258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/78b3d1258722692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/8jbpf1258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/8jbpf1258722692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/9vucp1258722692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587227961ok1wjxqu8q5s9n/9vucp1258722692.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