*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: Thu, 17 Dec 2009 08:03:56 -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/Dec/17/t1261062280ykb5mif7pn7h3gy.htm/, Retrieved Thu, 17 Dec 2009 16:04:52 +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/Dec/17/t1261062280ykb5mif7pn7h3gy.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 «

97.4 97 105.4 102.7 98.1 104.5 87.4 89.9 109.8 111.7 98.6 96.9 95.1 97 112.7 102.9 97.4 111.4 87.4 96.8 114.1 110.3 103.9 101.6 94.6 95.9 104.7 102.8 98.1 113.9 80.9 95.7 113.2 105.9 108.8 102.3 99 100.7 115.5 100.7 109.9 114.6 85.4 100.5 114.8 116.5 112.9 102 106 105.3 118.8 106.1 109.3 117.2 92.5 104.2 112.5 122.4 113.3 100 110.7 112.8 109.8 117.3 109.1 115.9 96 99.8 116.8

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 Industriële_Productie[t] = + 93.8576 + 0.837555555555539M1[t] + 1.63471111111111M2[t] + 11.1485333333333M3[t] + 5.22902222222222M4[t] + 3.27617777777778M5[t] + 12.3566666666667M6[t] -12.4795111111111M7[t] -3.11568888888888M8[t] + 12.4148M9[t] + 13.1723555555556M10[t] + 7.12617777777778M11[t] + 0.186177777777778t + 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) 93.8576 1.835331 51.1393 0 0 M1 0.837555555555539 2.234457 0.3748 0.709198 0.354599 M2 1.63471111111111 2.233443 0.7319 0.467267 0.233634 M3 11.1485333333333 2.232655 4.9934 6e-06 3e-06 M4 5.22902222222222 2.232092 2.3427 0.022726 0.011363 M5 3.27617777777778 2.231754 1.468 0.147703 0.073852 M6 12.3566666666667 2.231641 5.537 1e-06 0 M7 -12.4795111111111 2.231754 -5.5918 1e-06 0 M8 -3.11568888888888 2.232092 -1.3959 0.168266 0.084133 M9 12.4148 2.232655 5.5606 1e-06 0 M10 13.1723555555556 2.331304 5.6502 1e-06 0 M11 7.12617777777778 2.330981 3.0572 0.003422 0.001711 t 0.186177777777778 0.022429 8.3008 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.926946080712693 R-squared 0.859229036548622 Adjusted R-squared 0.829063830094755 F-TEST (value) 28.4841092621954 F-TEST (DF numerator) 12 F-TEST (DF denominator) 56 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.68543355335440 Sum Squared Residuals 760.615546666666 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.4 94.8813333333334 2.51866666666658 2 97 95.8646666666667 1.13533333333334 3 105.4 105.564666666667 -0.164666666666663 4 102.7 99.8313333333334 2.86866666666665 5 98.1 98.0646666666667 0.0353333333333238 6 104.5 107.331333333333 -2.83133333333333 7 87.4 82.6813333333333 4.71866666666669 8 89.9 92.2313333333333 -2.33133333333333 9 109.8 107.948 1.852 10 111.7 108.891733333333 2.80826666666668 11 98.6 103.031733333333 -4.43173333333333 12 96.9 96.0917333333333 0.808266666666674 13 95.1 97.1154666666666 -2.01546666666665 14 97 98.0988 -1.09880000000000 15 112.7 107.7988 4.9012 16 102.9 102.065466666667 0.834533333333344 17 97.4 100.2988 -2.89879999999999 18 111.4 109.565466666667 1.83453333333334 19 87.4 84.9154666666667 2.48453333333333 20 96.8 94.4654666666667 2.33453333333333 21 114.1 110.182133333333 3.91786666666667 22 110.3 111.125866666667 -0.825866666666677 23 103.9 105.265866666667 -1.36586666666666 24 101.6 98.3258666666667 3.27413333333333 25 94.6 99.3496 -4.74959999999998 26 95.9 100.332933333333 -4.43293333333333 27 104.7 110.032933333333 -5.33293333333333 28 102.8 104.2996 -1.49960000000000 29 98.1 102.532933333333 -4.43293333333334 30 113.9 111.7996 2.10040000000000 31 80.9 87.1496 -6.2496 32 95.7 96.6996 -0.999599999999998 33 113.2 112.416266666667 0.78373333333334 34 105.9 113.36 -7.46 35 108.8 107.5 1.30000000000000 36 102.3 100.56 1.74 37 99 101.583733333333 -2.58373333333331 38 100.7 102.567066666667 -1.86706666666666 39 115.5 112.267066666667 3.23293333333333 40 100.7 106.533733333333 -5.83373333333333 41 109.9 104.767066666667 5.13293333333334 42 114.6 114.033733333333 0.566266666666658 43 85.4 89.3837333333333 -3.98373333333334 44 100.5 98.9337333333333 1.56626666666666 45 114.8 114.6504 0.149600000000000 46 116.5 115.594133333333 0.905866666666657 47 112.9 109.734133333333 3.16586666666667 48 102 102.794133333333 -0.794133333333331 49 106 103.817866666667 2.18213333333335 50 105.3 104.8012 0.498799999999996 51 118.8 114.5012 4.2988 52 106.1 108.767866666667 -2.66786666666667 53 109.3 107.0012 2.29880000000000 54 117.2 116.267866666667 0.932133333333332 55 92.5 91.6178666666667 0.882133333333323 56 104.2 101.167866666667 3.03213333333333 57 112.5 116.884533333333 -4.38453333333333 58 122.4 117.828266666667 4.57173333333333 59 113.3 111.968266666667 1.33173333333333 60 100 105.028266666667 -5.02826666666667 61 110.7 106.052 4.64800000000002 62 112.8 107.035333333333 5.76466666666666 63 109.8 116.735333333333 -6.93533333333334 64 117.3 111.002 6.298 65 109.1 109.235333333333 -0.135333333333341 66 115.9 118.502 -2.602 67 96 93.852 2.14799999999999 68 99.8 103.402 -3.60200000000001 69 116.8 119.118666666667 -2.31866666666667 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.358646519782559 0.717293039565117 0.641353480217441 17 0.223271324607279 0.446542649214558 0.776728675392721 18 0.253529097851506 0.507058195703011 0.746470902148495 19 0.174032855485592 0.348065710971184 0.825967144514408 20 0.179218304604889 0.358436609209778 0.820781695395111 21 0.140217790407952 0.280435580815904 0.859782209592048 22 0.114436388409565 0.228872776819130 0.885563611590435 23 0.0842070477435407 0.168414095487081 0.91579295225646 24 0.0675256672768516 0.135051334553703 0.932474332723148 25 0.0880667933460909 0.176133586692182 0.91193320665391 26 0.0820703539050278 0.164140707810056 0.917929646094972 27 0.123165941912915 0.246331883825829 0.876834058087085 28 0.0826587070505485 0.165317414101097 0.917341292949452 29 0.0660888584897663 0.132177716979533 0.933911141510234 30 0.0719660645476714 0.143932129095343 0.928033935452329 31 0.144113631885910 0.288227263771820 0.85588636811409 32 0.102605821528227 0.205211643056455 0.897394178471773 33 0.0761796279159942 0.152359255831988 0.923820372084006 34 0.161457882730653 0.322915765461306 0.838542117269347 35 0.190073533060576 0.380147066121152 0.809926466939424 36 0.169895558432604 0.339791116865208 0.830104441567396 37 0.163665080665551 0.327330161331103 0.836334919334449 38 0.156355034919952 0.312710069839905 0.843644965080048 39 0.183690028734275 0.367380057468549 0.816309971265725 40 0.283574940928842 0.567149881857684 0.716425059071158 41 0.409538465353641 0.819076930707282 0.590461534646359 42 0.330116081424072 0.660232162848145 0.669883918575928 43 0.371704935817369 0.743409871634738 0.628295064182631 44 0.30412501172365 0.6082500234473 0.69587498827635 45 0.239362912475451 0.478725824950902 0.760637087524549 46 0.220714586141189 0.441429172282378 0.779285413858811 47 0.185872920650523 0.371745841301045 0.814127079349477 48 0.142826386919290 0.285652773838580 0.85717361308071 49 0.116310085726659 0.232620171453319 0.88368991427334 50 0.119414423614903 0.238828847229805 0.880585576385097 51 0.296497056665637 0.592994113331274 0.703502943334363 52 0.693899066076727 0.612201867846546 0.306100933923273 53 0.532266738864283 0.935466522271434 0.467733261135717 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/Dec/17/t1261062280ykb5mif7pn7h3gy/108cq61261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/108cq61261062231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/11ed01261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/11ed01261062231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/20hlx1261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/20hlx1261062231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/3twd21261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/3twd21261062231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/4yehw1261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/4yehw1261062231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/58c9s1261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/58c9s1261062231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/6cyf11261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/6cyf11261062231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/73wtz1261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/73wtz1261062231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/8aibz1261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/8aibz1261062231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/94xcg1261062231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261062280ykb5mif7pn7h3gy/94xcg1261062231.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