WS 8 - Multiple Regression

*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, 29 Nov 2010 20:22:31 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9.htm/, Retrieved Mon, 29 Nov 2010 21:20:46 +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/2010/Nov/29/t1291062036anfpwg4qknq5hj9.htm/}, year = {2010}, } @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 = {2010}, 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 «

1576.23 1546.37 1545.05 1552.34 1594.3 1605.78 1673.21 1612.94 1566.34 1530.17 1582.54 1702.16 1701.93 1811.15 1924.2 2034.25 2011.13 2013.04 2151.67 1902.09 1944.01 1916.67 1967.31 2119.88 2216.38 2522.83 2647.64 2631.23 2693.41 3021.76 2953.67 2796.8 2672.05 2251.23 2046.08 2420.04 2608.89 2660.47 2493.98 2541.7 2554.6 2699.61 2805.48 2956.66 3149.51 3372.5 3379.33 3517.54 3527.34 3281.06 3089.65 3222.76 3165.76 3232.43 3229.54 3071.74 2850.17

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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation PrijsCacao[t] = + 1384.86440476191 + 62.0890992063496M1[t] + 65.1430793650795M2[t] + 5.70305952380944M3[t] + 26.8870396825396M4[t] -0.89698015873049M5[t] + 74.6190000000001M6[t] + 87.6409801587303M7[t] -42.1950396825399M8[t] -108.993059523809M9[t] -101.926460317460M10[t] -160.92198015873M11[t] + 35.1680198412698t + 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) 1384.86440476191 141.256226 9.8039 0 0 M1 62.0890992063496 170.633079 0.3639 0.717694 0.358847 M2 65.1430793650795 170.520603 0.382 0.704282 0.352141 M3 5.70305952380944 170.43307 0.0335 0.973457 0.486729 M4 26.8870396825396 170.370519 0.1578 0.875325 0.437662 M5 -0.89698015873049 170.332977 -0.0053 0.995822 0.497911 M6 74.6190000000001 170.320461 0.4381 0.66345 0.331725 M7 87.6409801587303 170.332977 0.5145 0.609458 0.304729 M8 -42.1950396825399 170.370519 -0.2477 0.805545 0.402772 M9 -108.993059523809 170.43307 -0.6395 0.525809 0.262905 M10 -101.926460317460 179.581019 -0.5676 0.573206 0.286603 M11 -160.92198015873 179.545404 -0.8963 0.374984 0.187492 t 35.1680198412698 2.064832 17.0319 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.933280547277717 R-squared 0.871012579926995 Adjusted R-squared 0.835834192634357 F-TEST (value) 24.7598780660784 F-TEST (DF numerator) 12 F-TEST (DF denominator) 44 p-value 1.11022302462516e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 253.898753064943 Sum Squared Residuals 2836441.37954905 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1576.23 1482.12152380952 94.1084761904785 2 1546.37 1520.34352380952 26.0264761904762 3 1545.05 1496.07152380952 48.9784761904758 4 1552.34 1552.42352380952 -0.0835238095243298 5 1594.3 1559.80752380952 34.4924761904758 6 1605.78 1670.49152380952 -64.7115238095235 7 1673.21 1718.68152380952 -45.471523809524 8 1612.94 1624.01352380952 -11.0735238095240 9 1566.34 1592.38352380952 -26.0435238095238 10 1530.17 1634.61814285714 -104.448142857143 11 1582.54 1610.79064285714 -28.2506428571428 12 1702.16 1806.88064285714 -104.720642857143 13 1701.93 1904.13776190476 -202.207761904763 14 1811.15 1942.35976190476 -131.209761904762 15 1924.2 1918.08776190476 6.11223809523815 16 2034.25 1974.43976190476 59.8102380952381 17 2011.13 1981.82376190476 29.3062380952381 18 2013.04 2092.50776190476 -79.4677619047621 19 2151.67 2140.69776190476 10.9722380952381 20 1902.09 2046.02976190476 -143.939761904762 21 1944.01 2014.39976190476 -70.3897619047622 22 1916.67 2056.63438095238 -139.964380952381 23 1967.31 2032.80688095238 -65.496880952381 24 2119.88 2228.89688095238 -109.016880952381 25 2216.38 2326.154 -109.774000000000 26 2522.83 2364.376 158.454 27 2647.64 2340.104 307.536 28 2631.23 2396.456 234.774 29 2693.41 2403.84 289.57 30 3021.76 2514.524 507.236 31 2953.67 2562.714 390.956 32 2796.8 2468.046 328.754000000000 33 2672.05 2436.416 235.634 34 2251.23 2478.65061904762 -227.420619047619 35 2046.08 2454.82311904762 -408.743119047619 36 2420.04 2650.91311904762 -230.873119047619 37 2608.89 2748.17023809524 -139.280238095239 38 2660.47 2786.39223809524 -125.922238095238 39 2493.98 2762.12023809524 -268.140238095238 40 2541.7 2818.47223809524 -276.772238095238 41 2554.6 2825.85623809524 -271.256238095238 42 2699.61 2936.54023809524 -236.930238095238 43 2805.48 2984.73023809524 -179.250238095238 44 2956.66 2890.06223809524 66.597761904762 45 3149.51 2858.43223809524 291.077761904762 46 3372.5 2900.66685714286 471.833142857143 47 3379.33 2876.83935714286 502.490642857143 48 3517.54 3072.92935714286 444.610642857143 49 3527.34 3170.18647619048 357.153523809523 50 3281.06 3208.40847619048 72.6515238095239 51 3089.65 3184.13647619048 -94.486476190476 52 3222.76 3240.48847619048 -17.7284761904757 53 3165.76 3247.87247619048 -82.1124761904755 54 3232.43 3358.55647619048 -126.126476190476 55 3229.54 3406.74647619048 -177.206476190476 56 3071.74 3312.07847619048 -240.338476190477 57 2850.17 3280.44847619048 -430.278476190476 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0534408628463813 0.106881725692763 0.946559137153619 17 0.0180730556149191 0.0361461112298383 0.98192694438508 18 0.00539897911882325 0.0107979582376465 0.994601020881177 19 0.00207034874621668 0.00414069749243336 0.997929651253783 20 0.000620827484517941 0.00124165496903588 0.999379172515482 21 0.000149768377125842 0.000299536754251683 0.999850231622874 22 3.77219869932395e-05 7.5443973986479e-05 0.999962278013007 23 8.29500279427866e-06 1.65900055885573e-05 0.999991704997206 24 2.14612819336265e-06 4.29225638672529e-06 0.999997853871807 25 5.16074589248048e-07 1.03214917849610e-06 0.99999948392541 26 3.73500236851257e-06 7.47000473702514e-06 0.999996264997632 27 1.54093603165815e-05 3.0818720633163e-05 0.999984590639683 28 1.04637407524590e-05 2.09274815049180e-05 0.999989536259247 29 1.02893830125257e-05 2.05787660250513e-05 0.999989710616987 30 0.000380295971627725 0.00076059194325545 0.999619704028372 31 0.000873736243283737 0.00174747248656747 0.999126263756716 32 0.0016161393165666 0.0032322786331332 0.998383860683433 33 0.00226565900434571 0.00453131800869141 0.997734340995654 34 0.00349041694675453 0.00698083389350905 0.996509583053246 35 0.0390729983308826 0.0781459966617652 0.960927001669117 36 0.08376744741804 0.167534894836080 0.91623255258196 37 0.126150256245002 0.252300512490005 0.873849743754998 38 0.10837779145557 0.21675558291114 0.89162220854443 39 0.127956232954975 0.25591246590995 0.872043767045025 40 0.166109992062036 0.332219984124071 0.833890007937964 41 0.199979676821416 0.399959353642832 0.800020323178584 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.615384615384615 NOK 5% type I error level 18 0.692307692307692 NOK 10% type I error level 19 0.730769230769231 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/102tna1291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/102tna1291062143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/1waqg1291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/1waqg1291062143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/2waqg1291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/2waqg1291062143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/3okpj1291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/3okpj1291062143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/4okpj1291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/4okpj1291062143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/5okpj1291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/5okpj1291062143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/6hb641291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/6hb641291062143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/7sk5p1291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/7sk5p1291062143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/8sk5p1291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/8sk5p1291062143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/9sk5p1291062143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291062036anfpwg4qknq5hj9/9sk5p1291062143.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