shw-ws7

*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 05:24:28 -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/t1258720091oudhlwqjrfn36ja.htm/, Retrieved Fri, 20 Nov 2009 13:28:24 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja.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:

Workshop 7 Model 3

Dataseries X:

» Textbox « » Textfile « » CSV «

2529 314 2196 318 3202 320 2718 323 2728 325 2354 327 2697 330 2651 331 2067 332 2641 334 2539 334 2294 334 2712 339 2314 345 3092 346 2677 352 2813 355 2668 358 2939 361 2617 363 2231 364 2481 365 2421 366 2408 370 2560 371 2100 371 3315 372 2801 373 2403 373 3024 374 2507 375 2980 375 2211 376 2471 376 2594 377 2452 377 2232 378 2373 379 3127 380 2802 384 2641 389 2787 390 2619 391 2806 392 2193 393 2323 394 2529 394 2412 395 2262 396 2154 397 3230 398 2295 399 2715 400 2733 400 2317 401 2730 401 1913 406 2390 407 2484 423 1960 427

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 Y[t] = + 1219.03242808287 + 3.60792597062968X[t] + 141.869234895728M1[t] -90.4173137531013M2[t] + 879.025648762825M3[t] + 341.574344531617M4[t] + 343.009381076913M5[t] + 399.130758398712M6[t] + 303.20896533226M7[t] + 449.295098236438M8[t] -183.026694830014M9[t] + 159.537852880037M10[t] + 206.721793066452M11[t] -7.97247368068132t + 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) 1219.03242808287 1238.875109 0.984 0.330271 0.165136 X 3.60792597062968 3.807662 0.9475 0.348313 0.174157 M1 141.869234895728 105.916484 1.3394 0.187007 0.093503 M2 -90.4173137531013 105.36043 -0.8582 0.395247 0.197624 M3 879.025648762825 105.377042 8.3417 0 0 M4 341.574344531617 104.718085 3.2618 0.002089 0.001045 M5 343.009381076913 104.46064 3.2836 0.001963 0.000981 M6 399.130758398712 104.393318 3.8233 0.000394 0.000197 M7 303.20896533226 104.265455 2.908 0.005582 0.002791 M8 449.295098236438 104.365828 4.305 8.7e-05 4.3e-05 M9 -183.026694830014 104.250564 -1.7556 0.085807 0.042903 M10 159.537852880037 104.368433 1.5286 0.133211 0.066605 M11 206.721793066452 103.985934 1.988 0.052785 0.026392 t -7.97247368068132 6.056208 -1.3164 0.194557 0.097279 Multiple Linear Regression - Regression Statistics Multiple R 0.88502400689593 R-squared 0.783267492782127 Adjusted R-squared 0.722017001611859 F-TEST (value) 12.7879381506467 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 3.25499627251702e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 164.397756950770 Sum Squared Residuals 1243224.63456045 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2529 2485.81794407564 43.182055924359 2 2196 2259.99062562865 -63.9906256286457 3 3202 3228.67696640515 -26.6769664051491 4 2718 2694.07696640515 23.92303359485 5 2728 2694.75538121102 33.2446187889761 6 2354 2750.1201367934 -396.120136793402 7 2697 2657.04964795816 39.950352041843 8 2651 2798.77123315228 -147.771233152283 9 2067 2162.08489237578 -95.084892375779 10 2641 2503.89281834641 137.107181653591 11 2539 2543.10428485214 -4.10428485214211 12 2294 2328.41001810501 -34.4100181050085 13 2712 2480.34640917320 231.653590826796 14 2314 2261.73494266747 52.2650573325285 15 3092 3226.81335747335 -134.813357473346 16 2677 2703.03713538523 -26.0371353852344 17 2813 2707.32347616174 105.676523838262 18 2668 2766.29615771475 -98.2961577147456 19 2939 2673.2256688795 265.774331120499 20 2617 2818.55518004426 -201.555180044257 21 2231 2181.86883926775 49.1311607322468 22 2481 2520.06883926775 -39.0688392677533 23 2421 2562.88823174412 -141.888231744116 24 2408 2362.6256688795 45.3743311204986 25 2560 2500.13035606518 59.8696439348223 26 2100 2259.87133373567 -159.871333735667 27 3315 3224.94974854154 90.0502514584587 28 2801 2683.13389660028 117.866103399718 29 2403 2676.59645946490 -273.596459464896 30 3024 2728.35328907664 295.646710923355 31 2507 2628.06694830014 -121.066948300141 32 2980 2766.18060752364 213.819392476363 33 2211 2129.49426674713 81.5057332528665 34 2471 2464.08634077650 6.9136592234963 35 2594 2506.90573325287 87.0942667471334 36 2452 2292.21146650573 159.788533494267 37 2232 2429.71615369141 -197.716153691409 38 2373 2193.06505733253 179.934942667471 39 3127 3158.1434721384 -31.1434721384029 40 2802 2627.15139810903 174.848601890968 41 2641 2638.65359082680 2.34640917320456 42 2787 2690.41042043854 96.5895795614564 43 2619 2590.12407966204 28.8759203379601 44 2806 2731.84566485617 74.1543351438341 45 2193 2095.15932407966 97.840675920338 46 2323 2433.35932407966 -110.359324079662 47 2529 2472.57079058540 56.4292094146048 48 2412 2261.48444980889 150.515550191108 49 2262 2398.98913699457 -136.989136994568 50 2154 2162.33804063569 -8.338040635687 51 3230 3127.41645544156 102.583544558439 52 2295 2585.6006035003 -290.600603500302 53 2715 2582.67109233555 132.328907664454 54 2733 2630.81999597666 102.180004023336 55 2317 2530.53365520016 -213.533655200161 56 2730 2668.64731442366 61.352685576343 57 1913 2046.39267752967 -133.392677529672 58 2390 2384.59267752967 5.407322470328 59 2484 2481.53095956548 2.46904043451999 60 1960 2281.26839670087 -321.268396700865 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.207156993390945 0.414313986781891 0.792843006609055 18 0.271657818398231 0.543315636796463 0.728342181601769 19 0.205038652327296 0.410077304654592 0.794961347672704 20 0.272101342788480 0.544202685576961 0.72789865721152 21 0.172253850321008 0.344507700642017 0.827746149678992 22 0.243378775721128 0.486757551442256 0.756621224278872 23 0.28926292481579 0.57852584963158 0.71073707518421 24 0.204907696583617 0.409815393167235 0.795092303416383 25 0.207554649955512 0.415109299911024 0.792445350044488 26 0.242505923579627 0.485011847159253 0.757494076420373 27 0.226666741254722 0.453333482509444 0.773333258745278 28 0.18298808271442 0.36597616542884 0.81701191728558 29 0.562667134631599 0.874665730736803 0.437332865368401 30 0.856752556201363 0.286494887597275 0.143247443798637 31 0.876601059424375 0.246797881151250 0.123398940575625 32 0.879061420344476 0.241877159311047 0.120938579655524 33 0.817320470876678 0.365359058246644 0.182679529123322 34 0.75606702570207 0.48786594859586 0.24393297429793 35 0.707056402945614 0.585887194108771 0.292943597054386 36 0.606921411557687 0.786157176884626 0.393078588442313 37 0.664926886426022 0.670146227147957 0.335073113573978 38 0.576196325761579 0.847607348476842 0.423803674238421 39 0.588404960270117 0.823190079459766 0.411595039729883 40 0.724214801814022 0.551570396371957 0.275785198185979 41 0.675317112776742 0.649365774446516 0.324682887223258 42 0.540677183115028 0.918645633769944 0.459322816884972 43 0.483889316478553 0.967778632957107 0.516110683521447 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/10hjwu1258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/10hjwu1258719864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/1mz9d1258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/1mz9d1258719864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/2fr071258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/2fr071258719864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/3ue351258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/3ue351258719864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/4s5nn1258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/4s5nn1258719864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/5hwzo1258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/5hwzo1258719864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/6qniq1258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/6qniq1258719864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/7p1e01258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/7p1e01258719864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/86hsq1258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/86hsq1258719864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/9ypep1258719864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258720091oudhlwqjrfn36ja/9ypep1258719864.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