Model 2

*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: Sat, 19 Dec 2009 18:00:02 -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/20/t12612708496t4ifxlqkvwvbj6.htm/, Retrieved Sun, 20 Dec 2009 02:01:01 +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/20/t12612708496t4ifxlqkvwvbj6.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 «

3016 0 2155 0 2172 0 2150 0 2533 0 2058 0 2160 0 2260 0 2498 0 2695 0 2799 0 2946 0 2930 0 2318 0 2540 0 2570 0 2669 0 2450 0 2842 0 3440 0 2678 0 2981 0 2260 0 2844 0 2546 0 2456 0 2295 0 2379 0 2479 0 2057 0 2280 0 2351 0 2276 0 2548 1 2311 1 2201 1 2725 1 2408 1 2139 1 1898 1 2537 1 2068 1 2063 1 2520 1 2434 1 2190 1 2794 1 2070 1 2615 1 2265 1 2139 1 2428 1 2137 1 1823 1 2063 1 1806 1 1758 1 2243 1 1993 1 1932 1 2465 1

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] = + 2589.64081632653 -318.401360544218x[t] + 285.726530612245M1[t] -141.880272108844M2[t] -205.280272108843M3[t] -177.280272108844M4[t] + 8.71972789115645M5[t] -371.080272108844M6[t] -180.680272108843M7[t] + 13.1197278911563M8[t] -133.480272108844M9[t] + 132.800000000000M10[t] + 32.7999999999998M11[t] + 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) 2589.64081632653 131.781034 19.6511 0 0 x -318.401360544218 72.543072 -4.3891 6.2e-05 3.1e-05 M1 285.726530612245 168.574865 1.695 0.096563 0.048282 M2 -141.880272108844 176.504911 -0.8038 0.425457 0.212729 M3 -205.280272108843 176.504911 -1.163 0.250567 0.125284 M4 -177.280272108844 176.504911 -1.0044 0.320226 0.160113 M5 8.71972789115645 176.504911 0.0494 0.960804 0.480402 M6 -371.080272108844 176.504911 -2.1024 0.040791 0.020395 M7 -180.680272108843 176.504911 -1.0237 0.311129 0.155565 M8 13.1197278911563 176.504911 0.0743 0.941056 0.470528 M9 -133.480272108844 176.504911 -0.7562 0.4532 0.2266 M10 132.800000000000 175.9076 0.7549 0.453972 0.226986 M11 32.7999999999998 175.9076 0.1865 0.852869 0.426434 Multiple Linear Regression - Regression Statistics Multiple R 0.668376440301656 R-squared 0.446727065950313 Adjusted R-squared 0.308408832437891 F-TEST (value) 3.22970482348008 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.00186216208043333 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 278.134337495171 Sum Squared Residuals 3713218.06530612 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3016 2875.36734693877 140.632653061227 2 2155 2447.76054421769 -292.760544217687 3 2172 2384.36054421769 -212.360544217686 4 2150 2412.36054421769 -262.360544217687 5 2533 2598.36054421769 -65.3605442176871 6 2058 2218.56054421769 -160.560544217687 7 2160 2408.96054421769 -248.960544217687 8 2260 2602.76054421769 -342.760544217687 9 2498 2456.16054421769 41.8394557823126 10 2695 2722.44081632653 -27.4408163265305 11 2799 2622.44081632653 176.559183673469 12 2946 2589.64081632653 356.359183673469 13 2930 2875.36734693878 54.6326530612238 14 2318 2447.76054421769 -129.760544217687 15 2540 2384.36054421769 155.639455782313 16 2570 2412.36054421769 157.639455782313 17 2669 2598.36054421769 70.6394557823127 18 2450 2218.56054421769 231.439455782313 19 2842 2408.96054421769 433.039455782313 20 3440 2602.76054421769 837.239455782313 21 2678 2456.16054421769 221.839455782313 22 2981 2722.44081632653 258.559183673469 23 2260 2622.44081632653 -362.440816326531 24 2844 2589.64081632653 254.359183673469 25 2546 2875.36734693878 -329.367346938776 26 2456 2447.76054421769 8.23945578231283 27 2295 2384.36054421769 -89.3605442176873 28 2379 2412.36054421769 -33.3605442176871 29 2479 2598.36054421769 -119.360544217687 30 2057 2218.56054421769 -161.560544217687 31 2280 2408.96054421769 -128.960544217687 32 2351 2602.76054421769 -251.760544217687 33 2276 2456.16054421769 -180.160544217687 34 2548 2404.03945578231 143.960544217687 35 2311 2304.03945578231 6.96054421768714 36 2201 2271.23945578231 -70.239455782313 37 2725 2556.96598639456 168.034013605442 38 2408 2129.35918367347 278.640816326531 39 2139 2065.95918367347 73.0408163265305 40 1898 2093.95918367347 -195.959183673469 41 2537 2279.95918367347 257.040816326531 42 2068 1900.15918367347 167.840816326531 43 2063 2090.55918367347 -27.5591836734693 44 2520 2284.35918367347 235.640816326531 45 2434 2137.75918367347 296.240816326531 46 2190 2404.03945578231 -214.039455782313 47 2794 2304.03945578231 489.960544217687 48 2070 2271.23945578231 -201.239455782313 49 2615 2556.96598639456 58.0340136054416 50 2265 2129.35918367347 135.640816326531 51 2139 2065.95918367347 73.0408163265305 52 2428 2093.95918367347 334.040816326531 53 2137 2279.95918367347 -142.959183673469 54 1823 1900.15918367347 -77.1591836734694 55 2063 2090.55918367347 -27.5591836734693 56 1806 2284.35918367347 -478.359183673469 57 1758 2137.75918367347 -379.759183673469 58 2243 2404.03945578231 -161.039455782313 59 1993 2304.03945578231 -311.039455782313 60 1932 2271.23945578231 -339.239455782313 61 2465 2556.96598639456 -91.9659863945585 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.405071279018884 0.810142558037769 0.594928720981116 17 0.257128781594800 0.514257563189599 0.7428712184052 18 0.261586775669138 0.523173551338276 0.738413224330862 19 0.489501291843967 0.979002583687934 0.510498708156033 20 0.964647663358868 0.0707046732822645 0.0353523366411322 21 0.951693750654695 0.0966124986906091 0.0483062493453046 22 0.94930925419321 0.101381491613580 0.0506907458067902 23 0.953688452997632 0.0926230940047352 0.0463115470023676 24 0.966362832117088 0.0672743357658242 0.0336371678829121 25 0.964210395491895 0.0715792090162093 0.0357896045081047 26 0.944981243223733 0.110037513552534 0.0550187567762668 27 0.912141472789958 0.175717054420084 0.0878585272100422 28 0.866559125826477 0.266881748347047 0.133440874173523 29 0.810261225170478 0.379477549659044 0.189738774829522 30 0.749662402331894 0.500675195336213 0.250337597668106 31 0.681061825501475 0.63787634899705 0.318938174498525 32 0.66421273832309 0.671574523353822 0.335787261676911 33 0.598290304130007 0.803419391739985 0.401709695869993 34 0.549681424948808 0.900637150102384 0.450318575051192 35 0.453237961303298 0.906475922606595 0.546762038696702 36 0.40682603215476 0.81365206430952 0.59317396784524 37 0.341917219104340 0.683834438208681 0.65808278089566 38 0.289154246547123 0.578308493094247 0.710845753452877 39 0.204276211329097 0.408552422658195 0.795723788670903 40 0.208809895661752 0.417619791323504 0.791190104338248 41 0.181750602305841 0.363501204611683 0.818249397694159 42 0.12633226134003 0.25266452268006 0.87366773865997 43 0.0731834807906737 0.146366961581347 0.926816519209326 44 0.115764348601026 0.231528697202051 0.884235651398974 45 0.196524679852474 0.393049359704949 0.803475320147526 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 5 0.166666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/10ewfb1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/10ewfb1261270796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/1z7av1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/1z7av1261270796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/2rkcq1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/2rkcq1261270796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/3wa2f1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/3wa2f1261270796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/41o4y1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/41o4y1261270796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/56znt1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/56znt1261270796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/6argw1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/6argw1261270796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/7s9gi1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/7s9gi1261270796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/8dsev1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/8dsev1261270796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/92xnx1261270796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12612708496t4ifxlqkvwvbj6/92xnx1261270796.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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