*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: Sun, 23 Nov 2008 07:14:40 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz.htm/, Retrieved Sun, 23 Nov 2008 14:15:25 +0000

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/2008/Nov/23/t12274497251zwzgsah2oeqyrz.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

299.63 0 305.945 0 382.252 0 348.846 0 335.367 0 373.617 0 312.612 0 312.232 0 337.161 0 331.476 0 350.103 0 345.127 0 297.256 0 295.979 0 361.007 0 321.803 0 354.937 0 349.432 0 290.979 0 349.576 0 327.625 0 349.377 0 336.777 0 339.134 0 323.321 0 318.86 0 373.583 0 333.03 0 408.556 0 414.646 0 291.514 0 348.857 0 349.368 0 375.765 0 364.136 0 349.53 0 348.167 1 332.856 1 360.551 1 346.969 1 392.815 1 372.02 1 371.027 1 342.672 1 367.343 1 390.786 1 343.785 1 362.6 1 349.468 1 340.624 1 369.536 1 407.782 1 392.239 1 404.824 1 373.669 1 344.902 1 396.7 1 398.911 1 366.009 1 392.484 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation x[t] = + 324.873316666667 + 2.36008333333332y[t] -24.4417625M1[t] -30.045075M2[t] + 19.6002125M3[t] + 1.01269999999999M4[t] + 25.2217875M5[t] + 30.459075M6[t] -25.3762375M7[t] -14.5763500000000M8[t] + 0.527537500000004M9[t] + 13.263425M10[t] -4.72528749999999M11[t] + 0.8877125t + 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) 324.873316666667 10.99601 29.5447 0 0 y 2.36008333333332 9.874708 0.239 0.812165 0.406083 M1 -24.4417625 12.257506 -1.994 0.052097 0.026048 M2 -30.045075 12.1877 -2.4652 0.017486 0.008743 M3 19.6002125 12.124196 1.6166 0.112799 0.056399 M4 1.01269999999999 12.067093 0.0839 0.933482 0.466741 M5 25.2217875 12.016483 2.0989 0.041341 0.02067 M6 30.459075 11.972448 2.5441 0.014379 0.00719 M7 -25.3762375 11.93506 -2.1262 0.038885 0.019443 M8 -14.5763500000000 11.904383 -1.2245 0.227016 0.113508 M9 0.527537500000004 11.880468 0.0444 0.964775 0.482387 M10 13.263425 11.863357 1.118 0.269365 0.134683 M11 -4.72528749999999 11.853078 -0.3987 0.691992 0.345996 t 0.8877125 0.285058 3.1141 0.003171 0.001586 Multiple Linear Regression - Regression Statistics Multiple R 0.8433620513171 R-squared 0.711259549601787 Adjusted R-squared 0.629658987532727 F-TEST (value) 8.71635600989898 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.44897841503067e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 18.7359418727401 Sum Squared Residuals 16147.6338215 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 299.63 301.319266666667 -1.68926666666680 2 305.945 296.603666666667 9.34133333333333 3 382.252 347.136666666667 35.1153333333333 4 348.846 329.436866666667 19.4091333333333 5 335.367 354.533666666667 -19.1666666666666 6 373.617 360.658666666667 12.9583333333333 7 312.612 305.711066666667 6.90093333333334 8 312.232 317.398666666667 -5.16666666666666 9 337.161 333.390266666667 3.77073333333334 10 331.476 347.013866666667 -15.5378666666667 11 350.103 329.912866666667 20.1901333333333 12 345.127 335.525866666667 9.60113333333335 13 297.256 311.971816666667 -14.7158166666667 14 295.979 307.256216666667 -11.2772166666667 15 361.007 357.789216666667 3.21778333333334 16 321.803 340.089416666667 -18.2864166666667 17 354.937 365.186216666667 -10.2492166666666 18 349.432 371.311216666667 -21.8792166666667 19 290.979 316.363616666667 -25.3846166666667 20 349.576 328.051216666667 21.5247833333333 21 327.625 344.042816666667 -16.4178166666667 22 349.377 357.666416666667 -8.28941666666665 23 336.777 340.565416666667 -3.78841666666669 24 339.134 346.178416666667 -7.04441666666665 25 323.321 322.624366666667 0.696633333333397 26 318.86 317.908766666667 0.951233333333345 27 373.583 368.441766666667 5.14123333333335 28 333.03 350.741966666667 -17.7119666666667 29 408.556 375.838766666667 32.7172333333333 30 414.646 381.963766666667 32.6822333333333 31 291.514 327.016166666667 -35.5021666666667 32 348.857 338.703766666667 10.1532333333333 33 349.368 354.695366666667 -5.32736666666667 34 375.765 368.318966666667 7.44603333333333 35 364.136 351.217966666667 12.9180333333333 36 349.53 356.830966666667 -7.30096666666669 37 348.167 335.637 12.5300000000000 38 332.856 330.9214 1.9346 39 360.551 381.4544 -20.9034 40 346.969 363.7546 -16.7856 41 392.815 388.8514 3.96360000000000 42 372.02 394.9764 -22.9564000000000 43 371.027 340.0288 30.9982 44 342.672 351.7164 -9.04439999999999 45 367.343 367.708 -0.364999999999983 46 390.786 381.3316 9.4544 47 343.785 364.2306 -20.4456 48 362.6 369.8436 -7.24359999999997 49 349.468 346.28955 3.17845000000006 50 340.624 341.57395 -0.94994999999998 51 369.536 392.10695 -22.57095 52 407.782 374.40715 33.37485 53 392.239 399.50395 -7.26495000000003 54 404.824 405.62895 -0.804949999999996 55 373.669 350.68135 22.98765 56 344.902 362.36895 -17.4669500000000 57 396.7 378.36055 18.3394500000000 58 398.911 391.98415 6.92684999999999 59 366.009 374.88315 -8.87415 60 392.484 380.49615 11.9878500000000 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.392690568268703 0.785381136537405 0.607309431731297 18 0.287972194914696 0.575944389829391 0.712027805085304 19 0.199361544126477 0.398723088252954 0.800638455873523 20 0.513262742042454 0.973474515915091 0.486737257957546 21 0.395402764173719 0.790805528347438 0.604597235826281 22 0.362454799214017 0.724909598428034 0.637545200785983 23 0.26763209703504 0.53526419407008 0.73236790296496 24 0.180743969076835 0.361487938153669 0.819256030923165 25 0.213869402383029 0.427738804766057 0.786130597616971 26 0.172758440489734 0.345516880979467 0.827241559510266 27 0.128880278564970 0.257760557129940 0.87111972143503 28 0.104941494761536 0.209882989523072 0.895058505238464 29 0.396238477449608 0.792476954899215 0.603761522550393 30 0.639109094794706 0.721781810410587 0.360890905205294 31 0.923251319911214 0.153497360177572 0.076748680088786 32 0.913285133825728 0.173429732348544 0.086714866174272 33 0.893928670874063 0.212142658251873 0.106071329125937 34 0.863367933682914 0.273264132634173 0.136632066317086 35 0.886922904021902 0.226154191956197 0.113077095978098 36 0.823808557103805 0.352382885792390 0.176191442896195 37 0.776839161588902 0.446321676822196 0.223160838411098 38 0.704677657404058 0.590644685191884 0.295322342595942 39 0.685487302891729 0.629025394216542 0.314512697108271 40 0.8967214317602 0.206557136479599 0.103278568239800 41 0.878616624076128 0.242766751847745 0.121383375923872 42 0.854344188181562 0.291311623636876 0.145655811818438 43 0.841084729166525 0.317830541666951 0.158915270833476 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://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/10rzfa1227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/10rzfa1227449669.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/1im781227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/1im781227449669.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/2gcpk1227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/2gcpk1227449669.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/37kea1227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/37kea1227449669.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/4s37g1227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/4s37g1227449669.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/59id41227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/59id41227449669.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/68ruh1227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/68ruh1227449669.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/7vxuw1227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/7vxuw1227449669.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/8jo8y1227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/8jo8y1227449669.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/9o3ta1227449669.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274497251zwzgsah2oeqyrz/9o3ta1227449669.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