paper multiple regression 1

*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: Wed, 30 Dec 2009 09:08:31 -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/30/t1262189405cl3n6vsqnwm6tub.htm/, Retrieved Wed, 30 Dec 2009 17:10:17 +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/30/t1262189405cl3n6vsqnwm6tub.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 «

4223,4 401 4627,3 394 5175,3 372 4550,7 334 4639,3 320 5498,7 334 5031,0 400 4033,3 427 4643,5 423 4873,2 395 4608,7 373 4733,5 377 3955,6 391 4590,9 398 5127,5 393 5257,3 375 5416,9 371 5813,3 364 5261,9 400 4669,2 406 5855,8 407 5274,6 397 5516,7 389 5819,5 394 5156,0 399 5377,3 401 6386,8 396 5144,0 392 6138,5 384 5567,8 370 5822,6 380 5145,5 376 5706,6 378 6078,5 376 6074,5 373 5577,6 374 5727,5 379 6067,0 376 7069,9 371 5490,0 375 5948,3 360 6177,5 338 6890,1 352 5756,2 344 6528,8 330 6792,0 334 6657,4 333 5753,7 343 5750,9 350 5968,4 341 5871,7 320 7004,9 302 6363,4 287 6694,7 304 7101,6 370 5364,0 385 6958,6 365 6503,3 333 5316,0 313 5312,7 330 4478,0 367

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 Werkloosheid[t] = + 476.464276058344 -0.0194424865219139Export[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) 476.464276058344 25.368791 18.7815 0 0 Export -0.0194424865219139 0.004496 -4.3244 6e-05 3e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.490586298742127 R-squared 0.2406749165135 Adjusted R-squared 0.227804999844237 F-TEST (value) 18.7005807961682 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 5.97589999016268e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 27.2726845465216 Sum Squared Residuals 43884.1600200707 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 401 394.350878481693 6.64912151830708 2 394 386.498058175492 7.50194182450838 3 372 375.843575561483 -3.84357556148283 4 334 387.98735264307 -53.9873526430703 5 320 386.264748337229 -66.2647483372287 6 334 369.555875420296 -35.5558754202959 7 400 378.649126366595 21.350873633405 8 427 398.046895169509 28.9531048304915 9 423 386.183089893837 36.8169101061633 10 395 381.717150739753 13.2828492602470 11 373 386.859688424799 -13.8596884247993 12 377 384.433266106864 -7.43326610686441 13 391 399.557576372261 -8.55757637226126 14 398 387.205764684889 10.7942353151107 15 393 376.772926417230 16.2270735827697 16 375 374.249291666686 0.750708333314111 17 371 371.146270817788 -0.146270817788435 18 364 363.439269160502 0.56073083949826 19 400 374.159856228685 25.8401437713149 20 406 385.683417990223 20.3165820097765 21 407 362.612963483320 44.3870365166796 22 397 373.912936649857 23.0870633501432 23 389 369.205910662901 19.7940893370986 24 394 363.318725744066 30.6812742559341 25 399 376.218815551356 22.7811844486442 26 401 371.916193284056 29.0838067159438 27 396 352.289003140184 43.7109968598159 28 392 376.452125389619 15.5478746103813 29 384 357.116572543575 26.8834274564247 30 370 368.212399601632 1.78760039836839 31 380 363.258454035848 16.7415459641521 32 376 376.422961659836 -0.42296165983587 33 378 365.51378247239 12.4862175276100 34 376 358.28312173489 17.7168782651098 35 373 358.360891680978 14.6391083190222 36 374 368.021863233717 5.97813676628315 37 379 365.107434504082 13.8925654959180 38 376 358.506710329892 17.4932896701078 39 371 339.007840597065 31.9921594029353 40 375 369.725025053037 5.27497494696348 41 360 360.814533480043 -0.814533480043359 42 338 356.358315569221 -18.3583155692207 43 352 342.503599673705 9.49640032629519 44 344 364.549435140903 -20.5494351409030 45 330 349.528170054072 -19.5281700540723 46 334 344.410907601505 -10.4109076015046 47 333 347.027866287354 -14.0278662873542 48 343 364.598041357208 -21.5980413572078 49 350 364.652480319469 -14.6524803194692 50 341 360.423739500953 -19.4237395009529 51 320 362.303827947622 -42.303827947622 52 302 340.271602220989 -38.2716022209891 53 287 352.743957324797 -65.7439573247969 54 304 346.302661540087 -42.3026615400868 55 370 338.39151377432 31.60848622568 56 385 372.174778354798 12.8252216452023 57 365 341.171789346954 23.8282106530463 58 333 350.023953460381 -17.0239534603811 59 313 373.108017707850 -60.1080177078495 60 330 373.172177913372 -43.1721779133719 61 367 389.400821413213 -22.4008214132134 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.931861322632518 0.136277354734964 0.0681386773674818 6 0.885509991472717 0.228980017054567 0.114490008527283 7 0.922089365870157 0.155821268259685 0.0779106341298425 8 0.928673249683818 0.142653500632364 0.0713267503161822 9 0.95442230905977 0.091155381880459 0.0455776909402295 10 0.93719004240425 0.125619915191500 0.0628099575957499 11 0.906668214762078 0.186663570475844 0.0933317852379222 12 0.860699355032118 0.278601289935764 0.139300644967882 13 0.816287829097434 0.367424341805132 0.183712170902566 14 0.763535944016242 0.472928111967516 0.236464055983758 15 0.735438019577342 0.529123960845316 0.264561980422658 16 0.663060938843228 0.673878122313544 0.336939061156772 17 0.584193592647828 0.831612814704343 0.415806407352172 18 0.503847710636126 0.992304578727749 0.496152289363874 19 0.499435774284706 0.998871548569412 0.500564225715294 20 0.46051231273412 0.92102462546824 0.53948768726588 21 0.560760608639875 0.87847878272025 0.439239391360125 22 0.524607668296477 0.950784663407046 0.475392331703523 23 0.472709963017314 0.945419926034628 0.527290036982686 24 0.457414902259972 0.914829804519943 0.542585097740028 25 0.429750928052056 0.859501856104112 0.570249071947944 26 0.43079835516571 0.86159671033142 0.56920164483429 27 0.472567628454629 0.945135256909258 0.527432371545371 28 0.435763286834766 0.871526573669532 0.564236713165234 29 0.418406363999017 0.836812727998033 0.581593636000983 30 0.367086126976714 0.734172253953427 0.632913873023286 31 0.334795971696212 0.669591943392423 0.665204028303788 32 0.286695792208616 0.573391584417232 0.713304207791384 33 0.257527575285199 0.515055150570399 0.7424724247148 34 0.237909246908226 0.475818493816453 0.762090753091774 35 0.216817254231128 0.433634508462257 0.783182745768872 36 0.193444228110993 0.386888456221986 0.806555771889007 37 0.190625125763498 0.381250251526997 0.809374874236502 38 0.194317375964097 0.388634751928194 0.805682624035903 39 0.221276286336568 0.442552572673136 0.778723713663432 40 0.224510715653408 0.449021431306816 0.775489284346592 41 0.210319228052948 0.420638456105895 0.789680771947052 42 0.208508806765768 0.417017613531535 0.791491193234232 43 0.189657371460219 0.379314742920437 0.810342628539781 44 0.171354465540834 0.342708931081667 0.828645534459166 45 0.157541398972327 0.315082797944655 0.842458601027673 46 0.126786378206141 0.253572756412282 0.87321362179386 47 0.099081722133218 0.198163444266436 0.900918277866782 48 0.0772243033748388 0.154448606749678 0.922775696625161 49 0.0570345795969958 0.114069159193992 0.942965420403004 50 0.0402480854684436 0.0804961709368873 0.959751914531556 51 0.0412469198688111 0.0824938397376222 0.95875308013119 52 0.0475754215844513 0.0951508431689027 0.952424578415549 53 0.181149859747236 0.362299719494472 0.818850140252764 54 0.319352569635326 0.638705139270651 0.680647430364674 55 0.256474849333099 0.512949698666198 0.743525150666901 56 0.363738775433133 0.727477550866266 0.636261224566867 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 4 0.0769230769230769 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/10tfru1262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/10tfru1262189305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/1wgfk1262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/1wgfk1262189305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/2s8tc1262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/2s8tc1262189305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/3opzt1262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/3opzt1262189305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/4ur231262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/4ur231262189305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/50sw91262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/50sw91262189305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/6iser1262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/6iser1262189305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/7jyii1262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/7jyii1262189305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/87kuf1262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/87kuf1262189305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/9liri1262189305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262189405cl3n6vsqnwm6tub/9liri1262189305.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal 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