Multiple regression (with seiz, no linear)

*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: Thu, 16 Dec 2010 12:34:00 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx.htm/, Retrieved Thu, 16 Dec 2010 13:32:53 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

300 2,26 302 2,57 400 3,07 392 2,76 373 2,51 379 2,87 303 3,14 324 3,11 353 3,16 392 2,47 327 2,57 376 2,89 329 2,63 359 2,38 413 1,69 338 1,96 422 2,19 390 1,87 370 1,60 367 1,63 406 1,22 418 1,21 346 1,49 350 1,64 330 1,66 318 1,77 382 1,82 337 1,78 372 1,28 422 1,29 428 1,37 426 1,12 396 1,51 458 2,24 315 2,94 337 3,09 386 3,46 352 3,64 383 4,39 439 4,15 397 5,21 453 5,80 363 5,91 365 5,39 474 5,46 373 4,72 403 3,14 384 2,63 364 2,32 361 1,93 419 0,62 352 0,60 363 -0,37 410 -1,10 361 -1,68 383 -0,78 342 -1,19 369 -0,79 361 -0,12 317 0,26 386 0,62 318 0,70 407 1,66 393 1,80 404 2,27 498 2,46 438 2,57

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Aantal_vergunningen[t] = + 344.887869993583 + 3.76409610200597Inflatie[t] -3.84537741374636M1[t] -18.0371380544264M2[t] + 47.4664177811533M3[t] + 22.0918876512202M4[t] + 35.4001270105402M5[t] + 72.1707254088401M6[t] + 24.1797165602670M7[t] + 20.2301127688160M8[t] + 41.6634867271404M9[t] + 49.6968606854647M10[t] -2.03111858200344M11[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) 344.887869993583 17.130084 20.1335 0 0 Inflatie 3.76409610200597 2.746641 1.3704 0.176221 0.088111 M1 -3.84537741374636 21.837745 -0.1761 0.860883 0.430442 M2 -18.0371380544264 21.837882 -0.826 0.412463 0.206232 M3 47.4664177811533 21.83915 2.1735 0.03415 0.017075 M4 22.0918876512202 21.838117 1.0116 0.316231 0.158116 M5 35.4001270105402 21.838293 1.621 0.110841 0.05542 M6 72.1707254088401 21.838799 3.3047 0.001693 0.000846 M7 24.1797165602670 21.837623 1.1073 0.273094 0.136547 M8 20.2301127688160 22.808219 0.887 0.379031 0.189515 M9 41.6634867271404 22.809018 1.8266 0.073286 0.036643 M10 49.6968606854647 22.811089 2.1786 0.033743 0.016872 M11 -2.03111858200344 22.809796 -0.089 0.929375 0.464687 Multiple Linear Regression - Regression Statistics Multiple R 0.63324623870236 R-squared 0.401000798830687 Adjusted R-squared 0.267889865237506 F-TEST (value) 3.01253088687848 F-TEST (DF numerator) 12 F-TEST (DF denominator) 54 p-value 0.00271853806529065 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 36.0629432196610 Sum Squared Residuals 70228.9371778828 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 300 349.549349770371 -49.5493497703707 2 302 336.524458921312 -34.5244589213124 3 400 403.910062807895 -3.91006280789517 4 392 377.36866288634 14.6313371136598 5 373 389.735878220159 -16.7358782201586 6 379 427.861551215181 -48.8615512151807 7 303 380.886848314149 -77.8868483141492 8 324 376.824321639638 -52.8243216396381 9 353 398.445900403063 -45.4459004030628 10 392 403.882048051003 -11.8820480510030 11 327 352.530478393735 -25.5304783937354 12 376 355.766107728381 20.2338922716193 13 329 350.942065328113 -21.9420653281128 14 359 335.809280661931 23.1907193380687 15 413 398.715610187127 14.2843898128731 16 338 374.357386004735 -36.3573860047354 17 422 388.531367467517 33.4686325324833 18 390 424.097455113175 -34.0974551131747 19 370 375.09014031706 -5.09014031706005 20 367 371.253459408669 -4.25345940866923 21 406 391.143553965171 14.8564460348288 22 418 399.139286962475 18.8607130375245 23 346 348.465254603569 -2.46525460356892 24 350 351.060987600873 -1.06098760087327 25 330 347.290892109167 -17.290892109167 26 318 333.513182039708 -15.5131820397076 27 382 399.204942680388 -17.2049426803877 28 337 373.679848706374 -36.6798487063743 29 372 385.106040014691 -13.1060400146913 30 422 421.914279374011 0.0857206259887766 31 428 374.224398213599 53.7756017864013 32 426 369.333770396646 56.6662296033538 33 396 392.235141834753 3.76485816524712 34 458 403.016305947542 54.9836940524584 35 315 353.923193951478 -38.9231939514776 36 337 356.518926948782 -19.5189269487819 37 386 354.066265092778 31.9337349072222 38 352 340.552041750459 11.4479582495412 39 383 408.878669662543 -25.8786696625430 40 439 382.600756468129 56.3992435318715 41 397 399.898937695575 -2.89893769557475 42 453 438.890352794058 14.1096472059418 43 363 391.313394516706 -28.3133945167058 44 365 385.406460752212 -20.4064607522117 45 474 407.103321437676 66.8966785623235 46 373 412.351264280516 -39.3512642805164 47 403 354.676013171879 48.3239868281212 48 384 354.787442741859 29.2125572581408 49 364 349.775195536491 14.2248044635090 50 361 334.115437416029 26.8845625839714 51 419 394.688027357981 24.3119726420195 52 352 369.238215306007 -17.2382153060072 53 363 378.895281446381 -15.8952814463814 54 410 412.918089690217 -2.91808969021696 55 361 362.743905102480 -1.74390510248044 56 383 362.181987802835 20.8180121971652 57 342 382.072082359337 -40.0720823593368 58 369 391.611094758463 -22.6110947584635 59 361 342.405059879339 18.5949401206607 60 317 345.866534980105 -28.866534980105 61 386 343.376232163081 42.6237678369192 62 318 329.485599210561 -11.4855992105612 63 407 398.602687304067 8.39731269593327 64 393 373.755130628414 19.2448693715856 65 404 388.832495155677 15.1675048443228 66 498 426.318271813358 71.6817281866418 67 438 378.741313536006 59.2586864639942 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.59908612221523 0.80182775556954 0.40091387778477 17 0.598353404883313 0.803293190233375 0.401646595116687 18 0.487696061475254 0.975392122950508 0.512303938524746 19 0.501794354759725 0.99641129048055 0.498205645240275 20 0.39246273319668 0.78492546639336 0.60753726680332 21 0.286932291178657 0.573864582357315 0.713067708821343 22 0.200026599025582 0.400053198051165 0.799973400974418 23 0.131154741769357 0.262309483538715 0.868845258230643 24 0.140169420558922 0.280338841117843 0.859830579441078 25 0.101966191576194 0.203932383152388 0.898033808423806 26 0.0785374615114525 0.157074923022905 0.921462538488548 27 0.0681485728854703 0.136297145770941 0.93185142711453 28 0.0769707561914664 0.153941512382933 0.923029243808534 29 0.0709010267859813 0.141802053571963 0.929098973214019 30 0.0548138166216803 0.109627633243361 0.94518618337832 31 0.135183295392389 0.270366590784779 0.86481670460761 32 0.188438700890983 0.376877401781966 0.811561299109017 33 0.134110181353631 0.268220362707261 0.86588981864637 34 0.261857150174289 0.523714300348578 0.738142849825711 35 0.309885408400268 0.619770816800537 0.690114591599732 36 0.249335136321457 0.498670272642914 0.750664863678543 37 0.37317267373791 0.74634534747582 0.62682732626209 38 0.333365011926117 0.666730023852234 0.666634988073883 39 0.321137246708866 0.642274493417732 0.678862753291134 40 0.492771235609845 0.98554247121969 0.507228764390155 41 0.409809197584304 0.819618395168608 0.590190802415696 42 0.411812704855208 0.823625409710417 0.588187295144792 43 0.596484009578301 0.807031980843398 0.403515990421699 44 0.821431320701878 0.357137358596245 0.178568679298122 45 0.855704571760646 0.288590856478709 0.144295428239354 46 0.96861489534424 0.0627702093115183 0.0313851046557592 47 0.955602735033233 0.0887945299335332 0.0443972649667666 48 0.92978795015533 0.140424099689341 0.0702120498446703 49 0.975955619582938 0.0480887608341234 0.0240443804170617 50 0.952832160426082 0.094335679147837 0.0471678395739185 51 0.962175081251788 0.0756498374964238 0.0378249187482119 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 1 0.0277777777777778 OK 10% type I error level 5 0.138888888888889 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/10jv6i1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/10jv6i1292502832.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/15l8r1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/15l8r1292502832.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/25l8r1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/25l8r1292502832.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/35l8r1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/35l8r1292502832.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/4gdpc1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/4gdpc1292502832.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/5gdpc1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/5gdpc1292502832.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/6gdpc1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/6gdpc1292502832.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/7rm6f1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/7rm6f1292502832.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/8jv6i1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/8jv6i1292502832.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/9jv6i1292502832.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292502760y5ith70tfdju0bx/9jv6i1292502832.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