Workshop7 Multiple regression

*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, 19 Nov 2009 13:07:34 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp.htm/, Retrieved Thu, 19 Nov 2009 21:09:20 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp.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 «

562325 0 560854 0 555332 0 543599 0 536662 0 542722 0 593530 0 610763 0 612613 0 611324 0 594167 0 595454 0 590865 0 589379 0 584428 0 573100 0 567456 0 569028 0 620735 0 628884 0 628232 0 612117 0 595404 0 597141 0 593408 0 590072 0 579799 0 574205 0 572775 0 572942 0 619567 0 625809 0 619916 0 587625 0 565742 0 557274 0 560576 0 548854 0 531673 0 525919 0 511038 0 498662 1 555362 1 564591 1 541657 1 527070 1 509846 1 514258 1 516922 1 507561 1 492622 1 490243 1 469357 1 477580 1 528379 1 533590 1 517945 1 506174 1 501866 1 516141 1 528222 1 532638 1 536322 1 536535 1 523597 1 536214 1 586570 1 596594 1 580523 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 580812.390243902 -54846.6402439024X[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) 580812.390243902 4715.637085 123.1673 0 0 X -54846.6402439024 7402.627635 -7.4091 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.671076763329065 R-squared 0.450344022280214 Adjusted R-squared 0.442140201717233 F-TEST (value) 54.894426178981 F-TEST (DF numerator) 1 F-TEST (DF denominator) 67 p-value 2.79369416489317e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 30194.8101167264 Sum Squared Residuals 61085679385.0061 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 562325 580812.390243903 -18487.3902439027 2 560854 580812.390243903 -19958.3902439026 3 555332 580812.390243902 -25480.3902439024 4 543599 580812.390243902 -37213.3902439024 5 536662 580812.390243902 -44150.3902439024 6 542722 580812.390243902 -38090.3902439024 7 593530 580812.390243902 12717.6097560976 8 610763 580812.390243902 29950.6097560976 9 612613 580812.390243902 31800.6097560976 10 611324 580812.390243902 30511.6097560976 11 594167 580812.390243902 13354.6097560976 12 595454 580812.390243902 14641.6097560976 13 590865 580812.390243902 10052.6097560976 14 589379 580812.390243902 8566.60975609757 15 584428 580812.390243902 3615.60975609757 16 573100 580812.390243902 -7712.39024390243 17 567456 580812.390243902 -13356.3902439024 18 569028 580812.390243902 -11784.3902439024 19 620735 580812.390243902 39922.6097560976 20 628884 580812.390243902 48071.6097560976 21 628232 580812.390243902 47419.6097560976 22 612117 580812.390243902 31304.6097560976 23 595404 580812.390243902 14591.6097560976 24 597141 580812.390243902 16328.6097560976 25 593408 580812.390243902 12595.6097560976 26 590072 580812.390243902 9259.60975609757 27 579799 580812.390243902 -1013.39024390243 28 574205 580812.390243902 -6607.39024390243 29 572775 580812.390243902 -8037.39024390243 30 572942 580812.390243902 -7870.39024390243 31 619567 580812.390243902 38754.6097560976 32 625809 580812.390243902 44996.6097560976 33 619916 580812.390243902 39103.6097560976 34 587625 580812.390243902 6812.60975609757 35 565742 580812.390243902 -15070.3902439024 36 557274 580812.390243902 -23538.3902439024 37 560576 580812.390243902 -20236.3902439024 38 548854 580812.390243902 -31958.3902439024 39 531673 580812.390243902 -49139.3902439024 40 525919 580812.390243902 -54893.3902439024 41 511038 580812.390243902 -69774.3902439024 42 498662 525965.75 -27303.75 43 555362 525965.75 29396.25 44 564591 525965.75 38625.25 45 541657 525965.75 15691.25 46 527070 525965.75 1104.25 47 509846 525965.75 -16119.75 48 514258 525965.75 -11707.75 49 516922 525965.75 -9043.75 50 507561 525965.75 -18404.75 51 492622 525965.75 -33343.75 52 490243 525965.75 -35722.75 53 469357 525965.75 -56608.75 54 477580 525965.75 -48385.75 55 528379 525965.75 2413.25 56 533590 525965.75 7624.25 57 517945 525965.75 -8020.75 58 506174 525965.75 -19791.75 59 501866 525965.75 -24099.75 60 516141 525965.75 -9824.75 61 528222 525965.75 2256.25 62 532638 525965.75 6672.25 63 536322 525965.75 10356.25 64 536535 525965.75 10569.25 65 523597 525965.75 -2368.75 66 536214 525965.75 10248.25 67 586570 525965.75 60604.25 68 596594 525965.75 70628.25 69 580523 525965.75 54557.25 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0943754528294783 0.188750905658957 0.905624547170522 6 0.0427780746539920 0.0855561493079839 0.957221925346008 7 0.213412658758288 0.426825317516576 0.786587341241712 8 0.483749350260541 0.967498700521082 0.516250649739459 9 0.620725232303911 0.758549535392178 0.379274767696089 10 0.672587022474654 0.654825955050692 0.327412977525346 11 0.609369262134408 0.781261475731184 0.390630737865592 12 0.545433147274105 0.90913370545179 0.454566852725895 13 0.464726663334253 0.929453326668506 0.535273336665747 14 0.382773889429880 0.765547778859759 0.61722611057012 15 0.300036358019258 0.600072716038516 0.699963641980742 16 0.228987785208966 0.457975570417931 0.771012214791034 17 0.176110870515050 0.352221741030101 0.82388912948495 18 0.130437206863877 0.260874413727754 0.869562793136123 19 0.184301050254191 0.368602100508381 0.81569894974581 20 0.285884413075631 0.571768826151263 0.714115586924369 21 0.383117273876077 0.766234547752155 0.616882726123923 22 0.380268257543892 0.760536515087784 0.619731742456108 23 0.323154946695548 0.646309893391096 0.676845053304452 24 0.274783343844225 0.54956668768845 0.725216656155775 25 0.225650406573868 0.451300813147737 0.774349593426132 26 0.179634419672802 0.359268839345603 0.820365580327198 27 0.137866315569352 0.275732631138704 0.862133684430648 28 0.105445463901237 0.210890927802475 0.894554536098763 29 0.0795617585005525 0.159123517001105 0.920438241499447 30 0.0586520817276273 0.117304163455255 0.941347918272373 31 0.0830344240753289 0.166068848150658 0.916965575924671 32 0.152719357647601 0.305438715295201 0.8472806423524 33 0.251230669683645 0.502461339367291 0.748769330316355 34 0.248866447282469 0.497732894564938 0.751133552717531 35 0.233422581920117 0.466845163840234 0.766577418079883 36 0.227199981055003 0.454399962110006 0.772800018944997 37 0.224090452483391 0.448180904966782 0.775909547516609 38 0.235930523181729 0.471861046363458 0.76406947681827 39 0.280809876686331 0.561619753372663 0.719190123313669 40 0.333868330809795 0.66773666161959 0.666131669190205 41 0.424774107458312 0.849548214916623 0.575225892541688 42 0.387735064569712 0.775470129139423 0.612264935430288 43 0.400850330118209 0.801700660236417 0.599149669881791 44 0.427801894688571 0.855603789377142 0.572198105311429 45 0.370876937805996 0.741753875611991 0.629123062194004 46 0.306321316622901 0.612642633245802 0.693678683377099 47 0.265060233843978 0.530120467687956 0.734939766156022 48 0.215759115568895 0.431518231137791 0.784240884431105 49 0.167979056000904 0.335958112001807 0.832020943999096 50 0.137253241338285 0.27450648267657 0.862746758661715 51 0.140403561862805 0.280807123725610 0.859596438137195 52 0.152896373043751 0.305792746087502 0.847103626956249 53 0.303002295401248 0.606004590802495 0.696997704598752 54 0.477063311109767 0.954126622219533 0.522936688890233 55 0.398947720239339 0.797895440478678 0.601052279760661 56 0.32022055798588 0.64044111597176 0.67977944201412 57 0.268985821317961 0.537971642635923 0.731014178682039 58 0.274959640412203 0.549919280824407 0.725040359587797 59 0.340311351916900 0.680622703833800 0.6596886480831 60 0.343427507709543 0.686855015419086 0.656572492290457 61 0.298007775857895 0.59601555171579 0.701992224142105 62 0.245357118032340 0.490714236064679 0.75464288196766 63 0.190684337680386 0.381368675360773 0.809315662319614 64 0.149215203006349 0.298430406012698 0.850784796993651 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 1 0.0166666666666667 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/10jnpn1258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/10jnpn1258661246.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/1smwt1258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/1smwt1258661246.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/2bwbj1258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/2bwbj1258661246.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/3pj5o1258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/3pj5o1258661246.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/45zb31258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/45zb31258661246.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/5i60x1258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/5i60x1258661246.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/6o67z1258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/6o67z1258661246.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/7cvlj1258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/7cvlj1258661246.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/83re91258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/83re91258661246.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/91dhn1258661246.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586613480qc9yzjiefy0gyp/91dhn1258661246.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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