*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, 22 Nov 2009 09:44:25 -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/22/t1258908592stua5w1uqbr30lm.htm/, Retrieved Sun, 22 Nov 2009 17:50:04 +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/22/t1258908592stua5w1uqbr30lm.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 «

99.9 98.8 98.6 100.5 107.2 110.4 95.7 96.4 93.7 101.9 106.7 106.2 86.7 81 95.3 94.7 99.3 101 101.8 109.4 96 102.3 91.7 90.7 95.3 96.2 96.6 96.1 107.2 106 108 103.1 98.4 102 103.1 104.7 81.1 86 96.6 92.1 103.7 106.9 106.6 112.6 97.6 101.7 87.6 92 99.4 97.4 98.5 97 105.2 105.4 104.6 102.7 97.5 98.1 108.9 104.5 86.8 87.4 88.9 89.9 110.3 109.8 114.8 111.7 94.6 98.6 92 96.9 93.8 95.1 93.8 97 107.6 112.7 101 102.9 95.4 97.4 96.5 111.4 89.2 87.4 87.1 96.8 110.5 114.1 110.8 110.3 104.2 103.9 88.9 101.6 89.8 94.6 90 95.9 93.9 104.7 91.3 102.8 87.8 98.1 99.7 113.9 73.5 80.9 79.2 95.7 96.9 113.2 95.2 105.9 95.6 108.8 89.7 102.3

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 TotProd[t] = + 16.3383193313249 + 0.800894252855453ProdMetal[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) 16.3383193313249 8.960284 1.8234 0.073395 0.036697 ProdMetal 0.800894252855453 0.088747 9.0245 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.764233944431027 R-squared 0.584053521820605 Adjusted R-squared 0.576882030817512 F-TEST (value) 81.441017156503 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.20836674000202e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.39997904308062 Sum Squared Residuals 1691.26687261117 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99.9 95.4666715134433 4.43332848655675 2 98.6 96.8281917432979 1.77180825670211 3 107.2 104.757044846567 2.44295515343312 4 95.7 93.5445253065905 2.15547469340946 5 93.7 97.9494436972955 -4.24944369729553 6 106.7 101.393288984574 5.30671101542603 7 86.7 81.2107538126166 5.48924618738344 8 95.3 92.1830050767363 3.11699492326373 9 99.3 97.2286388697256 2.07136113027438 10 101.8 103.956150593711 -2.15615059371143 11 96 98.2698013984377 -2.26980139843770 12 91.7 88.9794280653145 2.72057193468554 13 95.3 93.3843464560194 1.91565354398055 14 96.6 93.3042570307339 3.2957429692661 15 107.2 101.233110134003 5.96688986599712 16 108 98.9105168007221 9.08948319927793 17 98.4 98.029533122581 0.370466877418934 18 103.1 100.191947605291 2.9080523947092 19 81.1 85.2152250768938 -4.11522507689383 20 96.6 90.100680019312 6.49931998068791 21 103.7 101.953914961573 1.74608503842721 22 106.6 106.519012202849 0.0809877971511289 23 97.6 97.7892648467244 -0.189264846724444 24 87.6 90.0205905940265 -2.42059059402655 25 99.4 94.345419559446 5.05458044055401 26 98.5 94.0250618583038 4.47493814169619 27 105.2 100.752573582290 4.44742641771039 28 104.6 98.59015909958 6.0098409004201 29 97.5 94.9060455364448 2.5939544635552 30 108.9 100.031768754720 8.8682312452803 31 86.8 86.3364770308915 0.46352296910853 32 88.9 88.3387126630301 0.561287336969906 33 110.3 104.276508294854 6.0234917051464 34 114.8 105.798207375279 9.00179262472103 35 94.6 95.3064926628725 -0.706492662872534 36 92 93.9449724330183 -1.94497243301827 37 93.8 92.5033627778785 1.29663722212155 38 93.8 94.0250618583038 -0.225061858303811 39 107.6 106.599101628134 1.00089837186558 40 101 98.750337950151 2.24966204984902 41 95.4 94.345419559446 1.05458044055401 42 96.5 105.557939099422 -9.05793909942233 43 89.2 86.3364770308915 2.86352296910854 44 87.1 93.8648830077327 -6.76488300773272 45 110.5 107.720353582132 2.77964641786796 46 110.8 104.676955421281 6.12304457871867 47 104.2 99.5512322030064 4.64876779699357 48 88.9 97.7091754214389 -8.80917542143888 49 89.8 92.1029156514507 -2.30291565145072 50 90 93.1440781801628 -3.14407818016281 51 93.9 100.191947605291 -6.29194760529079 52 91.3 98.6702485248654 -7.37024852486543 53 87.8 94.9060455364448 -7.1060455364448 54 99.7 107.560174731561 -7.86017473156096 55 73.5 81.130664387331 -7.63066438733102 56 79.2 92.9838993295917 -13.7838993295917 57 96.9 106.999548754562 -10.0995487545621 58 95.2 101.153020708717 -5.95302070871734 59 95.6 103.475614041998 -7.87561404199815 60 89.7 98.2698013984377 -8.5698013984377 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.28820754290826 0.57641508581652 0.71179245709174 6 0.226556915056469 0.453113830112939 0.77344308494353 7 0.150701834725078 0.301403669450155 0.849298165274922 8 0.0800047727624063 0.160009545524813 0.919995227237594 9 0.0394187152855106 0.0788374305710212 0.96058128471449 10 0.0286418034517264 0.0572836069034528 0.971358196548274 11 0.0248373215388841 0.0496746430777683 0.975162678461116 12 0.0123144313391165 0.024628862678233 0.987685568660883 13 0.00572207118084090 0.0114441423616818 0.99427792881916 14 0.00275724838707733 0.00551449677415467 0.997242751612923 15 0.00463152921769821 0.00926305843539641 0.995368470782302 16 0.0189473385279612 0.0378946770559224 0.981052661472039 17 0.0114716912849693 0.0229433825699385 0.98852830871503 18 0.0065113870350074 0.0130227740700148 0.993488612964993 19 0.0137924120037852 0.0275848240075705 0.986207587996215 20 0.0153634994966310 0.0307269989932620 0.984636500503369 21 0.00897658253776942 0.0179531650755388 0.99102341746223 22 0.00533959181080014 0.0106791836216003 0.9946604081892 23 0.00328417967476883 0.00656835934953766 0.996715820325231 24 0.00308648300203185 0.00617296600406371 0.996913516997968 25 0.00262495605021207 0.00524991210042414 0.997375043949788 26 0.00200202607384317 0.00400405214768634 0.997997973926157 27 0.00151140952428088 0.00302281904856177 0.998488590475719 28 0.00172843289761186 0.00345686579522372 0.998271567102388 29 0.00106935808663315 0.0021387161732663 0.998930641913367 30 0.003908105076335 0.00781621015267 0.996091894923665 31 0.00262076068560393 0.00524152137120786 0.997379239314396 32 0.00175926858902727 0.00351853717805453 0.998240731410973 33 0.00221464041566051 0.00442928083132103 0.99778535958434 34 0.00946502032425226 0.0189300406485045 0.990534979675748 35 0.00742764904941115 0.0148552980988223 0.992572350950589 36 0.00620167445654422 0.0124033489130884 0.993798325543456 37 0.0049002854835543 0.0098005709671086 0.995099714516446 38 0.00372855913482909 0.00745711826965817 0.996271440865171 39 0.00324248520971889 0.00648497041943778 0.996757514790281 40 0.00332971732905676 0.00665943465811352 0.996670282670943 41 0.00312271192739483 0.00624542385478965 0.996877288072605 42 0.0181100341126104 0.0362200682252208 0.98188996588739 43 0.0349249230363314 0.0698498460726629 0.965075076963669 44 0.0436932079988374 0.087386415997675 0.956306792001163 45 0.051088789150932 0.102177578301864 0.948911210849068 46 0.249038905974161 0.498077811948322 0.750961094025839 47 0.822652210993679 0.354695578012642 0.177347789006321 48 0.839430151663031 0.321139696673937 0.160569848336969 49 0.895918189522991 0.208163620954017 0.104081810477009 50 0.94809230187468 0.103815396250640 0.0519076981253202 51 0.936959971829901 0.126080056340198 0.0630400281700988 52 0.907779015019027 0.184441969961947 0.0922209849809733 53 0.867510026484353 0.264979947031293 0.132489973515647 54 0.789059669004185 0.421880661991630 0.210940330995815 55 0.77499353320415 0.4500129335917 0.22500646679585 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 18 0.352941176470588 NOK 5% type I error level 32 0.627450980392157 NOK 10% type I error level 36 0.705882352941177 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/10hnts1258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/10hnts1258908258.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/1hb1b1258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/1hb1b1258908258.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/2586t1258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/2586t1258908258.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/3t6wc1258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/3t6wc1258908258.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/4s66r1258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/4s66r1258908258.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/5t10c1258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/5t10c1258908258.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/6j62u1258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/6j62u1258908258.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/7s2go1258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/7s2go1258908258.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/8l9nc1258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/8l9nc1258908258.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/9tg001258908258.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258908592stua5w1uqbr30lm/9tg001258908258.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