Multiple Linear 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: Sat, 18 Dec 2010 10:17:24 +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/18/t1292667436933xhsmo3sij3ym.htm/, Retrieved Sat, 18 Dec 2010 11:17:32 +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/18/t1292667436933xhsmo3sij3ym.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 «

94,6 116,1 95,9 107,5 104,7 116,7 102,8 112,5 98,1 113 113,9 126,4 80,9 114,1 95,7 112,5 113,2 112,4 105,9 113,1 108,8 116,3 102,3 111,7 99 118,8 100,7 116,5 115,5 125,1 100,7 113,1 109,9 119,6 114,6 114,4 85,4 114 100,5 117,8 114,8 117 116,5 120,9 112,9 115 102 117,3 106 119,4 105,3 114,9 118,8 125,8 106,1 117,6 109,3 117,6 117,2 114,9 92,5 121,9 104,2 117 112,5 106,4 122,4 110,5 113,3 113,6 100 114,2 110,7 125,4 112,8 124,6 109,8 120,2 117,3 120,8 109,1 111,4 115,9 124,1 96 120,2 99,8 125,5 116,8 116 115,7 117 99,4 105,7 94,3 102 91 106,4 93,2 96,9 103,1 107,6 94,1 98,8 91,8 101,1 102,7 105,7 82,6 104,6 89,1 103,2 104,5 101,6 105,1 106,7 95,1 99,5 88,7 101

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 29 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation I.P.C.N.[t] = + 72.839181298607 + 0.393111879092869T.I.P.[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) 72.839181298607 8.996611 8.0963 0 0 T.I.P. 0.393111879092869 0.086193 4.5608 2.7e-05 1.3e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.513779201282127 R-squared 0.263969067670101 Adjusted R-squared 0.251278879181654 F-TEST (value) 20.801036005921 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 2.68695476206560e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.47910918842311 Sum Squared Residuals 2434.77364077951 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 116.1 110.027565060792 6.0724349392077 2 107.5 110.538610503613 -3.03861050361307 3 116.7 113.997995039630 2.70200496036969 4 112.5 113.251082469354 -0.751082469353855 5 113 111.403456637617 1.59654336238263 6 126.4 117.614624327285 8.7853756727153 7 114.1 104.64193231722 9.45806768277997 8 112.5 110.459988127794 2.04001187220551 9 112.4 117.339446011920 -4.93944601191969 10 113.1 114.469729294542 -1.36972929454176 11 116.3 115.609753743911 0.690246256088929 12 111.7 113.054526529807 -1.35452652980742 13 118.8 111.757257328801 7.04274267119904 14 116.5 112.425547523259 4.07445247674117 15 125.1 118.243603333833 6.8563966661667 16 113.1 112.425547523259 0.674452476741162 17 119.6 116.042176810913 3.55782318908677 18 114.4 117.889802642650 -3.4898026426497 19 114 106.410935773138 7.58906422686206 20 117.8 112.346925147440 5.45307485255974 21 117 117.968425018468 -0.968425018468282 22 120.9 118.636715212926 2.26328478707385 23 115 117.221512448192 -2.22151244819183 24 117.3 112.936592966080 4.36340703392044 25 119.4 114.509040482451 4.89095951754897 26 114.9 114.233862167086 0.666137832913978 27 125.8 119.540872534840 6.25912746516024 28 117.6 114.548351670360 3.05164832963967 29 117.6 115.806309683457 1.79369031654249 30 114.9 118.911893528291 -4.01189352829116 31 121.9 109.202030114697 12.6979698853027 32 117 113.801439100084 3.19856089991613 33 106.4 117.064267696555 -10.6642676965547 34 110.5 120.956075299574 -10.4560752995741 35 113.6 117.378757199829 -3.77875719982898 36 114.2 112.150369207894 2.04963079210618 37 125.4 116.356666314188 9.04333368581248 38 124.6 117.182201260283 7.41779873971745 39 120.2 116.002865623004 4.19713437699607 40 120.8 118.951204716200 1.84879528379954 41 111.4 115.727687307639 -4.32768730763892 42 124.1 118.400848085470 5.69915191452955 43 120.2 110.577921691522 9.62207830847765 44 125.5 112.071746832075 13.4282531679248 45 116 118.754648776654 -2.75464877665402 46 117 118.322225709652 -1.32222570965187 47 105.7 111.914502080438 -6.2145020804381 48 102 109.909631497064 -7.90963149706447 49 106.4 108.612362296058 -2.21236229605800 50 96.9 109.477208430062 -12.5772084300623 51 107.6 113.369016033082 -5.76901603308172 52 98.8 109.831009121246 -11.0310091212459 53 101.1 108.926851799332 -7.8268517993323 54 105.7 113.211771281445 -7.51177128144457 55 104.6 105.310222511678 -0.710222511677907 56 103.2 107.865449725782 -4.66544972578155 57 101.6 113.919372663812 -12.3193726638117 58 106.7 114.155239791267 -7.45523979126745 59 99.5 110.224121000339 -10.7241210003388 60 101 107.708204974144 -6.7082049741444 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.210720654013049 0.421441308026098 0.789279345986951 6 0.177464261642847 0.354928523285694 0.822535738357153 7 0.312803064400155 0.62560612880031 0.687196935599845 8 0.206346295454725 0.41269259090945 0.793653704545275 9 0.216098523567857 0.432197047135714 0.783901476432143 10 0.149384320224055 0.29876864044811 0.850615679775945 11 0.090727999210081 0.181455998420162 0.909272000789919 12 0.0611007152597041 0.122201430519408 0.938899284740296 13 0.0564593635084676 0.112918727016935 0.943540636491532 14 0.0357852850890879 0.0715705701781759 0.964214714910912 15 0.0472756128728817 0.0945512257457633 0.952724387127118 16 0.0295378298019811 0.0590756596039622 0.970462170198019 17 0.01856133931403 0.03712267862806 0.98143866068597 18 0.0148125966505389 0.0296251933010779 0.98518740334946 19 0.0120800528443884 0.0241601056887768 0.987919947155612 20 0.00875933748316333 0.0175186749663267 0.991240662516837 21 0.00486368025503348 0.00972736051006695 0.995136319744967 22 0.00285760178155233 0.00571520356310465 0.997142398218448 23 0.00176212577008145 0.00352425154016289 0.998237874229918 24 0.00108612534552317 0.00217225069104634 0.998913874654477 25 0.000767118666466525 0.00153423733293305 0.999232881333533 26 0.000395953556412454 0.000791907112824908 0.999604046443588 27 0.00057006977366842 0.00114013954733684 0.999429930226332 28 0.000313850182687613 0.000627700365375225 0.999686149817312 29 0.000157334499624379 0.000314668999248759 0.999842665500376 30 0.000122791541724234 0.000245583083448469 0.999877208458276 31 0.00103801594199871 0.00207603188399742 0.998961984058001 32 0.000662178703632911 0.00132435740726582 0.999337821296367 33 0.00450326406319998 0.00900652812639996 0.9954967359368 34 0.0127959862360739 0.0255919724721479 0.987204013763926 35 0.00969458807065116 0.0193891761413023 0.990305411929349 36 0.0067925513682854 0.0135851027365708 0.993207448631715 37 0.0156558424491415 0.0313116848982831 0.984344157550858 38 0.0237527691143980 0.0475055382287959 0.976247230885602 39 0.0209721525907927 0.0419443051815854 0.979027847409207 40 0.0146720057327009 0.0293440114654017 0.9853279942673 41 0.0115628557045015 0.0231257114090029 0.988437144295499 42 0.0159863891703456 0.0319727783406911 0.984013610829654 43 0.0708195603094888 0.141639120618978 0.929180439690511 44 0.821554255070212 0.356891489859577 0.178445744929788 45 0.803158892897666 0.393682214204667 0.196841107102334 46 0.92355270383289 0.152894592334219 0.0764472961671095 47 0.922687710888008 0.154624578223984 0.0773122891119922 48 0.917898668315098 0.164202663369805 0.0821013316849024 49 0.927630904758388 0.144738190483223 0.0723690952416116 50 0.971803898439624 0.0563922031207515 0.0281961015603758 51 0.972578129972113 0.054843740055775 0.0274218700278875 52 0.9778629206443 0.0442741587114013 0.0221370793557007 53 0.958082610129438 0.083834779741123 0.0419173898705616 54 0.926074780480197 0.147850439039605 0.0739252195198025 55 0.887604339292053 0.224791321415895 0.112395660707947 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.254901960784314 NOK 5% type I error level 27 0.529411764705882 NOK 10% type I error level 33 0.647058823529412 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/10feg41292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/10feg41292667414.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/18vja1292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/18vja1292667414.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/2j4iv1292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/2j4iv1292667414.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/3j4iv1292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/3j4iv1292667414.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/4j4iv1292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/4j4iv1292667414.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/5j4iv1292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/5j4iv1292667414.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/6cvhg1292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/6cvhg1292667414.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/7m5hj1292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/7m5hj1292667414.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/8m5hj1292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/8m5hj1292667414.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/9feg41292667414.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292667436933xhsmo3sij3ym/9feg41292667414.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