multi

*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: Fri, 20 Nov 2009 09:46:48 -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/20/t1258735713hhfkch2dgo9ajb5.htm/, Retrieved Fri, 20 Nov 2009 17:48:45 +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/20/t1258735713hhfkch2dgo9ajb5.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 «

8.9 6.3 8.2 6.2 7.6 6.1 7.7 6.3 8.1 6.5 8.3 6.6 8.3 6.5 7.9 6.2 7.8 6.2 8 5.9 8.5 6.1 8.6 6.1 8.5 6.1 8 6.1 7.8 6.1 8 6.4 8.2 6.7 8.3 6.9 8.2 7 8.1 7 8 6.8 7.8 6.4 7.8 5.9 7.7 5.5 7.6 5.5 7.6 5.6 7.6 5.8 7.8 5.9 8 6.1 8 6.1 7.9 6 7.7 6 7.4 5.9 6.9 5.5 6.7 5.6 6.5 5.4 6.4 5.2 6.7 5.2 6.8 5.2 6.9 5.5 6.9 5.8 6.7 5.8 6.4 5.5 6.2 5.3 5.9 5.1 6.1 5.2 6.7 5.8 6.8 5.8 6.6 5.5 6.4 5 6.4 4.9 6.7 5.3 7.1 6.1 7.1 6.5 6.9 6.8 6.4 6.6 6 6.4 6 6.4

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation wm[t] = + 3.03276769585098 + 0.397803021818835wv[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) 3.03276769585098 0.537548 5.6419 1e-06 0 wv 0.397803021818835 0.072424 5.4927 1e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.591710957513193 R-squared 0.35012185724118 Adjusted R-squared 0.338516890406201 F-TEST (value) 30.1700006746995 F-TEST (DF numerator) 1 F-TEST (DF denominator) 56 p-value 1.00029949146041e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.430961123434683 Sum Squared Residuals 10.4007394350767 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.3 6.57321459003865 -0.273214590038652 2 6.2 6.29475247476543 -0.0947524747654248 3 6.1 6.05607066167412 0.0439293383258764 4 6.3 6.09585096385601 0.204149036143993 5 6.5 6.25497217258354 0.245027827416459 6 6.6 6.33453277694731 0.265467223052692 7 6.5 6.33453277694731 0.165467223052692 8 6.2 6.17541156821977 0.0245884317802262 9 6.2 6.13563126603789 0.0643687339621099 10 5.9 6.21519187040166 -0.315191870401657 11 6.1 6.41409338131107 -0.314093381311075 12 6.1 6.45387368349296 -0.353873683492958 13 6.1 6.41409338131107 -0.314093381311075 14 6.1 6.21519187040166 -0.115191870401658 15 6.1 6.13563126603789 -0.0356312660378906 16 6.4 6.21519187040166 0.184808129598343 17 6.7 6.29475247476542 0.405247525234576 18 6.9 6.33453277694731 0.565467223052692 19 7 6.29475247476542 0.705247525234576 20 7 6.25497217258354 0.74502782741646 21 6.8 6.21519187040166 0.584808129598342 22 6.4 6.13563126603789 0.26436873396211 23 5.9 6.13563126603789 -0.23563126603789 24 5.5 6.09585096385601 -0.595850963856007 25 5.5 6.05607066167412 -0.556070661674123 26 5.6 6.05607066167412 -0.456070661674124 27 5.8 6.05607066167412 -0.256070661674123 28 5.9 6.13563126603789 -0.23563126603789 29 6.1 6.21519187040166 -0.115191870401658 30 6.1 6.21519187040166 -0.115191870401658 31 6 6.17541156821977 -0.175411568219774 32 6 6.09585096385601 -0.095850963856007 33 5.9 5.97651005731036 -0.0765100573103562 34 5.5 5.77760854640094 -0.277608546400939 35 5.6 5.69804794203717 -0.0980479420371725 36 5.4 5.6184873376734 -0.218487337673405 37 5.2 5.57870703549152 -0.378707035491522 38 5.2 5.69804794203717 -0.498047942037172 39 5.2 5.73782824421906 -0.537828244219055 40 5.5 5.77760854640094 -0.277608546400939 41 5.8 5.77760854640094 0.0223914535990607 42 5.8 5.69804794203717 0.101952057962828 43 5.5 5.57870703549152 -0.0787070354915217 44 5.3 5.49914643112775 -0.199146431127755 45 5.1 5.3798055245821 -0.279805524582105 46 5.2 5.45936612894587 -0.259366128945871 47 5.8 5.69804794203717 0.101952057962828 48 5.8 5.73782824421906 0.0621717557809444 49 5.5 5.65826763985529 -0.158267639855288 50 5 5.57870703549152 -0.578707035491522 51 4.9 5.57870703549152 -0.678707035491521 52 5.3 5.69804794203717 -0.398047942037172 53 6.1 5.8571691507647 0.242830849235294 54 6.5 5.8571691507647 0.642830849235294 55 6.8 5.77760854640094 1.02239145359906 56 6.6 5.57870703549152 1.02129296450848 57 6.4 5.41958582676399 0.980414173236013 58 6.4 5.41958582676399 0.980414173236013 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0605137986150324 0.121027597230065 0.939486201384968 6 0.0577400812090342 0.115480162418068 0.942259918790966 7 0.0264304204017075 0.0528608408034151 0.973569579598292 8 0.0109075027624124 0.0218150055248249 0.989092497237588 9 0.00396170849199775 0.0079234169839955 0.996038291508002 10 0.00941642718787281 0.0188328543757456 0.990583572812127 11 0.00698519159346992 0.0139703831869398 0.99301480840653 12 0.00455869755960083 0.00911739511920166 0.9954413024404 13 0.00257203556198926 0.00514407112397851 0.99742796443801 14 0.00121128112040257 0.00242256224080514 0.998788718879597 15 0.000519111005473103 0.00103822201094621 0.999480888994527 16 0.000285792232864783 0.000571584465729566 0.999714207767135 17 0.000810441515594277 0.00162088303118855 0.999189558484406 18 0.00453914078980642 0.00907828157961284 0.995460859210194 19 0.0214704836273814 0.0429409672547629 0.978529516372619 20 0.0631246429752348 0.126249285950470 0.936875357024765 21 0.087514990767626 0.175029981535252 0.912485009232374 22 0.06805737867638 0.13611475735276 0.93194262132362 23 0.066226952738082 0.132453905476164 0.933773047261918 24 0.126956688683435 0.25391337736687 0.873043311316565 25 0.167320688148515 0.334641376297031 0.832679311851485 26 0.167148055586969 0.334296111173938 0.832851944413031 27 0.131663295363510 0.263326590727020 0.86833670463649 28 0.100688142182938 0.201376284365876 0.899311857817062 29 0.0714846444001248 0.142969288800250 0.928515355599875 30 0.0492378504388421 0.0984757008776843 0.950762149561158 31 0.0335909898773240 0.0671819797546479 0.966409010122676 32 0.0214066528679148 0.0428133057358296 0.978593347132085 33 0.0131713171236495 0.026342634247299 0.98682868287635 34 0.0085838239856091 0.0171676479712182 0.99141617601439 35 0.00516969979295925 0.0103393995859185 0.99483030020704 36 0.00310603293510551 0.00621206587021103 0.996893967064894 37 0.00219133479420471 0.00438266958840942 0.997808665205795 38 0.002046242983419 0.004092485966838 0.99795375701658 39 0.00229345274006921 0.00458690548013841 0.99770654725993 40 0.00160362517363808 0.00320725034727616 0.998396374826362 41 0.000992864205933396 0.00198572841186679 0.999007135794067 42 0.000649557541350113 0.00129911508270023 0.99935044245865 43 0.000367384038595838 0.000734768077191676 0.999632615961404 44 0.000212180766065121 0.000424361532130241 0.999787819233935 45 0.000147316029121720 0.000294632058243440 0.999852683970878 46 0.000127104168340875 0.000254208336681751 0.99987289583166 47 6.66496981859274e-05 0.000133299396371855 0.999933350301814 48 3.12618384571333e-05 6.25236769142666e-05 0.999968738161543 49 1.97475682157916e-05 3.94951364315832e-05 0.999980252431784 50 0.000161791436775501 0.000323582873551002 0.999838208563224 51 0.0184690128250700 0.0369380256501401 0.98153098717493 52 0.573063903468884 0.853872193062232 0.426936096531116 53 0.869064250688448 0.261871498623105 0.130935749311552 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.469387755102041 NOK 5% type I error level 32 0.653061224489796 NOK 10% type I error level 35 0.714285714285714 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/10jzby1258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/10jzby1258735601.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/1wj781258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/1wj781258735601.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/26k1h1258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/26k1h1258735601.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/38a8h1258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/38a8h1258735601.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/4qpav1258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/4qpav1258735601.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/5j6e81258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/5j6e81258735601.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/6ghag1258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/6ghag1258735601.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/7obn51258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/7obn51258735601.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/8pab01258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/8pab01258735601.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/9xkye1258735601.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735713hhfkch2dgo9ajb5/9xkye1258735601.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