WS7

*Unverified author*

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 02:03:08 -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/t12587080498rbpcz4plcyd5wd.htm/, Retrieved Fri, 20 Nov 2009 10:07:41 +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/t12587080498rbpcz4plcyd5wd.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 «

3922 22782 3759 16169 4138 13807 4634 29743 3995 25591 4308 29096 4143 26482 4429 22404 5219 27044 4929 17970 5755 18730 5592 19684 4163 19785 4962 18479 5208 10698 4755 31956 4491 29506 5732 34506 5731 27165 5040 26736 6102 23691 4904 18157 5369 17328 5578 18205 4619 20995 4731 17382 5011 9367 5299 31124 4146 26551 4625 30651 4736 25859 4219 25100 5116 25778 4205 20418 4121 18688 5103 20424 4300 24776 4578 19814 3809 12738 5526 31566 4247 30111 3830 30019 4394 31934 4826 25826 4409 26835 4569 20205 4106 17789 4794 20520 3914 22518 3793 15572 4405 11509 4022 25447 4100 24090 4788 27786 3163 26195 3585 20516 3903 22759 4178 19028 3863 16971 4187 20036

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 bouwaanvragen[t] = + 4316.33430518384 + 0.0110801554434606inschrijvingen_autos[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) 4316.33430518384 329.916793 13.0831 0 0 inschrijvingen_autos 0.0110801554434606 0.014082 0.7868 0.434587 0.217294 Multiple Linear Regression - Regression Statistics Multiple R 0.102768301789830 R-squared 0.0105613238527655 Adjusted R-squared -0.00649796366701438 F-TEST (value) 0.619095248879525 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.434587018191825 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 627.789965491755 Sum Squared Residuals 22858973.9647841 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3922 4568.76240649678 -646.762406496776 2 3759 4495.48933854916 -736.489338549158 3 4138 4469.31801139171 -331.318011391705 4 4634 4645.89136853869 -11.8913685386938 5 3995 4599.88656313745 -604.886563137445 6 4308 4638.72250796678 -330.722507966775 7 4143 4609.75898163757 -466.758981637569 8 4429 4564.57410773914 -135.574107739136 9 5219 4615.98602899679 603.013971003207 10 4929 4515.44469850283 413.555301497168 11 5755 4523.86561663986 1231.13438336014 12 5592 4534.43608493292 1057.56391506708 13 4163 4535.55518063271 -372.555180632713 14 4962 4521.08449762355 440.915502376447 15 5208 4434.86980811799 773.130191882014 16 4755 4670.41175253507 84.5882474649278 17 4491 4643.26537169859 -152.265371698594 18 5732 4698.6661489159 1033.33385108410 19 5731 4617.32672780545 1113.67327219455 20 5040 4612.57334112021 427.426658879792 21 6102 4578.83426779487 1523.16573220513 22 4904 4517.51668757076 386.483312429241 23 5369 4508.33123870813 860.66876129187 24 5578 4518.04853503205 1059.95146496795 25 4619 4548.9621687193 70.0378312807 26 4731 4508.92956710208 222.070432897923 27 5011 4420.12212122274 590.87787877726 28 5299 4661.19306320611 637.806936793887 29 4146 4610.52351236317 -464.523512363167 30 4625 4655.95214968136 -30.9521496813561 31 4736 4602.85604479629 133.143955203707 32 4219 4594.44620681471 -375.446206814706 33 5116 4601.95855220537 514.041447794628 34 4205 4542.56891902842 -337.568919028423 35 4121 4523.40025011124 -402.400250111236 36 5103 4542.63539996108 560.364600038916 37 4300 4590.85623645102 -290.856236451025 38 4578 4535.87650514057 42.1234948594270 39 3809 4457.47332522265 -648.473325222645 40 5526 4666.09049191212 859.909508087877 41 4247 4649.96886574189 -402.968865741887 42 3830 4648.94949144109 -818.949491441089 43 4394 4670.16798911532 -276.167989115316 44 4826 4602.49039966666 223.509600333342 45 4409 4613.67027650911 -204.670276509110 46 4569 4540.20884591897 28.7911540810339 47 4106 4513.43919036757 -407.439190367565 48 4794 4543.69909488366 250.300905116344 49 3914 4565.83724545969 -651.83724545969 50 3793 4488.87448574941 -695.874485749413 51 4405 4443.85581418263 -38.8558141826323 52 4022 4598.29102075359 -576.291020753587 53 4100 4583.25524981681 -483.255249816811 54 4788 4624.20750433584 163.792495664159 55 3163 4606.57897702530 -1443.57897702530 56 3585 4543.65477426188 -958.654774261882 57 3903 4568.50756292156 -665.507562921565 58 4178 4527.16750296201 -349.167502962013 59 3863 4504.37562321481 -641.375623214814 60 4187 4538.33629964902 -351.336299649021 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.129349209030593 0.258698418061187 0.870650790969407 6 0.0497226287893394 0.0994452575786789 0.95027737121066 7 0.018580179201132 0.037160358402264 0.981419820798868 8 0.0126618264193884 0.0253236528387767 0.987338173580612 9 0.0958925350080506 0.191785070016101 0.90410746499195 10 0.162912083438284 0.325824166876569 0.837087916561716 11 0.563905278912074 0.872189442175851 0.436094721087926 12 0.712629761482145 0.57474047703571 0.287370238517855 13 0.65631172704128 0.687376545917439 0.343688272958719 14 0.600256722049485 0.79948655590103 0.399743277950515 15 0.582035238417111 0.835929523165778 0.417964761582889 16 0.509765546294941 0.980468907410118 0.490234453705059 17 0.424154044027371 0.848308088054742 0.575845955972629 18 0.588829231877188 0.822341536245624 0.411170768122812 19 0.714962019049523 0.570075961900953 0.285037980950477 20 0.66682144570697 0.66635710858606 0.33317855429303 21 0.898322195980536 0.203355608038929 0.101677804019464 22 0.872319944976305 0.255360110047391 0.127680055023695 23 0.89993179351886 0.200136412962281 0.100068206481141 24 0.952415461636498 0.0951690767270034 0.0475845383635017 25 0.935010438693762 0.129979122612476 0.0649895613062381 26 0.918372962249314 0.163254075501371 0.0816270377506857 27 0.941440477132764 0.117119045734472 0.058559522867236 28 0.947410073932567 0.105179852134866 0.0525899260674331 29 0.942711695227769 0.114576609544463 0.0572883047722314 30 0.919623929627446 0.160752140745108 0.0803760703725542 31 0.897004885200274 0.205990229599452 0.102995114799726 32 0.876897793702558 0.246204412594884 0.123102206297442 33 0.890251562311802 0.219496875376396 0.109748437688198 34 0.866625679618426 0.266748640763148 0.133374320381574 35 0.84243342710119 0.315133145797621 0.157566572898810 36 0.885774706412105 0.22845058717579 0.114225293587895 37 0.853087827689057 0.293824344621886 0.146912172310943 38 0.828539915989134 0.342920168021732 0.171460084010866 39 0.814679064723102 0.370641870553796 0.185320935276898 40 0.94209676004102 0.115806479917960 0.0579032399589798 41 0.921153526473788 0.157692947052425 0.0788464735262123 42 0.92355327642166 0.152893447156681 0.0764467235783404 43 0.891116767171938 0.217766465656124 0.108883232828062 44 0.905949695744399 0.188100608511202 0.094050304255601 45 0.879842427298586 0.240315145402829 0.120157572701414 46 0.867641647653206 0.264716704693588 0.132358352346794 47 0.816550489876367 0.366899020247265 0.183449510123633 48 0.87389186254734 0.252216274905319 0.126108137452660 49 0.825029197410252 0.349941605179497 0.174970802589748 50 0.780681757029495 0.438636485941010 0.219318242970505 51 0.735424299717114 0.529151400565772 0.264575700282886 52 0.634038431268874 0.731923137462252 0.365961568731126 53 0.51317856133544 0.97364287732912 0.48682143866456 54 0.895604050174316 0.208791899651368 0.104395949825684 55 0.888187215568403 0.223625568863193 0.111812784431597 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 2 0.0392156862745098 OK 10% type I error level 4 0.0784313725490196 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/101ajc1258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/101ajc1258707783.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/18xx21258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/18xx21258707783.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/2r11l1258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/2r11l1258707783.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/3qfdz1258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/3qfdz1258707783.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/4u4rb1258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/4u4rb1258707783.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/5520q1258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/5520q1258707783.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/6szl01258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/6szl01258707783.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/71h8j1258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/71h8j1258707783.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/8i0yo1258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/8i0yo1258707783.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/9gtt31258707783.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587080498rbpcz4plcyd5wd/9gtt31258707783.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