WS07 - ReviewModel2

*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: Wed, 25 Nov 2009 10:23:59 -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/25/t1259170139ejbxbdrwasqhwp2.htm/, Retrieved Wed, 25 Nov 2009 18:29:11 +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/25/t1259170139ejbxbdrwasqhwp2.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 «

8.00 96.80 8.10 114.10 7.70 110.30 7.50 103.90 7.60 101.60 7.80 94.60 7.80 95.90 7.80 104.70 7.50 102.80 7.50 98.10 7.10 113.90 7.50 80.90 7.50 95.70 7.60 113.20 7.70 105.90 7.70 108.80 7.90 102.30 8.10 99.00 8.20 100.70 8.20 115.50 8.20 100.70 7.90 109.90 7.30 114.60 6.90 85.40 6.60 100.50 6.70 114.80 6.90 116.50 7.00 112.90 7.10 102.00 7.20 106.00 7.10 105.30 6.90 118.80 7.00 106.10 6.80 109.30 6.40 117.20 6.70 92.50 6.60 104.20 6.40 112.50 6.30 122.40 6.20 113.30 6.50 100.00 6.80 110.70 6.80 112.80 6.40 109.80 6.10 117.30 5.80 109.10 6.10 115.90 7.20 96.00 7.30 99.80 6.90 116.80 6.10 115.70 5.80 99.40 6.20 94.30 7.10 91.00 7.70 93.20 7.90 103.10 7.70 94.10 7.40 91.80 7.50 102.70 8.00 82.60

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Wman[t] = + 10.4164058531152 -0.025382099948453Ecogr[t] -0.0192854525358532t + 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) 10.4164058531152 0.777349 13.3999 0 0 Ecogr -0.025382099948453 0.007259 -3.4968 0.00092 0.00046 t -0.0192854525358532 0.003934 -4.9017 8e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.615703458936818 R-squared 0.379090749346763 Adjusted R-squared 0.357304459850158 F-TEST (value) 17.4004274296383 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 1.26270445377497e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.52726940432956 Sum Squared Residuals 15.8467424102968 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 7.9401331255691 0.0598668744309016 2 8.1 7.481737343925 0.618262656075 3 7.7 7.55890387119326 0.141096128806736 4 7.5 7.70206385832751 -0.202063858327513 5 7.6 7.7411572356731 -0.141157235673102 6 7.8 7.89954648277642 -0.0995464827764202 7 7.8 7.84726430030758 -0.0472643003075778 8 7.8 7.60461636822534 0.195383631774662 9 7.5 7.63355690559155 -0.133556905591546 10 7.5 7.73356732281342 -0.233567322813422 11 7.1 7.31324469109201 -0.213244691092011 12 7.5 8.1315685368551 -0.631568536855107 13 7.5 7.73662800508215 -0.236628005082149 14 7.6 7.27315580344837 0.326844196551631 15 7.7 7.43915968053622 0.260840319463778 16 7.7 7.34626613814986 0.353733861850144 17 7.9 7.49196433527895 0.408035664721053 18 8.1 7.55643981257299 0.543560187427011 19 8.2 7.49400479012477 0.705995209875234 20 8.2 7.09906425835181 1.10093574164819 21 8.2 7.45543388505306 0.74456611494694 22 7.9 7.20263311299144 0.697366887008562 23 7.3 7.06405179069786 0.235948209302143 24 6.9 7.78592365665683 -0.88592365665683 25 6.6 7.38336849489934 -0.783368494899338 26 6.7 7.0011190131006 -0.301119013100606 27 6.9 6.93868399065238 -0.0386839906523827 28 7 7.01077409793096 -0.0107740979309606 29 7.1 7.26815353483325 -0.168153534833246 30 7.2 7.14733968250358 0.05266031749642 31 7.1 7.14582169993164 -0.0458216999316445 32 6.9 6.78387789809168 0.116122101908325 33 7 7.08694511490118 -0.0869451149011756 34 6.8 6.98643694253027 -0.186436942530273 35 6.4 6.76663290040164 -0.36663290040164 36 6.7 7.37428531659258 -0.674285316592577 37 6.6 7.05802929465982 -0.458029294659824 38 6.4 6.82807241255181 -0.42807241255181 39 6.3 6.55750417052627 -0.257504170526273 40 6.2 6.76919582752134 -0.569195827521342 41 6.5 7.08749230429991 -0.587492304299914 42 6.8 6.79661838231561 0.00338161768438656 43 6.8 6.72403051988801 0.0759694801119909 44 6.4 6.78089136719751 -0.380891367197514 45 6.1 6.57124016504826 -0.471240165048265 46 5.8 6.76008793208973 -0.960087932089726 47 6.1 6.56820419990439 -0.468204199904392 48 7.2 7.05402253634275 0.145977463657247 49 7.3 6.93828510400278 0.361714895997221 50 6.9 6.48750395234322 0.412496047656775 51 6.1 6.49613880975067 -0.39613880975067 52 5.8 6.8905815863746 -1.0905815863746 53 6.2 7.00074484357586 -0.800744843575858 54 7.1 7.0652203208699 0.0347796791300999 55 7.7 6.99009424844745 0.70990575155255 56 7.9 6.71952600642191 1.18047399357809 57 7.7 6.92867945342214 0.771320546577864 58 7.4 6.96777283076772 0.432227169232275 59 7.5 6.67182248879373 0.828177511206266 60 8 7.16271724522179 0.837282754778214 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0903249971622906 0.180649994324581 0.90967500283771 7 0.0454168657712525 0.0908337315425049 0.954583134228748 8 0.0204064332956071 0.0408128665912143 0.979593566704393 9 0.00785681234175401 0.0157136246835080 0.992143187658246 10 0.00253986862930999 0.00507973725861999 0.99746013137069 11 0.00168799620858192 0.00337599241716384 0.998312003791418 12 0.000604941626702323 0.00120988325340465 0.999395058373298 13 0.000268979843018697 0.000537959686037394 0.999731020156981 14 0.000346875315716867 0.000693750631433734 0.999653124684283 15 0.000326748863188730 0.000653497726377461 0.999673251136811 16 0.00022507580843412 0.00045015161686824 0.999774924191566 17 0.000313950041144808 0.000627900082289617 0.999686049958855 18 0.000725527195231403 0.00145105439046281 0.999274472804769 19 0.00147758006019192 0.00295516012038384 0.998522419939808 20 0.00331979594394545 0.0066395918878909 0.996680204056055 21 0.00472180657502634 0.00944361315005268 0.995278193424974 22 0.00562598495685947 0.0112519699137189 0.99437401504314 23 0.0142707688049946 0.0285415376099893 0.985729231195005 24 0.0462346647435626 0.0924693294871253 0.953765335256437 25 0.141625324509694 0.283250649019387 0.858374675490306 26 0.202039373301667 0.404078746603335 0.797960626698333 27 0.198865389407924 0.397730778815847 0.801134610592076 28 0.176025184750191 0.352050369500381 0.823974815249809 29 0.135898358587873 0.271796717175747 0.864101641412127 30 0.116452666816436 0.232905333632873 0.883547333183564 31 0.0975141129043033 0.195028225808607 0.902485887095697 32 0.10625149386293 0.21250298772586 0.89374850613707 33 0.0967984003058166 0.193596800611633 0.903201599694183 34 0.0896265431674792 0.179253086334958 0.910373456832521 35 0.0936439053184516 0.187287810636903 0.906356094681548 36 0.0675378508549491 0.135075701709898 0.932462149145051 37 0.0509974359785744 0.101994871957149 0.949002564021426 38 0.0411233287325455 0.082246657465091 0.958876671267455 39 0.0358562701775443 0.0717125403550885 0.964143729822456 40 0.0272169196643573 0.0544338393287146 0.972783080335643 41 0.0168528910723861 0.0337057821447723 0.983147108927614 42 0.0178642549025567 0.0357285098051134 0.982135745097443 43 0.0251530289526603 0.0503060579053205 0.97484697104734 44 0.0182293099056828 0.0364586198113655 0.981770690094317 45 0.0121163587022265 0.0242327174044530 0.987883641297774 46 0.0122858682995929 0.0245717365991859 0.987714131700407 47 0.00719444606876881 0.0143888921375376 0.992805553931231 48 0.0160594809360182 0.0321189618720364 0.983940519063982 49 0.103307156676546 0.206614313353092 0.896692843323454 50 0.194279539285227 0.388559078570455 0.805720460714773 51 0.126287762873867 0.252575525747735 0.873712237126133 52 0.223043984638691 0.446087969277383 0.776956015361309 53 0.606476015151 0.787047969698 0.393523984849 54 0.721908142993627 0.556183714012747 0.278091857006373 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.244897959183673 NOK 5% type I error level 23 0.469387755102041 NOK 10% type I error level 29 0.591836734693878 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/108ctw1259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/108ctw1259169834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/1lees1259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/1lees1259169834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/2l1xl1259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/2l1xl1259169834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/3c8131259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/3c8131259169834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/4u0kh1259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/4u0kh1259169834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/5tqwa1259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/5tqwa1259169834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/6eygs1259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/6eygs1259169834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/7c7961259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/7c7961259169834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/8d9vx1259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/8d9vx1259169834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/9oghb1259169834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259170139ejbxbdrwasqhwp2/9oghb1259169834.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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