Paper Dummy Variables

*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: Tue, 16 Dec 2008 12:45:52 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc.htm/, Retrieved Tue, 16 Dec 2008 20:47:01 +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/2008/Dec/16/t12294568213j6yoygckibd5kc.htm/}, year = {2008}, } @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 = {2008}, 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 «

94.7 0 101.8 0 102.5 0 105.3 0 110.3 0 109.8 0 117.3 0 118.8 0 131.3 0 125.9 0 133.1 0 147 0 145.8 0 164.4 0 149.8 0 137.7 0 151.7 0 156.8 0 180 0 180.4 0 170.4 0 191.6 0 199.5 0 218.2 1 217.5 1 205 1 194 0 199.3 0 219.3 1 211.1 1 215.2 1 240.2 1 242.2 1 240.7 1 255.4 1 253 1 218.2 1 203.7 1 205.6 1 215.6 1 188.5 1 202.9 1 214 1 230.3 1 230 1 241 1 259.6 1 247.8 1 270.3 1 289.7 1 322.7 1 315 1 320.2 1 329.5 1 360.6 1 382.2 1 435.4 1 464 1 468.8 1 403 1 351.6 1

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 144.768 + 129.898666666667D[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) 144.768 12.797342 11.3123 0 0 D 129.898666666667 16.658406 7.7978 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.712414843906071 R-squared 0.507534909817711 Adjusted R-squared 0.499188043882418 F-TEST (value) 60.8054464696387 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 1.20324861185850e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 63.9867113322653 Sum Squared Residuals 241563.6544 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 94.7 144.768 -50.0679999999999 2 101.8 144.768 -42.9680000000001 3 102.5 144.768 -42.268 4 105.3 144.768 -39.468 5 110.3 144.768 -34.468 6 109.8 144.768 -34.968 7 117.3 144.768 -27.468 8 118.8 144.768 -25.968 9 131.3 144.768 -13.468 10 125.9 144.768 -18.868 11 133.1 144.768 -11.6680000000000 12 147 144.768 2.232 13 145.8 144.768 1.03200000000001 14 164.4 144.768 19.632 15 149.8 144.768 5.03200000000001 16 137.7 144.768 -7.06800000000002 17 151.7 144.768 6.93199999999999 18 156.8 144.768 12.0320000000000 19 180 144.768 35.232 20 180.4 144.768 35.632 21 170.4 144.768 25.632 22 191.6 144.768 46.832 23 199.5 144.768 54.732 24 218.2 274.666666666667 -56.4666666666667 25 217.5 274.666666666667 -57.1666666666667 26 205 274.666666666667 -69.6666666666667 27 194 144.768 49.232 28 199.3 144.768 54.532 29 219.3 274.666666666667 -55.3666666666667 30 211.1 274.666666666667 -63.5666666666667 31 215.2 274.666666666667 -59.4666666666667 32 240.2 274.666666666667 -34.4666666666667 33 242.2 274.666666666667 -32.4666666666667 34 240.7 274.666666666667 -33.9666666666667 35 255.4 274.666666666667 -19.2666666666667 36 253 274.666666666667 -21.6666666666667 37 218.2 274.666666666667 -56.4666666666667 38 203.7 274.666666666667 -70.9666666666667 39 205.6 274.666666666667 -69.0666666666667 40 215.6 274.666666666667 -59.0666666666667 41 188.5 274.666666666667 -86.1666666666667 42 202.9 274.666666666667 -71.7666666666667 43 214 274.666666666667 -60.6666666666667 44 230.3 274.666666666667 -44.3666666666667 45 230 274.666666666667 -44.6666666666667 46 241 274.666666666667 -33.6666666666667 47 259.6 274.666666666667 -15.0666666666666 48 247.8 274.666666666667 -26.8666666666667 49 270.3 274.666666666667 -4.36666666666665 50 289.7 274.666666666667 15.0333333333333 51 322.7 274.666666666667 48.0333333333333 52 315 274.666666666667 40.3333333333333 53 320.2 274.666666666667 45.5333333333333 54 329.5 274.666666666667 54.8333333333333 55 360.6 274.666666666667 85.9333333333334 56 382.2 274.666666666667 107.533333333333 57 435.4 274.666666666667 160.733333333333 58 464 274.666666666667 189.333333333333 59 468.8 274.666666666667 194.133333333333 60 403 274.666666666667 128.333333333333 61 351.6 274.666666666667 76.9333333333334 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00140529262540386 0.00281058525080772 0.998594707374596 6 0.000195868992498785 0.000391737984997571 0.999804131007501 7 8.02075444807798e-05 0.000160415088961560 0.99991979245552 8 2.58913485624334e-05 5.17826971248669e-05 0.999974108651438 9 3.7892565574932e-05 7.5785131149864e-05 0.999962107434425 10 1.36201601058841e-05 2.72403202117682e-05 0.999986379839894 11 8.36022136617385e-06 1.67204427323477e-05 0.999991639778634 12 1.58338237394955e-05 3.16676474789911e-05 0.99998416617626 13 1.36772237625704e-05 2.73544475251408e-05 0.999986322776237 14 3.86081388609191e-05 7.72162777218382e-05 0.99996139186114 15 2.29771657379327e-05 4.59543314758653e-05 0.999977022834262 16 8.19707425877212e-06 1.63941485175442e-05 0.999991802925741 17 4.70098357439579e-06 9.40196714879158e-06 0.999995299016426 18 3.16811807288251e-06 6.33623614576502e-06 0.999996831881927 19 7.53975382511244e-06 1.50795076502249e-05 0.999992460246175 20 1.14283789648216e-05 2.28567579296431e-05 0.999988571621035 21 8.56642897985676e-06 1.71328579597135e-05 0.99999143357102 22 1.48189633721079e-05 2.96379267442158e-05 0.999985181036628 23 2.73764049054581e-05 5.47528098109163e-05 0.999972623595095 24 1.19112283377746e-05 2.38224566755492e-05 0.999988088771662 25 5.14901647224441e-06 1.02980329444888e-05 0.999994850983528 26 2.52299810194725e-06 5.04599620389451e-06 0.999997477001898 27 3.06809110942965e-06 6.1361822188593e-06 0.99999693190889 28 3.86442241994174e-06 7.72884483988348e-06 0.99999613557758 29 1.77195621072267e-06 3.54391242144533e-06 0.99999822804379 30 8.62602671645233e-07 1.72520534329047e-06 0.999999137397328 31 4.10824798166279e-07 8.21649596332559e-07 0.999999589175202 32 2.10197696377074e-07 4.20395392754148e-07 0.999999789802304 33 1.03539886231574e-07 2.07079772463149e-07 0.999999896460114 34 4.79131042462929e-08 9.58262084925858e-08 0.999999952086896 35 2.62308511152649e-08 5.24617022305298e-08 0.999999973769149 36 1.27434052842484e-08 2.54868105684967e-08 0.999999987256595 37 6.76909337667732e-09 1.35381867533546e-08 0.999999993230907 38 5.81388340566969e-09 1.16277668113394e-08 0.999999994186117 39 5.13422990538869e-09 1.02684598107774e-08 0.99999999486577 40 3.77753325955153e-09 7.55506651910307e-09 0.999999996222467 41 1.04301252358051e-08 2.08602504716102e-08 0.999999989569875 42 1.90951249321186e-08 3.81902498642372e-08 0.999999980904875 43 3.22458150132755e-08 6.4491630026551e-08 0.999999967754185 44 4.90465370159535e-08 9.8093074031907e-08 0.999999950953463 45 1.10539094573963e-07 2.21078189147926e-07 0.999999889460905 46 3.04736994623648e-07 6.09473989247297e-07 0.999999695263005 47 9.53542945107505e-07 1.90708589021501e-06 0.999999046457055 48 6.43280447507626e-06 1.28656089501525e-05 0.999993567195525 49 4.6265267915978e-05 9.2530535831956e-05 0.999953734732084 50 0.000334327477781172 0.000668654955562343 0.999665672522219 51 0.00171132453567962 0.00342264907135925 0.99828867546432 52 0.00704725544631968 0.0140945108926394 0.99295274455368 53 0.0268032652268435 0.0536065304536871 0.973196734773156 54 0.089904574324757 0.179809148649514 0.910095425675243 55 0.157903262557552 0.315806525115104 0.842096737442448 56 0.184921948730133 0.369843897460266 0.815078051269867 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 47 0.903846153846154 NOK 5% type I error level 48 0.923076923076923 NOK 10% type I error level 49 0.942307692307692 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/10iqx01229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/10iqx01229456741.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/1ddls1229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/1ddls1229456741.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/26hui1229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/26hui1229456741.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/3pd981229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/3pd981229456741.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/4dw3j1229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/4dw3j1229456741.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/5zil01229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/5zil01229456741.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/6yrkv1229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/6yrkv1229456741.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/7wadg1229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/7wadg1229456741.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/81vsu1229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/81vsu1229456741.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/915451229456741.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294568213j6yoygckibd5kc/915451229456741.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