Multiple Lineair Regression Totaal niet-werkende werkzoekende mannen Vlaams gewest

*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: Sun, 21 Dec 2008 11:55:53 -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/21/t1229886110b7quu5h7ppfupoa.htm/, Retrieved Sun, 21 Dec 2008 20:01:51 +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/21/t1229886110b7quu5h7ppfupoa.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:

Multiple Lineair Regression Totaal niet-werkende werkzoekende mannen Vlaams gewest

Dataseries X:

» Textbox « » Textfile « » CSV «

106099 0 103235 0 98857 0 101107 0 102700 0 101477 0 99639 0 96622 0 94697 0 95093 0 112885 0 121162 0 113624 0 111632 0 106707 0 108827 0 108413 0 106249 0 104861 0 102382 0 100320 0 100228 0 117089 0 121523 0 114948 0 112831 0 107605 0 108928 1 101993 1 102850 1 99925 1 101536 1 99450 1 98305 1 110159 1 109483 1 106810 1 96279 1 91982 1 90276 1 90999 1 86622 1 83117 1 80367 1 77550 1 77443 1 92844 1 92175 1 84822 1 81632 1 78872 1 81485 1 80651 1 78192 1 76844 1 76335 1 71415 1 73899 1 86822 1 86371 1 83469 1 82662 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation werkl.man[t] = + 112849.848864994 -2665.41084656085Wetswijziging[t] -465.962749615975t + 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) 112849.848864994 2397.069776 47.0782 0 0 Wetswijziging -2665.41084656085 4387.208383 -0.6075 0.545823 0.272912 t -465.962749615975 121.553623 -3.8334 0.000309 0.000155 Multiple Linear Regression - Regression Statistics Multiple R 0.742938557631786 R-squared 0.551957700415998 Adjusted R-squared 0.536769825853829 F-TEST (value) 36.3419975689576 F-TEST (DF numerator) 2 F-TEST (DF denominator) 59 p-value 5.17500486907352e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8772.09641237543 Sum Squared Residuals 4540030852.61258 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 106099 112383.886115378 -6284.88611537807 2 103235 111917.923365762 -8682.92336576207 3 98857 111451.960616146 -12594.9606161461 4 101107 110985.997866530 -9878.99786653012 5 102700 110520.035116914 -7820.03511691415 6 101477 110054.072367298 -8577.07236729817 7 99639 109588.109617682 -9949.1096176822 8 96622 109122.146868066 -12500.1468680662 9 94697 108656.184118450 -13959.1841184502 10 95093 108190.221368834 -13097.2213688343 11 112885 107724.258619218 5160.7413807817 12 121162 107258.295869602 13903.7041303977 13 113624 106792.333119986 6831.66688001365 14 111632 106326.370370370 5305.62962962963 15 106707 105860.407620754 846.592379245605 16 108827 105394.444871138 3432.55512886158 17 108413 104928.482121522 3484.51787847756 18 106249 104462.519371906 1786.48062809353 19 104861 103996.556622290 864.443377709507 20 102382 103530.593872675 -1148.59387267452 21 100320 103064.631123059 -2744.63112305854 22 100228 102598.668373443 -2370.66837344257 23 117089 102132.705623827 14956.2943761734 24 121523 101666.742874211 19856.2571257894 25 114948 101200.780124595 13747.2198754054 26 112831 100734.817374979 12096.1826250213 27 107605 100268.854625363 7336.14537463731 28 108928 97137.4810291859 11790.5189708141 29 101993 96671.5182795699 5321.48172043011 30 102850 96205.555529954 6644.44447004608 31 99925 95739.592780338 4185.40721966206 32 101536 95273.630030722 6262.36996927803 33 99450 94807.667281106 4642.33271889401 34 98305 94341.70453149 3963.29546850998 35 110159 93875.741781874 16283.2582181260 36 109483 93409.779032258 16073.2209677419 37 106810 92943.816282642 13866.1837173579 38 96279 92477.8535330261 3801.14646697389 39 91982 92011.8907834101 -29.8907834101374 40 90276 91545.9280337942 -1269.92803379416 41 90999 91079.9652841782 -80.9652841781868 42 86622 90614.0025345622 -3992.00253456221 43 83117 90148.0397849462 -7031.03978494624 44 80367 89682.0770353303 -9315.07703533026 45 77550 89216.1142857143 -11666.1142857143 46 77443 88750.1515360983 -11307.1515360983 47 92844 88284.1887864823 4559.81121351767 48 92175 87818.2260368664 4356.77396313364 49 84822 87352.2632872504 -2530.26328725039 50 81632 86886.3005376344 -5254.30053763441 51 78872 86420.3377880184 -7548.33778801843 52 81485 85954.3750384025 -4469.37503840246 53 80651 85488.4122887865 -4837.41228878648 54 78192 85022.4495391705 -6830.4495391705 55 76844 84556.4867895545 -7712.48678955453 56 76335 84090.5240399386 -7755.52403993856 57 71415 83624.5612903226 -12209.5612903226 58 73899 83158.5985407066 -9259.5985407066 59 86822 82692.6357910906 4129.36420890937 60 86371 82226.6730414747 4144.32695852534 61 83469 81760.7102918587 1708.28970814132 62 82662 81294.7475422427 1367.25245775729 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0425408972658898 0.0850817945317796 0.95745910273411 7 0.0122349386364021 0.0244698772728043 0.987765061363598 8 0.00527817592409731 0.0105563518481946 0.994721824075903 9 0.00289600010195743 0.00579200020391487 0.997103999898043 10 0.00135472856781241 0.00270945713562482 0.998645271432188 11 0.311291531497712 0.622583062995423 0.688708468502288 12 0.761446092510763 0.477107814978474 0.238553907489237 13 0.71800990961202 0.56398018077596 0.28199009038798 14 0.636834057683694 0.726331884632612 0.363165942316306 15 0.581387803992383 0.837224392015235 0.418612196007617 16 0.501572139540117 0.996855720919767 0.498427860459883 17 0.427519539560687 0.855039079121374 0.572480460439313 18 0.384345303769132 0.768690607538264 0.615654696230868 19 0.364061955705056 0.728123911410111 0.635938044294944 20 0.396029203653837 0.792058407307675 0.603970796346163 21 0.490830227578143 0.981660455156286 0.509169772421857 22 0.614167886997022 0.771664226005957 0.385832113002978 23 0.633270015687174 0.733459968625652 0.366729984312826 24 0.716043479663792 0.567913040672415 0.283956520336208 25 0.661613231645575 0.67677353670885 0.338386768354425 26 0.594557443338344 0.810885113323313 0.405442556661656 27 0.53970742737452 0.92058514525096 0.46029257262548 28 0.48059721386582 0.96119442773164 0.51940278613418 29 0.429545197781117 0.859090395562235 0.570454802218883 30 0.362310844058895 0.724621688117789 0.637689155941105 31 0.312047559222711 0.624095118445421 0.68795244077729 32 0.253530295508500 0.507060591017001 0.7464697044915 33 0.208422373185014 0.416844746370029 0.791577626814986 34 0.17205525582251 0.34411051164502 0.82794474417749 35 0.242367755908735 0.484735511817469 0.757632244091265 36 0.392831391168409 0.785662782336817 0.607168608831591 37 0.623608874707684 0.752782250584631 0.376391125292316 38 0.697422909618643 0.605154180762714 0.302577090381357 39 0.75838518219633 0.48322963560734 0.24161481780367 40 0.803295479100258 0.393409041799485 0.196704520899742 41 0.842785181946875 0.31442963610625 0.157214818053125 42 0.865790931855256 0.268418136289488 0.134209068144744 43 0.881865727577061 0.236268544845878 0.118134272422939 44 0.896889393947282 0.206221212105436 0.103110606052718 45 0.923293823257252 0.153412353485496 0.076706176742748 46 0.942590081077194 0.114819837845612 0.057409918922806 47 0.951202887966212 0.0975942240675755 0.0487971120337878 48 0.978253031580564 0.0434939368388726 0.0217469684194363 49 0.977006461955063 0.0459870760898737 0.0229935380449368 50 0.968453378341736 0.0630932433165276 0.0315466216582638 51 0.948420911308899 0.103158177382203 0.0515790886911013 52 0.934956645104804 0.130086709790392 0.065043354895196 53 0.924082034472122 0.151835931055756 0.075917965527878 54 0.891432987085416 0.217134025829168 0.108567012914584 55 0.826040991554437 0.347918016891125 0.173959008445563 56 0.709308265333966 0.581383469332068 0.290691734666034 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0392156862745098 NOK 5% type I error level 6 0.117647058823529 NOK 10% type I error level 9 0.176470588235294 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/10x2gv1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/10x2gv1229885740.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/168cy1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/168cy1229885740.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/2ohsr1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/2ohsr1229885740.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/3t9fx1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/3t9fx1229885740.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/4h02x1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/4h02x1229885740.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/57rxf1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/57rxf1229885740.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/64ppu1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/64ppu1229885740.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/7kh1p1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/7kh1p1229885740.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/8la1z1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/8la1z1229885740.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/9513d1229885740.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229886110b7quu5h7ppfupoa/9513d1229885740.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