Paper: multiple linear regression prijsindexcijfers grondstoffen

*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: Thu, 18 Dec 2008 04:09:27 -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/18/t1229598808537o1w3o298agx4.htm/, Retrieved Thu, 18 Dec 2008 12:13:56 +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/18/t1229598808537o1w3o298agx4.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 «

90.8 0 96.4 0 90 0 92.1 0 97.2 0 95.1 0 88.5 0 91 0 90.5 1 75 1 66.3 1 66 0 68.4 0 70.6 0 83.9 0 90.1 0 90.6 0 87.1 0 90.8 0 94.1 0 99.8 0 96.8 0 87 0 96.3 0 107.1 0 115.2 0 106.1 1 89.5 1 91.3 1 97.6 1 100.7 1 104.6 1 94.7 1 101.8 1 102.5 1 105.3 1 110.3 1 109.8 1 117.3 1 118.8 1 131.3 1 125.9 1 133.1 1 147 1 145.8 1 164.4 1 149.8 1 137.7 1 151.7 1 156.8 1 180 1 180.4 1 170.4 1 191.6 1 199.5 1 218.2 1 217.5 1 205 1 194 1 199.3 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 92.7881818181819 + 46.8863636363636t -5.88272727272737M1[t] -1.78272727272727M2[t] -5.46000000000002M3[t] -6.74000000000003M4[t] -4.76000000000004M5[t] -1.46000000000003M6[t] + 1.59999999999998M7[t] + 10.0600000000000M8[t] -0.637272727272797M9[t] -1.69727272727277M10[t] -10.3772727272728M11[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) 92.7881818181819 18.041613 5.143 5e-06 3e-06 t 46.8863636363636 10.41633 4.5012 4.4e-05 2.2e-05 M1 -5.88272727272737 24.025396 -0.2449 0.807635 0.403817 M2 -1.78272727272727 24.025396 -0.0742 0.941165 0.470582 M3 -5.46000000000002 23.934905 -0.2281 0.820544 0.410272 M4 -6.74000000000003 23.934905 -0.2816 0.779489 0.389744 M5 -4.76000000000004 23.934905 -0.1989 0.84322 0.42161 M6 -1.46000000000003 23.934905 -0.061 0.951619 0.475809 M7 1.59999999999998 23.934905 0.0668 0.946986 0.473493 M8 10.0600000000000 23.934905 0.4203 0.676177 0.338088 M9 -0.637272727272797 24.025396 -0.0265 0.978951 0.489475 M10 -1.69727272727277 24.025396 -0.0706 0.94398 0.47199 M11 -10.3772727272728 24.025396 -0.4319 0.667768 0.333884 Multiple Linear Regression - Regression Statistics Multiple R 0.569875575470858 R-squared 0.324758171518242 Adjusted R-squared 0.152356002544176 F-TEST (value) 1.88372439541114 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.061421781836675 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 37.8444075368895 Sum Squared Residuals 67313.3615454545 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 90.8 86.9054545454548 3.89454545454518 2 96.4 91.0054545454544 5.39454545454557 3 90 87.3281818181818 2.67181818181818 4 92.1 86.0481818181818 6.05181818181817 5 97.2 88.0281818181818 9.17181818181817 6 95.1 91.3281818181818 3.77181818181818 7 88.5 94.3881818181818 -5.88818181818182 8 91 102.848181818182 -11.8481818181818 9 90.5 139.037272727273 -48.5372727272728 10 75 137.977272727273 -62.9772727272727 11 66.3 129.297272727273 -62.9972727272727 12 66 92.7881818181818 -26.7881818181818 13 68.4 86.9054545454545 -18.5054545454545 14 70.6 91.0054545454546 -20.4054545454546 15 83.9 87.3281818181818 -3.42818181818181 16 90.1 86.0481818181818 4.05181818181817 17 90.6 88.0281818181818 2.57181818181818 18 87.1 91.3281818181818 -4.22818181818182 19 90.8 94.3881818181818 -3.58818181818181 20 94.1 102.848181818182 -8.74818181818182 21 99.8 92.150909090909 7.64909090909092 22 96.8 91.090909090909 5.70909090909092 23 87 82.4109090909091 4.58909090909089 24 96.3 92.7881818181818 3.51181818181815 25 107.1 86.9054545454545 20.1945454545455 26 115.2 91.0054545454546 24.1945454545454 27 106.1 134.214545454545 -28.1145454545455 28 89.5 132.934545454545 -43.4345454545455 29 91.3 134.914545454545 -43.6145454545454 30 97.6 138.214545454545 -40.6145454545455 31 100.7 141.274545454545 -40.5745454545454 32 104.6 149.734545454545 -45.1345454545454 33 94.7 139.037272727273 -44.3372727272727 34 101.8 137.977272727273 -36.1772727272727 35 102.5 129.297272727273 -26.7972727272727 36 105.3 139.674545454545 -34.3745454545455 37 110.3 133.791818181818 -23.4918181818181 38 109.8 137.891818181818 -28.0918181818182 39 117.3 134.214545454545 -16.9145454545455 40 118.8 132.934545454545 -14.1345454545455 41 131.3 134.914545454545 -3.61454545454543 42 125.9 138.214545454545 -12.3145454545454 43 133.1 141.274545454545 -8.17454545454545 44 147 149.734545454545 -2.73454545454544 45 145.8 139.037272727273 6.7627272727273 46 164.4 137.977272727273 26.4227272727273 47 149.8 129.297272727273 20.5027272727273 48 137.7 139.674545454545 -1.97454545454549 49 151.7 133.791818181818 17.9081818181819 50 156.8 137.891818181818 18.9081818181818 51 180 134.214545454545 45.7854545454545 52 180.4 132.934545454545 47.4654545454545 53 170.4 134.914545454545 35.4854545454545 54 191.6 138.214545454545 53.3854545454545 55 199.5 141.274545454545 58.2254545454546 56 218.2 149.734545454545 68.4654545454545 57 217.5 139.037272727273 78.4627272727272 58 205 137.977272727273 67.0227272727273 59 194 129.297272727273 64.7027272727273 60 199.3 139.674545454545 59.6254545454545 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0522012120784709 0.104402424156942 0.947798787921529 17 0.0151531995210809 0.0303063990421617 0.98484680047892 18 0.0042031508049899 0.0084063016099798 0.99579684919501 19 0.000974993626989444 0.00194998725397889 0.99902500637301 20 0.000212485453142479 0.000424970906284957 0.999787514546858 21 4.15938514427031e-05 8.31877028854063e-05 0.999958406148557 22 9.84089157083578e-06 1.96817831416716e-05 0.99999015910843 23 1.90561936816029e-06 3.81123873632057e-06 0.999998094380632 24 4.44408544601107e-06 8.88817089202213e-06 0.999995555914554 25 6.5666734138744e-06 1.31333468277488e-05 0.999993433326586 26 1.08035257406464e-05 2.16070514812929e-05 0.99998919647426 27 1.32880486068361e-05 2.65760972136722e-05 0.999986711951393 28 5.02086047424913e-06 1.00417209484983e-05 0.999994979139526 29 1.87257014502639e-06 3.74514029005279e-06 0.999998127429855 30 8.51309731405404e-07 1.70261946281081e-06 0.999999148690269 31 4.81366772870414e-07 9.62733545740829e-07 0.999999518633227 32 3.71001670497966e-07 7.42003340995931e-07 0.99999962899833 33 3.42871811367738e-07 6.85743622735476e-07 0.999999657128189 34 5.54082236267273e-07 1.10816447253455e-06 0.999999445917764 35 1.3123887471066e-06 2.6247774942132e-06 0.999998687611253 36 2.08749566129524e-06 4.17499132259049e-06 0.99999791250434 37 1.31958807548039e-06 2.63917615096079e-06 0.999998680411925 38 7.18818309983695e-07 1.43763661996739e-06 0.99999928118169 39 1.02033203750534e-06 2.04066407501067e-06 0.999998979667962 40 1.90051741052438e-06 3.80103482104876e-06 0.99999809948259 41 3.10675406517404e-06 6.21350813034808e-06 0.999996893245935 42 8.3933566966478e-06 1.67867133932956e-05 0.999991606643303 43 3.7544384370212e-05 7.5088768740424e-05 0.99996245561563 44 0.000405357214655421 0.000810714429310842 0.999594642785345 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.93103448275862 NOK 5% type I error level 28 0.96551724137931 NOK 10% type I error level 28 0.96551724137931 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/10nzop1229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/10nzop1229598562.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/1apq21229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/1apq21229598562.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/2z0ye1229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/2z0ye1229598562.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/35i7j1229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/35i7j1229598562.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/4nztt1229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/4nztt1229598562.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/5lgdr1229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/5lgdr1229598562.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/6ertt1229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/6ertt1229598562.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/7rpqr1229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/7rpqr1229598562.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/8fh1c1229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/8fh1c1229598562.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/92j4q1229598562.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/18/t1229598808537o1w3o298agx4/92j4q1229598562.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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