Paper - Multiple Regression - Gas met Trend & Monthly dummies

*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: Mon, 22 Dec 2008 04:36:38 -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/22/t1229945851at42i5xg05eip8e.htm/, Retrieved Mon, 22 Dec 2008 12:37:40 +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/22/t1229945851at42i5xg05eip8e.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 «

127,96 0 127,47 0 126,47 0 125,75 0 125,42 0 125,14 0 125,15 0 125,51 0 125,63 0 126,22 0 126,88 0 127,96 0 128,74 0 129,6 0 131,2 0 132,72 0 134,67 0 135,94 0 136,39 0 136,74 0 137,2 0 137,36 0 138,63 0 141,07 0 143,32 0 147,91 0 152,56 0 151,61 0 156,56 0 157,45 0 158,13 0 159,18 0 159,47 0 159,79 0 161,65 0 162,77 0 163,48 0 166,16 0 163,86 0 162,12 0 149,08 0 145,32 0 141,21 0 134,68 0 133,65 0 139,17 0 138,61 0 144,96 1 157,99 1 167,18 1 174,48 1 182,77 1 190,00 1 189,70 1 188,90 1 198,28 1 201,18 1 204,14 1 221,02 1 221,12 1 220,68 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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Gasindex[t] = + 119.901837133550 + 22.9243078175896dumivariable[t] + 3.21734826275786M1[t] + 1.14631704668838M2[t] + 2.34897149837134M3[t] + 2.78162595005429M4[t] + 2.08628040173724M5[t] + 0.802934853420195M6[t] -0.798410694896848M7[t] -0.723756243213888M8[t] -1.02310179153094M9[t] + 0.0395526601520119M10[t] + 3.21420711183497M11[t] + 0.847345548317046t + 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) 119.901837133550 7.473799 16.043 0 0 dumivariable 22.9243078175896 6.412429 3.575 0.000823 0.000412 M1 3.21734826275786 8.537864 0.3768 0.707994 0.353997 M2 1.14631704668838 8.961486 0.1279 0.898761 0.449381 M3 2.34897149837134 8.952668 0.2624 0.794178 0.397089 M4 2.78162595005429 8.946479 0.3109 0.757237 0.378619 M5 2.08628040173724 8.942923 0.2333 0.816551 0.408275 M6 0.802934853420195 8.942005 0.0898 0.928833 0.464416 M7 -0.798410694896848 8.943725 -0.0893 0.929246 0.464623 M8 -0.723756243213888 8.948081 -0.0809 0.935878 0.467939 M9 -1.02310179153094 8.955069 -0.1142 0.909528 0.454764 M10 0.0395526601520119 8.964685 0.0044 0.996498 0.498249 M11 3.21420711183497 8.976918 0.3581 0.721907 0.360954 t 0.847345548317046 0.153592 5.5169 1e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.880299423307561 R-squared 0.774927074675625 Adjusted R-squared 0.712672861288032 F-TEST (value) 12.447785178024 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 3.85795839719094e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.0718642559844 Sum Squared Residuals 9306.81609102606 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 127.96 123.966530944625 3.9934690553745 2 127.47 122.742845276873 4.72715472312704 3 126.47 124.792845276873 1.67715472312703 4 125.75 126.072845276873 -0.322845276872946 5 125.42 126.224845276873 -0.80484527687297 6 125.14 125.788845276873 -0.648845276872942 7 125.15 125.034845276873 0.115154723127050 8 125.51 125.956845276873 -0.446845276872959 9 125.63 126.504845276873 -0.874845276872957 10 126.22 128.414845276873 -2.19484527687295 11 126.88 132.436845276873 -5.55684527687297 12 127.96 130.069983713355 -2.10998371335504 13 128.74 134.13467752443 -5.39467752442994 14 129.6 132.910991856677 -3.31099185667749 15 131.2 134.960991856678 -3.76099185667753 16 132.72 136.240991856678 -3.52099185667753 17 134.67 136.392991856678 -1.72299185667753 18 135.94 135.956991856678 -0.0169918566775213 19 136.39 135.202991856678 1.18700814332246 20 136.74 136.124991856678 0.61500814332248 21 137.2 136.672991856678 0.52700814332247 22 137.36 138.582991856678 -1.22299185667751 23 138.63 142.604991856678 -3.97499185667753 24 141.07 140.238130293160 0.831869706840392 25 143.32 144.302824104235 -0.982824104234517 26 147.91 143.079138436482 4.8308615635179 27 152.56 145.129138436482 7.43086156351792 28 151.61 146.409138436482 5.20086156351792 29 156.56 146.561138436482 9.99886156351792 30 157.45 146.125138436482 11.3248615635179 31 158.13 145.371138436482 12.7588615635179 32 159.18 146.293138436482 12.8868615635179 33 159.47 146.841138436482 12.6288615635179 34 159.79 148.751138436482 11.0388615635179 35 161.65 152.773138436482 8.87686156351792 36 162.77 150.406276872964 12.3637231270358 37 163.48 154.470970684039 9.00902931596092 38 166.16 153.247285016287 12.9127149837133 39 163.86 155.297285016287 8.56271498371337 40 162.12 156.577285016287 5.54271498371334 41 149.08 156.729285016287 -7.64928501628663 42 145.32 156.293285016287 -10.9732850162867 43 141.21 155.539285016287 -14.3292850162866 44 134.68 156.461285016287 -21.7812850162867 45 133.65 157.009285016287 -23.3592850162866 46 139.17 158.919285016287 -19.7492850162867 47 138.61 162.941285016287 -24.3312850162866 48 144.96 183.498731270358 -38.5387312703583 49 157.99 187.563425081433 -29.5734250814332 50 167.18 186.339739413681 -19.1597394136808 51 174.48 188.389739413681 -13.9097394136808 52 182.77 189.669739413681 -6.89973941368078 53 190 189.821739413681 0.178260586319218 54 189.7 189.385739413681 0.314260586319207 55 188.9 188.631739413681 0.268260586319221 56 198.28 189.553739413681 8.7262605863192 57 201.18 190.101739413681 11.0782605863192 58 204.14 192.011739413681 12.1282605863192 59 221.02 196.033739413681 24.9862605863192 60 221.12 193.666877850163 27.4531221498371 61 220.68 197.731571661238 22.9484283387622 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00588384082611425 0.0117676816522285 0.994116159173886 18 0.00198031574967425 0.0039606314993485 0.998019684250326 19 0.00057703507244227 0.00115407014488454 0.999422964927558 20 0.000143818841764617 0.000287637683529234 0.999856181158235 21 3.4222792429422e-05 6.8445584858844e-05 0.99996577720757 22 6.93090576233189e-06 1.38618115246638e-05 0.999993069094238 23 1.51272192791410e-06 3.02544385582819e-06 0.999998487278072 24 3.84167650924973e-07 7.68335301849946e-07 0.999999615832349 25 6.13041655685933e-08 1.22608331137187e-07 0.999999938695834 26 2.41779760792633e-08 4.83559521585266e-08 0.999999975822024 27 3.18056106517874e-08 6.36112213035747e-08 0.99999996819439 28 1.29066964470506e-08 2.58133928941012e-08 0.999999987093304 29 1.45019807035197e-08 2.90039614070394e-08 0.99999998549802 30 1.13198193445803e-08 2.26396386891607e-08 0.99999998868018 31 8.05627498938564e-09 1.61125499787713e-08 0.999999991943725 32 5.96261051979116e-09 1.19252210395823e-08 0.99999999403739 33 4.52880664570084e-09 9.05761329140168e-09 0.999999995471193 34 3.89021525060495e-09 7.78043050120991e-09 0.999999996109785 35 5.95343355625792e-09 1.19068671125158e-08 0.999999994046566 36 1.67522678860134e-08 3.35045357720268e-08 0.999999983247732 37 1.29829791788559e-07 2.59659583577118e-07 0.999999870170208 38 1.27136566005498e-06 2.54273132010996e-06 0.99999872863434 39 2.65406248108387e-05 5.30812496216773e-05 0.99997345937519 40 0.00120491532777429 0.00240983065554858 0.998795084672226 41 0.0215124462769844 0.0430248925539687 0.978487553723016 42 0.157327596403240 0.314655192806480 0.84267240359676 43 0.574912520004765 0.85017495999047 0.425087479995235 44 0.615024947613821 0.769950104772358 0.384975052386179 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.821428571428571 NOK 5% type I error level 25 0.892857142857143 NOK 10% type I error level 25 0.892857142857143 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/10gia91229945794.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/10gia91229945794.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/1o6o91229945793.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/1o6o91229945793.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/2vfyf1229945793.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/2vfyf1229945793.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/3s3w51229945793.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/3s3w51229945793.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/403l31229945793.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/403l31229945793.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/5l0wp1229945793.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/5l0wp1229945793.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/6hzu31229945793.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/6hzu31229945793.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/7rlkr1229945793.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/7rlkr1229945793.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/8bvpr1229945793.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/8bvpr1229945793.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/9ie3h1229945794.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229945851at42i5xg05eip8e/9ie3h1229945794.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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