Blog paper

*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, 01 Dec 2008 11:42:46 -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/01/t1228157310sov8hi0ipyysgeo.htm/, Retrieved Mon, 01 Dec 2008 18:48:30 +0000

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/01/t1228157310sov8hi0ipyysgeo.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

blog paper

Dataseries X:

» Textbox « » Textfile « » CSV «

14929387,5 0 14717825,3 0 15826281,2 0 16301309,6 0 15033016,9 0 16998460,6 0 14066462,7 0 13328937,3 0 17319718,2 0 17586426,8 0 15887037,4 0 17935679,1 0 15869489 0 15892510,9 0 17556558,1 0 16791643 0 15953688,5 0 18144913,6 0 14390881 0 13885708,7 0 17332571,5 0 17152595,8 0 16003877,1 0 16841467,1 0 14783398,1 0 14667847,5 0 17714362,2 0 16282088 1 15014866,2 1 17722582,4 1 13876509,4 1 15495489,6 1 17799521,1 1 17920079,1 1 17248022,4 1 18813782,4 0 16249688,3 0 17823358,5 0 20424438,3 0 17814218,7 0 19699959,6 0 19776328,1 0 15679833,1 0 17119266,5 0 20092613 0 20863688,3 0 20925203,1 0 21032593 0 20664684,3 0 19711511,4 0 22553293,4 0 19498332,9 0 20722827,8 0 21321275 0 17960847,7 0 17789654,9 0 20003708,5 0 21169851,7 0 20422839,4 0 19810562,3 0

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 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation x[t] = + 15509742.34 -1300033.25625y[t] -1355603.48333334M1[t] -1386129.82666667M2[t] + 772438.47M3[t] -538830.702083335M4[t] -685284.965416666M5[t] + 728747.55125M6[t] -2962865.23208333M7[t] -2727768.23541667M8[t] + 164239.201249999M9[t] + 499333.457916666M10[t] -435606.625416667M11[t] + 93807.6233333334t + 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) 15509742.34 490286.670042 31.634 0 0 y -1300033.25625 369567.482782 -3.5177 0.000992 0.000496 M1 -1355603.48333334 596461.173308 -2.2727 0.02776 0.01388 M2 -1386129.82666667 595570.013859 -2.3274 0.024399 0.0122 M3 772438.47 594762.576172 1.2987 0.200508 0.100254 M4 -538830.702083335 598619.894471 -0.9001 0.372744 0.186372 M5 -685284.965416666 597985.78544 -1.146 0.257727 0.128863 M6 728747.55125 597435.67987 1.2198 0.228761 0.11438 M7 -2962865.23208333 596969.809989 -4.9632 1e-05 5e-06 M8 -2727768.23541667 596588.373133 -4.5723 3.6e-05 1.8e-05 M9 164239.201249999 596291.531332 0.2754 0.784216 0.392108 M10 499333.457916666 596079.41097 0.8377 0.406533 0.203266 M11 -435606.625416667 595952.102512 -0.7309 0.468521 0.234261 t 93807.6233333334 7112.329522 13.1894 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.931218982255859 R-squared 0.867168792913637 Adjusted R-squared 0.829629538737056 F-TEST (value) 23.1003202363735 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 6.66133814775094e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 934939.995781152 Sum Squared Residuals 40209188602718 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14929387.5 14247946.48 681441.019999986 2 14717825.3 14311227.76 406597.540000002 3 15826281.2 16563603.68 -737322.48 4 16301309.6 15346142.13125 955167.46875 5 15033016.9 15293495.49125 -260478.591249999 6 16998460.6 16801335.63125 197124.968750001 7 14066462.7 13203530.47125 862932.22875 8 13328937.3 13532435.09125 -203497.791249998 9 17319718.2 16518250.15125 801468.048749998 10 17586426.8 16947152.03125 639274.76875 11 15887037.4 16106019.57125 -218982.171249999 12 17935679.1 16635433.82 1300245.28 13 15869489 15373637.96 495851.040000003 14 15892510.9 15436919.24 455591.66 15 17556558.1 17689295.16 -132737.059999998 16 16791643 16471833.61125 319809.388750001 17 15953688.5 16419186.97125 -465498.47125 18 18144913.6 17927027.11125 217886.488750001 19 14390881 14329221.95125 61659.0487500003 20 13885708.7 14658126.57125 -772417.87125 21 17332571.5 17643941.63125 -311370.131250000 22 17152595.8 18072843.51125 -920247.71125 23 16003877.1 17231711.05125 -1227833.95125 24 16841467.1 17761125.3 -919658.199999999 25 14783398.1 16499329.44 -1715931.34000000 26 14667847.5 16562610.72 -1894763.22 27 17714362.2 18814986.64 -1100624.44 28 16282088 16297491.835 -15403.8349999994 29 15014866.2 16244845.195 -1229978.995 30 17722582.4 17752685.335 -30102.9350000024 31 13876509.4 14154880.175 -278370.775000000 32 15495489.6 14483784.795 1011704.805 33 17799521.1 17469599.855 329921.245000002 34 17920079.1 17898501.735 21577.3650000011 35 17248022.4 17057369.275 190653.124999999 36 18813782.4 18886816.78 -73034.3800000027 37 16249688.3 17625020.92 -1375332.62000000 38 17823358.5 17688302.2 135056.299999999 39 20424438.3 19940678.12 483760.180000001 40 17814218.7 18723216.57125 -908997.87125 41 19699959.6 18670569.93125 1029389.66875 42 19776328.1 20178410.07125 -402081.971249999 43 15679833.1 16580604.91125 -900771.81125 44 17119266.5 16909509.53125 209756.96875 45 20092613 19895324.59125 197288.408750000 46 20863688.3 20324226.47125 539461.828749999 47 20925203.1 19483094.01125 1442109.08875 48 21032593 20012508.26 1020084.74000000 49 20664684.3 18750712.4 1913971.90000000 50 19711511.4 18813993.68 897517.719999999 51 22553293.4 21066369.6 1486923.80000000 52 19498332.9 19848908.05125 -350575.151250001 53 20722827.8 19796261.41125 926566.38875 54 21321275 21304101.55125 17173.4487499993 55 17960847.7 17706296.39125 254551.30875 56 17789654.9 18035201.01125 -245546.111250002 57 20003708.5 21021016.07125 -1017307.57125 58 21169851.7 21449917.95125 -280066.251250001 59 20422839.4 20608785.49125 -185946.091250001 60 19810562.3 21138199.74 -1327637.44 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0709588295254343 0.141917659050869 0.929041170474566 18 0.0235756382923711 0.0471512765847422 0.976424361707629 19 0.0207745625719652 0.0415491251439303 0.979225437428035 20 0.0088173479052919 0.0176346958105838 0.991182652094708 21 0.01046780519969 0.02093561039938 0.98953219480031 22 0.0192459459000394 0.0384918918000788 0.98075405409996 23 0.0108231054133701 0.0216462108267402 0.98917689458663 24 0.0324814894434821 0.0649629788869643 0.967518510556518 25 0.0478858301202923 0.0957716602405846 0.952114169879708 26 0.0649681583989401 0.129936316797880 0.93503184160106 27 0.0721556166453576 0.144311233290715 0.927844383354642 28 0.0448496016747905 0.089699203349581 0.95515039832521 29 0.0618207497445462 0.123641499489092 0.938179250255454 30 0.0393584276862305 0.078716855372461 0.96064157231377 31 0.0222610367805544 0.0445220735611089 0.977738963219446 32 0.0677661584650804 0.135532316930161 0.93223384153492 33 0.0475509580364586 0.0951019160729171 0.952449041963541 34 0.028167610872742 0.056335221745484 0.971832389127258 35 0.0214551762281633 0.0429103524563266 0.978544823771837 36 0.0158833816395535 0.0317667632791071 0.984116618360447 37 0.114825583896799 0.229651167793598 0.885174416103201 38 0.186986249171174 0.373972498342347 0.813013750828826 39 0.336119060486811 0.672238120973622 0.663880939513189 40 0.300866448612211 0.601732897224422 0.699133551387789 41 0.352238384649477 0.704476769298954 0.647761615350523 42 0.311789208437203 0.623578416874407 0.688210791562797 43 0.67338485983745 0.653230280325101 0.326615140162550 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 9 0.333333333333333 NOK 10% type I error level 15 0.555555555555556 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/101nw1228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/101nw1228156954.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/1029sk1228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/1029sk1228156954.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/2zxep1228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/2zxep1228156954.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/3f9m41228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/3f9m41228156954.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/48i131228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/48i131228156954.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/5fu6c1228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/5fu6c1228156954.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/6jy911228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/6jy911228156954.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/72ybr1228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/72ybr1228156954.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/8np001228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/8np001228156954.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/9ttyi1228156954.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157310sov8hi0ipyysgeo/9ttyi1228156954.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