*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, 22 Nov 2009 09:29:33 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7.htm/, Retrieved Sun, 22 Nov 2009 17:32:02 +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/2009/Nov/22/t12589075103xmm0mqrbm85ub7.htm/}, year = {2009}, } @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 = {2009}, 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:

ws7m3.1

Dataseries X:

» Textbox « » Textfile « » CSV «

2921,44 0 2849,27 2756,76 2981,85 0 2921,44 2849,27 3080,58 0 2981,85 2921,44 3106,22 0 3080,58 2981,85 3119,31 0 3106,22 3080,58 3061,26 0 3119,31 3106,22 3097,31 0 3061,26 3119,31 3161,69 0 3097,31 3061,26 3257,16 0 3161,69 3097,31 3277,01 0 3257,16 3161,69 3295,32 0 3277,01 3257,16 3363,99 0 3295,32 3277,01 3494,17 0 3363,99 3295,32 3667,03 1 3494,17 3363,99 3813,06 1 3667,03 3494,17 3917,96 1 3813,06 3667,03 3895,51 1 3917,96 3813,06 3801,06 1 3895,51 3917,96 3570,12 0 3801,06 3895,51 3701,61 1 3570,12 3801,06 3862,27 1 3701,61 3570,12 3970,1 1 3862,27 3701,61 4138,52 1 3970,1 3862,27 4199,75 1 4138,52 3970,1 4290,89 1 4199,75 4138,52 4443,91 1 4290,89 4199,75 4502,64 1 4443,91 4290,89 4356,98 1 4502,64 4443,91 4591,27 1 4356,98 4502,64 4696,96 1 4591,27 4356,98 4621,4 1 4696,96 4591,27 4562,84 1 4621,4 4696,96 4202,52 1 4562,84 4621,4 4296,49 1 4202,52 4562,84 4435,23 1 4296,49 4202,52 4105,18 1 4435,23 4296,49 4116,68 1 4105,18 4435,23 3844,49 1 4116,68 4105,18 3720 etc...

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 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Yt[t] = + 201.730817279814 -11.4118617949622X[t] + 1.21358991033072`Yt-1`[t] -0.250695764968951`Yt-2 `[t] -2.51442156104134t + 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) 201.730817279814 182.919049 1.1028 0.275078 0.137539 X -11.4118617949622 86.199045 -0.1324 0.895177 0.447589 `Yt-1` 1.21358991033072 0.136841 8.8686 0 0 `Yt-2 ` -0.250695764968951 0.134741 -1.8606 0.068356 0.034178 t -2.51442156104134 1.824066 -1.3785 0.173849 0.086925 Multiple Linear Regression - Regression Statistics Multiple R 0.980150857748208 R-squared 0.960695703944548 Adjusted R-squared 0.957729341978098 F-TEST (value) 323.863275894988 F-TEST (DF numerator) 4 F-TEST (DF denominator) 53 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 176.239030887663 Sum Squared Residuals 1646190.38843581 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2921.44 2965.95366249096 -44.5136624909641 2 2981.85 3027.83215954121 -45.9821595412143 3 3080.58 3080.53799110544 0.0420088945597845 4 3106.22 3182.69677022958 -76.4767702295774 5 3119.31 3186.54760109403 -67.2376010940309 6 3061.26 3193.49123204541 -132.231232045415 7 3097.31 3117.24630862623 -19.9363086262322 8 3161.69 3173.03469248906 -11.3446924890602 9 3257.16 3239.61360702798 17.54639297202 10 3277.01 3336.82082085751 -59.8108208575104 11 3295.32 3334.46223433495 -39.1422343349486 12 3363.99 3349.19233309743 14.7976669025708 13 3494.17 3425.42489122222 68.7451087777843 14 3667.03 3552.26846421265 114.761535787353 15 3813.06 3726.89961986772 86.1603801322848 16 3917.96 3858.27046297973 59.689537020265 17 3895.51 3946.45252045397 -50.9425204539698 18 3801.06 3890.39501966076 -89.3350196607614 19 3570.12 3790.2970127875 -220.177012787499 20 3701.61 3519.78249054104 181.827509458963 21 3862.27 3734.73868625131 127.531313748689 22 3970.1 3894.23563354824 75.8643664517647 23 4138.52 3982.30583041824 156.214169581757 24 4199.75 4157.1516972185 42.5983027815003 25 4290.89 4186.72320513094 104.166794869064 26 4443.91 4279.46526630839 164.444733691612 27 4502.64 4439.80596080688 62.8340391931181 28 4356.98 4470.20420872402 -113.224208724016 29 4591.27 4276.19491854758 315.075081452426 30 4696.96 4594.52882220330 102.431177796705 31 4621.4 4661.54320749053 -40.1432074905315 32 4562.84 4540.83389690533 22.0061030946679 33 4202.52 4486.19422219638 -283.674222196379 34 4296.49 4061.07982814156 235.410171858444 35 4435.23 4262.93714848790 172.292851512096 36 4105.18 4405.23831005201 -300.058310052013 37 4116.68 3967.39700815453 149.282991845472 38 3844.49 4061.58100779029 -217.091007790292 39 3720.98 3725.85654723919 -4.87654723918963 40 3674.4 3641.6885161201 32.7114838798993 41 3857.62 3613.60851046717 244.01148953283 42 3801.06 3845.12544100918 -44.0654410091757 43 3504.37 3728.03789606222 -223.667896062218 44 3032.6 3379.6428364718 -347.042836471801 45 3047.03 2890.38389121764 156.646108782362 46 2962.34 3012.24045130711 -49.9004513071086 47 2197.82 2903.32956035166 -705.509560351657 48 2014.45 1994.23280487980 20.2171951202023 49 1862.83 1972.25618949044 -109.426189490438 50 1905.41 1831.70734814741 73.7026518525904 51 1810.99 1918.87807685284 -107.888076852843 52 1670.07 1791.10187028600 -121.031870285997 53 1864.44 1641.23905268952 223.200947310480 54 2052.02 1909.93814919888 142.081850801116 55 2029.6 2086.34118718066 -56.7411871806635 56 2070.83 2009.59256823713 61.2374317628683 57 2293.41 2062.73505772963 230.674942270370 58 2443.27 2320.00529202033 123.264707979671 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.0252343081545159 0.0504686163090318 0.974765691845484 9 0.00656368250845884 0.0131273650169177 0.993436317491541 10 0.00166380128279802 0.00332760256559605 0.998336198717202 11 0.00033551430099687 0.00067102860199374 0.999664485699003 12 9.94039969136015e-05 0.000198807993827203 0.999900596003086 13 8.87730405038833e-05 0.000177546081007767 0.999911226959496 14 1.83271373831402e-05 3.66542747662804e-05 0.999981672862617 15 3.87007249306162e-06 7.74014498612324e-06 0.999996129927507 16 8.85036074456956e-07 1.77007214891391e-06 0.999999114963926 17 2.81652575828228e-07 5.63305151656455e-07 0.999999718347424 18 8.36061282742958e-08 1.67212256548592e-07 0.999999916393872 19 1.32731845468959e-07 2.65463690937917e-07 0.999999867268154 20 6.09986080470123e-08 1.21997216094025e-07 0.999999939001392 21 9.65616590253932e-08 1.93123318050786e-07 0.99999990343834 22 3.27885151841174e-08 6.55770303682348e-08 0.999999967211485 23 3.86834103249615e-08 7.7366820649923e-08 0.99999996131659 24 9.83309044968994e-09 1.96661808993799e-08 0.99999999016691 25 1.96536410661777e-08 3.93072821323555e-08 0.99999998034636 26 1.44420318442585e-07 2.8884063688517e-07 0.999999855579682 27 5.75952884216035e-08 1.15190576843207e-07 0.999999942404712 28 4.78530189974977e-08 9.57060379949955e-08 0.999999952146981 29 8.50853888702006e-06 1.70170777740401e-05 0.999991491461113 30 4.94201208278786e-06 9.88402416557571e-06 0.999995057987917 31 2.04659715280308e-06 4.09319430560616e-06 0.999997953402847 32 7.36716771532692e-07 1.47343354306538e-06 0.999999263283228 33 0.000113982665160248 0.000227965330320495 0.99988601733484 34 8.6135049290247e-05 0.000172270098580494 0.99991386495071 35 8.46750682219367e-05 0.000169350136443873 0.999915324931778 36 0.00232037279934471 0.00464074559868942 0.997679627200655 37 0.00169233333964721 0.00338466667929442 0.998307666660353 38 0.00337692732193543 0.00675385464387087 0.996623072678064 39 0.00200072596466625 0.00400145192933250 0.997999274035334 40 0.00127840572456559 0.00255681144913119 0.998721594275434 41 0.0113589525210454 0.0227179050420909 0.988641047478955 42 0.0230178572558169 0.0460357145116339 0.976982142744183 43 0.0304533357676292 0.0609066715352584 0.96954666423237 44 0.0458646419024157 0.0917292838048314 0.954135358097584 45 0.0671960792496803 0.134392158499361 0.93280392075032 46 0.758823994072697 0.482352011854607 0.241176005927303 47 0.828669162538752 0.342661674922496 0.171330837461248 48 0.720514667027303 0.558970665945395 0.279485332972697 49 0.59699426609762 0.80601146780476 0.40300573390238 50 0.768820456703445 0.46235908659311 0.231179543296555 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 31 0.72093023255814 NOK 5% type I error level 34 0.790697674418605 NOK 10% type I error level 37 0.86046511627907 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/10ryuo1258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/10ryuo1258907368.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/1bcq71258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/1bcq71258907368.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/2c9lp1258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/2c9lp1258907368.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/3hisz1258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/3hisz1258907368.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/4pzhe1258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/4pzhe1258907368.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/59te61258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/59te61258907368.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/61h7r1258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/61h7r1258907368.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/79sdm1258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/79sdm1258907368.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/80yfu1258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/80yfu1258907368.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/97auw1258907368.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589075103xmm0mqrbm85ub7/97auw1258907368.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