*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: Tue, 17 Nov 2009 10:00:20 -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/17/t1258477257i4iseb14jpb6n4z.htm/, Retrieved Tue, 17 Nov 2009 18:01:12 +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/17/t1258477257i4iseb14jpb6n4z.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:

Dataseries X:

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19435,1 2,01 20604,6 20604,6 20604,6 22686,8 2,01 19435,1 18714,9 18714,9 20396,7 2,01 22686,8 19435,1 18492,6 19233,6 2,01 20396,7 22686,8 19435,1 22751 2,01 19233,6 20396,7 22686,8 19864 2,01 22751 19233,6 20396,7 17165,4 2,02 19864 22751 19233,6 22309,7 2,02 17165,4 19864 22751 21786,3 2,03 22309,7 17165,4 19864 21927,6 2,05 21786,3 22309,7 17165,4 20957,9 2,08 21927,6 21786,3 22309,7 19726 2,07 20957,9 21927,6 21786,3 21315,7 2,06 19726 20957,9 21927,6 24771,5 2,05 21315,7 19726 20957,9 22592,4 2,05 24771,5 21315,7 19726 21942,1 2,05 22592,4 24771,5 21315,7 23973,7 2,05 21942,1 22592,4 24771,5 20815,7 2,05 23973,7 21942,1 22592,4 19931,4 2,06 20815,7 23973,7 21942,1 24436,8 2,06 19931,4 20815,7 23973,7 22838,7 2,07 24436,8 19931,4 20815,7 24465,3 2,07 22838,7 24436,8 19931,4 23007,3 2,3 24465,3 22838,7 24436,8 22720,8 2,31 23007,3 24465,3 22838,7 23045,7 2,31 22720,8 23007,3 24465,3 27198,5 2,53 23045,7 22720,8 23007,3 22401,9 2,58 27198,5 23045,7 22720,8 25122,7 2,59 22401,9 27 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 9374.57467049922 + 1224.00784219445X[t] + 0.0977062231002342Y1[t] + 0.115952759659355Y2[t] + 0.182520307490899Y3[t] + 724.35433075174M1[t] + 3884.92719146509M2[t] + 1126.73655843838M3[t] + 815.94703196878M4[t] + 2970.75504633567M5[t] + 924.990992066053M6[t] -1503.43996923854M7[t] + 2712.33484127727M8[t] + 2530.80527012911M9[t] + 2003.66072271356M10[t] -1276.19185810045M11[t] + 10.7061192035070t + 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) 9374.57467049922 4382.637692 2.139 0.037341 0.01867 X 1224.00784219445 508.131503 2.4088 0.019729 0.009865 Y1 0.0977062231002342 0.155102 0.6299 0.531598 0.265799 Y2 0.115952759659355 0.14831 0.7818 0.438003 0.219001 Y3 0.182520307490899 0.152485 1.197 0.236961 0.118481 M1 724.35433075174 885.524156 0.818 0.417241 0.208621 M2 3884.92719146509 1009.660585 3.8478 0.000339 0.000169 M3 1126.73655843838 1123.54422 1.0028 0.320766 0.160383 M4 815.94703196878 929.463714 0.8779 0.384214 0.192107 M5 2970.75504633567 841.075887 3.5321 0.000897 0.000449 M6 924.990992066053 1055.792717 0.8761 0.38516 0.19258 M7 -1503.43996923854 910.49508 -1.6512 0.104959 0.052479 M8 2712.33484127727 880.596449 3.0801 0.003359 0.001679 M9 2530.80527012911 1204.331615 2.1014 0.040665 0.020333 M10 2003.66072271356 1199.433841 1.6705 0.101069 0.050534 M11 -1276.19185810045 916.869508 -1.3919 0.170113 0.085057 t 10.7061192035070 13.891099 0.7707 0.444501 0.22225 Multiple Linear Regression - Regression Statistics Multiple R 0.925534667128993 R-squared 0.856614420057577 Adjusted R-squared 0.810731034476001 F-TEST (value) 18.6693812847488 F-TEST (DF numerator) 16 F-TEST (DF denominator) 50 p-value 9.9920072216264e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1304.49441924886 Sum Squared Residuals 85085284.4925715 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 19435.1 20733.0066871605 -1297.90668716053 2 22686.8 23225.9936841678 -539.193684167838 3 20396.7 20839.1554091511 -442.455409151094 4 19233.6 20864.3839587577 -1630.78395875766 5 22751 23244.2138532124 -493.213853212446 6 19864 20999.9733763344 -1135.97337633439 7 17165.4 18507.9736137480 -1342.57361374802 8 22309.7 22778.0258422410 -468.325842240976 9 21786.3 22282.2263472698 -495.926347269849 10 21927.6 21843.0751184517 84.5248815482946 11 20957.9 19502.7043248508 1455.19567514917 12 19726 20603.4694951917 -877.469495191672 13 21315.7 21119.2762988946 196.423701105413 14 24771.5 24113.8066364537 657.693363546343 15 22592.4 21663.4586236527 928.941376347327 16 21942.1 21841.3256652779 100.774334722052 17 23973.7 24321.3824620196 -347.682462019612 18 20815.7 22011.6904081440 -1195.99040814404 19 19931.4 19414.5260624770 516.873937523033 20 24436.8 23559.234820803 877.565179196982 21 22838.7 23161.9209084131 -323.220908413080 22 24465.3 22850.3490205196 1614.95097948040 23 23007.3 20658.6761932666 2348.62380673344 24 22720.8 21712.281631173 1008.51836882699 25 23045.7 22547.1776567914 498.522343208602 26 27198.5 25720.1480399122 1478.35196008783 27 22401.9 23424.9993050065 -1023.09930500651 28 25122.7 23209.3277746570 1913.37222534305 29 26100.5 26013.8334239118 86.6665760881643 30 22904.9 23624.4807011610 -719.580701160957 31 22040.4 21724.8271251319 315.572874868083 32 25981.5 25723.731056566 257.768943433971 33 26157.1 25487.0360351552 670.063964844769 34 25975.4 25397.1081407847 578.29185921532 35 22589.8 23057.9818800585 -468.181880058498 36 25370.4 24122.9882453752 1247.41175462485 37 25091.1 24691.7569378872 399.343062112842 38 28760.9 27736.0653149108 1024.83468508915 39 24325.9 25944.6742440767 -1618.77424407666 40 25821.7 25585.8092528767 235.890747123276 41 27645.7 28248.8761450524 -603.176145052429 42 26296.9 25853.9195624730 442.980437526976 43 24141.5 23813.4004330618 328.09956693822 44 27268.1 28140.4461907871 -872.346190787106 45 29060.3 27742.2828118083 1318.01718819168 46 28226.4 27382.3271826435 844.072817356496 47 23268.5 24859.1423445400 -1590.64234454005 48 26938.2 25892.0425271406 1046.1574728594 49 27217.5 26209.6093187872 1007.89068121278 50 27540.5 28953.2522132727 -1412.75221327267 51 29167.6 27012.9476582147 2154.65234178528 52 26671.5 26960.2727098072 -288.772709807198 53 30184 29068.3227423487 1115.67725765132 54 28422.3 27543.1280443401 879.171955659858 55 23774.3 25002.8899051544 -1228.58990515438 56 29601 29395.6620896029 205.337910397129 57 28523.6 29692.5338973535 -1168.93389735352 58 23622 26743.8405376005 -3121.84053760050 59 21320.3 23065.2952572841 -1744.99525728407 60 20423.6 22848.2181011196 -2424.61810111956 61 21174.9 21979.1731004791 -804.273100479103 62 23050.2 24259.1341112828 -1208.93411128282 63 21202.9 21202.1647598983 0.735240101653204 64 20476.4 20806.8806386235 -330.480638623518 65 23173.3 22931.571373455 241.728626545 66 22468 20738.6079075474 1729.39209245255 67 19842.7 18432.0828604269 1410.61713957306 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.0816056498610319 0.163211299722064 0.918394350138968 21 0.0299617613426773 0.0599235226853546 0.970038238657323 22 0.0113498581141185 0.0226997162282370 0.988650141885882 23 0.00722906310381757 0.0144581262076351 0.992770936896182 24 0.0112282167004081 0.0224564334008161 0.988771783299592 25 0.00765274627435896 0.0153054925487179 0.99234725372564 26 0.00369158059669901 0.00738316119339802 0.9963084194033 27 0.0202067771017378 0.0404135542034756 0.979793222898262 28 0.0213375190151760 0.0426750380303519 0.978662480984824 29 0.0119109741862005 0.0238219483724011 0.9880890258138 30 0.0112884877094765 0.0225769754189529 0.988711512290524 31 0.00621730237121023 0.0124346047424205 0.99378269762879 32 0.00289306204301792 0.00578612408603584 0.997106937956982 33 0.00151150193222967 0.00302300386445934 0.99848849806777 34 0.000653375929573666 0.00130675185914733 0.999346624070426 35 0.00359270749651043 0.00718541499302085 0.99640729250349 36 0.00209269907292152 0.00418539814584304 0.997907300927078 37 0.000918090525713133 0.00183618105142627 0.999081909474287 38 0.000746394849588602 0.00149278969917720 0.999253605150411 39 0.00128929258863516 0.00257858517727031 0.998710707411365 40 0.000582627873034116 0.00116525574606823 0.999417372126966 41 0.000559597435552029 0.00111919487110406 0.999440402564448 42 0.00149952642286708 0.00299905284573416 0.998500473577133 43 0.00543435397719858 0.0108687079543972 0.994565646022801 44 0.0241537736651623 0.0483075473303245 0.975846226334838 45 0.136307200407508 0.272614400815016 0.863692799592492 46 0.102343399308495 0.204686798616990 0.897656600691505 47 0.118313166294788 0.236626332589577 0.881686833705212 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.428571428571429 NOK 5% type I error level 23 0.821428571428571 NOK 10% type I error level 24 0.857142857142857 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/10u6141258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/10u6141258477215.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/1ty3q1258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/1ty3q1258477215.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/2sf9t1258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/2sf9t1258477215.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/39qno1258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/39qno1258477215.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/4c5mh1258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/4c5mh1258477215.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/5n9r41258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/5n9r41258477215.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/6v3f11258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/6v3f11258477215.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/7epo61258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/7epo61258477215.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/84dn41258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/84dn41258477215.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/9yopk1258477215.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477257i4iseb14jpb6n4z/9yopk1258477215.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') }

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