11.1 the seatbelt law Q3.2

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 24 Nov 2008 00:44:50 -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/Nov/24/t1227512753kj4mhuuamnkcyv6.htm/, Retrieved Mon, 24 Nov 2008 07:46:03 +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/Nov/24/t1227512753kj4mhuuamnkcyv6.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}, }

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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90.7 0 94.3 0 104.6 0 111.1 0 110.8 0 107.2 0 99 0 99 0 91 0 96.2 0 96.9 0 96.2 0 100.1 0 99 0 115.4 0 106.9 0 107.1 0 99.3 0 99.2 0 108.3 0 105.6 0 99.5 0 107.4 0 93.1 0 88.1 0 110.7 0 113.1 0 99.6 0 93.6 0 98.6 0 99.6 0 114.3 0 107.8 0 101.2 0 112.5 0 100.5 0 93.9 0 116.2 0 112 0 106.4 0 95.7 0 96 0 95.8 0 103 0 102.2 0 98.4 0 111.4 1 86.6 1 91.3 1 107.9 1 101.8 1 104.4 1 93.4 1 100.1 1 98.5 1 112.9 1 101.4 1 107.1 1 110.8 1 90.3 1 95.5 1 111.4 1 113 1 107.5 1 95.9 1 106.3 1 105.2 1 117.2 1 106.9 1 108.2 1 113 1 97.2 1 99.9 1 108.1 1 118.1 1 109.1 1 93.3 1 112.1 1 111.8 1 112.5 1 116.3 1 110.3 1 117.1 1 103.4 1 96.2 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 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Prodintergoed[t] = + 90.012030075188 -2.50614035087720invest[t] -0.342100041771058M1[t] + 12.5193713450293M2[t] + 16.7216322055138M3[t] + 11.8667502088555M4[t] + 3.84043964076861M5[t] + 7.95698621553886M6[t] + 6.31638993316627M7[t] + 14.4757936507937M8[t] + 9.19234022556392M9[t] + 7.58031537176276M10[t] + 14.6834534252298M11[t] + 0.140596282372598t + 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) 90.012030075188 2.504657 35.9379 0 0 invest -2.50614035087720 2.435083 -1.0292 0.306888 0.153444 M1 -0.342100041771058 2.879975 -0.1188 0.905781 0.45289 M2 12.5193713450293 2.982784 4.1972 7.7e-05 3.9e-05 M3 16.7216322055138 2.979971 5.6113 0 0 M4 11.8667502088555 2.97798 3.9848 0.000162 8.1e-05 M5 3.84043964076861 2.976811 1.2901 0.201196 0.100598 M6 7.95698621553886 2.976467 2.6733 0.009312 0.004656 M7 6.31638993316627 2.976947 2.1218 0.037348 0.018674 M8 14.4757936507937 2.97825 4.8605 7e-06 3e-06 M9 9.19234022556392 2.980377 3.0843 0.002906 0.001453 M10 7.58031537176276 2.983325 2.5409 0.013244 0.006622 M11 14.6834534252298 2.971793 4.9409 5e-06 2e-06 t 0.140596282372598 0.049537 2.8382 0.005911 0.002955 Multiple Linear Regression - Regression Statistics Multiple R 0.756161698369131 R-squared 0.571780514080489 Adjusted R-squared 0.493374129334664 F-TEST (value) 7.29252491278689 F-TEST (DF numerator) 13 F-TEST (DF denominator) 71 p-value 8.20181944582998e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.55894331268494 Sum Squared Residuals 2194.03140350877 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 90.7 89.8105263157894 0.889473684210552 2 94.3 102.812593984962 -8.51259398496238 3 104.6 107.155451127820 -2.55545112781957 4 111.1 102.441165413534 8.65883458646611 5 110.8 94.5554511278195 16.2445488721805 6 107.2 98.8125939849624 8.38740601503758 7 99 97.3125939849624 1.68740601503759 8 99 105.612593984962 -6.61259398496243 9 91 100.469736842105 -9.46973684210527 10 96.2 98.9983082706767 -2.79830827067668 11 96.9 106.242042606516 -9.34204260651627 12 96.2 91.6991854636591 4.50081453634087 13 100.1 91.4976817042606 8.60231829573936 14 99 104.499749373434 -5.49974937343359 15 115.4 108.842606516291 6.55739348370928 16 106.9 104.128320802005 2.77167919799499 17 107.1 96.2426065162907 10.8573934837093 18 99.3 100.499749373434 -1.19974937343357 19 99.2 98.9997493734336 0.200250626566422 20 108.3 107.299749373434 1.00025062656642 21 105.6 102.156892230576 3.44310776942355 22 99.5 100.685463659148 -1.18546365914787 23 107.4 107.929197994987 -0.52919799498746 24 93.1 93.3863408521303 -0.286340852130322 25 88.1 93.1848370927318 -5.08483709273184 26 110.7 106.186904761905 4.51309523809523 27 113.1 110.529761904762 2.57023809523810 28 99.6 105.815476190476 -6.2154761904762 29 93.6 97.9297619047619 -4.32976190476191 30 98.6 102.186904761905 -3.58690476190476 31 99.6 100.686904761905 -1.08690476190477 32 114.3 108.986904761905 5.31309523809524 33 107.8 103.844047619048 3.95595238095238 34 101.2 102.372619047619 -1.17261904761905 35 112.5 109.616353383459 2.88364661654135 36 100.5 95.0734962406015 5.4265037593985 37 93.9 94.871992481203 -0.971992481203011 38 116.2 107.874060150376 8.32593984962405 39 112 112.216917293233 -0.21691729323308 40 106.4 107.502631578947 -1.10263157894737 41 95.7 99.616917293233 -3.91691729323308 42 96 103.874060150376 -7.87406015037593 43 95.8 102.374060150376 -6.57406015037594 44 103 110.674060150376 -7.67406015037594 45 102.2 105.531203007519 -3.3312030075188 46 98.4 104.059774436090 -5.65977443609022 47 111.4 108.797368421053 2.60263157894738 48 86.6 94.2545112781955 -7.65451127819548 49 91.3 94.053007518797 -2.753007518797 50 107.9 107.05507518797 0.844924812030074 51 101.8 111.397932330827 -9.59793233082706 52 104.4 106.683646616541 -2.28364661654135 53 93.4 98.797932330827 -5.39793233082706 54 100.1 103.05507518797 -2.95507518796992 55 98.5 101.55507518797 -3.05507518796992 56 112.9 109.85507518797 3.04492481203008 57 101.4 104.712218045113 -3.31221804511278 58 107.1 103.240789473684 3.85921052631578 59 110.8 110.484523809524 0.315476190476187 60 90.3 95.9416666666666 -5.64166666666666 61 95.5 95.7401629072682 -0.240162907268178 62 111.4 108.742230576441 2.65776942355890 63 113 113.085087719298 -0.0850877192982411 64 107.5 108.370802005013 -0.870802005012532 65 95.9 100.485087719298 -4.58508771929824 66 106.3 104.742230576441 1.55776942355890 67 105.2 103.242230576441 1.9577694235589 68 117.2 111.542230576441 5.6577694235589 69 106.9 106.399373433584 0.500626566416045 70 108.2 104.927944862155 3.27205513784461 71 113 112.171679197995 0.828320802005009 72 97.2 97.6288220551378 -0.428822055137831 73 99.9 97.4273182957394 2.47268170426065 74 108.1 110.429385964912 -2.32938596491230 75 118.1 114.772243107769 3.32775689223057 76 109.1 110.057957393484 -0.957957393483718 77 93.3 102.172243107769 -8.87224310776943 78 112.1 106.429385964912 5.67061403508772 79 111.8 104.929385964912 6.87061403508771 80 112.5 113.229385964912 -0.72938596491228 81 116.3 108.086528822055 8.21347117794486 82 110.3 106.615100250627 3.68489974937343 83 117.1 113.858834586466 3.24116541353382 84 103.4 99.315977443609 4.08402255639099 85 96.2 99.1144736842105 -2.91447368421053 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.8933608164659 0.213278367068200 0.106639183534100 18 0.93965865763969 0.120682684720620 0.0603413423603098 19 0.892228708320958 0.215542583358084 0.107771291679042 20 0.892665643090757 0.214668713818486 0.107334356909243 21 0.94716410374114 0.105671792517718 0.0528358962588591 22 0.912142606212288 0.175714787575424 0.0878573937877121 23 0.899077984588242 0.201844030823516 0.100922015411758 24 0.892505922049762 0.214988155900476 0.107494077950238 25 0.946522043177453 0.106955913645094 0.053477956822547 26 0.965179269517847 0.0696414609643066 0.0348207304821533 27 0.954647358322164 0.0907052833556711 0.0453526416778356 28 0.980458409507617 0.0390831809847654 0.0195415904923827 29 0.995888748570695 0.00822250285860991 0.00411125142930496 30 0.993834127482787 0.0123317450344255 0.00616587251721274 31 0.989415519380645 0.0211689612387102 0.0105844806193551 32 0.993263549038734 0.0134729019225322 0.00673645096126611 33 0.993960544083095 0.0120789118338103 0.00603945591690516 34 0.989832835120886 0.0203343297582285 0.0101671648791142 35 0.989121336733587 0.021757326532825 0.0108786632664125 36 0.994272328358256 0.0114553432834872 0.0057276716417436 37 0.99188547372061 0.0162290525587808 0.0081145262793904 38 0.99862903220291 0.00274193559418256 0.00137096779709128 39 0.99883082199577 0.00233835600846173 0.00116917800423087 40 0.998917513688528 0.00216497262294337 0.00108248631147169 41 0.99984874200295 0.000302515994098484 0.000151257997049242 42 0.99978977391238 0.000420452175238544 0.000210226087619272 43 0.999631949594785 0.000736100810429858 0.000368050405214929 44 0.999452727876391 0.00109454424721731 0.000547272123608653 45 0.999082520751984 0.00183495849603163 0.000917479248015814 46 0.99825369248802 0.00349261502395873 0.00174630751197936 47 0.997831809306776 0.00433638138644713 0.00216819069322356 48 0.997991127851752 0.0040177442964957 0.00200887214824785 49 0.996279053369044 0.00744189326191108 0.00372094663095554 50 0.994694876173659 0.0106102476526826 0.00530512382634131 51 0.997801921981877 0.00439615603624597 0.00219807801812299 52 0.995824220800275 0.00835155839944991 0.00417577919972496 53 0.99463417431809 0.0107316513638194 0.00536582568190971 54 0.992617061383172 0.0147658772336551 0.00738293861682753 55 0.992000214479254 0.0159995710414920 0.00799978552074599 56 0.989267638518864 0.0214647229622716 0.0107323614811358 57 0.989571611178428 0.0208567776431434 0.0104283888215717 58 0.98498488638896 0.0300302272220805 0.0150151136110403 59 0.972757763454064 0.0544844730918713 0.0272422365459356 60 0.976389167183622 0.0472216656327563 0.0236108328163782 61 0.956730079466974 0.0865398410660525 0.0432699205330263 62 0.952208316738919 0.0955833665221619 0.0477916832610809 63 0.920793199161258 0.158413601677485 0.0792068008387423 64 0.863622893098841 0.272754213802317 0.136377106901159 65 0.838198473361455 0.323603053277089 0.161801526638544 66 0.755942667069389 0.488114665861223 0.244057332930611 67 0.667511981914375 0.66497603617125 0.332488018085625 68 0.691349430700328 0.617301138599343 0.308650569299672 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 15 0.288461538461538 NOK 5% type I error level 32 0.615384615384615 NOK 10% type I error level 37 0.711538461538462 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/101mf01227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/101mf01227512684.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/1xkos1227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/1xkos1227512684.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/2agdo1227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/2agdo1227512684.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/3x1qf1227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/3x1qf1227512684.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/4kmzj1227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/4kmzj1227512684.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/5yddd1227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/5yddd1227512684.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/6uwhf1227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/6uwhf1227512684.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/73nwj1227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/73nwj1227512684.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/8chz71227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/8chz71227512684.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/9ufy91227512684.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227512753kj4mhuuamnkcyv6/9ufy91227512684.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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