Q3 task 6

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 20 Nov 2008 10:50:14 -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/20/t1227203695r0zzdx3k4gabomw.htm/, Retrieved Thu, 20 Nov 2008 17:55:05 +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/20/t1227203695r0zzdx3k4gabomw.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:

» Textbox « » Textfile « » CSV «

3353 0 3480 0 3098 0 2944 0 3389 0 3497 0 4404 0 3849 0 3734 0 3060 0 3507 0 3287 0 3215 0 3764 0 2734 0 2837 0 2766 0 3851 0 3289 0 3848 0 3348 0 3682 0 4058 0 3655 1 3811 1 3341 1 3032 1 3475 1 3353 1 3186 1 3902 1 4164 1 3499 1 4145 1 3796 1 3711 1 3949 1 3740 1 3243 1 4407 1 4814 1 3908 1 5250 1 3937 1 4004 1 5560 1 3922 1 3759 1 4138 1 4634 1 3996 1 4308 1 4142 1 4429 1 5219 1 4929 1 5754 1 5592 1 4163 1 4962 1 5208 1 4755 1 4491 1 5732 1 5730 1 5024 1 6056 1 4901 1 5353 1 5578 1 4618 1 4724 1 5011 1 5298 1 4143 1 4617 1 4727 1 4207 1 5112 1 4190 1 4098 1 5071 1 4177 1 4598 1 3757 1 5591 1 4218 1 3780 1 4336 1 4870 1 4422 1 4727 1 4459 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 3434.52173913043 + 1014.16397515528d[t] + 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) 3434.52173913043 136.435755 25.1732 0 0 d 1014.16397515528 157.260862 6.4489 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.560059691689458 R-squared 0.313666858255291 Adjusted R-squared 0.306124735818536 F-TEST (value) 41.5886722717066 F-TEST (DF numerator) 1 F-TEST (DF denominator) 91 p-value 5.31672972314823e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 654.322893245122 Sum Squared Residuals 38960598.8248447 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3353 3434.52173913044 -81.52173913044 2 3480 3434.52173913044 45.4782608695581 3 3098 3434.52173913043 -336.521739130434 4 2944 3434.52173913043 -490.521739130434 5 3389 3434.52173913043 -45.5217391304342 6 3497 3434.52173913043 62.4782608695658 7 4404 3434.52173913043 969.478260869566 8 3849 3434.52173913043 414.478260869566 9 3734 3434.52173913043 299.478260869566 10 3060 3434.52173913043 -374.521739130434 11 3507 3434.52173913043 72.4782608695658 12 3287 3434.52173913043 -147.521739130434 13 3215 3434.52173913043 -219.521739130434 14 3764 3434.52173913043 329.478260869566 15 2734 3434.52173913043 -700.521739130434 16 2837 3434.52173913043 -597.521739130434 17 2766 3434.52173913043 -668.521739130434 18 3851 3434.52173913043 416.478260869566 19 3289 3434.52173913043 -145.521739130434 20 3848 3434.52173913043 413.478260869566 21 3348 3434.52173913043 -86.5217391304342 22 3682 3434.52173913043 247.478260869566 23 4058 3434.52173913043 623.478260869566 24 3655 4448.68571428571 -793.685714285714 25 3811 4448.68571428571 -637.685714285714 26 3341 4448.68571428571 -1107.68571428571 27 3032 4448.68571428571 -1416.68571428571 28 3475 4448.68571428571 -973.685714285714 29 3353 4448.68571428571 -1095.68571428571 30 3186 4448.68571428571 -1262.68571428571 31 3902 4448.68571428571 -546.685714285714 32 4164 4448.68571428571 -284.685714285714 33 3499 4448.68571428571 -949.685714285714 34 4145 4448.68571428571 -303.685714285714 35 3796 4448.68571428571 -652.685714285714 36 3711 4448.68571428571 -737.685714285714 37 3949 4448.68571428571 -499.685714285714 38 3740 4448.68571428571 -708.685714285714 39 3243 4448.68571428571 -1205.68571428571 40 4407 4448.68571428571 -41.6857142857143 41 4814 4448.68571428571 365.314285714286 42 3908 4448.68571428571 -540.685714285714 43 5250 4448.68571428571 801.314285714286 44 3937 4448.68571428571 -511.685714285714 45 4004 4448.68571428571 -444.685714285714 46 5560 4448.68571428571 1111.31428571429 47 3922 4448.68571428571 -526.685714285714 48 3759 4448.68571428571 -689.685714285714 49 4138 4448.68571428571 -310.685714285714 50 4634 4448.68571428571 185.314285714286 51 3996 4448.68571428571 -452.685714285714 52 4308 4448.68571428571 -140.685714285714 53 4142 4448.68571428571 -306.685714285714 54 4429 4448.68571428571 -19.6857142857143 55 5219 4448.68571428571 770.314285714286 56 4929 4448.68571428571 480.314285714286 57 5754 4448.68571428571 1305.31428571429 58 5592 4448.68571428571 1143.31428571429 59 4163 4448.68571428571 -285.685714285714 60 4962 4448.68571428571 513.314285714286 61 5208 4448.68571428571 759.314285714286 62 4755 4448.68571428571 306.314285714286 63 4491 4448.68571428571 42.3142857142857 64 5732 4448.68571428571 1283.31428571429 65 5730 4448.68571428571 1281.31428571429 66 5024 4448.68571428571 575.314285714286 67 6056 4448.68571428571 1607.31428571429 68 4901 4448.68571428571 452.314285714286 69 5353 4448.68571428571 904.314285714286 70 5578 4448.68571428571 1129.31428571429 71 4618 4448.68571428571 169.314285714286 72 4724 4448.68571428571 275.314285714286 73 5011 4448.68571428571 562.314285714286 74 5298 4448.68571428571 849.314285714286 75 4143 4448.68571428571 -305.685714285714 76 4617 4448.68571428571 168.314285714286 77 4727 4448.68571428571 278.314285714286 78 4207 4448.68571428571 -241.685714285714 79 5112 4448.68571428571 663.314285714286 80 4190 4448.68571428571 -258.685714285714 81 4098 4448.68571428571 -350.685714285714 82 5071 4448.68571428571 622.314285714286 83 4177 4448.68571428571 -271.685714285714 84 4598 4448.68571428571 149.314285714286 85 3757 4448.68571428571 -691.685714285714 86 5591 4448.68571428571 1142.31428571429 87 4218 4448.68571428571 -230.685714285714 88 3780 4448.68571428571 -668.685714285714 89 4336 4448.68571428571 -112.685714285714 90 4870 4448.68571428571 421.314285714286 91 4422 4448.68571428571 -26.6857142857143 92 4727 4448.68571428571 278.314285714286 93 4459 4448.68571428571 10.3142857142857 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0695320536772172 0.139064107354434 0.930467946322783 6 0.0319811966361307 0.0639623932722613 0.96801880336387 7 0.286992763666627 0.573985527333254 0.713007236333373 8 0.218480528999665 0.436961057999331 0.781519471000335 9 0.145024639162072 0.290049278324145 0.854975360837928 10 0.118887791374066 0.237775582748132 0.881112208625934 11 0.0702131784200659 0.140426356840132 0.929786821579934 12 0.0423831321977843 0.0847662643955687 0.957616867802216 13 0.0262201257661325 0.052440251532265 0.973779874233867 14 0.0174354119796008 0.0348708239592015 0.9825645880204 15 0.0279097875853900 0.0558195751707801 0.97209021241461 16 0.0294456233504225 0.0588912467008451 0.970554376649577 17 0.0336433939291433 0.0672867878582866 0.966356606070857 18 0.0291101040170915 0.058220208034183 0.970889895982909 19 0.0180319627030471 0.0360639254060941 0.981968037296953 20 0.0149726169803694 0.0299452339607388 0.98502738301963 21 0.00900591648905715 0.0180118329781143 0.990994083510943 22 0.00595249352147577 0.0119049870429515 0.994047506478524 23 0.00672380631761243 0.0134476126352249 0.993276193682388 24 0.00434254029969528 0.00868508059939055 0.995657459700305 25 0.00271654870402296 0.00543309740804591 0.997283451295977 26 0.00242555876604868 0.00485111753209737 0.997574441233951 27 0.00352510852815969 0.00705021705631938 0.99647489147184 28 0.00267057469395155 0.00534114938790311 0.997329425306049 29 0.00228511052544093 0.00457022105088186 0.99771488947456 30 0.00255739575094159 0.00511479150188318 0.997442604249058 31 0.00252245938351050 0.00504491876702101 0.99747754061649 32 0.00326360479982389 0.00652720959964778 0.996736395200176 33 0.00291550617470025 0.00583101234940051 0.9970844938253 34 0.00321572008339069 0.00643144016678138 0.99678427991661 35 0.00261620724806713 0.00523241449613426 0.997383792751933 36 0.00220201379018150 0.00440402758036299 0.997797986209819 37 0.00188779743138373 0.00377559486276747 0.998112202568616 38 0.00163798145835859 0.00327596291671717 0.998362018541641 39 0.00329524301499723 0.00659048602999446 0.996704756985003 40 0.00503464335200077 0.0100692867040015 0.994965356648 41 0.0147315198032422 0.0294630396064844 0.985268480196758 42 0.0133607634860258 0.0267215269720517 0.986639236513974 43 0.0642497694455793 0.128499538891159 0.93575023055442 44 0.0592489793151269 0.118497958630254 0.940751020684873 45 0.0540789647848663 0.108157929569733 0.945921035215134 46 0.217812741315184 0.435625482630368 0.782187258684816 47 0.212157660261039 0.424315320522078 0.78784233973896 48 0.231283730274672 0.462567460549343 0.768716269725328 49 0.216492786028896 0.432985572057792 0.783507213971104 50 0.211092686837880 0.422185373675759 0.78890731316212 51 0.209360564493771 0.418721128987542 0.79063943550623 52 0.192044015131911 0.384088030263822 0.807955984868089 53 0.18319688545641 0.36639377091282 0.81680311454359 54 0.167507051270752 0.335014102541504 0.832492948729248 55 0.232649638589694 0.465299277179387 0.767350361410306 56 0.239553753099754 0.479107506199508 0.760446246900246 57 0.466799277782808 0.933598555565615 0.533200722217192 58 0.618027791256643 0.763944417486714 0.381972208743357 59 0.598554674579709 0.802890650840583 0.401445325420291 60 0.578838416603899 0.842323166792203 0.421161583396101 61 0.597481985848118 0.805036028303764 0.402518014151882 62 0.550969875399415 0.898060249201171 0.449030124600585 63 0.499458394299076 0.998916788598151 0.500541605700924 64 0.658627535087569 0.682744929824862 0.341372464912431 65 0.792053932297558 0.415892135404885 0.207946067702442 66 0.768758761471405 0.46248247705719 0.231241238528595 67 0.941561687108304 0.116876625783392 0.0584383128916962 68 0.925568927703615 0.148862144592771 0.0744310722963853 69 0.942240674497365 0.11551865100527 0.057759325502635 70 0.975178200929974 0.0496435981400521 0.0248217990700260 71 0.962012029834431 0.0759759403311376 0.0379879701655688 72 0.945235067780436 0.109529864439127 0.0547649322195637 73 0.938198145007286 0.123603709985427 0.0618018549927134 74 0.957718500258613 0.0845629994827738 0.0422814997413869 75 0.94356122297052 0.112877554058961 0.0564387770294803 76 0.916051612258019 0.167896775483963 0.0839483877419813 77 0.884122202664548 0.231755594670903 0.115877797335452 78 0.845033209184225 0.309933581631551 0.154966790815776 79 0.85249774865969 0.29500450268062 0.14750225134031 80 0.802672314455744 0.394655371088513 0.197327685544256 81 0.757188631391698 0.485622737216605 0.242811368608302 82 0.752491641654993 0.495016716690014 0.247508358345007 83 0.678161778137415 0.643676443725171 0.321838221862585 84 0.57293140383339 0.854137192333221 0.427068596166611 85 0.613562215866382 0.772875568267235 0.386437784133618 86 0.892653561157428 0.214692877685145 0.107346438842572 87 0.81047172746424 0.37905654507152 0.18952827253576 88 0.928979830089846 0.142040339820307 0.0710201699101536 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.190476190476190 NOK 5% type I error level 26 0.309523809523810 NOK 10% type I error level 35 0.416666666666667 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/10i28s1227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/10i28s1227203408.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/1v0w81227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/1v0w81227203408.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/2nz8p1227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/2nz8p1227203408.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/36do61227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/36do61227203408.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/44cgn1227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/44cgn1227203408.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/527se1227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/527se1227203408.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/62hyc1227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/62hyc1227203408.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/7cri31227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/7cri31227203408.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/805ve1227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/805ve1227203408.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/9b0df1227203408.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227203695r0zzdx3k4gabomw/9b0df1227203408.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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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