WS 7.2

*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: Fri, 20 Nov 2009 14:37:01 -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/20/t12587531888nxzpa7p71o807h.htm/, Retrieved Fri, 20 Nov 2009 22:40:00 +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/20/t12587531888nxzpa7p71o807h.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:

» Textbox « » Textfile « » CSV «

474605 0 470390 0 461251 0 454724 0 455626 0 516847 0 525192 0 522975 0 518585 0 509239 0 512238 0 519164 0 517009 0 509933 0 509127 0 500875 0 506971 0 569323 0 579714 0 577992 0 565644 0 547344 0 554788 0 562325 0 560854 0 555332 0 543599 0 536662 0 542722 0 593530 0 610763 0 612613 0 611324 0 594167 0 595454 0 590865 0 589379 0 584428 0 573100 0 567456 0 569028 0 620735 0 628884 0 628232 0 612117 0 595404 0 597141 0 593408 0 590072 0 579799 0 574205 0 572775 0 572942 0 619567 0 625809 0 619916 0 587625 0 565724 0 557274 0 560576 0 548854 0 531673 0 525919 0 511038 0 498662 0 555362 0 564591 0 541667 0 527070 0 509846 0 514258 0 516922 0 507561 0 492622 0 490243 0 469357 0 477580 0 528379 0 533590 0 517945 1 506174 1 501866 1 516441 1 528222 1 532638 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkzoekend[t] = + 559199.923875432 -42917.4671280275Crisis[t] -13713.7404844296M1[t] -27174.6381611466M2[t] -33850.7810182896M3[t] -43073.2095897181M4[t] -41552.6381611467M5[t] + 12763.3618388533M6[t] + 22020.5046959962M7[t] + 21408.2857142858M8[t] + 8151.0000000001M9[t] -6841.71428571418M10[t] -3412.57142857133M11[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) 559199.923875432 15587.838076 35.8741 0 0 Crisis -42917.4671280275 17908.148812 -2.3965 0.01915 0.009575 M1 -13713.7404844296 21057.524001 -0.6513 0.516958 0.258479 M2 -27174.6381611466 21895.580768 -1.2411 0.218597 0.109298 M3 -33850.7810182896 21895.580768 -1.546 0.126487 0.063243 M4 -43073.2095897181 21895.580768 -1.9672 0.053012 0.026506 M5 -41552.6381611467 21895.580768 -1.8978 0.061737 0.030868 M6 12763.3618388533 21895.580768 0.5829 0.561769 0.280884 M7 22020.5046959962 21895.580768 1.0057 0.317925 0.158963 M8 21408.2857142858 21745.609272 0.9845 0.328173 0.164086 M9 8151.0000000001 21745.609272 0.3748 0.708886 0.354443 M10 -6841.71428571418 21745.609272 -0.3146 0.753956 0.376978 M11 -3412.57142857133 21745.609272 -0.1569 0.875738 0.437869 Multiple Linear Regression - Regression Statistics Multiple R 0.531838683334346 R-squared 0.282852385090811 Adjusted R-squared 0.163327782605946 F-TEST (value) 2.36647835851726 F-TEST (DF numerator) 12 F-TEST (DF denominator) 72 p-value 0.0125047098828625 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 40682.3097810769 Sum Squared Residuals 119163623696.892 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 474605 545486.183391008 -70881.1833910078 2 470390 532025.285714286 -61635.2857142858 3 461251 525349.142857143 -64098.1428571429 4 454724 516126.714285714 -61402.7142857144 5 455626 517647.285714286 -62021.2857142857 6 516847 571963.285714286 -55116.2857142857 7 525192 581220.428571429 -56028.4285714286 8 522975 580608.209589718 -57633.2095897182 9 518585 567350.923875432 -48765.9238754325 10 509239 552358.209589718 -43119.2095897182 11 512238 555787.352446861 -43549.3524468611 12 519164 559199.923875432 -40035.9238754324 13 517009 545486.183391003 -28477.1833910028 14 509933 532025.285714286 -22092.2857142857 15 509127 525349.142857143 -16222.1428571428 16 500875 516126.714285714 -15251.7142857143 17 506971 517647.285714286 -10676.2857142857 18 569323 571963.285714286 -2640.28571428572 19 579714 581220.428571429 -1506.42857142856 20 577992 580608.209589718 -2616.20958971819 21 565644 567350.923875432 -1706.92387543250 22 547344 552358.209589718 -5014.20958971823 23 554788 555787.352446861 -999.352446861071 24 562325 559199.923875432 3125.0761245676 25 560854 545486.183391003 15367.8166089972 26 555332 532025.285714286 23306.7142857143 27 543599 525349.142857143 18249.8571428571 28 536662 516126.714285714 20535.2857142857 29 542722 517647.285714286 25074.7142857143 30 593530 571963.285714286 21566.7142857143 31 610763 581220.428571429 29542.5714285714 32 612613 580608.209589718 32004.7904102818 33 611324 567350.923875433 43973.0761245675 34 594167 552358.209589718 41808.7904102818 35 595454 555787.352446861 39666.6475531389 36 590865 559199.923875432 31665.0761245676 37 589379 545486.183391003 43892.8166089972 38 584428 532025.285714286 52402.7142857143 39 573100 525349.142857143 47750.8571428572 40 567456 516126.714285714 51329.2857142857 41 569028 517647.285714286 51380.7142857143 42 620735 571963.285714286 48771.7142857142 43 628884 581220.428571429 47663.5714285714 44 628232 580608.209589718 47623.7904102818 45 612117 567350.923875433 44766.0761245675 46 595404 552358.209589718 43045.7904102818 47 597141 555787.352446861 41353.6475531389 48 593408 559199.923875432 34208.0761245676 49 590072 545486.183391003 44585.8166089972 50 579799 532025.285714286 47773.7142857143 51 574205 525349.142857143 48855.8571428572 52 572775 516126.714285714 56648.2857142857 53 572942 517647.285714286 55294.7142857143 54 619567 571963.285714286 47603.7142857142 55 625809 581220.428571429 44588.5714285714 56 619916 580608.209589718 39307.7904102818 57 587625 567350.923875432 20274.0761245675 58 565724 552358.209589718 13365.7904102818 59 557274 555787.352446861 1486.64755313893 60 560576 559199.923875432 1376.0761245676 61 548854 545486.183391003 3367.81660899717 62 531673 532025.285714286 -352.285714285744 63 525919 525349.142857143 569.857142857151 64 511038 516126.714285714 -5088.71428571429 65 498662 517647.285714286 -18985.2857142857 66 555362 571963.285714286 -16601.2857142857 67 564591 581220.428571429 -16629.4285714286 68 541667 580608.209589718 -38941.2095897182 69 527070 567350.923875432 -40280.9238754325 70 509846 552358.209589718 -42512.2095897182 71 514258 555787.352446861 -41529.3524468611 72 516922 559199.923875432 -42277.9238754324 73 507561 545486.183391003 -37925.1833910028 74 492622 532025.285714286 -39403.2857142857 75 490243 525349.142857143 -35106.1428571428 76 469357 516126.714285714 -46769.7142857143 77 477580 517647.285714286 -40067.2857142857 78 528379 571963.285714286 -43584.2857142857 79 533590 581220.428571429 -47630.4285714286 80 517945 537690.742461691 -19745.7424616907 81 506174 524433.456747405 -18259.4567474050 82 501866 509440.742461691 -7574.74246169069 83 516441 512869.885318834 3571.11468116647 84 528222 516282.456747405 11939.5432525951 85 532638 502568.716262975 30069.2837370247 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.617403206735115 0.76519358652977 0.382596793264885 17 0.600965132848332 0.798069734303335 0.399034867151668 18 0.589055564733813 0.821888870532373 0.410944435266187 19 0.58485065131457 0.83029869737086 0.41514934868543 20 0.580952275229976 0.838095449540047 0.419047724770024 21 0.546805436121793 0.906389127756414 0.453194563878207 22 0.489661681145459 0.979323362290918 0.510338318854541 23 0.445762712380416 0.891525424760831 0.554237287619584 24 0.404531162436484 0.809062324872968 0.595468837563516 25 0.467145051587443 0.934290103174887 0.532854948412557 26 0.516049407414814 0.967901185170372 0.483950592585186 27 0.528765818983166 0.942468362033667 0.471234181016834 28 0.537788416863812 0.924423166272375 0.462211583136188 29 0.550986022167159 0.898027955665682 0.449013977832841 30 0.530629833602215 0.93874033279557 0.469370166397785 31 0.531917440661541 0.936165118676917 0.468082559338459 32 0.539208376024389 0.921583247951221 0.460791623975611 33 0.573516674784913 0.852966650430174 0.426483325215087 34 0.59297744640855 0.814045107182899 0.407022553591449 35 0.599352995488711 0.801294009022577 0.400647004511289 36 0.575989552655586 0.848020894688828 0.424010447344414 37 0.604912088358545 0.79017582328291 0.395087911641455 38 0.645832639487874 0.708334721024252 0.354167360512126 39 0.665687569724628 0.668624860550743 0.334312430275372 40 0.692606978098263 0.614786043803474 0.307393021901737 41 0.715459634936827 0.569080730126347 0.284540365063174 42 0.727949143403772 0.544101713192455 0.272050856596228 43 0.736980165873872 0.526039668252255 0.263019834126128 44 0.744103389125491 0.511793221749019 0.255896610874509 45 0.748480389599051 0.503039220801898 0.251519610400949 46 0.75080302099476 0.498393958010481 0.249196979005240 47 0.751613503281029 0.496772993437942 0.248386496718971 48 0.735107073392556 0.529785853214888 0.264892926607444 49 0.739610892713334 0.520778214573332 0.260389107286666 50 0.76602486293386 0.467950274132282 0.233975137066141 51 0.792708683474933 0.414582633050133 0.207291316525067 52 0.857514011549832 0.284971976900336 0.142485988450168 53 0.916580214258664 0.166839571482671 0.0834197857413355 54 0.950976171753465 0.0980476564930702 0.0490238282465351 55 0.975888314376066 0.0482233712478673 0.0241116856239336 56 0.991860811891904 0.0162783762161915 0.00813918810809575 57 0.996091557226512 0.00781688554697657 0.00390844277348828 58 0.99783100427238 0.00433799145523869 0.00216899572761934 59 0.997760268155111 0.00447946368977777 0.00223973184488888 60 0.997573623715257 0.00485275256948543 0.00242637628474272 61 0.996490192259003 0.00701961548199387 0.00350980774099694 62 0.996354291400646 0.007291417198709 0.0036457085993545 63 0.995849350407293 0.0083012991854136 0.0041506495927068 64 0.997025845678415 0.00594830864316975 0.00297415432158488 65 0.994324225479868 0.0113515490402634 0.0056757745201317 66 0.991582393225954 0.0168352135480918 0.0084176067740459 67 0.990804376286321 0.0183912474273578 0.00919562371367889 68 0.985139552773126 0.0297208944537477 0.0148604472268738 69 0.98197106810043 0.0360578637991393 0.0180289318995697 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.148148148148148 NOK 5% type I error level 15 0.277777777777778 NOK 10% type I error level 16 0.296296296296296 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/102oqq1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/102oqq1258753016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/1dk2g1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/1dk2g1258753016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/2olhe1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/2olhe1258753016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/3746x1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/3746x1258753016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/4wcko1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/4wcko1258753016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/5t9zf1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/5t9zf1258753016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/64p6k1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/64p6k1258753016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/7broa1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/7broa1258753016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/8b5nm1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/8b5nm1258753016.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/9mc3y1258753016.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587531888nxzpa7p71o807h/9mc3y1258753016.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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