WS 7.1

*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 13:55:39 -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/t12587508432vtnhk81musgpax.htm/, Retrieved Fri, 20 Nov 2009 22:00:55 +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/t12587508432vtnhk81musgpax.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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkzoekend[t] = + 549627.29113924 -32412.9578059071Crisis[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) 549627.29113924 4944.478592 111.1598 0 0 Crisis -32412.9578059071 18610.34133 -1.7417 0.085272 0.042636 Multiple Linear Regression - Regression Statistics Multiple R 0.18777198628528 R-squared 0.0352583188335194 Adjusted R-squared 0.0236349250845257 F-TEST (value) 3.03339279344054 F-TEST (DF numerator) 1 F-TEST (DF denominator) 83 p-value 0.0852721030934667 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 43947.4870174879 Sum Squared Residuals 160304674057.638 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 474605 549627.291139245 -75022.2911392455 2 470390 549627.29113924 -79237.2911392404 3 461251 549627.29113924 -88376.2911392404 4 454724 549627.29113924 -94903.2911392404 5 455626 549627.29113924 -94001.2911392404 6 516847 549627.29113924 -32780.2911392404 7 525192 549627.29113924 -24435.2911392404 8 522975 549627.29113924 -26652.2911392404 9 518585 549627.29113924 -31042.2911392404 10 509239 549627.29113924 -40388.2911392404 11 512238 549627.29113924 -37389.2911392404 12 519164 549627.29113924 -30463.2911392404 13 517009 549627.29113924 -32618.2911392404 14 509933 549627.29113924 -39694.2911392404 15 509127 549627.29113924 -40500.2911392404 16 500875 549627.29113924 -48752.2911392404 17 506971 549627.29113924 -42656.2911392404 18 569323 549627.29113924 19695.7088607596 19 579714 549627.29113924 30086.7088607596 20 577992 549627.29113924 28364.7088607596 21 565644 549627.29113924 16016.7088607596 22 547344 549627.29113924 -2283.29113924044 23 554788 549627.29113924 5160.70886075956 24 562325 549627.29113924 12697.7088607596 25 560854 549627.29113924 11226.7088607596 26 555332 549627.29113924 5704.70886075956 27 543599 549627.29113924 -6028.29113924044 28 536662 549627.29113924 -12965.2911392404 29 542722 549627.29113924 -6905.29113924044 30 593530 549627.29113924 43902.7088607596 31 610763 549627.29113924 61135.7088607595 32 612613 549627.29113924 62985.7088607595 33 611324 549627.29113924 61696.7088607595 34 594167 549627.29113924 44539.7088607596 35 595454 549627.29113924 45826.7088607596 36 590865 549627.29113924 41237.7088607596 37 589379 549627.29113924 39751.7088607596 38 584428 549627.29113924 34800.7088607596 39 573100 549627.29113924 23472.7088607596 40 567456 549627.29113924 17828.7088607596 41 569028 549627.29113924 19400.7088607596 42 620735 549627.29113924 71107.7088607596 43 628884 549627.29113924 79256.7088607596 44 628232 549627.29113924 78604.7088607596 45 612117 549627.29113924 62489.7088607595 46 595404 549627.29113924 45776.7088607596 47 597141 549627.29113924 47513.7088607596 48 593408 549627.29113924 43780.7088607596 49 590072 549627.29113924 40444.7088607596 50 579799 549627.29113924 30171.7088607596 51 574205 549627.29113924 24577.7088607596 52 572775 549627.29113924 23147.7088607596 53 572942 549627.29113924 23314.7088607596 54 619567 549627.29113924 69939.7088607596 55 625809 549627.29113924 76181.7088607596 56 619916 549627.29113924 70288.7088607596 57 587625 549627.29113924 37997.7088607596 58 565724 549627.29113924 16096.7088607596 59 557274 549627.29113924 7646.70886075956 60 560576 549627.29113924 10948.7088607596 61 548854 549627.29113924 -773.291139240441 62 531673 549627.29113924 -17954.2911392404 63 525919 549627.29113924 -23708.2911392404 64 511038 549627.29113924 -38589.2911392404 65 498662 549627.29113924 -50965.2911392404 66 555362 549627.29113924 5734.70886075956 67 564591 549627.29113924 14963.7088607596 68 541667 549627.29113924 -7960.29113924044 69 527070 549627.29113924 -22557.2911392404 70 509846 549627.29113924 -39781.2911392404 71 514258 549627.29113924 -35369.2911392404 72 516922 549627.29113924 -32705.2911392404 73 507561 549627.29113924 -42066.2911392404 74 492622 549627.29113924 -57005.2911392404 75 490243 549627.29113924 -59384.2911392404 76 469357 549627.29113924 -80270.2911392404 77 477580 549627.29113924 -72047.2911392404 78 528379 549627.29113924 -21248.2911392404 79 533590 549627.29113924 -16037.2911392404 80 517945 517214.333333333 730.666666666674 81 506174 517214.333333333 -11040.3333333333 82 501866 517214.333333333 -15348.3333333333 83 516441 517214.333333333 -773.333333333326 84 528222 517214.333333333 11007.6666666667 85 532638 517214.333333333 15423.6666666667 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0232551441096575 0.046510288219315 0.976744855890342 6 0.219378016782612 0.438756033565224 0.780621983217388 7 0.344440485193853 0.688880970387706 0.655559514806147 8 0.366884529952654 0.733769059905307 0.633115470047346 9 0.335984938334841 0.671969876669682 0.664015061665159 10 0.271339947875506 0.542679895751012 0.728660052124494 11 0.219919507932298 0.439839015864596 0.780080492067702 12 0.187535818392979 0.375071636785958 0.812464181607021 13 0.152644130038922 0.305288260077844 0.847355869961078 14 0.115942144458136 0.231884288916272 0.884057855541864 15 0.086878519462809 0.173757038925618 0.913121480537191 16 0.0645455071207046 0.129091014241409 0.935454492879295 17 0.0479577078586455 0.0959154157172911 0.952042292141354 18 0.147142433000596 0.294284866001192 0.852857566999404 19 0.314950956475233 0.629901912950466 0.685049043524767 20 0.445989860807001 0.891979721614001 0.554010139192999 21 0.484163832800266 0.968327665600532 0.515836167199734 22 0.454036802812098 0.908073605624197 0.545963197187902 23 0.437237054006461 0.874474108012921 0.562762945993539 24 0.435092400882792 0.870184801765585 0.564907599117208 25 0.42180500771189 0.84361001542378 0.57819499228811 26 0.39158674123503 0.78317348247006 0.60841325876497 27 0.344530261858519 0.689060523717037 0.655469738141481 28 0.29654079672407 0.59308159344814 0.70345920327593 29 0.254364525942429 0.508729051884858 0.745635474057571 30 0.327671740415583 0.655343480831165 0.672328259584417 31 0.470930311081536 0.941860622163072 0.529069688918464 32 0.599764170765365 0.80047165846927 0.400235829234635 33 0.69484289095747 0.61031421808506 0.30515710904253 34 0.713938960844189 0.572122078311622 0.286061039155811 35 0.731099197224108 0.537801605551784 0.268900802775892 36 0.732625612475513 0.534748775048974 0.267374387524487 37 0.728146173061368 0.543707653877264 0.271853826938632 38 0.711251168032895 0.577497663934209 0.288748831967104 39 0.673303819369436 0.653392361261127 0.326696180630564 40 0.625689504827463 0.748620990345075 0.374310495172537 41 0.57748232551746 0.84503534896508 0.42251767448254 42 0.670192412064462 0.659615175871075 0.329807587935538 43 0.7816142562509 0.436771487498199 0.218385743749099 44 0.86693229188499 0.266135416230018 0.133067708115009 45 0.898510375898738 0.202979248202524 0.101489624101262 46 0.901820860394436 0.196358279211128 0.0981791396055638 47 0.908629768458096 0.182740463083807 0.0913702315419036 48 0.911799344052437 0.176401311895126 0.0882006559475628 49 0.912329135751392 0.175341728497215 0.0876708642486077 50 0.902573450848286 0.194853098303428 0.097426549151714 51 0.887106911185042 0.225786177629915 0.112893088814958 52 0.86938912145057 0.261221757098859 0.130610878549430 53 0.851268941158777 0.297462117682447 0.148731058841223 54 0.928223131191925 0.143553737616150 0.0717768688080751 55 0.983034909351414 0.0339301812971724 0.0169650906485862 56 0.998088670617823 0.00382265876435486 0.00191132938217743 57 0.999366860554405 0.00126627889118925 0.000633139445594627 58 0.999517925165496 0.000964149669008244 0.000482074834504122 59 0.999538124528106 0.000923750943788806 0.000461875471894403 60 0.999670241276268 0.000659517447464739 0.000329758723732369 61 0.999649272260684 0.000701455478632899 0.000350727739316449 62 0.999411284194083 0.00117743161183453 0.000588715805917266 63 0.998953437266038 0.00209312546792377 0.00104656273396188 64 0.998166450920346 0.00366709815930842 0.00183354907965421 65 0.997441551486053 0.00511689702789377 0.00255844851394688 66 0.998150560210196 0.00369887957960766 0.00184943978980383 67 0.999601145018537 0.000797709962925191 0.000398854981462596 68 0.999725924891008 0.000548150217983337 0.000274075108991669 69 0.999626336322706 0.000747327354587012 0.000373663677293506 70 0.99917751809055 0.00164496381890219 0.000822481909451097 71 0.998367226355246 0.00326554728950801 0.00163277364475401 72 0.997136121515038 0.00572775696992319 0.00286387848496159 73 0.994103078340222 0.0117938433195563 0.00589692165977813 74 0.988296054016861 0.0234078919662775 0.0117039459831387 75 0.978481057712286 0.0430378845754277 0.0215189422877139 76 0.988435260021496 0.0231294799570076 0.0115647399785038 77 0.999193276776816 0.00161344644636830 0.000806723223184148 78 0.996636083149036 0.00672783370192849 0.00336391685096424 79 0.986366172741715 0.0272676545165696 0.0136338272582848 80 0.949991214326124 0.100017571347753 0.0500087856738765 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.25 NOK 5% type I error level 26 0.342105263157895 NOK 10% type I error level 27 0.355263157894737 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/10jj1p1258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/10jj1p1258750534.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/1jcr21258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/1jcr21258750534.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/2uy5j1258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/2uy5j1258750534.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/3vbl41258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/3vbl41258750534.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/4oysb1258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/4oysb1258750534.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/5slv71258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/5slv71258750534.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/6959i1258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/6959i1258750534.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/77r361258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/77r361258750534.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/87j1s1258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/87j1s1258750534.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/9lbcy1258750534.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587508432vtnhk81musgpax/9lbcy1258750534.ps (open in new window)

Parameters (Session):

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