WS 7.3

*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 15:04:53 -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/t12587548700032orglez0s8fj.htm/, Retrieved Fri, 20 Nov 2009 23:08:02 +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/t12587548700032orglez0s8fj.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] = + 547894.545081966 -54536.8842213115Crisis[t] -12570.6784586022M1[t] -26133.4516552008M2[t] -33079.7048356996M3[t] -42572.2437304839M4[t] -41321.7826252682M5[t] + 12724.107051376M6[t] + 21711.1395851631M7[t] + 22488.7270077089M8[t] + 8961.33097006743M9[t] -6301.49363900264M10[t] -3142.46110521557M11[t] + 270.110323355782t + 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) 547894.545081966 17618.587546 31.0975 0 0 Crisis -54536.8842213115 19779.076066 -2.7573 0.007404 0.003702 M1 -12570.6784586022 20955.558696 -0.5999 0.5505 0.27525 M2 -26133.4516552008 21785.417137 -1.1996 0.234289 0.117144 M3 -33079.7048356996 21779.247823 -1.5189 0.133238 0.066619 M4 -42572.2437304839 21774.916487 -1.9551 0.054508 0.027254 M5 -41321.7826252682 21772.424226 -1.8979 0.061776 0.030888 M6 12724.107051376 21771.771671 0.5844 0.560783 0.280391 M7 21711.1395851631 21772.958988 0.9972 0.322073 0.161036 M8 22488.7270077089 21637.444658 1.0393 0.302172 0.151086 M9 8961.33097006743 21630.96401 0.4143 0.679916 0.339958 M10 -6301.49363900264 21626.333787 -0.2914 0.77161 0.385805 M11 -3142.46110521557 21623.555178 -0.1453 0.884865 0.442433 t 270.110323355782 200.145413 1.3496 0.181441 0.090721 Multiple Linear Regression - Regression Statistics Multiple R 0.548442358230699 R-squared 0.300789020301651 Adjusted R-squared 0.17276447472308 F-TEST (value) 2.34946368247057 F-TEST (DF numerator) 13 F-TEST (DF denominator) 71 p-value 0.0113830623123323 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 40452.2345635065 Sum Squared Residuals 116183212963.847 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 474605 535593.976946726 -60988.9769467255 2 470390 522301.314073478 -51911.3140734776 3 461251 515625.171216335 -54374.1712163348 4 454724 506402.742644906 -51678.7426449063 5 455626 507923.314073478 -52297.3140734775 6 516847 562239.314073478 -45392.3140734776 7 525192 571496.45693062 -46304.4569306204 8 522975 572544.154676522 -49569.1546765221 9 518585 559286.868962236 -40701.8689622364 10 509239 544294.154676522 -35055.1546765221 11 512238 547723.297533665 -35485.297533665 12 519164 551135.868962236 -31971.8689622363 13 517009 538835.30082699 -21826.3008269899 14 509933 525542.637953747 -15609.6379537470 15 509127 518866.495096604 -9739.4950966041 16 500875 509644.066525176 -8769.06652517551 17 506971 511164.637953747 -4193.63795374696 18 569323 565480.637953747 3842.36204625303 19 579714 574737.78081089 4976.2191891102 20 577992 575785.478556791 2206.52144320857 21 565644 562528.192842506 3115.80715749427 22 547344 547535.478556791 -191.478556791467 23 554788 550964.621413934 3823.3785860657 24 562325 554377.192842506 7947.80715749434 25 560854 542076.624707259 18777.3752927408 26 555332 528783.961834016 26548.0381659836 27 543599 522107.818976873 21491.1810231265 28 536662 512885.390405445 23776.6095945551 29 542722 514405.961834016 28316.0381659837 30 593530 568721.961834016 24808.0381659837 31 610763 577979.104691159 32783.8953088408 32 612613 579026.802437061 33586.1975629392 33 611324 565769.516722775 45554.4832772248 34 594167 550776.802437061 43390.1975629392 35 595454 554205.945294204 41248.0547057963 36 590865 557618.516722775 33246.4832772250 37 589379 545317.948587529 44061.0514124714 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 582268.12631733 45963.8736826698 45 612117 569010.840603044 43106.1593969555 46 595404 554018.12631733 41385.8736826698 47 597141 557447.269174473 39693.7308255269 48 593408 560859.840603044 32548.1593969556 49 590072 548559.272467798 41512.727532202 50 579799 535266.609594555 44532.3904054449 51 574205 528590.466737412 45614.5332625878 52 572775 519368.038165984 53406.9618340164 53 572942 520888.609594555 52053.3904054449 54 619567 575204.609594555 44362.3904054449 55 625809 584461.752451698 41347.247548302 56 619916 585509.4501976 34406.5498024004 57 587625 572252.164483314 15372.8355166861 58 565724 557259.4501976 8464.5498024004 59 557274 560688.593054742 -3414.59305474245 60 560576 564101.164483314 -3525.16448331379 61 548854 551800.596348067 -2946.59634806739 62 531673 538507.933474825 -6834.9334748245 63 525919 531831.790617682 -5912.79061768161 64 511038 522609.362046253 -11571.3620462530 65 498662 524129.933474824 -25467.9334748245 66 555362 578445.933474825 -23083.9334748245 67 564591 587703.076331967 -23112.0763319673 68 541667 588750.774077869 -47083.774077869 69 527070 575493.488363583 -48423.4883635833 70 509846 560500.774077869 -50654.774077869 71 514258 563929.916935012 -49671.9169350118 72 516922 567342.488363583 -50420.4883635832 73 507561 555041.920228337 -47480.9202283368 74 492622 541749.257355094 -49127.2573550939 75 490243 535073.114497951 -44830.114497951 76 469357 525850.685926522 -56493.6859265224 77 477580 527371.257355094 -49791.2573550939 78 528379 581687.257355094 -53308.2573550939 79 533590 590944.400212237 -57354.4002122367 80 517945 537455.213736827 -19510.2137368268 81 506174 524197.928022541 -18023.9280225411 82 501866 509205.213736827 -7339.21373682682 83 516441 512634.35659397 3806.64340603034 84 528222 516046.928022541 12175.0719774590 85 532638 503746.359887295 28891.6401127054 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00285103342350996 0.00570206684701992 0.99714896657649 18 0.00070215004566882 0.00140430009133764 0.999297849954331 19 0.000222029280372321 0.000444058560744642 0.999777970719628 20 6.63979426628915e-05 0.000132795885325783 0.999933602057337 21 1.16197437744086e-05 2.32394875488172e-05 0.999988380256226 22 8.4511313592429e-06 1.69022627184858e-05 0.99999154886864 23 2.49800476908221e-06 4.99600953816442e-06 0.99999750199523 24 7.07506729370806e-07 1.41501345874161e-06 0.99999929249327 25 2.68149950694364e-07 5.36299901388728e-07 0.99999973185005 26 8.2455309414264e-08 1.64910618828528e-07 0.99999991754469 27 1.22458850699506e-07 2.44917701399013e-07 0.99999987754115 28 1.23429418026245e-07 2.46858836052489e-07 0.999999876570582 29 7.51472220865512e-08 1.50294444173102e-07 0.999999924852778 30 7.46294995202157e-07 1.49258999040431e-06 0.999999253705005 31 8.3231299303966e-07 1.66462598607932e-06 0.999999167687007 32 4.5283627036236e-07 9.0567254072472e-07 0.99999954716373 33 2.29726090926200e-07 4.59452181852401e-07 0.99999977027391 34 9.41811414063898e-08 1.88362282812780e-07 0.999999905818859 35 4.46741678102294e-08 8.93483356204589e-08 0.999999955325832 36 2.86509496495823e-07 5.73018992991645e-07 0.999999713490503 37 1.25233139116123e-06 2.50466278232246e-06 0.99999874766861 38 1.66595086397308e-06 3.33190172794617e-06 0.999998334049136 39 6.34079227666844e-06 1.26815845533369e-05 0.999993659207723 40 1.18002812142076e-05 2.36005624284152e-05 0.999988199718786 41 3.00110984863519e-05 6.00221969727038e-05 0.999969988901514 42 0.000218571189440816 0.000437142378881633 0.99978142881056 43 0.00215277597318077 0.00430555194636155 0.99784722402682 44 0.00390106375229021 0.00780212750458043 0.99609893624771 45 0.0147403522853691 0.0294807045707382 0.98525964771463 46 0.0355121916914067 0.0710243833828134 0.964487808308593 47 0.0682572669771837 0.136514533954367 0.931742733022816 48 0.171964155018529 0.343928310037058 0.828035844981471 49 0.288155708778725 0.57631141755745 0.711844291221275 50 0.361692893645831 0.723385787291662 0.638307106354169 51 0.387256910074024 0.774513820148048 0.612743089925976 52 0.378410427937602 0.756820855875203 0.621589572062398 53 0.393645821133725 0.787291642267451 0.606354178866275 54 0.425900460622553 0.851800921245107 0.574099539377447 55 0.466873576016493 0.933747152032987 0.533126423983507 56 0.863954369253164 0.272091261493671 0.136045630746835 57 0.980223387973681 0.0395532240526373 0.0197766120263187 58 0.996297761159333 0.00740447768133352 0.00370223884066676 59 0.99763521568421 0.0047295686315791 0.00236478431578955 60 0.997480069116805 0.00503986176638963 0.00251993088319482 61 0.996811071790302 0.00637785641939619 0.00318892820969809 62 0.995928436056815 0.0081431278863694 0.0040715639431847 63 0.993209801201282 0.0135803975974365 0.00679019879871823 64 0.990758093376726 0.0184838132465487 0.00924190662327436 65 0.984080827064646 0.0318383458707083 0.0159191729353542 66 0.967197044513315 0.0656059109733698 0.0328029554866849 67 0.928943708410469 0.142112583179061 0.0710562915895307 68 0.909962801700666 0.180074396598668 0.090037198299334 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 33 0.634615384615385 NOK 5% type I error level 38 0.730769230769231 NOK 10% type I error level 40 0.769230769230769 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/10ilm91258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/10ilm91258754687.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/15ivq1258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/15ivq1258754687.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/2cn3u1258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/2cn3u1258754687.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/3tzjj1258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/3tzjj1258754687.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/46ov91258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/46ov91258754687.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/5evtz1258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/5evtz1258754687.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/6ck851258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/6ck851258754687.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/77zmv1258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/77zmv1258754687.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/8a05h1258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/8a05h1258754687.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/93k1a1258754687.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587548700032orglez0s8fj/93k1a1258754687.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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