Multiple regression

*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: Sat, 13 Dec 2008 08:07:42 -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/Dec/13/t1229181242dgjcgcur9tkbv69.htm/, Retrieved Sat, 13 Dec 2008 16:14:11 +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/2008/Dec/13/t1229181242dgjcgcur9tkbv69.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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32,68 10967,87 31,54 10433,56 32,43 10665,78 26,54 10666,71 25,85 10682,74 27,6 10777,22 25,71 10052,6 25,38 10213,97 28,57 10546,82 27,64 10767,2 25,36 10444,5 25,9 10314,68 26,29 9042,56 21,74 9220,75 19,2 9721,84 19,32 9978,53 19,82 9923,81 20,36 9892,56 24,31 10500,98 25,97 10179,35 25,61 10080,48 24,67 9492,44 25,59 8616,49 26,09 8685,4 28,37 8160,67 27,34 8048,1 24,46 8641,21 27,46 8526,63 30,23 8474,21 32,33 7916,13 29,87 7977,64 24,87 8334,59 25,48 8623,36 27,28 9098,03 28,24 9154,34 29,58 9284,73 26,95 9492,49 29,08 9682,35 28,76 9762,12 29,59 10124,63 30,7 10540,05 30,52 10601,61 32,67 10323,73 33,19 10418,4 37,13 10092,96 35,54 10364,91 37,75 10152,09 41,84 10032,8 42,94 10204,59 49,14 10001,6 44,61 10411,75 40,22 10673,38 44,23 10539,51 45,85 10723,78 53,38 10682,06 53,26 10283,19 51,8 10377,18 55,3 10486,64 57,81 10545,38 63,96 10554,27 63,77 10532,54 59,15 10324,31 56,12 10695,25 57,42 10827,81 63,52 10872,48 61,71 10971,19 63,01 11145,65 68,18 11234,68 72,03 11333,88 69,75 10997,97 74,41 11036,89 74,33 11257,35 64,24 11533,59 60,03 11963,12 59,44 12185,15 62,5 12377,62 55,04 12512,89 58,34 12631,48 61,92 12268,53 67,65 12754,8 67,68 13407,75 70,3 13480,21 75,26 13673,28 71,44 13239,71 76,36 13557,69 81,71 13901,28 92,6 13200,58 90,6 13406,97 92,23 12538,12 94,09 12419,57 102,79 12193,88 109,65 12656,63 124,05 12812,48 132,69 12056,67 135,81 11322,38 116,07 11530,75 101,42 11114,08 75,73 9181,73 55,48 8614,55

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 5 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Olieprijs[t] = -91.0219947586744 + 0.0132666552699718DowJones[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) -91.0219947586744 15.3699 -5.9221 0 0 DowJones 0.0132666552699718 0.00143 9.2779 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.6856927421105 R-squared 0.470174536583017 Adjusted R-squared 0.46471241840346 F-TEST (value) 86.0791585108454 F-TEST (DF numerator) 1 F-TEST (DF denominator) 97 p-value 4.88498130835069e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 20.046807600406 Sum Squared Residuals 38981.8260118664 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 32.68 54.4849555771916 -21.8049555771916 2 31.54 47.396448999893 -15.8564489998930 3 32.43 50.4772316866859 -18.0472316866859 4 26.54 50.489569676087 -23.9495696760870 5 25.85 50.7022341600646 -24.8522341600646 6 27.6 51.9556677499716 -24.3556677499715 7 25.71 42.3423840082446 -16.6323840082446 8 25.38 44.4832241691599 -19.1032241691599 9 28.57 48.89903037577 -20.3290303757700 10 27.64 51.8227358641664 -24.1827358641664 11 25.36 47.5415862085465 -22.1815862085465 12 25.9 45.8193090213988 -19.9193090213988 13 26.29 28.9425315193622 -2.65253151936219 14 21.74 31.3065168219185 -9.56651682191848 15 19.2 37.9543051111487 -18.7543051111487 16 19.32 41.3597228523978 -22.0397228523978 17 19.82 40.6337714760249 -20.8137714760249 18 20.36 40.2191884988383 -19.8591884988383 19 24.31 48.2908868981945 -23.9808868981945 20 25.97 44.0239325637135 -18.0539325637135 21 25.61 42.7122583571714 -17.1022583571714 22 24.67 34.9109343922171 -10.2409343922171 23 25.59 23.2900077084853 2.29999229151471 24 26.09 24.2042129231390 1.88578707686096 25 28.37 17.2428009033267 11.1271990966733 26 27.34 15.749373519586 11.5906264804140 27 24.46 23.617959426759 0.842040573241023 28 27.46 22.0978660659256 5.36213393407439 29 30.23 21.4024279966737 8.82757200332632 30 32.33 13.9985730236078 18.3314269763922 31 29.87 14.8146049892638 15.0553950107362 32 24.87 19.5501375878802 5.31986241211978 33 25.48 23.38114963019 2.09885036981 34 27.28 29.6784328871875 -2.39843288718754 35 28.24 30.4254782454396 -2.18547824543965 36 29.58 32.1553174260913 -2.57531742609127 37 26.95 34.9115977249806 -7.96159772498062 38 29.08 37.4304048945375 -8.35040489453749 39 28.76 38.4886859854231 -9.72868598542315 40 29.59 43.2979811873406 -13.7079811873406 41 30.7 48.8092151195923 -18.1092151195923 42 30.52 49.6259104180118 -19.1059104180118 43 32.67 45.939372251592 -13.2693722515920 44 33.19 47.1953265060003 -14.0053265060003 45 37.13 42.8778262149406 -5.74782621494061 46 35.54 46.4856931156095 -10.9456931156095 47 37.75 43.6622835410541 -5.91228354105406 48 41.84 42.0797042338991 -0.239704233899102 49 42.94 44.3587829427276 -1.41878294272758 50 49.14 41.665784589476 7.474215410524 51 44.61 47.1071032484549 -2.49710324845495 52 40.22 50.5780582667377 -10.3580582667377 53 44.23 48.8020511257466 -4.57205112574656 54 45.85 51.2466976923443 -5.39669769234427 55 53.38 50.693212834481 2.68678716551897 56 53.26 45.4015420469474 7.85845795305262 57 51.8 46.648474975772 5.15152502422797 58 55.3 48.1006430616231 7.19935693837687 59 57.81 48.8799263921813 8.93007360781873 60 63.96 48.9978669575313 14.9621330424687 61 63.77 48.7095825385149 15.0604174614851 62 59.15 45.9470669116486 13.2029330883514 63 56.12 50.868200017492 5.25179998250803 64 57.42 52.6268278400794 4.79317215992057 65 63.52 53.2194493309891 10.3005506690109 66 61.71 54.529000872688 7.180999127312 67 63.01 56.8435015510873 6.16649844891272 68 68.18 58.0246318697729 10.1553681302271 69 72.03 59.3406840725541 12.6893159274459 70 69.75 54.8842819008178 14.8657180991822 71 74.41 55.4006201239251 19.0093798760749 72 74.33 58.3253869447431 16.0046130552569 73 64.24 61.9901677965202 2.24983220347983 74 60.03 67.6885942346312 -7.65859423463118 75 59.44 70.634189704223 -11.1941897042230 76 62.5 73.1876228440345 -10.6876228440345 77 55.04 74.9822033024036 -19.9422033024036 78 58.34 76.5554959508696 -18.2154959508696 79 61.92 71.7403634206333 -9.82036342063329 80 67.65 78.1915398787625 -10.5415398787625 81 67.68 86.8540024372906 -19.1740024372906 82 70.3 87.8153042781528 -17.5153042781528 83 75.26 90.3766974111262 -15.1166974111262 84 71.44 84.6246736857245 -13.1846736857245 85 76.36 88.8432047284702 -12.4832047284702 86 81.71 93.4014948126798 -11.6914948126798 87 92.6 84.1055494650105 8.49445053498946 88 90.6 86.84365444618 3.75634555381997 89 92.23 75.316921014865 16.913078985135 90 94.09 73.7441590326098 20.3458409673902 91 102.79 70.7500076047299 32.0399923952701 92 109.65 76.8891523309093 32.7608476690907 93 124.05 78.9567605547345 45.0932394452655 94 132.69 68.929689835137 63.760310164863 95 135.81 59.1881175369494 76.6218824630506 96 116.07 61.9524904955534 54.1175095044465 97 101.42 56.4246732442143 44.9953267557857 98 75.73 30.7888519332842 44.9411480667158 99 55.48 23.2642703972615 32.2157296027385 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00932788959825223 0.0186557791965045 0.990672110401748 6 0.00182157449450672 0.00364314898901344 0.998178425505493 7 0.000301953201631945 0.000603906403263889 0.999698046798368 8 4.88576203897938e-05 9.77152407795876e-05 0.99995114237961 9 6.74527373850741e-06 1.34905474770148e-05 0.999993254726262 10 1.15133237740779e-06 2.30266475481559e-06 0.999998848667623 11 2.22062017883440e-07 4.44124035766879e-07 0.999999777937982 12 3.05627038676783e-08 6.11254077353566e-08 0.999999969437296 13 8.79075708775795e-09 1.75815141755159e-08 0.999999991209243 14 1.86225272060819e-09 3.72450544121638e-09 0.999999998137747 15 2.29592746022157e-09 4.59185492044314e-09 0.999999997704073 16 2.41642629921222e-09 4.83285259842445e-09 0.999999997583574 17 1.22883064180320e-09 2.45766128360641e-09 0.99999999877117 18 4.24414659726153e-10 8.48829319452307e-10 0.999999999575585 19 1.16461097149908e-10 2.32922194299816e-10 0.999999999883539 20 2.29222297928963e-11 4.58444595857926e-11 0.999999999977078 21 4.46357220400291e-12 8.92714440800582e-12 0.999999999995536 22 1.04509486236769e-12 2.09018972473539e-12 0.999999999998955 23 8.15907267971364e-13 1.63181453594273e-12 0.999999999999184 24 3.15957659725210e-13 6.31915319450421e-13 0.999999999999684 25 2.55263928973565e-13 5.1052785794713e-13 0.999999999999745 26 7.73091753291321e-14 1.54618350658264e-13 0.999999999999923 27 1.35857939899368e-14 2.71715879798735e-14 0.999999999999986 28 3.15142714039660e-15 6.30285428079319e-15 0.999999999999997 29 1.49396531104728e-15 2.98793062209455e-15 0.999999999999998 30 1.31589065174373e-15 2.63178130348746e-15 0.999999999999999 31 3.49904724667506e-16 6.99809449335013e-16 1 32 6.73714173516559e-17 1.34742834703312e-16 1 33 1.19575912272439e-17 2.39151824544877e-17 1 34 2.16407400681091e-18 4.32814801362181e-18 1 35 4.285314917236e-19 8.570629834472e-19 1 36 1.09980311561590e-19 2.19960623123179e-19 1 37 2.08865111554660e-20 4.17730223109319e-20 1 38 5.32815137839852e-21 1.06563027567970e-20 1 39 1.32761816888743e-21 2.65523633777487e-21 1 40 4.52324572647249e-22 9.04649145294498e-22 1 41 2.48577357329243e-22 4.97154714658486e-22 1 42 1.32938308993346e-22 2.65876617986691e-22 1 43 1.30208747028193e-22 2.60417494056387e-22 1 44 1.50143222030686e-22 3.00286444061371e-22 1 45 1.00748662930387e-21 2.01497325860774e-21 1 46 2.10909430694302e-21 4.21818861388604e-21 1 47 9.61438940676912e-21 1.92287788135382e-20 1 48 2.37089486810563e-19 4.74178973621126e-19 1 49 4.11826793735065e-18 8.2365358747013e-18 1 50 6.15057981096615e-16 1.23011596219323e-15 1 51 3.62059054626058e-15 7.24118109252116e-15 0.999999999999996 52 6.04548076442821e-15 1.20909615288564e-14 0.999999999999994 53 2.03709815901611e-14 4.07419631803223e-14 0.99999999999998 54 7.5992720459085e-14 1.5198544091817e-13 0.999999999999924 55 1.30283516470309e-12 2.60567032940618e-12 0.999999999998697 56 1.34504341515884e-11 2.69008683031769e-11 0.99999999998655 57 6.06838424001425e-11 1.21367684800285e-10 0.999999999939316 58 3.49932369150609e-10 6.99864738301219e-10 0.999999999650068 59 1.99540316966616e-09 3.99080633933233e-09 0.999999998004597 60 1.98447813463368e-08 3.96895626926735e-08 0.999999980155219 61 1.04860242420405e-07 2.09720484840811e-07 0.999999895139758 62 2.59217062541461e-07 5.18434125082923e-07 0.999999740782938 63 3.8972949083206e-07 7.7945898166412e-07 0.99999961027051 64 5.71116611267081e-07 1.14223322253416e-06 0.999999428883389 65 1.07324039418405e-06 2.1464807883681e-06 0.999998926759606 66 1.54880402509400e-06 3.09760805018799e-06 0.999998451195975 67 2.03643741056712e-06 4.07287482113424e-06 0.99999796356259 68 2.95857780500068e-06 5.91715561000136e-06 0.999997041422195 69 4.36573311094871e-06 8.73146622189742e-06 0.99999563426689 70 6.19637590001591e-06 1.23927518000318e-05 0.9999938036241 71 9.78636715803816e-06 1.95727343160763e-05 0.999990213632842 72 1.16375883465191e-05 2.32751766930382e-05 0.999988362411653 73 9.9650933343207e-06 1.99301866686414e-05 0.999990034906666 74 9.45457649372108e-06 1.89091529874422e-05 0.999990545423506 75 9.98949971247623e-06 1.99789994249525e-05 0.999990010500287 76 9.5642502652271e-06 1.91285005304542e-05 0.999990435749735 77 1.87129580050215e-05 3.7425916010043e-05 0.999981287041995 78 3.22454606654716e-05 6.44909213309432e-05 0.999967754539335 79 4.88682429205454e-05 9.77364858410908e-05 0.99995113175708 80 5.59296344684277e-05 0.000111859268936855 0.999944070365532 81 7.58318919889454e-05 0.000151663783977891 0.99992416810801 82 0.000104661594397472 0.000209323188794944 0.999895338405603 83 0.000130009503576978 0.000260019007153957 0.999869990496423 84 0.000288688003900893 0.000577376007801785 0.9997113119961 85 0.000713647778279406 0.00142729555655881 0.99928635222172 86 0.00257100704877403 0.00514201409754806 0.997428992951226 87 0.00464237835882267 0.00928475671764535 0.995357621641177 88 0.0191794927520892 0.0383589855041784 0.98082050724791 89 0.0514955264109693 0.102991052821939 0.94850447358903 90 0.143482605364242 0.286965210728483 0.856517394635758 91 0.2210458009488 0.4420916018976 0.7789541990512 92 0.432800794679254 0.865601589358508 0.567199205320746 93 0.654237198725713 0.691525602548573 0.345762801274287 94 0.571233095798175 0.85753380840365 0.428766904201825 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 82 0.911111111111111 NOK 5% type I error level 84 0.933333333333333 NOK 10% type I error level 84 0.933333333333333 NOK

Charts produced by software:

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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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