Multiple Regression Paper Dma

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

Title produced by software: Multiple Regression

Date of computation: Thu, 09 Dec 2010 15:50:37 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/09/t129190976304t19qqniylek9o.htm/, Retrieved Thu, 09 Dec 2010 16:49:34 +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/2010/Dec/09/t129190976304t19qqniylek9o.htm/}, year = {2010}, } @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 = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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Paper Dma

Dataseries X:

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3030,29 25,64 2803,47 27,97 2767,63 27,62 2882,6 23,31 2863,36 29,07 2897,06 29,58 3012,61 28,63 3142,95 29,92 3032,93 32,68 3045,78 31,54 3110,52 32,43 3013,24 26,54 2987,1 25,85 2995,55 27,6 2833,18 25,71 2848,96 25,38 2794,83 28,57 2845,26 27,64 2915,03 25,36 2892,63 25,9 2604,42 26,29 2641,65 21,74 2659,81 19,2 2638,53 19,32 2720,25 19,82 2745,88 20,36 2735,7 24,31 2811,7 25,97 2799,43 25,61 2555,28 24,67 2304,98 25,59 2214,95 26,09 2065,81 28,37 1940,49 27,34 2042 24,46 1995,37 27,46 1946,81 30,23 1765,9 32,33 1635,25 29,87 1833,42 24,87 1910,43 25,48 1959,67 27,28 1969,6 28,24 2061,41 29,58 2093,48 26,95 2120,88 29,08 2174,56 28,76 2196,72 29,59 2350,44 30,7 2440,25 30,52 2408,64 32,67 2472,81 33,19 2407,6 37,13 2454,62 35,54 2448,05 37,75 2497,84 41,84 2645,64 42,94 2756,76 49,14 2849,27 44,61 2921,44 40,22 2981,85 44,23 3080,58 45,85 3106,22 53,38 3119,31 53,26 3061,26 51,8 3097,31 55,3 3161,69 57,81 3257,16 63,96 3277,01 63,77 3295, etc...

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 18 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Aandelenkoers[t] = + 2162.31450281125 + 17.8087322021762Olieprijs[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) 2162.31450281125 122.062371 17.7148 0 0 Olieprijs 17.8087322021762 2.202828 8.0845 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.615794096477654 R-squared 0.379202369256731 Adjusted R-squared 0.373400522240438 F-TEST (value) 65.3589052230932 F-TEST (DF numerator) 1 F-TEST (DF denominator) 107 p-value 1.03117514527185e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 612.982636516373 Sum Squared Residuals 40205005.2557504 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3030.29 2618.93039647503 411.359603524969 2 2803.47 2660.42474250612 143.045257493879 3 2767.63 2654.19168623536 113.438313764641 4 2882.6 2577.43605044398 305.163949556021 5 2863.36 2680.01434792851 183.345652071486 6 2897.06 2689.09680135162 207.963198648376 7 3012.61 2672.17850575956 340.431494240443 8 3142.95 2695.15177030036 447.798229699636 9 3032.93 2744.30387117837 288.626128821630 10 3045.78 2724.00191646789 321.778083532111 11 3110.52 2739.85168812783 370.668311872174 12 3013.24 2634.95825545701 378.281744542991 13 2987.1 2622.67023023751 364.429769762493 14 2995.55 2653.83551159132 341.714488408685 15 2833.18 2620.1770077292 213.002992270798 16 2848.96 2614.30012610248 234.659873897516 17 2794.83 2671.10998182743 123.720018172574 18 2845.26 2654.5478608794 190.712139120598 19 2915.03 2613.94395145844 301.086048541560 20 2892.63 2623.56066684762 269.069333152385 21 2604.42 2630.50607240646 -26.0860724064642 22 2641.65 2549.47634088656 92.1736591134375 23 2659.81 2504.24216109304 155.567838906965 24 2638.53 2506.37920895730 132.150791042704 25 2720.25 2515.28357505838 204.966424941616 26 2745.88 2524.90029044756 220.979709552441 27 2735.7 2595.24478264616 140.455217353844 28 2811.7 2624.80727810177 186.892721898232 29 2799.43 2618.39613450898 181.033865491015 30 2555.28 2601.65592623894 -46.3759262389387 31 2304.98 2618.03995986494 -313.059959864941 32 2214.95 2626.94432596603 -411.994325966029 33 2065.81 2667.54823538699 -601.738235386991 34 1940.49 2649.20524121875 -708.71524121875 35 2042 2597.91609247648 -555.916092476482 36 1995.37 2651.34228908301 -655.972289083011 37 1946.81 2700.67247728304 -753.862477283039 38 1765.9 2738.07081490761 -972.170814907609 39 1635.25 2694.26133369026 -1059.01133369026 40 1833.42 2605.21767267937 -771.797672679374 41 1910.43 2616.0809993227 -705.650999322702 42 1959.67 2648.13671728662 -688.466717286619 43 1969.6 2665.23310020071 -695.633100200708 44 2061.41 2689.09680135162 -627.686801351624 45 2093.48 2642.2598356599 -548.779835659901 46 2120.88 2680.19243525054 -559.312435250536 47 2174.56 2674.49364094584 -499.93364094584 48 2196.72 2689.27488867365 -492.554888673646 49 2350.44 2709.04258141806 -358.602581418061 50 2440.25 2705.83700962167 -265.587009621670 51 2408.64 2744.12578385635 -335.485783856349 52 2472.81 2753.38632460148 -280.57632460148 53 2407.6 2823.55272947805 -415.952729478055 54 2454.62 2795.23684527659 -340.616845276594 55 2448.05 2834.59414344340 -386.544143443404 56 2497.84 2907.43185815030 -409.591858150304 57 2645.64 2927.0214635727 -281.381463572698 58 2756.76 3037.43560322619 -280.675603226190 59 2849.27 2956.76204635033 -107.492046350332 60 2921.44 2878.58171198278 42.8582880172212 61 2981.85 2949.99472811351 31.8552718864944 62 3080.58 2978.84487428103 101.735125718969 63 3106.22 3112.94462776342 -6.72462776341806 64 3119.31 3110.80757989916 8.50242010084331 65 3061.26 3084.80683088398 -23.5468308839791 66 3097.31 3147.13739359160 -49.8273935915961 67 3161.69 3191.83731141906 -30.1473114190584 68 3257.16 3301.36101446244 -44.2010144624423 69 3277.01 3297.97735534403 -20.9673553440284 70 3295.32 3215.70101256997 79.6189874300257 71 3363.99 3161.74055399738 202.249446002619 72 3494.17 3184.89190586021 309.278094139790 73 3667.03 3293.52517229348 373.504827706516 74 3813.06 3261.29136700755 551.768632992454 75 3917.96 3284.44271887037 633.517281129625 76 3895.51 3376.51386435563 518.996135644374 77 3801.06 3445.077483334 355.982516665996 78 3570.12 3404.47357391304 165.646426086958 79 3701.61 3487.46226597518 214.147734024817 80 3862.27 3486.03756739901 376.232432600991 81 3970.1 3306.34745947905 663.752540520949 82 4138.52 3231.37269690789 907.147303092111 83 4199.75 3220.86554490861 978.884455091394 84 4290.89 3275.36026544726 1015.52973455274 85 4443.91 3142.50712321903 1301.40287678097 86 4502.64 3201.27593948621 1301.36406051379 87 4356.98 3265.03120077 1091.94879923000 88 4591.27 3367.07523628847 1224.19476371153 89 4696.96 3367.60949825454 1329.35050174546 90 4621.4 3414.26837662424 1207.13162337576 91 4562.84 3502.59968834703 1060.24031165297 92 4202.52 3434.57033133472 767.94966866528 93 4296.49 3522.18929376943 774.300706230573 94 4435.23 3617.46601105107 817.76398894893 95 4105.18 3811.40310473277 293.776895267232 96 4116.68 3775.78564032842 340.894359671584 97 3844.49 3804.81387381796 39.6761261820362 98 3720.98 3837.93811571401 -116.958115714011 99 3674.4 3992.87408587294 -318.474085872944 100 3857.62 4115.04198877987 -257.421988779873 101 3801.06 4371.48773249121 -570.42773249121 102 3504.37 4525.35517871801 -1020.98517871801 103 3032.6 4580.9184231888 -1548.31842318880 104 3047.03 4229.37404951784 -1182.34404951784 105 2962.34 3968.47612275596 -1006.13612275596 106 2197.82 3510.96979248206 -1313.14979248206 107 2014.45 3150.34296538799 -1135.89296538799 108 1862.83 2942.33697326657 -1079.50697326657 109 1905.41 2968.87198424781 -1063.46198424781 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00634866350063285 0.0126973270012657 0.993651336499367 6 0.00109944415458633 0.00219888830917266 0.998900555845414 7 0.000407405881342841 0.000814811762685682 0.999592594118657 8 0.000396687360254249 0.000793374720508497 0.999603312639746 9 7.89989492083425e-05 0.000157997898416685 0.999921001050792 10 1.56000342593247e-05 3.12000685186494e-05 0.99998439996574 11 3.47934867552619e-06 6.95869735105238e-06 0.999996520651324 12 8.43522122863114e-07 1.68704424572623e-06 0.999999156477877 13 1.72563395771515e-07 3.4512679154303e-07 0.999999827436604 14 3.03703810351365e-08 6.0740762070273e-08 0.999999969629619 15 6.61432919038221e-09 1.32286583807644e-08 0.99999999338567 16 1.17242631018082e-09 2.34485262036164e-09 0.999999998827574 17 6.04693341791086e-10 1.20938668358217e-09 0.999999999395307 18 1.33824094530015e-10 2.67648189060030e-10 0.999999999866176 19 2.20790651931684e-11 4.41581303863368e-11 0.99999999997792 20 3.40437459334908e-12 6.80874918669816e-12 0.999999999996596 21 1.71527742004615e-11 3.43055484009230e-11 0.999999999982847 22 4.90422684527615e-12 9.8084536905523e-12 0.999999999995096 23 8.54286203555252e-13 1.70857240711050e-12 0.999999999999146 24 1.4883271850137e-13 2.9766543700274e-13 0.999999999999851 25 2.6316848782319e-14 5.2633697564638e-14 0.999999999999974 26 4.65056854065379e-15 9.30113708130758e-15 0.999999999999995 27 9.73093895442232e-16 1.94618779088446e-15 0.999999999999999 28 1.71652245029617e-16 3.43304490059234e-16 1 29 2.99415835069793e-17 5.98831670139587e-17 1 30 7.34969047059646e-17 1.46993809411929e-16 1 31 2.89328572553853e-14 5.78657145107706e-14 0.999999999999971 32 3.73049417117161e-12 7.46098834234322e-12 0.99999999999627 33 9.0905888230635e-10 1.8181177646127e-09 0.999999999090941 34 5.02344651377052e-08 1.00468930275410e-07 0.999999949765535 35 1.94017699726980e-07 3.88035399453959e-07 0.9999998059823 36 1.02220485424182e-06 2.04440970848364e-06 0.999998977795146 37 5.64856331123564e-06 1.12971266224713e-05 0.999994351436689 38 4.30029463775195e-05 8.6005892755039e-05 0.999956997053622 39 0.000265015732940271 0.000530031465880541 0.99973498426706 40 0.000551649604694535 0.00110329920938907 0.999448350395305 41 0.000822086213573965 0.00164417242714793 0.999177913786426 42 0.00104262603374516 0.00208525206749032 0.998957373966255 43 0.00126201968791828 0.00252403937583656 0.998737980312082 44 0.00126053599217066 0.00252107198434132 0.99873946400783 45 0.00119986939024864 0.00239973878049728 0.998800130609751 46 0.00109430289502302 0.00218860579004604 0.998905697104977 47 0.000933178370819459 0.00186635674163892 0.99906682162918 48 0.000781238108752978 0.00156247621750596 0.999218761891247 49 0.00056170732674878 0.00112341465349756 0.999438292673251 50 0.000382645078637738 0.000765290157275476 0.999617354921362 51 0.000271434315446247 0.000542868630892494 0.999728565684554 52 0.000189455088191393 0.000378910176382786 0.999810544911809 53 0.000144896301378553 0.000289792602757107 0.999855103698621 54 0.000109645273496244 0.000219290546992487 0.999890354726504 55 8.84008046486952e-05 0.000176801609297390 0.999911599195351 56 7.5624237519692e-05 0.000151248475039384 0.99992437576248 57 6.42798129489181e-05 0.000128559625897836 0.99993572018705 58 5.71274500332933e-05 0.000114254900066587 0.999942872549967 59 4.75653519455404e-05 9.51307038910807e-05 0.999952434648054 60 3.91729220157931e-05 7.83458440315861e-05 0.999960827077984 61 3.25083773007257e-05 6.50167546014514e-05 0.9999674916227 62 2.71416714692925e-05 5.42833429385849e-05 0.99997285832853 63 2.05287267317932e-05 4.10574534635865e-05 0.999979471273268 64 1.48842479126919e-05 2.97684958253839e-05 0.999985115752087 65 1.07317147370203e-05 2.14634294740407e-05 0.999989268285263 66 7.5217681730469e-06 1.50435363460938e-05 0.999992478231827 67 5.09539505072553e-06 1.01907901014511e-05 0.99999490460495 68 3.20368402622022e-06 6.40736805244044e-06 0.999996796315974 69 1.98616297407777e-06 3.97232594815554e-06 0.999998013837026 70 1.27296806399650e-06 2.54593612799299e-06 0.999998727031936 71 8.54012664289732e-07 1.70802532857946e-06 0.999999145987336 72 5.84354147943654e-07 1.16870829588731e-06 0.999999415645852 73 3.82073442567538e-07 7.64146885135077e-07 0.999999617926557 74 3.05395294236347e-07 6.10790588472693e-07 0.999999694604706 75 2.56888852601365e-07 5.1377770520273e-07 0.999999743111147 76 1.57167275306546e-07 3.14334550613093e-07 0.999999842832725 77 7.62525425968029e-08 1.52505085193606e-07 0.999999923747457 78 3.56924241270577e-08 7.13848482541155e-08 0.999999964307576 79 1.58048020824701e-08 3.16096041649402e-08 0.999999984195198 80 7.18329003099816e-09 1.43665800619963e-08 0.99999999281671 81 4.95902083927676e-09 9.9180416785535e-09 0.99999999504098 82 6.44265686878811e-09 1.28853137375762e-08 0.999999993557343 83 9.69797526709203e-09 1.93959505341841e-08 0.999999990302025 84 1.49188768875025e-08 2.98377537750050e-08 0.999999985081123 85 7.13154348932393e-08 1.42630869786479e-07 0.999999928684565 86 2.98083847520212e-07 5.96167695040423e-07 0.999999701916153 87 5.52721106925748e-07 1.10544221385150e-06 0.999999447278893 88 1.88402810369839e-06 3.76805620739678e-06 0.999998115971896 89 1.22485388094627e-05 2.44970776189253e-05 0.99998775146119 90 6.61369893028782e-05 0.000132273978605756 0.999933863010697 91 0.000310120426824715 0.00062024085364943 0.999689879573175 92 0.00081525716071061 0.00163051432142122 0.99918474283929 93 0.00337931681014767 0.00675863362029535 0.996620683189852 94 0.0263271976519080 0.0526543953038160 0.973672802348092 95 0.0652154515159749 0.130430903031950 0.934784548484025 96 0.204528543963634 0.409057087927267 0.795471456036366 97 0.384929071650588 0.769858143301176 0.615070928349412 98 0.590636360157163 0.818727279685674 0.409363639842837 99 0.734119535937213 0.531760928125573 0.265880464062787 100 0.936189161998794 0.127621676002412 0.063810838001206 101 0.99146815675853 0.0170636864829407 0.00853184324147033 102 0.992958063442316 0.0140838731153672 0.0070419365576836 103 0.995977103090049 0.00804579381990261 0.00402289690995131 104 0.982380979016244 0.0352380419675118 0.0176190209837559 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 89 0.89 NOK 5% type I error level 93 0.93 NOK 10% type I error level 94 0.94 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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