*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: Mon, 06 Dec 2010 16:44:05 +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/06/t1291653750vcjg6r1te91v0af.htm/, Retrieved Mon, 06 Dec 2010 17:42:33 +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/06/t1291653750vcjg6r1te91v0af.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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3173,95 16505,21 10853,87 8388 -0,1 2 0,8 3,11 3280,37 3288,18 3411,13 3484,74 3165,26 17135,96 10704,02 8099 -0,9 -8 0,7 3,57 3173,95 3280,37 3288,18 3411,13 3092,71 18033,25 11052,23 7984 0 0 0,7 4,04 3165,26 3173,95 3280,37 3288,18 3053,05 17671 10935,47 7786 0,1 -2 0,9 4,21 3092,71 3165,26 3173,95 3280,37 3181,96 17544,22 10714,03 8086 2,6 3 1,2 4,36 3053,05 3092,71 3165,26 3173,95 2999,93 17677,9 10394,48 9315 6 5 1,3 4,75 3181,96 3053,05 3092,71 3165,26 3249,57 18470,97 10817,9 9113 6,4 8 1,5 4,43 2999,93 3181,96 3053,05 3092,71 3210,52 18409,96 11251,2 9023 8,6 8 1,9 4,7 3249,57 2999,93 3181,96 3053,05 3030,29 18941,6 11281,26 9026 6,4 9 1,8 4,81 3210,52 3249,57 2999,93 3181,96 2803,47 19685,53 10539,68 9787 7,7 11 1,9 5,01 3030,29 3210,52 3249,57 2999,93 2767,63 19834,71 10483,39 9536 9,2 13 2,2 5 2803,47 3030,29 3210,52 3249,57 2882,6 19598,93 10947,43 9490 8,6 12 2,1 4,81 2767,63 2803,47 3030,29 3210,52 2863,36 17039,97 10580,27 9736 7,4 13 2,2 5,11 2882,6 2767,63 2803,47 3030,29 2897,0 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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation BEL_20[t] = -260.706690926404 + 0.0250525591118981Nikkei[t] + 0.102631890756772DJ_Indust[t] + 0.006957119094245Goudprijs[t] -10.2630504431514Conjunct_Seizoenzuiver[t] + 12.9959047466191Cons_vertrouw[t] -7.33394620617855Alg_consumptie_index_BE[t] -165.868025333682Gem_rente_kasbon_5j[t] + 0.806808871155089Y1[t] -0.114390996301673Y2[t] + 0.181991078938916Y3[t] -0.0730288844595162Y4[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) -260.706690926404 157.937258 -1.6507 0.101505 0.050753 Nikkei 0.0250525591118981 0.007214 3.4727 0.000725 0.000362 DJ_Indust 0.102631890756772 0.019485 5.2673 1e-06 0 Goudprijs 0.006957119094245 0.003585 1.9404 0.054758 0.027379 Conjunct_Seizoenzuiver -10.2630504431514 3.080988 -3.3311 0.001161 0.00058 Cons_vertrouw 12.9959047466191 2.936731 4.4253 2.2e-05 1.1e-05 Alg_consumptie_index_BE -7.33394620617855 10.299251 -0.7121 0.477842 0.238921 Gem_rente_kasbon_5j -165.868025333682 25.569328 -6.487 0 0 Y1 0.806808871155089 0.085344 9.4536 0 0 Y2 -0.114390996301673 0.112468 -1.0171 0.311225 0.155613 Y3 0.181991078938916 0.111477 1.6325 0.105276 0.052638 Y4 -0.0730288844595162 0.073509 -0.9935 0.322547 0.161274 Multiple Linear Regression - Regression Statistics Multiple R 0.990305665787982 R-squared 0.980705311691779 Adjusted R-squared 0.978875642972896 F-TEST (value) 536.001573164823 F-TEST (DF numerator) 11 F-TEST (DF denominator) 116 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 110.643967300128 Sum Squared Residuals 1420082.14998977 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3173.95 3467.20395684971 -293.253956849713 2 3165.26 3166.33400475918 -1.07400475917596 3 3092.71 3253.24318993043 -160.533189930426 4 3053.05 3097.78756228371 -44.7375622837136 5 3181.96 3068.70449700179 113.255502998209 6 2999.93 3069.456961988 -69.5269619880009 7 3249.57 3054.34084232542 195.229157674582 8 3210.52 3274.95114213856 -64.431142138561 9 3030.29 3206.83415388342 -176.544153883423 10 2803.47 3051.18046483841 -247.710464838414 11 2767.63 2869.72876856238 -102.098768562381 12 2882.6 2903.61905785664 -21.0190578566352 13 2863.36 2847.09881908162 16.2611809183754 14 2897.06 2838.34280107637 58.7171989236346 15 3012.61 2894.63433382927 117.975666170726 16 3142.95 3017.40273750787 125.547262492127 17 3032.93 3022.07789545689 10.8521045431094 18 3045.78 2931.82647547135 113.953524528646 19 3110.52 3007.28256317961 103.237436820385 20 3013.24 3113.06577998169 -99.8257799816897 21 2987.1 3041.88708251668 -54.7870825166776 22 2995.55 2993.17053708436 2.37946291564232 23 2833.18 2925.48578054612 -92.3057805461233 24 2848.96 2831.00156340159 17.9584365984119 25 2794.83 2891.44310757541 -96.6131075754113 26 2845.26 2847.5657863162 -2.30578631619529 27 2915.02 2869.74220681775 45.277793182248 28 2892.63 2834.95818618887 57.6718138111332 29 2604.42 2694.55960606494 -90.1396060649419 30 2641.65 2440.86718632355 200.782813676453 31 2659.81 2537.94838063762 121.861619362381 32 2638.53 2556.12213401537 82.4078659846257 33 2720.25 2612.97029490253 107.27970509747 34 2745.88 2670.9421193209 74.937880679098 35 2735.7 2703.55951123119 32.1404887688128 36 2811.7 2644.58139788928 167.118602110724 37 2799.43 2718.25634371398 81.1736562860207 38 2555.28 2635.76316212094 -80.4831621209376 39 2304.98 2377.52624607223 -72.546246072231 40 2214.95 2224.75260418944 -9.80260418944235 41 2065.81 2111.78229724053 -45.9722972405347 42 1940.49 1961.13332624318 -20.6433262431819 43 2042 1916.43449107842 125.565508921578 44 1995.37 1942.15464530868 53.2153546913235 45 1946.81 1940.68266851602 6.12733148397533 46 1765.9 1861.52507793266 -95.6250779326628 47 1635.25 1679.2992266262 -44.0492266261958 48 1833.42 1684.40793861533 149.012061384672 49 1910.43 1960.85359447305 -50.4235944730495 50 1959.67 2113.92544550314 -154.255445503144 51 1969.6 2113.67604473085 -144.07604473085 52 2061.41 2057.96091296613 3.44908703386985 53 2093.48 2222.85733855074 -129.377338550744 54 2120.88 2084.87859936043 36.0014006395703 55 2174.56 2200.45878078965 -25.8987807896495 56 2196.72 2272.17210730731 -75.452107307312 57 2350.44 2363.07965422396 -12.6396542239555 58 2440.25 2514.1510039829 -73.9010039828977 59 2408.64 2551.04381936523 -142.403819365229 60 2472.81 2554.96536232685 -82.1553623268483 61 2407.6 2492.46870431742 -84.8687043174208 62 2454.62 2541.96451296828 -87.3445129682793 63 2448.05 2462.0851575135 -14.0351575135041 64 2497.84 2486.10729206841 11.7327079315883 65 2645.64 2555.70179728264 89.9382027173589 66 2756.76 2577.8866697528 178.873330247205 67 2849.27 2719.98898167275 129.281018327251 68 2921.44 2875.68162076734 45.7583792326578 69 2981.85 2927.90536002629 53.9446399737078 70 3080.58 3083.64995305582 -3.0699530558192 71 3106.22 3182.89048690123 -76.6704869012327 72 3119.31 3141.57100875059 -22.261008750592 73 3061.26 3105.43361308601 -44.1736130860067 74 3097.31 3088.5267282131 8.78327178690254 75 3161.69 3159.25726362594 2.43273637405727 76 3257.16 3202.99522520012 54.1647747998809 77 3277.01 3216.61090476407 60.3990952359311 78 3295.32 3286.42053872432 8.89946127567834 79 3363.99 3330.06419902913 33.9258009708689 80 3494.17 3450.17288204795 43.9971179520542 81 3667.03 3607.03597959154 59.9940204084565 82 3813.06 3748.01786280001 65.0421371999933 83 3917.96 3827.02259176097 90.9374082390337 84 3895.51 3904.05484933636 -8.54484933636026 85 3801.06 3825.15358404066 -24.093584040661 86 3570.12 3726.30798898261 -156.187988982612 87 3701.61 3522.69765525564 178.912344744364 88 3862.27 3684.16509310029 178.104906899715 89 3970.1 3818.89667219032 151.203327809679 90 4138.52 4021.74361967929 116.776380320708 91 4199.75 4184.15768023658 15.592319763425 92 4290.89 4124.76619869132 166.123801308677 93 4443.91 4317.89482363685 126.015176363151 94 4502.64 4447.52868376335 55.1113162366501 95 4356.98 4419.2951666984 -62.3151666984014 96 4591.27 4398.5798222448 192.6901777552 97 4696.96 4671.32137790449 25.638622095512 98 4621.4 4629.46956744007 -8.06956744006972 99 4562.84 4584.77454273744 -21.9345427374353 100 4202.52 4488.22698001557 -285.706980015571 101 4296.49 4225.08075205745 71.4092479425464 102 4435.23 4422.28089135067 12.9491086493269 103 4105.18 4281.92776215144 -176.747762151443 104 4116.68 4145.17490964838 -28.4949096483841 105 3844.49 4025.45204593649 -180.962045936491 106 3720.98 3789.66113769446 -68.6811376944588 107 3674.4 3746.02102646824 -71.6210264682444 108 3857.62 3730.77000425409 126.849995745909 109 3801.06 3811.2136841969 -10.1536841968965 110 3504.37 3569.6541459628 -65.2841459627997 111 3032.6 3196.51429632775 -163.914296327754 112 3047.03 2863.23668579991 183.79331420009 113 2962.34 2955.67556438988 6.66443561011828 114 2197.82 2520.05182648897 -322.231826488974 115 2014.45 1967.53999366548 46.9100063345203 116 1862.83 1940.45550701668 -77.6255070166772 117 1905.41 1767.08456288793 138.325437112074 118 1810.99 1801.85855741938 9.13144258061918 119 1670.07 1725.45280795337 -55.382807953366 120 1864.44 1732.7451791336 131.694820866399 121 2052.02 1959.21478275693 92.8052172430666 122 2029.6 2068.29470960025 -38.6947096002482 123 2070.83 2080.140736216 -9.31073621600352 124 2293.41 2253.81642931338 39.5935706866216 125 2443.27 2435.66568786687 7.60431213312592 126 2513.17 2506.78981830183 6.38018169816949 127 2466.92 2617.09648544227 -150.17648544227 128 2502.66 2545.26478975177 -42.6047897517722 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.800234918979488 0.399530162041024 0.199765081020512 16 0.823051761176634 0.353896477646733 0.176948238823366 17 0.732092649661197 0.535814700677606 0.267907350338803 18 0.645530426197792 0.708939147604415 0.354469573802208 19 0.556525169993537 0.886949660012927 0.443474830006463 20 0.621360135088531 0.757279729822938 0.378639864911469 21 0.851579884392403 0.296840231215195 0.148420115607598 22 0.849503734649504 0.300992530700991 0.150496265350496 23 0.853531494387146 0.292937011225709 0.146468505612854 24 0.805461517894422 0.389076964211157 0.194538482105578 25 0.790243436157216 0.419513127685568 0.209756563842784 26 0.738309825706358 0.523380348587284 0.261690174293642 27 0.68291693836554 0.63416612326892 0.31708306163446 28 0.613516650781789 0.772966698436422 0.386483349218211 29 0.561817216988133 0.876365566023734 0.438182783011867 30 0.544205104426856 0.911589791146287 0.455794895573144 31 0.493916554220293 0.987833108440586 0.506083445779707 32 0.441078290806547 0.882156581613095 0.558921709193453 33 0.403345603944057 0.806691207888114 0.596654396055943 34 0.373918652356706 0.747837304713412 0.626081347643294 35 0.363777786776246 0.727555573552492 0.636222213223754 36 0.380631613254466 0.761263226508931 0.619368386745534 37 0.356711551565587 0.713423103131173 0.643288448434413 38 0.375614734690629 0.751229469381258 0.624385265309371 39 0.407152803411803 0.814305606823607 0.592847196588197 40 0.370514333778512 0.741028667557024 0.629485666221488 41 0.318163418021143 0.636326836042285 0.681836581978857 42 0.280863380681593 0.561726761363185 0.719136619318407 43 0.274524921755102 0.549049843510203 0.725475078244898 44 0.267834489891606 0.535668979783213 0.732165510108394 45 0.237885225481387 0.475770450962773 0.762114774518613 46 0.229745884157173 0.459491768314346 0.770254115842827 47 0.251173235514476 0.502346471028953 0.748826764485524 48 0.250854209179385 0.501708418358769 0.749145790820615 49 0.208616347865302 0.417232695730604 0.791383652134698 50 0.212484221469893 0.424968442939787 0.787515778530107 51 0.238402714132839 0.476805428265677 0.761597285867161 52 0.196669643316529 0.393339286633058 0.803330356683471 53 0.203549416565612 0.407098833131224 0.796450583434388 54 0.179937276492628 0.359874552985257 0.820062723507372 55 0.149197021327318 0.298394042654635 0.850802978672682 56 0.151632331256674 0.303264662513347 0.848367668743326 57 0.127809807977873 0.255619615955747 0.872190192022127 58 0.110558747206927 0.221117494413854 0.889441252793073 59 0.134360876861917 0.268721753723833 0.865639123138083 60 0.192275946584294 0.384551893168589 0.807724053415706 61 0.201115532070412 0.402231064140825 0.798884467929588 62 0.330407322854191 0.660814645708382 0.669592677145809 63 0.364364544062077 0.728729088124155 0.635635455937923 64 0.439099571680268 0.878199143360536 0.560900428319732 65 0.529705095930943 0.940589808138114 0.470294904069057 66 0.678684462366506 0.642631075266988 0.321315537633494 67 0.727230046165453 0.545539907669094 0.272769953834547 68 0.710862256277352 0.578275487445297 0.289137743722648 69 0.709151289436885 0.58169742112623 0.290848710563115 70 0.687672089155393 0.624655821689214 0.312327910844607 71 0.659478005868322 0.681043988263357 0.340521994131678 72 0.628577114585647 0.742845770828707 0.371422885414353 73 0.581696031867412 0.836607936265176 0.418303968132588 74 0.531311927018301 0.937376145963398 0.468688072981699 75 0.480710325317521 0.961420650635042 0.519289674682479 76 0.463212918373693 0.926425836747387 0.536787081626307 77 0.445446557700905 0.89089311540181 0.554553442299095 78 0.414186759073793 0.828373518147587 0.585813240926207 79 0.374735853883382 0.749471707766763 0.625264146116618 80 0.46075915076549 0.92151830153098 0.53924084923451 81 0.581737151216341 0.836525697567317 0.418262848783659 82 0.644152405992217 0.711695188015566 0.355847594007783 83 0.611708432287915 0.77658313542417 0.388291567712085 84 0.77744156087068 0.445116878258641 0.222558439129321 85 0.916639283479316 0.166721433041369 0.0833607165206845 86 0.995618470371817 0.00876305925636527 0.00438152962818263 87 0.99507306806586 0.0098538638682794 0.0049269319341397 88 0.993192940407165 0.0136141191856695 0.00680705959283473 89 0.990316098267428 0.019367803465144 0.00968390173257202 90 0.989698661492204 0.0206026770155925 0.0103013385077963 91 0.987008884972325 0.0259822300553507 0.0129911150276754 92 0.981477428061323 0.0370451438773546 0.0185225719386773 93 0.9721779443476 0.0556441113047996 0.0278220556523998 94 0.96013224748218 0.0797355050356393 0.0398677525178196 95 0.959590826248709 0.0808183475025826 0.0404091737512913 96 0.957320016645879 0.085359966708242 0.042679983354121 97 0.949632764336396 0.100734471327209 0.0503672356636043 98 0.951134583938277 0.0977308321234454 0.0488654160617227 99 0.964437551289873 0.0711248974202543 0.0355624487101272 100 0.98213574393786 0.0357285121242792 0.0178642560621396 101 0.975629467511093 0.0487410649778131 0.0243705324889066 102 0.961643471247792 0.0767130575044162 0.0383565287522081 103 0.96557693360804 0.068846132783921 0.0344230663919605 104 0.98059433571448 0.0388113285710384 0.0194056642855192 105 0.990115036629306 0.0197699267413887 0.00988496337069437 106 0.990371477673414 0.0192570446531728 0.00962852232658638 107 0.983236257611605 0.0335274847767891 0.0167637423883946 108 0.97159630226495 0.0568073954701001 0.02840369773505 109 0.955848177307217 0.088303645385565 0.0441518226927825 110 0.917674184286031 0.164651631427938 0.082325815713969 111 0.978175269716317 0.0436494605673662 0.0218247302836831 112 0.948959952976141 0.102080094047717 0.0510400470238586 113 0.90667521277965 0.186649574440699 0.0933247872203497 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0202020202020202 NOK 5% type I error level 14 0.141414141414141 NOK 10% type I error level 24 0.242424242424242 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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Software written by Ed van Stee & Patrick Wessa

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