Apple inc - Multiple regression 3 Lanceringen

*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, 11 Dec 2010 14:46:41 +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/11/t12920789933a9thfu2w03x2mc.htm/, Retrieved Sat, 11 Dec 2010 15:50:03 +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/11/t12920789933a9thfu2w03x2mc.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)

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Dataseries X:

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25.94 23688100 39.18 3940.35 0.02740 144.7 0 28.66 13741000 35.78 4696.69 0.03220 140.8 0 33.95 14143500 42.54 4572.83 0.03760 137.1 0 31.01 16763800 27.92 3860.66 0.03070 137.7 0 21.00 16634600 25.05 3400.91 0.03190 144.7 0 26.19 13693300 32.03 3966.11 0.03730 139.2 0 25.41 10545800 27.95 3766.99 0.03660 143.0 0 30.47 9409900 27.95 4206.35 0.03410 140.8 0 12.88 39182200 24.15 3672.82 0.03450 142.5 0 9.78 37005800 27.57 3369.63 0.03450 135.8 0 8.25 15818500 22.97 2597.93 0.03450 132.6 0 7.44 16952000 17.37 2470.52 0.03390 128.6 0 10.81 24563400 24.45 2772.73 0.03730 115.7 0 9.12 14163200 23.62 2151.83 0.03530 109.2 0 11.03 18184800 21.90 1840.26 0.02920 116.9 0 12.74 20810300 27.12 2116.24 0.03270 109.9 0 9.98 12843000 27.70 2110.49 0.03620 116.1 0 11.62 13866700 29.23 2160.54 0.03250 118.9 0 9.40 15119200 26.50 2027.13 0.02720 116.3 0 9.27 8301600 22.84 1805.43 0.02720 114.0 0 7.76 14039600 20.49 1498.80 0.02650 97.0 0 8.78 12139700 23.28 1690.20 0.02130 85.3 0 10. 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation APPLE[t] = -42.9866879231334 + 6.63274331102043e-07VOLUME[t] + 3.80563439777418MICROSOFT[t] + 0.00110067474813571NASDAQ[t] -86.4307633236699INFLATION[t] -0.296771689245468CONS.CONF[t] + 93.305945123973LANCERINGEN[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) -42.9866879231334 16.584751 -2.5919 0.010699 0.005349 VOLUME 6.63274331102043e-07 0 2.9143 0.004236 0.002118 MICROSOFT 3.80563439777418 0.937704 4.0585 8.7e-05 4.4e-05 NASDAQ 0.00110067474813571 0.007095 0.1551 0.876977 0.438488 INFLATION -86.4307633236699 208.817893 -0.4139 0.679665 0.339832 CONS.CONF -0.296771689245468 0.179528 -1.6531 0.100868 0.050434 LANCERINGEN 93.305945123973 7.245541 12.8777 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.939050560655683 R-squared 0.881815955467753 Adjusted R-squared 0.876050880124716 F-TEST (value) 152.958270793960 F-TEST (DF numerator) 6 F-TEST (DF denominator) 123 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 26.9825524003687 Sum Squared Residuals 89551.1504867532 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25.94 80.8557538591651 -54.9157538591652 2 28.66 62.8939670709372 -34.2339670709372 3 33.95 89.3820230721157 -55.4320230721157 4 31.01 35.1160676244502 -4.10606762445021 5 21 21.4210479030977 -0.421047903097695 6 26.19 47.8211067460395 -21.6311067460395 7 25.41 28.9200652053225 -3.51006520532249 8 30.47 29.5192189746137 0.95078102538635 9 12.88 33.6786834555216 -20.7986834555215 10 9.78 46.9050595827561 -37.1250595827561 11 8.25 15.4464278200857 -7.19642782008572 12 7.44 -4.0145951078294 11.4545951078294 13 10.81 31.844867783762 -21.0348677837620 14 9.12 23.2064740909074 -14.0864740909074 15 11.03 17.2273553945034 -6.19735539450343 16 12.74 40.912852077269 -28.1728520772690 17 9.98 35.6867934250321 -25.7067934250322 18 11.62 41.7323298519303 -30.1123298519303 19 9.4 33.256547465217 -23.856547465217 20 9.27 15.2445417832451 -5.97454178324506 21 7.76 14.8752894138179 -7.11528941381792 22 8.78 28.3651923621954 -19.5851923621954 23 10.65 36.5429461996102 -25.8929461996102 24 10.95 36.3180660457339 -25.3680660457339 25 12.36 36.3481048323019 -23.9881048323019 26 10.85 28.9811615170905 -18.1311615170905 27 11.84 23.2465695030647 -11.4065695030647 28 12.14 12.0863985697903 0.0536014302096524 29 11.65 9.4250105671446 2.22498943285541 30 8.86 20.1291317007791 -11.2691317007791 31 7.63 10.4900600591011 -2.86006005910109 32 7.38 9.39593583803132 -2.01593583803132 33 7.25 2.37665249143934 4.87334750856066 34 8.03 21.6919446583704 -13.6619446583704 35 7.75 24.9529523412318 -17.2029523412318 36 7.16 16.5902166773755 -9.43021667737545 37 7.18 12.3461337239074 -5.16613372390735 38 7.51 14.5110443536876 -7.00104435368757 39 7.07 16.5107139813911 -9.44071398139105 40 7.11 20.1230006941788 -13.0130006941788 41 8.98 20.0440454008582 -11.0640454008582 42 9.53 17.7379461020554 -8.20794610205537 43 10.54 20.9087371484098 -10.3687371484098 44 11.31 19.1087202060284 -7.7987202060284 45 10.36 25.4573232422457 -15.0973232422457 46 11.44 20.9811767333467 -9.54117673334675 47 10.45 15.5290720046675 -5.07907200466753 48 10.69 20.5822900359451 -9.89229003594507 49 11.28 23.6376922778664 -12.3576922778664 50 11.96 19.7983458919574 -7.83834589195736 51 13.52 20.4620867002748 -6.9420867002748 52 12.89 20.4842713546142 -7.59427135461416 53 14.03 16.0309290824058 -2.00092908240577 54 16.27 23.4300821098814 -7.16008210988144 55 16.17 23.9172709786171 -7.74727097861713 56 17.25 20.6669218378919 -3.41692183789193 57 19.38 22.5893737726393 -3.20937377263930 58 26.2 34.1641842700399 -7.96418427003985 59 33.53 41.7645245460467 -8.2345245460467 60 32.2 35.4141296487356 -3.21412964873559 61 38.45 48.2737766299742 -9.82377662997417 62 44.86 39.9714736354894 4.88852636451063 63 41.67 25.7162406869496 15.9537593130504 64 36.06 36.9980806718056 -0.938080671805559 65 39.76 30.4368825468849 9.32311745311509 66 36.81 24.1136559628857 12.6963440371143 67 42.65 27.1113230938242 15.5386769061758 68 46.89 29.9743062169019 16.9156937830981 69 53.61 33.1307859107884 20.4792140892116 70 57.59 41.5217570491998 16.0682429508002 71 67.82 39.4413057599670 28.3786942400330 72 71.89 32.0752418882022 39.8147581117978 73 75.51 49.3215717322991 26.1884282677009 74 68.49 44.6748775051776 23.8151224948224 75 62.72 44.4570850692006 18.2629149307994 76 70.39 34.2420795780953 36.1479204219047 77 59.77 22.2687896909669 37.5012103090331 78 57.27 26.0419734563558 31.2280265436442 79 67.96 30.0294653580796 37.9305346419203 80 67.85 35.7774302627842 32.0725697372158 81 76.98 43.9292836445621 33.0507163554379 82 81.08 44.2908932282377 36.7891067717623 83 91.66 46.9144173754938 44.7455826245062 84 84.84 51.4902426796129 33.3497573203871 85 85.73 67.3441489863837 18.3858510136163 86 84.61 42.5805174141638 42.0294825858362 87 92.91 41.6322730937661 51.2777269062339 88 99.8 48.7216179284278 51.0783820715722 89 121.19 54.4810449769064 66.7089550230936 90 122.04 151.845718798019 -29.8057187980187 91 131.76 149.935150429140 -18.1751504291402 92 138.48 148.537841746340 -10.0578417463397 93 153.47 153.274189885879 0.195810114120932 94 189.95 178.204108301687 11.7458916983126 95 182.22 174.526196973170 7.69380302682965 96 198.08 171.234345665279 26.8456543347206 97 135.36 180.963594030809 -45.6035940308092 98 125.02 155.25417618831 -30.23417618831 99 143.5 159.810688589504 -16.3106885895037 100 173.95 159.253041148666 14.6969588513344 101 188.75 156.230959270286 32.519040729714 102 167.44 155.421514661960 12.0184853380404 103 158.95 147.293471790200 11.6565282098003 104 169.53 145.077721274670 24.4522787253302 105 113.66 155.662716347404 -42.0027163474043 106 107.59 162.703552474707 -55.1135524747073 107 92.67 141.217544664301 -48.5475446643008 108 85.35 133.764004887750 -48.4140048877502 109 90.13 135.241198990579 -45.1111989905786 110 89.31 121.506058528397 -32.1960585283973 111 105.12 128.927601682757 -23.8076016827570 112 125.83 128.924053867594 -3.09405386759378 113 135.81 125.959474681562 9.85052531843817 114 142.43 140.779898537027 1.65010146297288 115 163.39 139.288345224752 24.1016547752482 116 168.21 138.684711741000 29.5252882589995 117 185.35 145.244799792134 40.1052002078659 118 188.5 155.900438323179 32.5995616768208 119 199.91 156.230327175709 43.6796728242911 120 210.73 160.842996354633 49.8870036453667 121 192.06 252.486968263572 -60.4269682635722 122 204.62 251.931361863989 -47.3113618639892 123 235 251.424747720031 -16.4247477200307 124 261.09 256.233954376700 4.85604562329968 125 256.88 245.207240174050 11.6727598259504 126 251.53 234.202306900899 17.3276930991007 127 257.25 245.178047256480 12.0719527435204 128 243.1 228.718986789162 14.3810132108384 129 283.75 237.566757153433 46.1832428465669 130 300.98 245.684408487982 55.2955915120177 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.00883182622638272 0.0176636524527654 0.991168173773617 11 0.00385406293981634 0.00770812587963268 0.996145937060184 12 0.000714442499211908 0.00142888499842382 0.999285557500788 13 0.000201759181202269 0.000403518362404537 0.999798240818798 14 3.68802794864591e-05 7.37605589729181e-05 0.999963119720514 15 1.05259267654330e-05 2.10518535308660e-05 0.999989474073235 16 2.57232160790274e-06 5.14464321580548e-06 0.999997427678392 17 4.59744838510258e-07 9.19489677020516e-07 0.999999540255161 18 8.68607030947852e-08 1.73721406189570e-07 0.999999913139297 19 2.68182703280054e-08 5.36365406560107e-08 0.99999997318173 20 5.74445994089876e-09 1.14889198817975e-08 0.99999999425554 21 8.87565537715149e-10 1.77513107543030e-09 0.999999999112434 22 2.22863366541408e-10 4.45726733082815e-10 0.999999999777137 23 6.93060298338562e-11 1.38612059667712e-10 0.999999999930694 24 2.16361901884136e-11 4.32723803768273e-11 0.999999999978364 25 4.62657954982719e-12 9.25315909965439e-12 0.999999999995373 26 8.24991093731318e-13 1.64998218746264e-12 0.999999999999175 27 1.36525018800123e-13 2.73050037600245e-13 0.999999999999863 28 2.58446675724050e-14 5.16893351448099e-14 0.999999999999974 29 3.87061904033887e-15 7.74123808067774e-15 0.999999999999996 30 5.54459412905716e-16 1.10891882581143e-15 1 31 9.5974736985795e-17 1.9194947397159e-16 1 32 1.51837394087406e-17 3.03674788174812e-17 1 33 4.44904628550355e-18 8.8980925710071e-18 1 34 7.51841170712612e-19 1.50368234142522e-18 1 35 1.03911785052128e-19 2.07823570104256e-19 1 36 1.62425551247787e-20 3.24851102495573e-20 1 37 4.42849086817641e-21 8.85698173635282e-21 1 38 8.74656581669227e-22 1.74931316333845e-21 1 39 1.39654345922866e-22 2.79308691845732e-22 1 40 2.24914754338109e-23 4.49829508676218e-23 1 41 5.50016732403144e-24 1.10003346480629e-23 1 42 7.10717526563169e-25 1.42143505312634e-24 1 43 9.15157496165267e-26 1.83031499233053e-25 1 44 1.23779084457862e-26 2.47558168915725e-26 1 45 1.80240901739741e-27 3.60481803479483e-27 1 46 3.69104880193839e-28 7.38209760387679e-28 1 47 1.20980461893497e-28 2.41960923786993e-28 1 48 6.47068742109815e-29 1.29413748421963e-28 1 49 2.48461509538547e-29 4.96923019077095e-29 1 50 1.78864275654020e-29 3.57728551308041e-29 1 51 2.41104603248189e-29 4.82209206496378e-29 1 52 1.37164584432159e-29 2.74329168864317e-29 1 53 1.15957379986041e-29 2.31914759972083e-29 1 54 9.98527794834313e-29 1.99705558966863e-28 1 55 8.38041288679858e-28 1.67608257735972e-27 1 56 4.27593489576162e-27 8.55186979152323e-27 1 57 6.85289982928962e-26 1.37057996585792e-25 1 58 3.1052913760075e-21 6.210582752015e-21 1 59 1.81004499599053e-17 3.62008999198106e-17 1 60 1.46414043880065e-15 2.92828087760130e-15 0.999999999999999 61 1.95389619043278e-14 3.90779238086555e-14 0.99999999999998 62 8.35506308942592e-13 1.67101261788518e-12 0.999999999999164 63 3.77896007865473e-11 7.55792015730947e-11 0.99999999996221 64 3.8099270465788e-11 7.6198540931576e-11 0.9999999999619 65 3.24420038397389e-10 6.48840076794778e-10 0.99999999967558 66 1.99946513224456e-09 3.99893026448911e-09 0.999999998000535 67 2.95773615437723e-08 5.91547230875445e-08 0.999999970422638 68 5.60715027609537e-07 1.12143005521907e-06 0.999999439284972 69 2.48249147041372e-06 4.96498294082745e-06 0.99999751750853 70 3.55637144015454e-06 7.11274288030909e-06 0.99999644362856 71 7.35061210535285e-05 0.000147012242107057 0.999926493878946 72 0.00105665978305947 0.00211331956611895 0.99894334021694 73 0.00213409845041338 0.00426819690082675 0.997865901549587 74 0.00265930889235527 0.00531861778471053 0.997340691107645 75 0.00265089320322789 0.00530178640645577 0.997349106796772 76 0.00364043380323075 0.0072808676064615 0.99635956619677 77 0.00358053571580708 0.00716107143161416 0.996419464284193 78 0.00280244720597450 0.00560489441194901 0.997197552794026 79 0.00573022578353366 0.0114604515670673 0.994269774216466 80 0.00711668530709293 0.0142333706141859 0.992883314692907 81 0.0206846322548985 0.0413692645097970 0.979315367745101 82 0.0737007645661578 0.147401529132316 0.926299235433842 83 0.16474768390458 0.32949536780916 0.83525231609542 84 0.187114602411862 0.374229204823725 0.812885397588138 85 0.164615926176382 0.329231852352763 0.835384073823618 86 0.207319417376496 0.414638834752992 0.792680582623504 87 0.296394784407189 0.592789568814379 0.70360521559281 88 0.384694264790358 0.769388529580716 0.615305735209642 89 0.586660751404065 0.82667849719187 0.413339248595935 90 0.609506004764596 0.780987990470808 0.390493995235404 91 0.559066872235898 0.881866255528204 0.440933127764102 92 0.565579744663759 0.868840510672481 0.434420255336241 93 0.643747432839749 0.712505134320503 0.356252567160251 94 0.718631458139667 0.562737083720666 0.281368541860333 95 0.672235422846977 0.655529154306046 0.327764577153023 96 0.667180102586611 0.665639794826777 0.332819897413389 97 0.738409000323496 0.523181999353009 0.261590999676505 98 0.777504430507826 0.444991138984348 0.222495569492174 99 0.766085131224968 0.467829737550064 0.233914868775032 100 0.741070651210856 0.517858697578289 0.258929348789144 101 0.757102662037639 0.485794675924722 0.242897337962361 102 0.700483064110283 0.599033871779433 0.299516935889717 103 0.66470617451061 0.670587650978781 0.335293825489391 104 0.672078315992538 0.655843368014925 0.327921684007462 105 0.959373073223498 0.0812538535530046 0.0406269267765023 106 0.971192974643925 0.0576140507121501 0.0288070253560751 107 0.959246761852402 0.0815064762951969 0.0407532381475984 108 0.976947642738115 0.0461047145237691 0.0230523572618846 109 0.964328659539884 0.0713426809202314 0.0356713404601157 110 0.941990394292644 0.116019211414712 0.0580096057073558 111 0.91042668246964 0.179146635060721 0.0895733175303607 112 0.928593895383824 0.142812209232353 0.0714061046161764 113 0.94757798974919 0.104844020501618 0.0524220102508092 114 0.91662225009491 0.166755499810180 0.0833777499050899 115 0.920848355539454 0.158303288921092 0.0791516444605458 116 0.902770949550138 0.194458100899724 0.097229050449862 117 0.996293318816436 0.00741336236712839 0.00370668118356419 118 0.999781302649134 0.000437394701732014 0.000218697350866007 119 0.999856681969123 0.000286636061754886 0.000143318030877443 120 0.998421984016674 0.00315603196665226 0.00157801598332613 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 72 0.648648648648649 NOK 5% type I error level 77 0.693693693693694 NOK 10% type I error level 81 0.72972972972973 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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