Apple Inc - 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, 11 Dec 2010 10:33:09 +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/t12920636540n6d81osj9z0r91.htm/, Retrieved Sat, 11 Dec 2010 11:34:14 +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/t12920636540n6d81osj9z0r91.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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25.94 23688100 39.18 3940.35 0.02740 144.7 5.45 28.66 13741000 35.78 4696.69 0.03220 140.8 5.73 33.95 14143500 42.54 4572.83 0.03760 137.1 5.85 31.01 16763800 27.92 3860.66 0.03070 137.7 6.02 21.00 16634600 25.05 3400.91 0.03190 144.7 6.27 26.19 13693300 32.03 3966.11 0.03730 139.2 6.53 25.41 10545800 27.95 3766.99 0.03660 143.0 6.54 30.47 9409900 27.95 4206.35 0.03410 140.8 6.5 12.88 39182200 24.15 3672.82 0.03450 142.5 6.52 9.78 37005800 27.57 3369.63 0.03450 135.8 6.51 8.25 15818500 22.97 2597.93 0.03450 132.6 6.51 7.44 16952000 17.37 2470.52 0.03390 128.6 6.4 10.81 24563400 24.45 2772.73 0.03730 115.7 5.98 9.12 14163200 23.62 2151.83 0.03530 109.2 5.49 11.03 18184800 21.90 1840.26 0.02920 116.9 5.31 12.74 20810300 27.12 2116.24 0.03270 109.9 4.8 9.98 12843000 27.70 2110.49 0.03620 116.1 4.21 11.62 13866700 29.23 2160.54 0.03250 118.9 3.97 9.40 15119200 26.50 2027.13 0.02720 116.3 3.77 9.27 8301600 22.84 1805.43 0.02720 114.0 3.65 7.76 14039600 20.49 1498.80 0.02650 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation APPLE[t] = + 23.0147069811760 + 1.24781152025288e-06VOLUME[t] + 6.92603621715406MICROSOFT[t] + 0.0288863890196801NASDAQ[t] -605.881541364843INFLATION[t] -2.26245296320921CONS.CONF[t] + 3.36290179952853FED.FUNDS.RATE[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) 23.0147069811760 29.401198 0.7828 0.43526 0.21763 VOLUME 1.24781152025288e-06 0 3.288 0.001316 0.000658 MICROSOFT 6.92603621715406 1.398225 4.9535 2e-06 1e-06 NASDAQ 0.0288863890196801 0.010781 2.6793 0.008388 0.004194 INFLATION -605.881541364843 320.051667 -1.8931 0.060698 0.030349 CONS.CONF -2.26245296320921 0.264611 -8.5501 0 0 FED.FUNDS.RATE 3.36290179952853 4.016463 0.8373 0.404059 0.20203 Multiple Linear Regression - Regression Statistics Multiple R 0.85090925788459 R-squared 0.724046565153704 Adjusted R-squared 0.71058542199047 F-TEST (value) 53.7878957510289 F-TEST (DF numerator) 6 F-TEST (DF denominator) 123 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 41.230775839512 Sum Squared Residuals 209097.155788354 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25.94 112.107289813532 -86.167289813532 2 28.66 104.851539835078 -76.191539835078 3 33.95 154.098784512411 -120.148784512411 4 31.01 38.9325600394035 -7.9225600394035 5 21 -10.1104016462634 31.1104016462634 6 26.19 60.9358156410332 -34.7458156410332 7 25.41 14.8586681702057 10.5513318297943 8 30.47 32.4903872445283 -2.0203872445283 9 12.88 23.8886487920867 -11.0086487920867 10 9.78 51.2266972107049 -41.4466972107049 11 8.25 -22.1226033354753 30.3726033354753 12 7.44 -54.1310050386212 61.5710050386212 13 10.81 38.8459068386762 -28.0359068386762 14 9.12 16.4541339250056 -7.33413392500557 15 11.03 -13.7709145238127 24.8009145238127 16 12.74 45.6326947477347 -32.8926947477347 17 9.98 21.4101044631143 -11.4301044631143 18 11.62 29.8298852732551 -18.2098852732551 19 9.4 17.0519266840977 -7.65192668409769 20 9.27 -18.4084645373875 27.6784645373875 21 7.76 0.55319380019235 7.20680619980765 22 8.78 50.705773238009 -41.9257732380090 23 10.65 72.3251752953616 -61.6751752953616 24 10.95 56.3578358273062 -45.4078358273062 25 12.36 52.272717669608 -39.912717669608 26 10.85 38.2892794234368 -27.4392794234368 27 11.84 2.07387610056094 9.76612389943906 28 12.14 -19.2737140272184 31.4137140272184 29 11.65 -27.1269854246633 38.7769854246633 30 8.86 -4.13006140668979 12.9900614066898 31 7.63 -11.8839174968014 19.5139174968014 32 7.38 -10.93612626234 18.31612626234 33 7.25 -24.7883777880585 32.0383777880585 34 8.03 36.5906476378518 -28.5606476378518 35 7.75 35.1653652103446 -27.4153652103446 36 7.16 22.1719225461455 -15.0119225461455 37 7.18 16.4556029585753 -9.2756029585753 38 7.51 43.205241573673 -35.695241573673 39 7.07 52.5524889793366 -45.4824889793366 40 7.11 32.5745960284295 -25.4645960284295 41 8.98 32.4132598271319 -23.4332598271319 42 9.53 28.4163724064607 -18.8863724064607 43 10.54 47.6190898599333 -37.0790898599333 44 11.31 38.0705951634603 -26.7605951634603 45 10.36 56.3762006163701 -46.0162006163701 46 11.44 45.3650111959885 -33.9250111959885 47 10.45 18.7004367711593 -8.25043677115928 48 10.69 29.8586312706496 -19.1686312706496 49 11.28 29.0726853240445 -17.7926853240445 50 11.96 35.6482024961839 -23.6882024961839 51 13.52 35.988155465713 -22.468155465713 52 12.89 23.7105218869592 -10.8205218869592 53 14.03 13.5601293530061 0.469870646993858 54 16.27 11.2737845531827 4.99621544681726 55 16.17 4.9749414258126 11.1950585741874 56 17.25 11.7547561479335 5.49524385206646 57 19.38 21.3824388850511 -2.00243888505105 58 26.2 49.2918057854151 -23.0918057854151 59 33.53 66.1151775542645 -32.5851775542645 60 32.2 41.0096266626957 -8.8096266626957 61 38.45 59.8638894332297 -21.4138894332297 62 44.86 45.9330704932263 -1.07307049322626 63 41.67 20.1197837849421 21.5502162150579 64 36.06 47.3383881719415 -11.2783881719415 65 39.76 33.1141635446581 6.64583645534192 66 36.81 17.1680511039041 19.6419488960959 67 42.65 28.406637292986 14.2433627070140 68 46.89 28.0083844537446 18.8816155462554 69 53.61 60.7097258028334 -7.09972580283339 70 57.59 81.6255742932288 -24.0355742932288 71 67.82 62.6508344816763 5.16916551832365 72 71.89 39.7251622611629 32.1648377388371 73 75.51 67.2363104386734 8.27368956132664 74 68.49 67.4822451780627 1.00775482193725 75 62.72 61.7864707289808 0.933529271019227 76 70.39 38.6718484505559 31.7181515494441 77 59.77 19.0441821696929 40.7258178303071 78 57.27 23.6195628854511 33.6504371145489 79 67.96 28.1748047712402 39.7851952287598 80 67.85 54.1740020390498 13.6759979609502 81 76.98 69.2458688879706 7.7341311120294 82 81.08 77.223510438582 3.85648956141799 83 91.66 80.4761811863608 11.1838188136392 84 84.84 77.9632038546139 6.87679614538613 85 85.73 110.610419358969 -24.8804193589688 86 84.61 60.1242439410144 24.4857560589856 87 92.91 62.0695191702571 30.8404808297429 88 99.8 81.8652520403298 17.9347479596702 89 121.19 90.3648957017613 30.8251042982387 90 122.04 103.685347732840 18.3546522671602 91 131.76 88.9177861793443 42.8422138206557 92 138.48 99.3250352198196 39.1549647801804 93 153.47 117.518095468747 35.9519045312526 94 189.95 170.303839046355 19.6461609536453 95 182.22 167.037264092395 15.1827359076046 96 198.08 155.579887886391 42.5001121136088 97 135.36 170.210572672005 -34.8505726720045 98 125.02 137.309333586984 -12.2893335869840 99 143.5 162.695137803889 -19.1951378038885 100 173.95 170.403405502899 3.54659449710135 101 188.75 174.303344567796 14.4466554322041 102 167.44 174.733135116124 -7.29313511612409 103 158.95 155.616268362422 3.33373163757757 104 169.53 141.942972426113 27.587027573887 105 113.66 150.923166132943 -37.263166132943 106 107.59 196.551523085261 -88.9615230852615 107 92.67 150.733153409489 -58.0631534094888 108 85.35 152.302181993855 -66.9521819938554 109 90.13 212.101808696183 -121.971808696183 110 89.31 146.813987310572 -57.5039873105725 111 105.12 164.206110512103 -59.0861105121031 112 125.83 146.642303239864 -20.8123032398637 113 135.81 121.058253601411 14.7517463985886 114 142.43 160.054954981629 -17.6249549816289 115 163.39 167.191619488664 -3.80161948866429 116 168.21 151.740607503170 16.4693924968302 117 185.35 167.877751433817 17.4722485661827 118 188.5 188.381541243985 0.118458756015371 119 199.91 179.064691576324 20.8453084236755 120 210.73 181.823933068455 28.9060669315445 121 192.06 171.379745875497 20.6802541245033 122 204.62 192.081027405320 12.5389725946802 123 235 184.560880846088 50.4391191539117 124 261.09 186.259247243487 74.8307527565127 125 256.88 153.55716533577 103.322834664230 126 251.53 148.082028962810 103.447971037190 127 257.25 176.765918642905 80.4840813570952 128 243.1 139.224251011955 103.875748988045 129 283.75 170.344051583193 113.405948416807 130 300.98 186.482473148243 114.497526851757 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0026578751414828 0.0053157502829656 0.997342124858517 11 0.000785950465255728 0.00157190093051146 0.999214049534744 12 9.45105180145496e-05 0.000189021036029099 0.999905489481985 13 1.65499756536042e-05 3.30999513072083e-05 0.999983450024346 14 1.86935219100852e-06 3.73870438201703e-06 0.999998130647809 15 3.60583871968644e-07 7.21167743937288e-07 0.999999639416128 16 4.51245599911661e-08 9.02491199823323e-08 0.99999995487544 17 6.01429667309514e-09 1.20285933461903e-08 0.999999993985703 18 6.65214944748363e-10 1.33042988949673e-09 0.999999999334785 19 1.11340835863572e-10 2.22681671727144e-10 0.99999999988866 20 1.38933705726807e-11 2.77867411453614e-11 0.999999999986107 21 1.46353595036418e-12 2.92707190072836e-12 0.999999999998537 22 2.55232757362905e-13 5.1046551472581e-13 0.999999999999745 23 5.67539247561571e-14 1.13507849512314e-13 0.999999999999943 24 1.12154246086375e-14 2.2430849217275e-14 0.999999999999989 25 1.49975328372773e-15 2.99950656745547e-15 0.999999999999998 26 1.66673847794097e-16 3.33347695588195e-16 1 27 1.54821804800824e-17 3.09643609601648e-17 1 28 2.36151854034225e-18 4.7230370806845e-18 1 29 2.72597295534781e-19 5.45194591069561e-19 1 30 2.90652269543326e-20 5.81304539086653e-20 1 31 4.43332188901879e-21 8.86664377803758e-21 1 32 6.41793414546564e-22 1.28358682909313e-21 1 33 2.61909393137559e-22 5.23818786275119e-22 1 34 3.3459127697811e-23 6.6918255395622e-23 1 35 3.26179967193770e-24 6.52359934387539e-24 1 36 4.48720590291235e-25 8.9744118058247e-25 1 37 1.18226771299606e-25 2.36453542599211e-25 1 38 1.69337869303772e-26 3.38675738607544e-26 1 39 2.01653258482603e-27 4.03306516965207e-27 1 40 3.05942245766981e-28 6.11884491533963e-28 1 41 5.34545332063709e-29 1.06909066412742e-28 1 42 5.83822448945647e-30 1.16764489789129e-29 1 43 5.32259377760811e-31 1.06451875552162e-30 1 44 5.05011635935353e-32 1.01002327187071e-31 1 45 5.14853638251029e-33 1.02970727650206e-32 1 46 8.34542704857767e-34 1.66908540971553e-33 1 47 1.92184844952545e-34 3.84369689905091e-34 1 48 6.70367926551214e-35 1.34073585310243e-34 1 49 1.87011578771344e-35 3.74023157542687e-35 1 50 1.17086367664636e-35 2.34172735329272e-35 1 51 2.42784048076956e-35 4.85568096153912e-35 1 52 1.00715687718135e-35 2.0143137543627e-35 1 53 4.62677177348736e-36 9.25354354697472e-36 1 54 5.2902296786556e-36 1.05804593573112e-35 1 55 2.62044385831136e-36 5.24088771662272e-36 1 56 2.5121289635039e-36 5.0242579270078e-36 1 57 1.40648584224924e-35 2.81297168449848e-35 1 58 7.93659113980082e-31 1.58731822796016e-30 1 59 2.26827449967387e-26 4.53654899934774e-26 1 60 5.94742554854418e-24 1.18948510970884e-23 1 61 3.24116233171372e-22 6.48232466342743e-22 1 62 9.25530905943884e-20 1.85106181188777e-19 1 63 1.29065878887416e-17 2.58131757774831e-17 1 64 1.30666398916882e-17 2.61332797833763e-17 1 65 7.02459226681011e-16 1.40491845336202e-15 1 66 1.93533748642887e-14 3.87067497285775e-14 0.99999999999998 67 3.45197915025763e-12 6.90395830051526e-12 0.999999999996548 68 1.54218957709820e-10 3.08437915419640e-10 0.999999999845781 69 1.88504878956781e-09 3.77009757913561e-09 0.999999998114951 70 7.532373679655e-09 1.506474735931e-08 0.999999992467626 71 8.85397858636576e-07 1.77079571727315e-06 0.999999114602141 72 3.43206090658258e-05 6.86412181316516e-05 0.999965679390934 73 0.000104871834537243 0.000209743669074486 0.999895128165463 74 0.000210343744325836 0.000420687488651672 0.999789656255674 75 0.000632173260846289 0.00126434652169258 0.999367826739154 76 0.00170953147452145 0.00341906294904290 0.998290468525479 77 0.0017542919183331 0.0035085838366662 0.998245708081667 78 0.00138637528635591 0.00277275057271182 0.998613624713644 79 0.00294928141268399 0.00589856282536798 0.997050718587316 80 0.00519032002052606 0.0103806400410521 0.994809679979474 81 0.0128818865984262 0.0257637731968523 0.987118113401574 82 0.0272954154724497 0.0545908309448995 0.97270458452755 83 0.0475113645000775 0.095022729000155 0.952488635499922 84 0.0468405694223227 0.0936811388446454 0.953159430577677 85 0.0427853135858323 0.0855706271716647 0.957214686414168 86 0.0483748480749462 0.0967496961498923 0.951625151925054 87 0.0638226905372861 0.127645381074572 0.936177309462714 88 0.0823292101713032 0.164658420342606 0.917670789828697 89 0.133601097195363 0.267202194390726 0.866398902804637 90 0.145535290315397 0.291070580630795 0.854464709684603 91 0.165017966743053 0.330035933486107 0.834982033256947 92 0.194902107542545 0.38980421508509 0.805097892457455 93 0.280672225573413 0.561344451146825 0.719327774426587 94 0.425361243815582 0.850722487631164 0.574638756184418 95 0.479245282301641 0.958490564603281 0.520754717698359 96 0.913936717206399 0.172126565587202 0.0860632827936011 97 0.977461076913231 0.0450778461735377 0.0225389230867689 98 0.9708042052244 0.0583915895512002 0.0291957947756001 99 0.984902099710335 0.0301958005793297 0.0150979002896649 100 0.98781091679096 0.0243781664180798 0.0121890832090399 101 0.989066768792405 0.0218664624151907 0.0109332312075953 102 0.991265287903431 0.0174694241931372 0.00873471209656859 103 0.989008563900015 0.0219828721999697 0.0109914360999849 104 0.986204291938416 0.0275914161231684 0.0137957080615842 105 0.984385603836956 0.0312287923260876 0.0156143961630438 106 0.988859446705987 0.0222811065880267 0.0111405532940133 107 0.986582763682281 0.0268344726354378 0.0134172363177189 108 0.991989946309648 0.0160201073807035 0.00801005369035177 109 0.988400181775845 0.0231996364483095 0.0115998182241548 110 0.979273854348304 0.0414522913033922 0.0207261456516961 111 0.964785809448221 0.0704283811035574 0.0352141905517787 112 0.969031355989145 0.0619372880217108 0.0309686440108554 113 0.976985449199652 0.0460291016006966 0.0230145508003483 114 0.979008663460044 0.0419826730799112 0.0209913365399556 115 0.98800801733457 0.0239839653308617 0.0119919826654309 116 0.9856848715258 0.0286302569484009 0.0143151284742004 117 0.99777706612066 0.00444586775867862 0.00222293387933931 118 0.9994133231524 0.00117335369519848 0.00058667684759924 119 0.997507891716975 0.00498421656604941 0.00249210828302471 120 0.9995171574455 0.000965685109001376 0.000482842554500688 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 74 0.666666666666667 NOK 5% type I error level 93 0.837837837837838 NOK 10% type I error level 101 0.90990990990991 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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