apple Inc - Multiple regression model 5

*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: Tue, 14 Dec 2010 11:13:03 +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/14/t12923252144c81ietw6n88imv.htm/, Retrieved Tue, 14 Dec 2010 12:13:34 +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/14/t12923252144c81ietw6n88imv.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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10.81 24563400 -0.2643 24.45 115.7 9.12 14163200 -0.2643 23.62 109.2 11.03 18184800 -0.2643 21.90 116.9 12.74 20810300 -0.1918 27.12 109.9 9.98 12843000 -0.1918 27.70 116.1 11.62 13866700 -0.1918 29.23 118.9 9.40 15119200 -0.2246 26.50 116.3 9.27 8301600 -0.2246 22.84 114.0 7.76 14039600 -0.2246 20.49 97.0 8.78 12139700 0.3654 23.28 85.3 10.65 9649000 0.3654 25.71 84.9 10.95 8513600 0.3654 26.52 94.6 12.36 15278600 0.0447 25.51 97.8 10.85 15590900 0.0447 23.36 95.0 11.84 9691100 0.0447 24.15 110.7 12.14 10882700 -0.0312 20.92 108.5 11.65 10294800 -0.0312 20.38 110.3 8.86 16031900 -0.0312 21.90 106.3 7.63 13683600 -0.0048 19.21 97.4 7.38 8677200 -0.0048 19.65 94.5 7.25 9874100 -0.0048 17.51 93.7 8.03 10725500 0.0705 21.41 79.6 7.75 8348400 0.0705 23.09 84.9 7.16 8046200 0.0705 20.70 80.7 7.18 10862300 -0.0134 19.00 78.8 7.51 8100300 -0.0134 19.04 64.8 7.07 7287500 -0.0134 19.45 61.4 7.11 14002500 0.0812 20.54 81.0 8.98 19037900 0.0812 19.77 83.6 9.53 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation APPLE[t] = -167.424814295902 -4.30135459798218e-07VOLUME[t] -8.7281919063852REV.GROWTH[t] + 8.83841129074743MICROSOFT[t] -0.668738093829939CONS.CONF[t] + 6.27872053212584M1[t] + 10.5105860530226M2[t] + 15.0431505457878M3[t] + 18.1188764739719M4[t] + 24.7843160355318M5[t] + 17.5713482106206M6[t] + 22.3168235770649M7[t] + 19.8568271551398M8[t] + 21.4874096649532M9[t] + 3.79266552398289M10[t] + 0.0655665756245166M11[t] + 1.54972876039673t + 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) -167.424814295902 19.521004 -8.5766 0 0 VOLUME -4.30135459798218e-07 0 -1.6745 0.09715 0.048575 REV.GROWTH -8.7281919063852 10.288069 -0.8484 0.398253 0.199126 MICROSOFT 8.83841129074743 0.832748 10.6135 0 0 CONS.CONF -0.668738093829939 0.160171 -4.1751 6.4e-05 3.2e-05 M1 6.27872053212584 11.415391 0.55 0.58353 0.291765 M2 10.5105860530226 11.063574 0.95 0.344394 0.172197 M3 15.0431505457878 11.009187 1.3664 0.174872 0.087436 M4 18.1188764739719 10.957164 1.6536 0.101343 0.050671 M5 24.7843160355318 11.040727 2.2448 0.026983 0.013492 M6 17.5713482106206 10.992791 1.5984 0.1131 0.05655 M7 22.3168235770649 10.983927 2.0318 0.044829 0.022415 M8 19.8568271551398 11.066657 1.7943 0.075788 0.037894 M9 21.4874096649532 10.957483 1.961 0.052663 0.026331 M10 3.79266552398289 11.329386 0.3348 0.738505 0.369252 M11 0.0655665756245166 11.145967 0.0059 0.995318 0.497659 t 1.54972876039673 0.147584 10.5007 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.957710226610275 R-squared 0.917208878153904 Adjusted R-squared 0.903962298658528 F-TEST (value) 69.241186260507 F-TEST (DF numerator) 16 F-TEST (DF denominator) 100 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 23.5449579349573 Sum Squared Residuals 55436.5044158911 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.81 -29.1289346330788 39.9389346330788 2 9.12 -21.8629293042175 30.9829293042175 3 11.03 -37.8618195587563 48.8918195587563 4 12.74 15.8191941614227 -3.07919416142274 5 9.98 28.4414830991174 -18.4614830991174 6 11.62 33.9882169765273 -22.3682169765273 7 9.4 17.6408173547178 -8.24081735471784 8 9.27 -11.1474465044169 20.4174465044169 9 7.76 -19.8369714406765 27.5969714406765 10 8.78 -7.83100248695104 16.6110024869510 11 10.65 12.807800388855 -2.15780038885499 12 10.95 15.4526920100371 -4.50269201003714 13 12.36 12.1036487574918 0.256351242508247 14 10.85 0.620794122307112 10.2292058776929 15 11.84 5.72395740774698 6.11604259225302 16 12.14 -16.5775122145614 28.7175122145614 17 11.65 -14.085937921687 25.735937921687 18 8.86 -6.10756959535391 14.9675695953539 19 7.63 -16.8562599716215 24.4862599716215 20 7.38 -9.78485602718028 17.1648560271803 21 7.25 -25.4985835759382 32.7485835759382 22 8.03 1.23196201938234 6.79803798061766 23 7.75 11.3812859040640 -3.63128590406404 24 7.16 -5.31966796601335 12.4796679660134 25 7.18 -11.7249246568766 18.9049246568766 26 7.51 4.96057352962864 2.54942647037136 27 7.07 17.2899390327428 -10.2199390327428 28 7.11 14.7279488222825 -7.61794882228247 29 8.98 12.2329173121377 -3.25291731213772 30 9.53 17.5267717732773 -7.99677177327729 31 10.54 33.4039351298877 -22.8639351298877 32 11.31 30.6567552749443 -19.3467552749443 33 10.36 45.3067025210455 -34.9467025210455 34 11.44 13.0754575258651 -1.63545752586509 35 10.45 1.34700980675375 9.10299019324625 36 10.69 15.3627618198796 -4.67276181987964 37 11.28 20.3781618147631 -9.09816181476312 38 11.96 25.2728146088783 -13.3128146088783 39 13.52 16.2860072990066 -2.76600729900657 40 12.89 27.5456874102571 -14.6556874102571 41 14.03 39.0887992601857 -25.0587992601857 42 16.27 41.4498449447815 -25.1798449447815 43 16.17 43.7135139851325 -27.5435139851325 44 17.25 41.0322112286433 -23.7822112286433 45 19.38 47.9575666891021 -28.5775666891021 46 26.2 26.9848384046912 -0.784838404691183 47 33.53 37.5638539443876 -4.03385394438765 48 32.2 33.7655396498255 -1.56553964982551 49 38.45 27.1047709883906 11.3452290116094 50 44.86 28.2152262679419 16.6447737320581 51 41.67 34.4560788646183 7.2139211353817 52 36.06 47.4625291208946 -11.4025291208946 53 39.76 61.4918559914739 -21.7318559914739 54 36.81 47.5662944562746 -10.7562944562746 55 42.65 63.3982633935495 -20.7482633935495 56 46.89 77.702976868646 -30.812976868646 57 53.61 76.7894369524919 -23.1794369524919 58 57.59 56.2951178397668 1.29488216023321 59 67.82 66.0204213227497 1.79957867725034 60 71.89 51.718786966296 20.1712130337040 61 75.51 68.8686566886356 6.64134331136439 62 68.49 69.071756514849 -0.581756514848956 63 62.72 74.8427344024244 -12.1227344024244 64 70.39 53.2307715219882 17.1592284780118 65 59.77 58.454383717183 1.31561628281696 66 57.27 56.1779314592343 1.09206854076566 67 67.96 66.077489829688 1.88251017031203 68 67.85 85.6368170614178 -17.7868170614178 69 76.98 96.8473018730144 -19.8673018730144 70 81.08 96.5646572599 -15.4846572599 71 91.66 99.9915897284772 -8.33158972847717 72 84.84 99.4434417737356 -14.6034417737356 73 85.73 107.742314508485 -22.0123145084845 74 84.61 101.362023352933 -16.7520233529330 75 92.91 107.279193279517 -14.3691932795170 76 99.8 130.430002832845 -30.6300028328452 77 121.19 141.937008363237 -20.7470083632374 78 122.04 123.507306046263 -1.46730604626286 79 131.76 119.604966151996 12.1550338480041 80 138.48 123.584783515897 14.8952164841034 81 153.47 136.380498835399 17.0895011646011 82 189.95 184.374081452730 5.57591854727027 83 182.22 157.593166659243 24.6268333407566 84 198.08 180.179466815272 17.9005331847278 85 135.36 151.444911624608 -16.0849116246079 86 125.02 127.552761331245 -2.53276133124544 87 143.5 152.419301067743 -8.91930106774267 88 173.95 163.407098659772 10.5429013402276 89 188.75 176.900514929889 11.8494850701110 90 167.44 168.277653301704 -0.837653301703739 91 158.95 155.784767286365 3.16523271363483 92 169.53 168.650480585262 0.879519414737732 93 113.66 156.068556343813 -42.4085563438126 94 107.59 113.744518120294 -6.15451812029398 95 92.67 100.310998012182 -7.64099801218243 96 85.35 104.266506063077 -18.9165060630770 97 90.13 115.575994244093 -25.4459942440925 98 89.31 102.575169337853 -13.2651693378535 99 105.12 127.117420713788 -21.9974207137881 100 125.83 139.387273796008 -13.5572737960081 101 135.81 146.178344363205 -10.3683443632049 102 142.43 167.312791478231 -24.8827914782306 103 163.39 175.964235147452 -12.5742351474520 104 168.21 182.912081270901 -14.7020812709006 105 185.35 194.518521350729 -9.16852135072932 106 188.5 194.720369864322 -6.22036986432195 107 199.91 209.643874233287 -9.73387423328692 108 210.73 217.020472867890 -6.29047286789028 109 192.06 196.505400663489 -4.44540066348926 110 204.62 218.581810238581 -13.9618102385807 111 235 226.827187491169 8.17281250883057 112 261.09 236.567005889091 24.5229941109093 113 256.88 196.160630885258 60.7193691147421 114 251.53 174.100759159062 77.4292408409384 115 257.25 206.968271692833 50.2817283071672 116 243.1 190.026196725886 53.0738032741138 117 283.75 203.03697045102 80.71302954898 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.000298062341214584 0.000596124682429168 0.999701937658785 21 2.10230122855614e-05 4.20460245711228e-05 0.999978976987714 22 1.64092555787285e-06 3.2818511157457e-06 0.999998359074442 23 1.3134321322866e-07 2.6268642645732e-07 0.999999868656787 24 7.44455761869434e-09 1.48891152373887e-08 0.999999992555442 25 7.72586811908547e-10 1.54517362381709e-09 0.999999999227413 26 7.01162955072654e-11 1.40232591014531e-10 0.999999999929884 27 3.94236890332904e-12 7.88473780665807e-12 0.999999999996058 28 3.23935930685226e-12 6.47871861370452e-12 0.99999999999676 29 2.92733709203219e-13 5.85467418406437e-13 0.999999999999707 30 4.10100907222039e-14 8.20201814444079e-14 0.99999999999996 31 1.62761385744920e-14 3.25522771489839e-14 0.999999999999984 32 3.16374958345142e-15 6.32749916690283e-15 0.999999999999997 33 2.28967388533605e-16 4.57934777067209e-16 1 34 2.34307109995582e-17 4.68614219991165e-17 1 35 2.35543625184074e-18 4.71087250368148e-18 1 36 2.01910832410829e-19 4.03821664821657e-19 1 37 5.47175796241618e-20 1.09435159248324e-19 1 38 5.57354219456846e-21 1.11470843891369e-20 1 39 1.01640108419724e-21 2.03280216839449e-21 1 40 9.93576426012005e-23 1.98715285202401e-22 1 41 1.82343342329803e-23 3.64686684659607e-23 1 42 7.36302382080744e-24 1.47260476416149e-23 1 43 8.04296570733289e-25 1.60859314146658e-24 1 44 2.52162742389229e-25 5.04325484778459e-25 1 45 4.81289543358371e-25 9.62579086716742e-25 1 46 8.94377176984187e-24 1.78875435396837e-23 1 47 9.2873509559074e-22 1.85747019118148e-21 1 48 1.32624221022531e-21 2.65248442045062e-21 1 49 2.25831446702500e-22 4.51662893405001e-22 1 50 1.86554228222619e-21 3.73108456445239e-21 1 51 6.44577065763044e-19 1.28915413152609e-18 1 52 1.27760077447776e-19 2.55520154895553e-19 1 53 7.99468666600866e-19 1.59893733320173e-18 1 54 1.39816679161453e-18 2.79633358322907e-18 1 55 2.90173885318732e-16 5.80347770637465e-16 1 56 1.04121122947437e-14 2.08242245894873e-14 0.99999999999999 57 9.4640665569416e-13 1.89281331138832e-12 0.999999999999054 58 1.09682883884725e-11 2.19365767769449e-11 0.999999999989032 59 4.69579445067328e-10 9.39158890134655e-10 0.99999999953042 60 1.13161093849961e-08 2.26322187699922e-08 0.99999998868389 61 2.77285856367914e-08 5.54571712735827e-08 0.999999972271414 62 2.26406705423354e-08 4.52813410846707e-08 0.99999997735933 63 1.21880737176311e-08 2.43761474352623e-08 0.999999987811926 64 1.37858336903612e-08 2.75716673807225e-08 0.999999986214166 65 7.21431811323126e-09 1.44286362264625e-08 0.999999992785682 66 3.17745026574134e-09 6.35490053148268e-09 0.99999999682255 67 1.68933674434146e-09 3.37867348868292e-09 0.999999998310663 68 6.37325753804294e-10 1.27465150760859e-09 0.999999999362674 69 2.32560519979753e-10 4.65121039959507e-10 0.99999999976744 70 8.95938626215323e-11 1.79187725243065e-10 0.999999999910406 71 5.0587852257088e-11 1.01175704514176e-10 0.999999999949412 72 2.21416893512473e-11 4.42833787024946e-11 0.999999999977858 73 1.21613418981504e-11 2.43226837963008e-11 0.999999999987839 74 4.66958290754625e-12 9.33916581509249e-12 0.99999999999533 75 3.4537656171977e-12 6.9075312343954e-12 0.999999999996546 76 6.83441154359367e-12 1.36688230871873e-11 0.999999999993166 77 1.82677310352169e-10 3.65354620704339e-10 0.999999999817323 78 1.46059632304135e-09 2.92119264608271e-09 0.999999998539404 79 1.29112628497995e-08 2.5822525699599e-08 0.999999987088737 80 1.06272791761511e-07 2.12545583523021e-07 0.999999893727208 81 1.72609360441871e-06 3.45218720883741e-06 0.999998273906396 82 2.42790336043982e-05 4.85580672087964e-05 0.999975720966396 83 0.000110652464152783 0.000221304928305566 0.999889347535847 84 0.00248714269368214 0.00497428538736428 0.997512857306318 85 0.00169965019642761 0.00339930039285522 0.998300349803572 86 0.00171519202309680 0.00343038404619360 0.998284807976903 87 0.00173255273999131 0.00346510547998263 0.998267447260009 88 0.0144975911513671 0.0289951823027342 0.985502408848633 89 0.0524945685855242 0.104989137171048 0.947505431414476 90 0.112909635390278 0.225819270780557 0.887090364609722 91 0.157451085487678 0.314902170975357 0.842548914512322 92 0.962871427458067 0.0742571450838665 0.0371285725419332 93 0.984890666247923 0.0302186675041535 0.0151093337520767 94 0.974204228246163 0.0515915435076731 0.0257957717538365 95 0.953848227983957 0.0923035440320864 0.0461517720160432 96 0.920666898298993 0.158666203402014 0.079333101701007 97 0.82417167467208 0.35165665065584 0.17582832532792 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 68 0.871794871794872 NOK 5% type I error level 70 0.897435897435897 NOK 10% type I error level 73 0.935897435897436 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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