Apple Inc - Multiple regression model 4

*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 11:00:31 +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/t1292324308bkh3jkzn3juu09w.htm/, Retrieved Tue, 14 Dec 2010 11:58:31 +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/t1292324308bkh3jkzn3juu09w.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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation APPLE[t] = -154.134352998398 -6.20191077240239e-07VOLUME[t] -14.5370902407824REV.GROWTH[t] + 8.00707285496616MICROSOFT[t] -0.48366229262046CONS.CONF[t] + 1.73128519781195t + 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) -154.134352998398 17.180336 -8.9716 0 0 VOLUME -6.20191077240239e-07 0 -2.7004 0.008012 0.004006 REV.GROWTH -14.5370902407824 10.071044 -1.4435 0.151709 0.075854 MICROSOFT 8.00707285496616 0.797315 10.0425 0 0 CONS.CONF -0.48366229262046 0.147821 -3.272 0.001424 0.000712 t 1.73128519781195 0.133585 12.9601 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.952350239811516 R-squared 0.906970979269052 Adjusted R-squared 0.90278048283973 F-TEST (value) 216.435211094022 F-TEST (DF numerator) 5 F-TEST (DF denominator) 111 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 23.6893841431663 Sum Squared Residuals 62291.7482401575 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.81 -23.9817123088948 34.7917123088949 2 9.12 -19.3023814371578 28.4223814371578 3 11.03 -37.5616216392947 48.5916216392947 4 12.74 6.66996919403308 6.07003080596692 5 9.98 14.9878988031746 -5.0078988031746 6 11.62 26.9808614439767 -15.3608614439767 7 9.4 7.81038694419848 1.58961305580153 8 9.27 -14.4235765459456 23.6935765459456 9 7.76 -26.8453099839609 34.6053099839609 10 8.78 -4.51402491154679 13.2940249115468 11 10.65 18.4126221569634 -7.76262215696339 12 10.95 22.642277077978 -11.692277077978 13 12.36 15.2051515585774 -2.84515155857738 14 10.85 0.881798864127212 9.96820113587279 15 11.84 5.00417694072316 6.83582305927684 16 12.14 -17.6989806776047 29.8389806776047 17 11.65 -20.7974966138818 32.4474966138818 18 8.86 -8.51890973527437 17.3789097352744 19 7.63 -22.9494405886727 30.5794405886727 20 7.38 -13.1874980769808 20.5674980769808 21 7.25 -28.9467256550489 36.1967256550489 22 8.03 9.20910842478632 -1.17910842478632 23 7.75 23.3031220777607 -15.5531220777607 24 7.16 8.11630652475149 -0.956306524751487 25 7.18 -3.37233199631476 10.5523319963148 26 7.51 7.1634759677198 0.346524032280196 27 7.07 14.3262041385583 -7.25620413855832 28 7.11 9.76562599247612 -2.65562599247612 29 8.98 0.951032980815432 8.02896701918457 30 9.53 14.5013798060706 -4.97137980607063 31 10.54 23.8259811787228 -13.2859811787228 32 11.31 24.7410430241728 -13.4310430241728 33 10.36 35.8725128759893 -25.5125128759893 34 11.44 22.0594692033944 -10.6194692033944 35 10.45 16.8673638578927 -6.41736385789267 36 10.69 29.9358445389532 -19.2458445389532 37 11.28 28.9142696925118 -17.6342696925118 38 11.96 29.880610730741 -17.920610730741 39 13.52 15.9947127255577 -2.47471272555774 40 12.89 24.841221523558 -11.951221523558 41 14.03 31.0400322857486 -17.0100322857486 42 16.27 40.0977510422493 -23.8277510422493 43 16.17 37.5637580844737 -21.3937580844737 44 17.25 37.5931386179208 -20.3431386179208 45 19.38 42.4368549540992 -23.0568549540992 46 26.2 33.428273332118 -7.22827333211802 47 33.53 46.0664700494242 -12.5364700494242 48 32.2 45.4137781651326 -13.2137781651326 49 38.45 29.3817994357837 9.06820056421626 50 44.86 28.4940094398983 16.3659905601017 51 41.67 34.0440684398783 7.62593156012175 52 36.06 40.2420689809901 -4.18206898099008 53 39.76 50.5845979694464 -10.8245979694464 54 36.81 46.0134170964051 -9.20341709640509 55 42.65 57.2869169778635 -14.6369169778635 56 46.89 73.9115114369084 -27.0215114369084 57 53.61 68.1949221279359 -14.5849221279359 58 57.59 62.5314927606709 -4.94149276067093 59 67.82 78.815166642706 -10.995166642706 60 71.89 67.0599954154259 4.83000458457413 61 75.51 75.5970302172293 -0.0870302172293338 62 68.49 72.4775609145483 -3.98756091454829 63 62.72 74.5983102003232 -11.8783102003232 64 70.39 53.0672212059592 17.3227787940408 65 59.77 54.191857522708 5.57814247729202 66 57.27 58.4449839595593 -1.17498395955928 67 67.96 62.5386256660375 5.42137433396248 68 67.85 82.9376600622813 -15.0876600622813 69 76.98 91.7868115446541 -14.8068115446541 70 81.08 110.185281621652 -29.1052816216515 71 91.66 116.824994508139 -25.1649945081387 72 84.84 115.658415262778 -30.8184152627784 73 85.73 113.857770767879 -28.1277707678791 74 84.61 109.901822268718 -25.2918222687181 75 92.91 111.279596730886 -18.3695967308862 76 99.8 129.54925949676 -29.7492594967602 77 121.19 133.342199343209 -12.1521993432094 78 122.04 120.528084929799 1.51191507020111 79 131.76 112.67820483814 19.0817951618596 80 138.48 119.143235245062 19.3367647549377 81 153.47 128.538840864294 24.9311591357059 82 189.95 187.901722379001 2.04827762099925 83 182.22 164.338369536434 17.8816304635657 84 198.08 188.936547843334 9.14345215666628 85 135.36 149.920694939395 -14.5606949393949 86 125.02 126.928702254653 -1.90870225465328 87 143.5 145.39321692647 -1.89321692647006 88 173.95 154.245915526607 19.7040844733933 89 188.75 161.563092895461 27.1869071045388 90 167.44 159.406427976005 8.03357202399498 91 158.95 142.022712401786 16.9272875982136 92 169.53 159.304766416234 10.2252335837656 93 113.66 142.402740914777 -28.7427409147771 94 107.59 116.317650302864 -8.7276503028642 95 92.67 113.6554537575 -20.9854537574997 96 85.35 119.427615201161 -34.0776152011609 97 90.13 120.162068298861 -30.0320682988613 98 89.31 108.88444188496 -19.5744418849596 99 105.12 127.864806851276 -22.7448068512758 100 125.83 138.386232072637 -12.5562320726365 101 135.81 141.286914030145 -5.4769140301446 102 142.43 165.798287199066 -23.3682871990664 103 163.39 171.595383746337 -8.20538374633718 104 168.21 182.242844969093 -14.032844969093 105 185.35 190.668675483738 -5.3186754837375 106 188.5 203.918760294222 -15.4187602942221 107 199.91 222.836237710126 -22.9262377101257 108 210.73 229.53178474948 -18.8017847494797 109 192.06 202.123011991288 -10.0630119912882 110 204.62 219.606025936263 -14.9860259362633 111 235 224.402124651334 10.5978753486658 112 261.09 230.028807183237 31.0611928167628 113 256.88 185.609392276764 71.2706077232363 114 251.53 172.735498902359 78.7945010976412 115 257.25 197.969225317981 59.2807746820188 116 243.1 188.015412220696 55.0845877793041 117 283.75 196.926226455153 86.8237735448472 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.000117558264271682 0.000235116528543364 0.999882441735728 10 1.5781671081611e-05 3.15633421632221e-05 0.999984218328918 11 1.24465711461738e-06 2.48931422923476e-06 0.999998755342885 12 6.21287816322216e-08 1.24257563264443e-07 0.999999937871218 13 5.81582113990924e-09 1.16316422798185e-08 0.99999999418418 14 2.72623905391665e-10 5.4524781078333e-10 0.999999999727376 15 1.32348132953493e-11 2.64696265906986e-11 0.999999999986765 16 9.84608663156577e-13 1.96921732631315e-12 0.999999999999015 17 5.20143080275554e-14 1.04028616055111e-13 0.999999999999948 18 1.65889003858968e-13 3.31778007717937e-13 0.999999999999834 19 4.16690307087893e-14 8.33380614175786e-14 0.999999999999958 20 4.16813414520556e-15 8.33626829041112e-15 0.999999999999996 21 5.07659668604166e-16 1.01531933720833e-15 1 22 3.84276835817564e-17 7.68553671635128e-17 1 23 2.43921484952769e-18 4.87842969905539e-18 1 24 1.59794948600967e-19 3.19589897201934e-19 1 25 1.51282600666492e-20 3.02565201332984e-20 1 26 5.57374473679934e-21 1.11474894735987e-20 1 27 6.98294570623628e-22 1.39658914124726e-21 1 28 1.16184251177684e-22 2.32368502355369e-22 1 29 1.48452529062283e-23 2.96905058124567e-23 1 30 2.18120856310836e-24 4.36241712621673e-24 1 31 5.76931911710791e-25 1.15386382342158e-24 1 32 1.72774667746903e-25 3.45549335493806e-25 1 33 1.65522676613996e-26 3.31045353227992e-26 1 34 1.74271610626315e-27 3.48543221252629e-27 1 35 2.07894409053406e-28 4.15788818106811e-28 1 36 1.95377868097028e-29 3.90755736194056e-29 1 37 1.91155521026798e-30 3.82311042053596e-30 1 38 2.56266186094295e-31 5.1253237218859e-31 1 39 1.44762485719806e-31 2.89524971439612e-31 1 40 2.0996468904676e-32 4.1992937809352e-32 1 41 1.0280788118076e-32 2.05615762361521e-32 1 42 3.09129841125242e-33 6.18259682250483e-33 1 43 3.17466261881958e-34 6.34932523763916e-34 1 44 2.78527815999615e-34 5.5705563199923e-34 1 45 3.41772361435461e-33 6.83544722870922e-33 1 46 1.66022374650212e-31 3.32044749300424e-31 1 47 1.05978198987408e-28 2.11956397974817e-28 1 48 2.92138002643539e-28 5.84276005287078e-28 1 49 4.17251962415204e-29 8.34503924830408e-29 1 50 9.44861277674694e-27 1.88972255534939e-26 1 51 3.20713206220087e-23 6.41426412440174e-23 1 52 6.43046472181005e-24 1.28609294436201e-23 1 53 7.12021886398933e-23 1.42404377279787e-22 1 54 1.05038857469729e-22 2.10077714939457e-22 1 55 1.10695001900515e-20 2.2139000380103e-20 1 56 1.60250660180371e-18 3.20501320360741e-18 1 57 1.28469961382699e-15 2.56939922765397e-15 0.999999999999999 58 1.96481865246388e-14 3.92963730492775e-14 0.99999999999998 59 2.7115562191646e-12 5.4231124383292e-12 0.999999999997288 60 2.08395258344003e-10 4.16790516688005e-10 0.999999999791605 61 2.64833385004755e-10 5.2966677000951e-10 0.999999999735167 62 1.52321495214356e-10 3.04642990428711e-10 0.999999999847679 63 6.05907606525856e-11 1.21181521305171e-10 0.99999999993941 64 7.28177599765085e-11 1.45635519953017e-10 0.999999999927182 65 5.43163222072865e-11 1.08632644414573e-10 0.999999999945684 66 2.73506153883574e-11 5.47012307767148e-11 0.99999999997265 67 1.74704768075416e-11 3.49409536150831e-11 0.99999999998253 68 7.54493255916509e-12 1.50898651183302e-11 0.999999999992455 69 3.34769848357329e-12 6.69539696714658e-12 0.999999999996652 70 1.80007501491063e-12 3.60015002982125e-12 0.9999999999982 71 1.84266089007633e-12 3.68532178015265e-12 0.999999999998157 72 8.2568442578117e-13 1.65136885156234e-12 0.999999999999174 73 1.11008039735225e-12 2.22016079470449e-12 0.99999999999889 74 5.65832198119229e-13 1.13166439623846e-12 0.999999999999434 75 4.63913540800578e-13 9.27827081601156e-13 0.999999999999536 76 9.21740337737614e-13 1.84348067547523e-12 0.999999999999078 77 1.30240904133525e-11 2.6048180826705e-11 0.999999999986976 78 4.51201879881548e-11 9.02403759763095e-11 0.99999999995488 79 6.30556957821753e-10 1.26111391564351e-09 0.999999999369443 80 1.44697588778303e-08 2.89395177556607e-08 0.999999985530241 81 5.80358857583954e-07 1.16071771516791e-06 0.999999419641142 82 1.08611025440451e-05 2.17222050880902e-05 0.999989138897456 83 4.15039807861259e-05 8.30079615722518e-05 0.999958496019214 84 0.000235337775798581 0.000470675551597162 0.999764662224201 85 0.000315075316900027 0.000630150633800053 0.9996849246831 86 0.000216732032125856 0.000433464064251711 0.999783267967874 87 0.000130964097057365 0.000261928194114729 0.999869035902943 88 0.000521459273902975 0.00104291854780595 0.999478540726097 89 0.0204806739019154 0.0409613478038308 0.979519326098085 90 0.148605270468767 0.297210540937533 0.851394729531233 91 0.273413863234299 0.546827726468597 0.726586136765701 92 0.924576873671191 0.150846252657617 0.0754231263288087 93 0.953761746098275 0.0924765078034495 0.0462382539017248 94 0.964633471853987 0.070733056292027 0.0353665281460135 95 0.956874945418968 0.0862501091620633 0.0431250545810316 96 0.943875977239762 0.112248045520477 0.0561240227602383 97 0.936716410985758 0.126567178028485 0.0632835890142425 98 0.903928273930341 0.192143452139318 0.0960717260696588 99 0.857977465168466 0.284045069663069 0.142022534831534 100 0.833731009570916 0.332537980858167 0.166268990429084 101 0.782031973569102 0.435936052861796 0.217968026430898 102 0.729894490455296 0.540211019089408 0.270105509544704 103 0.724881252957973 0.550237494084055 0.275118747042027 104 0.623231057665734 0.753537884668532 0.376768942334266 105 0.508041824754431 0.983916350491139 0.491958175245569 106 0.5772193512088 0.8455612975824 0.4227806487912 107 0.6549927774616 0.6900144450768 0.3450072225384 108 0.483989508218386 0.967979016436772 0.516010491781614 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 80 0.8 NOK 5% type I error level 81 0.81 NOK 10% type I error level 84 0.84 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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