Multiple regression model 1

*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: Sun, 19 Dec 2010 11:46:47 +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/19/t1292759279ivlokg9tkbbpdql.htm/, Retrieved Sun, 19 Dec 2010 12:48:10 +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/19/t1292759279ivlokg9tkbbpdql.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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15561600 15,73 3,56 142,86 14917500 16,17 1,33 380,71 14805920 12,00 0,00 460,00 16958000 12,86 0,69 361,43 17605000 10,30 10,05 140,00 17131200 12,97 0,51 275,00 18474600 12,06 0,91 274,29 17286700 10,49 2,67 212,86 18574400 5,97 1,39 172,86 18056000 9,26 1,24 186,43 19701600 9,74 2,79 77,14 19061700 5,46 3,37 17,86 19681900 2,71 1,60 37,14 34521200 3,90 4,73 42,86 19922700 1,51 0,79 85,00 20177900 5,01 0,67 45,00 19759900 2,96 0,00 206,43 23076700 -1,97 0,60 178,57 22532000 -4,61 0,40 285,71 22029400 4,27 2,24 58,57 22587000 4,01 5,74 88,57 23256600 0,04 0,06 309,29 22680300 3,04 0,87 58,57 21916400 2,29 4,91 132,14 19640200 4,37 1,93 3,57 18813100 6,39 0,41 102,86 18730000 5,74 1,21 185,71 18154700 7,64 2,01 177,14 17848800 7,07 0,00 530,00 18077500 6,23 6,49 162,86 17133100 10,20 0,00 553,57 16602600 14,07 0,31 258,57 15878900 12,83 4,87 326,43 15789100 12,04 1,37 580,00 15422000 11,97 0,19 286,43 14661400 12,63 0,34 310,71 15879200 13,56 3,60 148,57 14339300 15,66 0,1 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 Kijkcijfers[t] = + 22305689.4422709 -434868.354365401Temperatuur[t] + 126331.126374946Neerslag[t] -2972.05562531293Zonneschijnduur[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) 22305689.4422709 416070.775861 53.6103 0 0 Temperatuur -434868.354365401 39673.981852 -10.961 0 0 Neerslag 126331.126374946 84185.188156 1.5006 0.136601 0.068301 Zonneschijnduur -2972.05562531293 1457.840352 -2.0387 0.044122 0.022061 Multiple Linear Regression - Regression Statistics Multiple R 0.869955756825498 R-squared 0.756823018833825 Adjusted R-squared 0.74952770939884 F-TEST (value) 103.741044239250 F-TEST (DF numerator) 3 F-TEST (DF denominator) 100 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1878997.01130159 Sum Squared Residuals 353062976848032 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15561600 15490361.1713656 71238.82863437 2 14917500 14310397.2531482 607102.74685184 3 14805920 15720123.6022421 -914203.602242134 4 16958000 15726260.8176737 1231739.18232631 5 17605000 18680085.4248317 -1075085.42483165 6 17131200 15912560.4636418 1218639.53635820 7 18474600 16360933.2761583 2113666.72384173 8 17286700 17448592.7519948 -161892.751994826 9 18574400 19371376.0969790 -796976.096979023 10 18056000 17881378.7473251 174621.252674884 11 19701600 18193271.1424013 1508328.85759866 12 19061700 20303963.2098513 -1242263.20985127 13 19681900 21218943.8582164 -1537043.85821644 14 34521200 21079866.7838984 13441333.2161016 15 19922700 21496215.0888637 -1573515.08886374 16 20177900 20077898.3384324 100001.661567644 17 19759900 20404957.6706159 -645057.670615948 18 23076700 22707458.8031836 369241.196816442 19 22532000 23511818.9937372 -979818.9937372 20 22029400 20557709.9942359 1471690.00576408 21 22587000 21023773.0399239 1563226.96007615 22 23256600 21376647.4913257 1879952.50867427 23 22680300 20919524.4269717 1760775.57302831 24 21916400 21537399.3109462 379000.689053752 25 19640200 20638523.5690154 -998323.56901536 26 18813100 19272970.77807 -459870.778070012 27 18730000 19410465.3009503 -680465.300950303 28 18154700 18710750.8454649 -556050.845464931 29 17848800 17655980.6954916 192819.304508351 30 18077500 19932319.6256094 -1854819.62560937 31 17133100 16224791.3952393 908308.60476068 32 16602600 15457769.9224888 1144830.07751123 33 15878900 16371392.9234379 -492492.923437881 34 15789100 15519155.8361636 269944.163836361 35 15422000 16273032.2617699 -851032.261769895 36 14661400 15932807.3062624 -1271407.30626237 37 15879200 16422108.3077731 -542908.307773114 38 14339300 13644389.1606875 694910.839312545 39 13169600 14045009.3962156 -875409.396215589 40 14528900 15044675.8666604 -515775.866660434 41 13375800 15713627.2075897 -2337827.20758967 42 12309900 14209486.8274132 -1899586.82741321 43 11933900 12375765.6740081 -441865.674008106 44 10061900 10547356.2663483 -485456.26634826 45 12609600 15085478.3946368 -2475878.39463682 46 11156500 12869085.5910468 -1712585.59104675 47 12187200 13686075.8341398 -1498875.83413984 48 11284300 13411471.7208982 -2127171.72089820 49 10177000 11726883.4857667 -1549883.48576668 50 10970720 12818577.6758419 -1847857.67584192 51 10820680 11923852.5686699 -1103172.56866989 52 11492390 13328455.2941103 -1836065.29411029 53 14573750 14744180.0705738 -170430.070573815 54 13992820 13995549.4203239 -2729.42032390920 55 14727070 14468087.6223733 258982.377626654 56 15685360 14448400.4412023 1236959.55879772 57 16736210 16250996.3468984 485213.653101552 58 17950180 17613352.1435398 336827.856460245 59 17002730 17251389.6148101 -248659.614810066 60 17415160 17325525.6271164 89634.3728836058 61 17929810 17027167.6447325 902642.355267474 62 17865790 18870225.1600536 -1004435.16005359 63 19202360 17939491.2281819 1262868.77181809 64 19085000 16860936.5607807 2224063.43921929 65 18188880 17671302.2578206 517577.742179371 66 18466410 19683499.7647815 -1217089.76478154 67 18520400 19643586.5450209 -1123186.54502089 68 20025500 23297614.3093677 -3272114.30936766 69 20636100 21416866.473144 -780766.473143981 70 20672000 21981400.5856402 -1309400.58564018 71 22589100 23552239.0192337 -963139.019233702 72 21864800 21674701.4033889 190098.596611128 73 22750100 20844991.8836037 1905108.11639635 74 22548746 22295637.4753704 253108.524629565 75 21325495 20620855.9058313 704639.09416875 76 21556563 23362470.7376630 -1805907.73766297 77 21415269 21401204.1372740 14064.8627259743 78 20401054 20058685.0723197 342368.927680251 79 19062253 20474555.5190159 -1412302.5190159 80 19085706 20780466.8950475 -1694760.89504747 81 19279967 17443381.1693506 1836585.83064943 82 18552045 17144197.2568856 1407847.74311437 83 17800733 19033628.5740772 -1232895.57407722 84 17142490 17220370.0643330 -77880.0643329632 85 17593173 16472263.5724632 1120909.42753676 86 17633859 16176998.1936560 1456860.80634404 87 17336613 15331354.6838832 2005258.31611680 88 17008347 18325063.0820513 -1316716.08205131 89 17951965 18513764.5725072 -561799.572507217 90 14520929 14302074.4030886 218854.596911391 91 16941217 15297482.7367568 1643734.26324323 92 15436824 13573699.5239808 1863124.47601917 93 14744261 14704077.7296071 40183.2703928535 94 14248004 14832295.4360174 -584291.43601736 95 11540953 11895620.9999059 -354667.999905936 96 12881661 10567608.3721974 2314052.62780264 97 15185757 11141637.3081690 4044119.69183104 98 13554339 12835639.9844747 718699.015525282 99 13575106 12358075.9516642 1217030.04833585 100 12238400 13716522.2798913 -1478122.27989129 101 13303614 14425383.1448668 -1121769.14486684 102 14151478 15295690.3474423 -1144212.34744231 103 14172009 14765178.6045297 -593169.604529667 104 14022320 14924024.6533574 -901704.653357371 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.188779342198484 0.377558684396969 0.811220657801516 8 0.129884772408617 0.259769544817234 0.870115227591383 9 0.0899890376606273 0.179978075321255 0.910010962339373 10 0.0419162018936496 0.0838324037872992 0.95808379810635 11 0.0281462428905723 0.0562924857811447 0.971853757109428 12 0.0207456026133941 0.0414912052267882 0.979254397386606 13 0.0115475071849243 0.0230950143698486 0.988452492815076 14 0.999999999876463 2.47073627798120e-10 1.23536813899060e-10 15 0.999999999917422 1.65155558565784e-10 8.25777792828921e-11 16 0.999999999759125 4.81750852227103e-10 2.40875426113552e-10 17 0.99999999939219 1.21561959645478e-09 6.07809798227388e-10 18 0.999999998316634 3.36673272220064e-09 1.68336636110032e-09 19 0.999999996148557 7.70288563352005e-09 3.85144281676003e-09 20 0.999999993377777 1.32444456700882e-08 6.62222283504409e-09 21 0.999999990201147 1.95977057799035e-08 9.79885288995176e-09 22 0.999999990800004 1.83999917004563e-08 9.19999585022817e-09 23 0.999999989429815 2.11403694849846e-08 1.05701847424923e-08 24 0.999999979762629 4.0474742660253e-08 2.02373713301265e-08 25 0.99999996858869 6.2822620748779e-08 3.14113103743895e-08 26 0.999999930916918 1.38166163183575e-07 6.90830815917875e-08 27 0.999999859915297 2.80169404998429e-07 1.40084702499214e-07 28 0.999999719868408 5.60263183294047e-07 2.80131591647023e-07 29 0.999999399265806 1.20146838851502e-06 6.00734194257508e-07 30 0.999999536486686 9.27026628746296e-07 4.63513314373148e-07 31 0.99999919792199 1.60415602175062e-06 8.02078010875312e-07 32 0.999998712243626 2.57551274822201e-06 1.28775637411100e-06 33 0.999997598324104 4.8033517927108e-06 2.4016758963554e-06 34 0.99999523722285 9.52555429910376e-06 4.76277714955188e-06 35 0.999991843296413 1.63134071744172e-05 8.1567035872086e-06 36 0.999988223088245 2.35538235104834e-05 1.17769117552417e-05 37 0.99997928990814 4.14201837212818e-05 2.07100918606409e-05 38 0.999965004430485 6.99911390292732e-05 3.49955695146366e-05 39 0.999943584811403 0.000112830377193601 5.64151885968005e-05 40 0.999901550940508 0.000196898118984857 9.84490594924286e-05 41 0.99993011357661 0.000139772846781835 6.98864233909177e-05 42 0.999930259980163 0.000139480039673531 6.97400198367655e-05 43 0.999881143683651 0.000237712632697800 0.000118856316348900 44 0.999810647023121 0.000378705953757276 0.000189352976878638 45 0.999880280486069 0.000239439027862868 0.000119719513931434 46 0.999879670387959 0.000240659224082631 0.000120329612041316 47 0.999863479728201 0.000273040543597533 0.000136520271798767 48 0.999906909111145 0.000186181777709998 9.3090888854999e-05 49 0.999921062490143 0.000157875019714198 7.89375098570989e-05 50 0.999949509753135 0.000100980493729532 5.04902468647660e-05 51 0.999954811233941 9.037753211747e-05 4.5188766058735e-05 52 0.99997868306701 4.26338659794134e-05 2.13169329897067e-05 53 0.999964206295605 7.15874087904927e-05 3.57937043952463e-05 54 0.999943381907024 0.000113236185951527 5.66180929757637e-05 55 0.99990433635821 0.000191327283580389 9.56636417901943e-05 56 0.999857553403479 0.000284893193042322 0.000142446596521161 57 0.999749443852844 0.000501112294311871 0.000250556147155935 58 0.999617996921863 0.000764006156273281 0.000382003078136641 59 0.999371671893314 0.00125665621337179 0.000628328106685895 60 0.998943067678293 0.00211386464341457 0.00105693232170729 61 0.998409717261602 0.00318056547679645 0.00159028273839823 62 0.997731611217619 0.00453677756476262 0.00226838878238131 63 0.997161509541829 0.00567698091634203 0.00283849045817102 64 0.997999314668914 0.00400137066217287 0.00200068533108644 65 0.997125200147017 0.005749599705966 0.002874799852983 66 0.996053966798651 0.00789206640269781 0.00394603320134891 67 0.994511981148273 0.0109760377034545 0.00548801885172723 68 0.997996211852906 0.00400757629418872 0.00200378814709436 69 0.996824370437496 0.00635125912500848 0.00317562956250424 70 0.995560601940072 0.00887879611985685 0.00443939805992843 71 0.993331133227067 0.0133377335458661 0.00666886677293305 72 0.99024181546079 0.0195163690784191 0.00975818453920953 73 0.994607094159168 0.0107858116816636 0.00539290584083181 74 0.992548358860804 0.0149032822783928 0.00745164113919642 75 0.992188187110604 0.0156236257787916 0.00781181288939582 76 0.989032532888038 0.0219349342239247 0.0109674671119624 77 0.985571284540928 0.0288574309181443 0.0144287154590722 78 0.985216461442364 0.0295670771152719 0.0147835385576360 79 0.980660224137828 0.0386795517243448 0.0193397758621724 80 0.976238543620657 0.0475229127586868 0.0237614563793434 81 0.988292863345485 0.0234142733090297 0.0117071366545149 82 0.992078333141645 0.0158433337167108 0.00792166685835539 83 0.986528096189582 0.0269438076208363 0.0134719038104181 84 0.97706978412898 0.0458604317420406 0.0229302158710203 85 0.964476315911105 0.0710473681777905 0.0355236840888952 86 0.948760676827734 0.102478646344532 0.051239323172266 87 0.95760065014942 0.0847986997011583 0.0423993498505792 88 0.933589407330734 0.132821185338532 0.0664105926692661 89 0.926729353910136 0.146541292179727 0.0732706460898636 90 0.892927747076305 0.214144505847391 0.107072252923695 91 0.935524257564216 0.128951484871567 0.0644757424357835 92 0.954226379569691 0.091547240860618 0.045773620430309 93 0.941788713598712 0.116422572802576 0.0582112864012881 94 0.95876480524841 0.0824703895031806 0.0412351947515903 95 0.96816364107888 0.0636727178422384 0.0318363589211192 96 0.963419013442774 0.073161973114451 0.0365809865572255 97 0.97245371352501 0.0550925729499817 0.0275462864749909 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 56 0.615384615384615 NOK 5% type I error level 73 0.802197802197802 NOK 10% type I error level 82 0.901098901098901 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/10gy1d1292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/10gy1d1292759197.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/19x411292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/19x411292759197.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/29x411292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/29x411292759197.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/3263m1292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/3263m1292759197.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/4263m1292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/4263m1292759197.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/5263m1292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/5263m1292759197.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/6dg3p1292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/6dg3p1292759197.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/75p2a1292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/75p2a1292759197.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/85p2a1292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/85p2a1292759197.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/95p2a1292759197.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292759279ivlokg9tkbbpdql/95p2a1292759197.ps (open in new window)

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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