Multiple Regression (invoer)

*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: Mon, 14 Dec 2009 04:36:12 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/14/t1260790772aq3k2jqmuflj0w2.htm/, Retrieved Mon, 14 Dec 2009 12:39:46 +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/2009/Dec/14/t1260790772aq3k2jqmuflj0w2.htm/}, year = {2009}, } @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 = {2009}, 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:

» Textbox « » Textfile « » CSV «

7787.0 0 8474.2 0 9154.7 0 8557.2 0 7951.1 0 9156.7 0 7865.7 0 7337.4 0 9131.7 0 8814.6 0 8598.8 0 8439.6 0 7451.8 0 8016.2 0 9544.1 0 8270.7 0 8102.2 0 9369.0 0 7657.7 0 7816.6 0 9391.3 0 9445.4 0 9533.1 0 10068.7 0 8955.5 0 10423.9 0 11617.2 0 9391.1 0 10872.0 0 10230.4 0 9221.0 0 9428.6 0 10934.5 0 10986.0 0 11724.6 0 11180.9 0 11163.2 0 11240.9 0 12107.1 0 10762.3 0 11340.4 0 11266.8 0 9542.7 0 9227.7 0 10571.9 1 10774.4 1 10392.8 1 9920.2 1 9884.9 1 10174.5 1 11395.4 1 10760.2 1 10570.1 1 10536.0 1 9902.6 1 8889.0 1 10837.3 1 11624.1 1 10509.0 1 10984.9 1 10649.1 1 10855.7 1 11677.4 1 10760.2 1 10046.2 1 10772.8 1 9987.7 1 8638.7 1 11063.7 1 11855.7 1 10684.5 1 11337.4 1 10478.0 1 11123.9 1 12909.3 1 11339.9 1 10462.2 1 12733.5 1 10519.2 1 10414.9 1 12476.8 1 12384. 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Invoer[t] = + 9489.82500000001 + 3199.11590909090Dummie[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) 9489.82500000001 286.797584 33.0889 0 0 Dummie 3199.11590909090 351.253871 9.1077 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.624122410216832 R-squared 0.389528782934867 Adjusted R-squared 0.384832850495905 F-TEST (value) 82.9502527981266 F-TEST (DF numerator) 1 F-TEST (DF denominator) 130 p-value 1.33226762955019e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1902.39995636125 Sum Squared Residuals 470486327.215227 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7787 9489.82499999992 -1702.82499999992 2 8474.2 9489.82499999998 -1015.62499999998 3 9154.7 9489.825 -335.125000000001 4 8557.2 9489.825 -932.625000000001 5 7951.1 9489.825 -1538.72500000000 6 9156.7 9489.825 -333.125000000001 7 7865.7 9489.825 -1624.12500000000 8 7337.4 9489.825 -2152.42500000000 9 9131.7 9489.825 -358.125000000001 10 8814.6 9489.825 -675.225000000002 11 8598.8 9489.825 -891.025000000003 12 8439.6 9489.825 -1050.22500000000 13 7451.8 9489.825 -2038.025 14 8016.2 9489.825 -1473.62500000000 15 9544.1 9489.825 54.2749999999982 16 8270.7 9489.825 -1219.12500000000 17 8102.2 9489.825 -1387.62500000000 18 9369 9489.825 -120.825000000002 19 7657.7 9489.825 -1832.12500000000 20 7816.6 9489.825 -1673.22500000000 21 9391.3 9489.825 -98.525000000003 22 9445.4 9489.825 -44.4250000000026 23 9533.1 9489.825 43.2749999999982 24 10068.7 9489.825 578.874999999999 25 8955.5 9489.825 -534.325000000002 26 10423.9 9489.825 934.074999999997 27 11617.2 9489.825 2127.375 28 9391.1 9489.825 -98.7250000000019 29 10872 9489.825 1382.17500000000 30 10230.4 9489.825 740.574999999997 31 9221 9489.825 -268.825000000002 32 9428.6 9489.825 -61.2250000000019 33 10934.5 9489.825 1444.67500000000 34 10986 9489.825 1496.17500000000 35 11724.6 9489.825 2234.775 36 11180.9 9489.825 1691.07500000000 37 11163.2 9489.825 1673.375 38 11240.9 9489.825 1751.07500000000 39 12107.1 9489.825 2617.275 40 10762.3 9489.825 1272.47500000000 41 11340.4 9489.825 1850.57500000000 42 11266.8 9489.825 1776.97500000000 43 9542.7 9489.825 52.8749999999985 44 9227.7 9489.825 -262.125000000001 45 10571.9 12688.9409090909 -2117.04090909091 46 10774.4 12688.9409090909 -1914.54090909091 47 10392.8 12688.9409090909 -2296.14090909091 48 9920.2 12688.9409090909 -2768.74090909091 49 9884.9 12688.9409090909 -2804.04090909091 50 10174.5 12688.9409090909 -2514.44090909091 51 11395.4 12688.9409090909 -1293.54090909091 52 10760.2 12688.9409090909 -1928.74090909091 53 10570.1 12688.9409090909 -2118.84090909091 54 10536 12688.9409090909 -2152.94090909091 55 9902.6 12688.9409090909 -2786.34090909091 56 8889 12688.9409090909 -3799.94090909091 57 10837.3 12688.9409090909 -1851.64090909091 58 11624.1 12688.9409090909 -1064.84090909091 59 10509 12688.9409090909 -2179.94090909091 60 10984.9 12688.9409090909 -1704.04090909091 61 10649.1 12688.9409090909 -2039.84090909091 62 10855.7 12688.9409090909 -1833.24090909091 63 11677.4 12688.9409090909 -1011.54090909091 64 10760.2 12688.9409090909 -1928.74090909091 65 10046.2 12688.9409090909 -2642.74090909091 66 10772.8 12688.9409090909 -1916.14090909091 67 9987.7 12688.9409090909 -2701.24090909091 68 8638.7 12688.9409090909 -4050.24090909091 69 11063.7 12688.9409090909 -1625.24090909091 70 11855.7 12688.9409090909 -833.240909090909 71 10684.5 12688.9409090909 -2004.44090909091 72 11337.4 12688.9409090909 -1351.54090909091 73 10478 12688.9409090909 -2210.94090909091 74 11123.9 12688.9409090909 -1565.04090909091 75 12909.3 12688.9409090909 220.359090909090 76 11339.9 12688.9409090909 -1349.04090909091 77 10462.2 12688.9409090909 -2226.74090909091 78 12733.5 12688.9409090909 44.5590909090904 79 10519.2 12688.9409090909 -2169.74090909091 80 10414.9 12688.9409090909 -2274.04090909091 81 12476.8 12688.9409090909 -212.140909090910 82 12384.6 12688.9409090909 -304.340909090909 83 12266.7 12688.9409090909 -422.240909090909 84 12919.9 12688.9409090909 230.95909090909 85 11497.3 12688.9409090909 -1191.64090909091 86 12142 12688.9409090909 -546.94090909091 87 13919.4 12688.9409090909 1230.45909090909 88 12656.8 12688.9409090909 -32.1409090909104 89 12034.1 12688.9409090909 -654.840909090909 90 13199.7 12688.9409090909 510.759090909091 91 10881.3 12688.9409090909 -1807.64090909091 92 11301.2 12688.9409090909 -1387.74090909091 93 13643.9 12688.9409090909 954.95909090909 94 12517 12688.9409090909 -171.940909090910 95 13981.1 12688.9409090909 1292.15909090909 96 14275.7 12688.9409090909 1586.75909090909 97 13435 12688.9409090909 746.05909090909 98 13565.7 12688.9409090909 876.759090909091 99 16216.3 12688.9409090909 3527.35909090909 100 12970 12688.9409090909 281.059090909090 101 14079.9 12688.9409090909 1390.95909090909 102 14235 12688.9409090909 1546.05909090909 103 12213.4 12688.9409090909 -475.54090909091 104 12581 12688.9409090909 -107.940909090910 105 14130.4 12688.9409090909 1441.45909090909 106 14210.8 12688.9409090909 1521.85909090909 107 14378.5 12688.9409090909 1689.55909090909 108 13142.8 12688.9409090909 453.85909090909 109 13714.7 12688.9409090909 1025.75909090909 110 13621.9 12688.9409090909 932.95909090909 111 15379.8 12688.9409090909 2690.85909090909 112 13306.3 12688.9409090909 617.35909090909 113 14391.2 12688.9409090909 1702.25909090909 114 14909.9 12688.9409090909 2220.95909090909 115 14025.4 12688.9409090909 1336.45909090909 116 12951.2 12688.9409090909 262.259090909091 117 14344.3 12688.9409090909 1655.35909090909 118 16093.4 12688.9409090909 3404.45909090909 119 15413.6 12688.9409090909 2724.65909090909 120 14705.7 12688.9409090909 2016.75909090909 121 15972.8 12688.9409090909 3283.85909090909 122 16241.4 12688.9409090909 3552.45909090909 123 16626.4 12688.9409090909 3937.45909090909 124 17136.2 12688.9409090909 4447.25909090909 125 15622.9 12688.9409090909 2933.95909090909 126 18003.9 12688.9409090909 5314.95909090909 127 16136.1 12688.9409090909 3447.15909090909 128 14423.7 12688.9409090909 1734.75909090909 129 16789.4 12688.9409090909 4100.45909090909 130 16782.2 12688.9409090909 4093.25909090909 131 14133.8 12688.9409090909 1444.85909090909 132 12607 12688.9409090909 -81.9409090909096 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0435320335579105 0.0870640671158211 0.95646796644209 6 0.0219496975877698 0.0438993951755395 0.97805030241223 7 0.0098220712508306 0.0196441425016612 0.99017792874917 8 0.00887198778725468 0.0177439755745094 0.991128012212745 9 0.00523163385622546 0.0104632677124509 0.994768366143775 10 0.00207117373445796 0.00414234746891591 0.997928826265542 11 0.000698396457421012 0.00139679291484202 0.999301603542579 12 0.000219667148365332 0.000439334296730664 0.999780332851635 13 0.000195066709257996 0.000390133418515992 0.999804933290742 14 7.27556890183157e-05 0.000145511378036631 0.999927244310982 15 9.23081156274626e-05 0.000184616231254925 0.999907691884373 16 3.27301669681818e-05 6.54603339363636e-05 0.999967269833032 17 1.23268213477734e-05 2.46536426955468e-05 0.999987673178652 18 1.00588344048050e-05 2.01176688096100e-05 0.999989941165595 19 6.59889150344053e-06 1.31977830068811e-05 0.999993401108497 20 3.39779444781356e-06 6.79558889562712e-06 0.999996602205552 21 2.93577469841226e-06 5.87154939682452e-06 0.999997064225302 22 2.48780235488490e-06 4.97560470976981e-06 0.999997512197645 23 2.19539545603819e-06 4.39079091207638e-06 0.999997804604544 24 4.4584936419605e-06 8.916987283921e-06 0.999995541506358 25 1.98386666530909e-06 3.96773333061819e-06 0.999998016133335 26 5.9765148415475e-06 1.1953029683095e-05 0.999994023485159 27 0.000121613069230337 0.000243226138460674 0.99987838693077 28 6.96036367277916e-05 0.000139207273455583 0.999930396363272 29 0.000143223485684398 0.000286446971368796 0.999856776514316 30 0.000127908055575743 0.000255816111151485 0.999872091944424 31 7.03388897784823e-05 0.000140677779556965 0.999929661110222 32 3.99583828826783e-05 7.99167657653566e-05 0.999960041617117 33 6.75814715197448e-05 0.000135162943039490 0.99993241852848 34 0.000103549634213137 0.000207099268426275 0.999896450365787 35 0.000309838361219956 0.000619676722439911 0.99969016163878 36 0.000417573957162283 0.000835147914324566 0.999582426042838 37 0.000508990820894148 0.00101798164178830 0.999491009179106 38 0.000613740296319303 0.00122748059263861 0.99938625970368 39 0.00140434772564135 0.0028086954512827 0.998595652274359 40 0.00115226719762606 0.00230453439525212 0.998847732802374 41 0.00126489336746329 0.00252978673492658 0.998735106632537 42 0.00132227449759666 0.00264454899519333 0.998677725502403 43 0.000815182721459778 0.00163036544291956 0.99918481727854 44 0.000498830102146202 0.000997660204292405 0.999501169897854 45 0.000340935528085759 0.000681871056171519 0.999659064471914 46 0.000227362785056008 0.000454725570112016 0.999772637214944 47 0.000160534775778938 0.000321069551557876 0.999839465224221 48 0.000130021962010223 0.000260043924020446 0.99986997803799 49 0.000105976085036841 0.000211952170073683 0.999894023914963 50 7.9696944194331e-05 0.000159393888388662 0.999920303055806 51 5.9952665513999e-05 0.000119905331027998 0.999940047334486 52 4.14641410125045e-05 8.2928282025009e-05 0.999958535858988 53 2.93869154583706e-05 5.87738309167413e-05 0.999970613084542 54 2.11183189251730e-05 4.22366378503459e-05 0.999978881681075 55 1.91617679720098e-05 3.83235359440195e-05 0.999980838232028 56 3.65750565742886e-05 7.31501131485772e-05 0.999963424943426 57 2.77825737532000e-05 5.55651475063999e-05 0.999972217426247 58 2.32837322138361e-05 4.65674644276722e-05 0.999976716267786 59 1.86788305158412e-05 3.73576610316825e-05 0.999981321169484 60 1.42116608815218e-05 2.84233217630436e-05 0.999985788339119 61 1.13990737477921e-05 2.27981474955842e-05 0.999988600926252 62 8.966592081345e-06 1.793318416269e-05 0.999991033407919 63 7.44865960501264e-06 1.48973192100253e-05 0.999992551340395 64 6.0941334496871e-06 1.21882668993742e-05 0.99999390586655 65 7.01548162669735e-06 1.40309632533947e-05 0.999992984518373 66 6.10407480152007e-06 1.22081496030401e-05 0.999993895925198 67 8.06961422743178e-06 1.61392284548636e-05 0.999991930385772 68 4.70230431969710e-05 9.40460863939421e-05 0.999952976956803 69 4.60896071040176e-05 9.21792142080351e-05 0.999953910392896 70 4.68496959765273e-05 9.36993919530545e-05 0.999953150304024 71 5.36393101842386e-05 0.000107278620368477 0.999946360689816 72 5.42661046248711e-05 0.000108532209249742 0.999945733895375 73 7.60938600399332e-05 0.000152187720079866 0.99992390613996 74 8.67289788654204e-05 0.000173457957730841 0.999913271021135 75 0.00013383637841467 0.00026767275682934 0.999866163621585 76 0.000148978479237533 0.000297956958475067 0.999851021520762 77 0.000260768932265808 0.000521537864531615 0.999739231067734 78 0.000341236635661175 0.00068247327132235 0.999658763364339 79 0.000641927923943562 0.00128385584788712 0.999358072076056 80 0.00144149664570763 0.00288299329141526 0.998558503354292 81 0.00177818356066824 0.00355636712133648 0.998221816439332 82 0.00213914667483306 0.00427829334966612 0.997860853325167 83 0.00255741068235145 0.0051148213647029 0.997442589317649 84 0.00319202249324949 0.00638404498649898 0.99680797750675 85 0.00461338754668645 0.0092267750933729 0.995386612453314 86 0.00574517033574783 0.0114903406714957 0.994254829664252 87 0.00907216597285097 0.0181443319457019 0.990927834027149 88 0.0103655693732422 0.0207311387464845 0.989634430626758 89 0.0132671218298498 0.0265342436596996 0.98673287817015 90 0.0153265430539038 0.0306530861078077 0.984673456946096 91 0.0372586322071288 0.0745172644142576 0.962741367792871 92 0.0738159026783511 0.147631805356702 0.926184097321649 93 0.086355975565023 0.172711951130046 0.913644024434977 94 0.108389648805648 0.216779297611296 0.891610351194352 95 0.124919115096481 0.249838230192962 0.875080884903519 96 0.144119734684898 0.288239469369796 0.855880265315102 97 0.153789784935610 0.307579569871220 0.84621021506439 98 0.162035556770591 0.324071113541182 0.83796444322941 99 0.289334816393236 0.578669632786473 0.710665183606764 100 0.307972217182779 0.615944434365557 0.692027782817221 101 0.305517790500643 0.611035581001286 0.694482209499357 102 0.300954030323369 0.601908060646739 0.69904596967663 103 0.384534430094487 0.769068860188973 0.615465569905513 104 0.456018299168851 0.912036598337702 0.543981700831149 105 0.448875172176523 0.897750344353047 0.551124827823477 106 0.438366001939919 0.876732003879838 0.561633998060081 107 0.423972252485158 0.847944504970315 0.576027747514842 108 0.462575123722345 0.92515024744469 0.537424876277655 109 0.469058213361755 0.93811642672351 0.530941786638245 110 0.485862245905059 0.971724491810119 0.514137754094941 111 0.474978061309863 0.949956122619726 0.525021938690137 112 0.522711236336122 0.954577527327755 0.477288763663878 113 0.503401320071244 0.993197359857511 0.496598679928756 114 0.470998903927465 0.94199780785493 0.529001096072535 115 0.470425562144354 0.940851124288707 0.529574437855646 116 0.612897520435735 0.77420495912853 0.387102479564265 117 0.613471398376847 0.773057203246306 0.386528601623153 118 0.582264565396957 0.835470869206086 0.417735434603043 119 0.528583316389466 0.942833367221067 0.471416683610534 120 0.495144751464018 0.990289502928035 0.504855248535982 121 0.434818504563408 0.869637009126816 0.565181495436592 122 0.377432672441073 0.754865344882146 0.622567327558927 123 0.33605651581398 0.67211303162796 0.66394348418602 124 0.335730820979752 0.671461641959504 0.664269179020248 125 0.244641485706065 0.489282971412129 0.755358514293936 126 0.376226105121989 0.752452210243978 0.623773894878011 127 0.285693857336726 0.571387714673451 0.714306142663274 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 76 0.617886178861789 NOK 5% type I error level 85 0.691056910569106 NOK 10% type I error level 87 0.707317073170732 NOK

Charts produced by software:

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Parameters (Session):

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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