Workshop 7

*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: Fri, 20 Nov 2009 09:10:52 -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/Nov/20/t1258733487ehmsf2to1dydc2j.htm/, Retrieved Fri, 20 Nov 2009 17:11:40 +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/Nov/20/t1258733487ehmsf2to1dydc2j.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 «

45 64 64 62 64 62 45 69 64 64 62 64 49 69 69 64 64 62 50 65 69 69 64 64 54 56 65 69 69 64 59 58 56 65 69 69 58 53 58 56 65 69 56 62 53 58 56 65 48 55 62 53 58 56 50 60 55 62 53 58 52 59 60 55 62 53 53 58 59 60 55 62 55 53 58 59 60 55 43 57 53 58 59 60 42 57 57 53 58 59 38 53 57 57 53 58 41 54 53 57 57 53 41 53 54 53 57 57 39 57 53 54 53 57 34 57 57 53 54 53 27 55 57 57 53 54 15 49 55 57 57 53 14 50 49 55 57 57 31 49 50 49 55 57 41 54 49 50 49 55 43 58 54 49 50 49 46 58 58 54 49 50 42 52 58 58 54 49 45 56 52 58 58 54 45 52 56 52 58 58 40 59 52 56 52 58 35 53 59 52 56 52 36 52 53 59 52 56 38 53 52 53 59 52 39 51 53 52 53 59 32 50 51 53 52 53 24 56 50 51 53 52 21 52 56 50 51 53 12 46 52 56 50 51 29 48 46 52 56 50 36 46 48 46 52 56 31 48 46 48 46 52 28 48 48 46 48 46 30 49 48 48 46 48 38 53 49 48 48 46 27 48 53 49 48 48 40 51 48 53 49 48 40 48 51 48 53 49 44 50 48 51 48 53 47 55 50 48 51 48 45 52 55 50 48 51 42 53 52 55 50 48 38 52 53 52 55 50 46 55 52 53 52 55 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 Y[t] = + 14.9840080784092 + 0.128775996030082X[t] + 0.322177095577494Y1[t] + 0.306776902898658Y2[t] -0.0185667559362691Y3[t] + 0.0387471558376612Y4[t] + 1.23353128598870M1[t] + 2.06185157166443M2[t] -0.0664360866287527M3[t] -2.62061359157391M4[t] -1.34841144304865M5[t] + 0.113812162922448M6[t] + 0.0743039899866369M7[t] + 0.0868559660332346M8[t] + 0.37792301509465M9[t] -0.439014422798539M10[t] -2.34191110722947M11[t] -0.0278283031022919t + 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) 14.9840080784092 4.956196 3.0233 0.003184 0.001592 X 0.128775996030082 0.033352 3.8611 0.000201 0.000101 Y1 0.322177095577494 0.095707 3.3663 0.001086 0.000543 Y2 0.306776902898658 0.100827 3.0426 0.003003 0.001502 Y3 -0.0185667559362691 0.100796 -0.1842 0.854233 0.427116 Y4 0.0387471558376612 0.093565 0.4141 0.679681 0.339841 M1 1.23353128598870 1.4089 0.8755 0.383406 0.191703 M2 2.06185157166443 1.446469 1.4254 0.157176 0.078588 M3 -0.0664360866287527 1.471872 -0.0451 0.964089 0.482044 M4 -2.62061359157391 1.420501 -1.8449 0.068049 0.034024 M5 -1.34841144304865 1.383135 -0.9749 0.331989 0.165994 M6 0.113812162922448 1.401763 0.0812 0.935453 0.467727 M7 0.0743039899866369 1.420277 0.0523 0.958382 0.479191 M8 0.0868559660332346 1.411249 0.0615 0.951049 0.475524 M9 0.37792301509465 1.405604 0.2689 0.78859 0.394295 M10 -0.439014422798539 1.437652 -0.3054 0.760726 0.380363 M11 -2.34191110722947 1.423159 -1.6456 0.103023 0.051512 t -0.0278283031022919 0.015057 -1.8482 0.067559 0.03378 Multiple Linear Regression - Regression Statistics Multiple R 0.909930197363436 R-squared 0.827972964073862 Adjusted R-squared 0.798432968005737 F-TEST (value) 28.0288786147568 F-TEST (DF numerator) 17 F-TEST (DF denominator) 99 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.98603176154760 Sum Squared Residuals 882.722182416138 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 64 62.8381842613397 1.16181573866032 2 69 64.3668578732581 4.63314212674191 3 69 64.2221035503226 4.77789644967742 4 65 63.3802525644738 1.61974743552617 5 56 63.7581882320258 -7.7581882320258 6 58 61.9034978224413 -3.90349782244125 7 53 59.6650144391852 -6.66501443918521 8 62 58.406966628055 3.59303337194503 9 55 58.6198488370661 -3.6198488370661 10 60 58.7087156365329 1.29128436346708 11 59 56.138153216042 2.861846783958 12 58 60.2714111292079 -2.27141112920789 13 53 60.7416482351333 -7.74164823513333 14 57 58.2914684196842 -1.29146841968424 15 57 55.741219930174 1.25878006982603 16 53 53.9253043734445 -0.925304373444513 17 54 53.9992950217144 0.000704978285632499 18 53 54.6837484319167 -1.68374843191669 19 57 54.4177267948847 2.58227320511534 20 57 54.566946587803 2.43305341219704 21 55 55.2131748849201 -0.213174884920069 22 49 52.0657288208259 -3.06572882082588 23 50 47.6146000813510 2.38539991864905 24 49 50.6365240080476 -1.63652400804760 25 54 53.1484929824983 0.851507017501724 26 58 55.2595958411585 2.74040415884154 27 58 56.3697146764304 1.63028532356958 28 52 54.3681315603383 -2.36813156033827 29 56 54.1852395758297 1.81476042417025 30 52 55.2226704669672 -3.22267046696723 31 59 54.561253775681 4.43874622431901 32 53 54.6234795671517 -1.62347956715167 33 52 55.4591257030622 -3.45912570306223 34 53 52.4241175262529 0.575882473747049 35 51 51.0201993539099 -0.0201993539098883 36 50 51.8813567184805 -1.88135671848047 37 56 51.0638069199775 4.93619308002252 38 52 53.1801372527372 -1.18013725273718 39 46 50.3580628064139 -4.35806280641388 40 48 46.654931054363 1.34506894563704 41 46 47.9111796045306 -1.91117960453058 42 48 48.6273064541583 -0.627306454158276 43 48 47.9348259284891 0.0651740715109061 44 49 48.9052832228387 0.0947167771612615 45 53 50.4062792090681 2.59372079093185 46 48 49.8179571086257 -1.81795710862572 47 51 49.1589754472545 1.84102455274554 48 48 50.8701851557134 -2.87018515571341 49 50 52.7926139477156 -2.79261394771563 50 55 53.4540213538412 1.54597864615878 51 52 53.4367344193922 -1.43673441939215 52 53 50.8823788716297 2.11762112837025 53 52 50.9981556518079 1.00184434819212 54 55 53.6967947772356 1.30320522276438 55 53 52.9954204973114 0.00457950268864583 56 53 53.8285385836909 -0.828538583690902 57 56 53.2550001041762 2.74499989582384 58 54 52.6287086570881 1.37129134291189 59 52 51.0252418714506 0.974758128549368 60 55 52.2832684028769 2.71673159712314 61 54 53.995323846084 0.00467615391604688 62 59 55.6111606340333 3.38883936596674 63 56 55.1410626522628 0.85893734773721 64 56 52.3597863232148 3.64021367678517 65 51 51.264488564122 -0.264488564121994 66 53 51.2086584400704 1.79134155992963 67 52 49.8779981811208 2.12200181887918 68 51 50.8908123241167 0.109187675883303 69 46 49.006467820238 -3.00646782023800 70 49 48.0141887144591 0.985811285540944 71 46 45.4959300992636 0.504069900736368 72 55 48.3330029333183 6.66699706668169 73 57 51.6548610087993 5.34513899120068 74 53 55.9038650479074 -2.90386504790738 75 52 52.4029242509696 -0.402924250969596 76 53 48.5832246264164 4.41677537358357 77 50 49.9947599999386 0.00524000006136917 78 54 51.1480830356796 2.85191696432043 79 53 51.7781383095718 1.22186169042822 80 50 52.1183599420293 -2.11835994202929 81 51 51.1753339991594 -0.175333999159375 82 52 50.6786260005128 1.32137399948718 83 47 49.2650321273968 -2.26503212739681 84 51 48.9812141688152 2.01878583118481 85 49 50.7345773956002 -1.73457739560016 86 53 52.3781797301623 0.621820269837660 87 52 50.2428875542559 1.75711244574411 88 45 49.6593663696593 -4.65936636965934 89 53 47.9324103156606 5.06758968433937 90 51 50.0991154381758 0.900884561824224 91 48 51.5465321417979 -3.54653214179793 92 48 48.2436466235578 -0.243646623557837 93 48 47.9336654193948 0.0663345806051861 94 48 47.1958816305629 0.8041183694371 95 40 43.4748272271256 -3.47482722712563 96 43 43.9841492428134 -0.98414924281335 97 40 43.4446162971828 -3.44461629718281 98 39 44.8625457333302 -5.86254573333022 99 39 41.3557964452113 -2.35579644521135 100 36 37.9950754894366 -1.99507548943662 101 41 37.4025873603699 3.5974126396301 102 39 38.8449102964421 0.155089703557867 103 40 39.3364764234609 0.663523576539106 104 39 40.1085080992919 -1.10850809929187 105 46 40.8447679356932 5.15523206430684 106 40 41.4660759051396 -1.46607590513964 107 37 39.807040576206 -2.80704057620599 108 37 38.7588882407270 -1.75888824072695 109 44 40.5858751056694 3.41412489433064 110 41 42.6921681138876 -1.69216811388761 111 40 41.7294937145674 -1.72949371456736 112 36 39.1915487670235 -3.19154876702346 113 38 39.5536956740005 -1.55369567400047 114 43 40.5652148369131 2.43478516308691 115 42 42.8866135084973 -0.886613508497264 116 45 45.3074584214651 -0.307458421465081 117 46 46.0863360872219 -0.0863360872219225 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.285994658454794 0.571989316909589 0.714005341545206 22 0.197032586853822 0.394065173707644 0.802967413146178 23 0.187265392839909 0.374530785679818 0.812734607160091 24 0.197075657448654 0.394151314897308 0.802924342551346 25 0.76561012781588 0.468779744368241 0.234389872184121 26 0.726925958284102 0.546148083431795 0.273074041715898 27 0.665740166878643 0.668519666242714 0.334259833121357 28 0.609538096646668 0.780923806706665 0.390461903353332 29 0.91043181655199 0.179136366896019 0.0895681834480097 30 0.895514289275396 0.208971421449208 0.104485710724604 31 0.91520285031009 0.169594299379820 0.0847971496899102 32 0.88940657469206 0.221186850615881 0.110593425307940 33 0.875808850902646 0.248382298194709 0.124191149097354 34 0.946139797343069 0.107720405313863 0.0538602026569314 35 0.936440384041047 0.127119231917905 0.0635596159589526 36 0.917977824018565 0.164044351962871 0.0820221759814355 37 0.960378564633657 0.0792428707326858 0.0396214353663429 38 0.95812092739511 0.0837581452097795 0.0418790726048898 39 0.987407646003622 0.0251847079927557 0.0125923539963778 40 0.984822637136238 0.0303547257275237 0.0151773628637619 41 0.981084712760985 0.0378305744780301 0.0189152872390150 42 0.975151776181413 0.0496964476371745 0.0248482238185873 43 0.96462487167666 0.070750256646678 0.035375128323339 44 0.95256423749455 0.0948715250108984 0.0474357625054492 45 0.955264103234424 0.0894717935311512 0.0447358967655756 46 0.946394500614695 0.107210998770611 0.0536054993853054 47 0.92959029430388 0.140819411392241 0.0704097056961207 48 0.9447021046715 0.110595790656998 0.0552978953284992 49 0.95589562118474 0.088208757630518 0.044104378815259 50 0.94027043020474 0.119459139590519 0.0597295697952597 51 0.934566993194217 0.130866013611566 0.0654330068057828 52 0.921123888724892 0.157752222550216 0.0788761112751082 53 0.928620006341283 0.142759987317434 0.0713799936587169 54 0.939337715069179 0.121324569861642 0.060662284930821 55 0.931444914501139 0.137110170997723 0.0685550854988615 56 0.928311545923896 0.143376908152209 0.0716884540761044 57 0.934965737675633 0.130068524648735 0.0650342623243674 58 0.922821586230855 0.154356827538290 0.0771784137691452 59 0.897882139016948 0.204235721966104 0.102117860983052 60 0.901415334341775 0.197169331316450 0.0985846656582248 61 0.890818103620244 0.218363792759512 0.109181896379756 62 0.879445083376436 0.241109833247127 0.120554916623564 63 0.846041518397792 0.307916963204415 0.153958481602208 64 0.853531968306343 0.292936063387314 0.146468031693657 65 0.826369120946401 0.347261758107197 0.173630879053599 66 0.790993983276362 0.418012033447275 0.209006016723638 67 0.758386934991663 0.483226130016674 0.241613065008337 68 0.711561418085557 0.576877163828887 0.288438581914443 69 0.796717649525641 0.406564700948717 0.203282350474359 70 0.769672643785784 0.460654712428433 0.230327356214216 71 0.723666239786245 0.55266752042751 0.276333760213755 72 0.77703479683099 0.44593040633802 0.22296520316901 73 0.806924960521783 0.386150078956435 0.193075039478217 74 0.817485563228481 0.365028873543038 0.182514436771519 75 0.777110156237569 0.445779687524862 0.222889843762431 76 0.916522183803596 0.166955632392809 0.0834778161964043 77 0.890400047995582 0.219199904008835 0.109599952004418 78 0.859043316720632 0.281913366558737 0.140956683279368 79 0.828172337797189 0.343655324405623 0.171827662202811 80 0.803463422848169 0.393073154303663 0.196536577151831 81 0.798713613416453 0.402572773167094 0.201286386583547 82 0.739221403125765 0.52155719374847 0.260778596874235 83 0.698049912818354 0.603900174363292 0.301950087181646 84 0.646949721848628 0.706100556302745 0.353050278151372 85 0.637409261866514 0.725181476266972 0.362590738133486 86 0.614648291134261 0.770703417731478 0.385351708865739 87 0.660508414074052 0.678983171851896 0.339491585925948 88 0.640745115038416 0.718509769923167 0.359254884961583 89 0.759561789100774 0.480876421798453 0.240438210899226 90 0.729422641981314 0.541154716037372 0.270577358018686 91 0.661490872268095 0.67701825546381 0.338509127731905 92 0.659757934518747 0.680484130962505 0.340242065481253 93 0.54353905074617 0.912921898507659 0.456460949253829 94 0.589991058356926 0.820017883286147 0.410008941643074 95 0.694968403050399 0.610063193899202 0.305031596949601 96 0.902659683107464 0.194680633785073 0.0973403168925365 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 4 0.0526315789473684 NOK 10% type I error level 10 0.131578947368421 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/10m3zc1258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/10m3zc1258733442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/1j2vf1258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/1j2vf1258733442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/2pz3p1258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/2pz3p1258733442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/320oz1258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/320oz1258733442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/4zzi61258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/4zzi61258733442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/586151258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/586151258733442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/6v9pc1258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/6v9pc1258733442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/7vnxz1258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/7vnxz1258733442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/8voqe1258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/8voqe1258733442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/9w4791258733442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733487ehmsf2to1dydc2j/9w4791258733442.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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