Multiple linear regression 2

*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, 19 Nov 2010 18:08:07 +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/Nov/19/t1290189983r3hp3qf3s7314tf.htm/, Retrieved Fri, 19 Nov 2010 19:06:26 +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/Nov/19/t1290189983r3hp3qf3s7314tf.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:

» Textbox « » Textfile « » CSV «

15 10 77 5 4 15 11 12 13 6 12 20 63 6 4 9 12 7 11 4 15 16 73 4 10 12 12 13 14 6 12 10 76 6 6 15 11 11 12 5 14 8 90 3 5 17 11 16 12 5 8 14 67 10 8 14 10 10 6 4 11 19 69 8 9 9 11 15 10 5 15 15 70 3 6 12 9 5 11 3 4 23 54 4 8 11 10 4 10 2 13 9 54 3 11 13 12 7 12 5 19 12 76 5 6 16 12 15 15 6 10 14 75 5 8 16 12 5 13 6 15 13 76 6 11 15 13 16 18 8 6 11 80 5 5 10 9 15 11 6 7 11 89 3 10 16 12 13 12 3 14 10 73 4 7 12 12 13 13 6 16 12 74 8 7 15 12 15 14 6 16 18 78 8 13 13 12 15 16 7 14 12 76 8 10 18 13 10 16 8 15 10 69 5 8 13 11 17 16 6 14 15 74 8 6 17 12 14 15 7 12 15 82 2 8 14 12 9 13 4 9 12 77 0 7 13 15 6 8 4 12 9 84 5 5 13 11 11 14 2 14 11 75 2 9 15 12 13 15 6 12 15 54 7 9 13 10 12 13 6 14 16 79 5 11 15 11 10 16 6 10 17 79 2 11 13 13 4 13 6 14 12 69 12 11 14 6 13 12 6 16 11 88 7 9 13 12 15 15 7 10 13 57 0 7 16 12 8 11 4 8 9 69 2 6 14 10 10 14 3 12 11 86 3 6 18 12 8 13 5 11 9 65 0 6 15 12 7 13 6 8 20 66 9 5 9 11 9 12 4 13 8 54 2 4 16 9 14 14 6 11 12 85 3 10 16 10 5 13 3 12 10 79 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Popularity[t] = -1.16892826150712 -0.080730628713802Depression[t] + 0.045325378873952Belonging[t] + 0.0964153521063372Weighted_popularity[t] + 0.077276519014147Parental_criticism[t] -0.0572765637003982Happiness[t] + 0.117596535487944FindingFriends[t] + 0.227711407897117KnowingPeople[t] + 0.34708334464486Liked[t] + 0.520075961070442Celebrity[t] -0.00640747869671335t + 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) -1.16892826150712 2.523234 -0.4633 0.643868 0.321934 Depression -0.080730628713802 0.063126 -1.2789 0.202984 0.101492 Belonging 0.045325378873952 0.016962 2.6722 0.008399 0.0042 Weighted_popularity 0.0964153521063372 0.05811 1.6592 0.09924 0.04962 Parental_criticism 0.077276519014147 0.064661 1.1951 0.233999 0.117 Happiness -0.0572765637003982 0.085478 -0.6701 0.503876 0.251938 FindingFriends 0.117596535487944 0.093661 1.2555 0.211299 0.10565 KnowingPeople 0.227711407897117 0.064294 3.5417 0.000535 0.000268 Liked 0.34708334464486 0.093825 3.6993 0.000306 0.000153 Celebrity 0.520075961070442 0.158405 3.2832 0.001286 0.000643 t -0.00640747869671335 0.003735 -1.7156 0.088366 0.044183 Multiple Linear Regression - Regression Statistics Multiple R 0.746865837404828 R-squared 0.557808579082415 Adjusted R-squared 0.527312619019133 F-TEST (value) 18.2912286717622 F-TEST (DF numerator) 10 F-TEST (DF denominator) 145 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.01899776486645 Sum Squared Residuals 591.071036307677 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15 13.0980845589734 1.90191544102664 2 12 9.33461110778379 2.66538889221621 3 15 13.651049054911 1.34895094508901 4 12 12.1896344205315 -0.189634420531492 5 14 13.6367248402493 0.363275159750659 6 8 9.09557557624667 -1.09557557624667 7 11 12.1115572596557 -1.11155725965572 8 15 8.38228593856976 6.61771406143024 9 4 6.33579814388996 -2.33579814388996 10 13 10.6532024118901 2.34679758810993 11 19 14.4193970585016 4.58060294149841 12 10 11.3894752132707 -1.38947521327073 13 15 17.2926358827324 -2.29263588273236 14 6 13.187466644766 -7.18746664476603 15 7 12.1231023366937 -5.12310233669369 16 14 13.4732227024492 0.526777297550778 17 16 14.3670172229621 1.6329827770379 18 16 15.8499823793242 0.150017620675771 19 14 15.2182109966908 -1.21821099669079 20 15 15.2172057096217 -0.21720570962175 21 14 14.5468136734371 -0.546813673437087 22 12 11.2579482302368 0.742051769763176 23 9 8.98851374333351 0.0114862566664852 24 12 11.5796185686613 0.420381431338651 25 14 13.909534855217 0.0904651447829507 26 12 12.0679306250819 -0.0679306250818915 27 14 13.6645199495633 0.335480050436709 28 10 11.2303635028932 -1.23036350289317 29 14 12.9603759144025 1.0396240855975 30 16 16.038856051892 -0.0388560518920173 31 10 8.92206925973075 1.07793074026925 32 8 10.7539999726585 -2.75399997265848 33 12 11.6968106073738 0.30318939262623 34 11 11.0749796177072 -0.0749796177071755 35 8 10.3105726476984 -2.31057264769841 36 13 12.2135908173589 0.786409182641092 37 11 10.0103046716484 0.989695328351577 38 12 9.8322784133195 2.1677215866805 39 16 15.2178558295842 0.782144170415826 40 16 13.0153748398144 2.98462516018557 41 13 13.9167692548379 -0.916769254837922 42 14 15.3107122591626 -1.31071225916263 43 5 5.59431254652437 -0.594312546524372 44 14 13.2795892107624 0.720410789237553 45 13 8.95837440464955 4.04162559535045 46 16 15.4360007531475 0.563999246852537 47 14 14.6266103305611 -0.626610330561147 48 15 14.1537475704718 0.846252429528169 49 15 13.3351955629063 1.66480443709368 50 11 12.2372811627391 -1.23728116273909 51 15 13.6847020933967 1.31529790660325 52 16 12.6660247146445 3.33397528535547 53 13 13.3258405899626 -0.325840589962623 54 11 13.7029888671069 -2.70298886710691 55 12 13.9250133183881 -1.92501331838815 56 12 11.609823336916 0.390176663083983 57 10 13.451342229255 -3.45134222925503 58 8 9.04261103534925 -1.04261103534925 59 9 9.69310574143097 -0.69310574143097 60 12 12.045387994039 -0.0453879940389715 61 14 13.910637057228 0.0893629427720449 62 12 13.0083713397218 -1.00837133972183 63 11 10.9769221353135 0.0230778646865252 64 14 13.4987255248902 0.501274475109798 65 7 11.6308647176515 -4.63086471765147 66 16 13.9993857476801 2.00061425231988 67 16 15.4900557409302 0.509944259069826 68 11 12.2982409395182 -1.29824093951819 69 16 15.287028491923 0.712971508076962 70 13 14.4142657678109 -1.4142657678109 71 11 10.9115304130367 0.0884695869632988 72 13 12.7985460057734 0.201453994226599 73 14 14.2807110397621 -0.28071103976212 74 15 13.4844566786343 1.51554332136573 75 10 9.29277056064779 0.707229439352216 76 15 14.6465623986928 0.353437601307165 77 11 12.9527797832399 -1.95277978323991 78 11 11.4043554773293 -0.40435547732932 79 6 8.4575735341726 -2.45757353417261 80 11 9.64537491771139 1.35462508228861 81 12 11.6342852343025 0.365714765697493 82 13 13.2443154760362 -0.244315476036222 83 12 12.6581126741542 -0.658112674154231 84 8 10.8160962310738 -2.81609623107383 85 9 10.7024135722567 -1.70241357225673 86 10 11.6212793989087 -1.62127939890874 87 16 13.2429145535025 2.75708544649748 88 15 12.7826600997677 2.21733990023233 89 14 13.5253803529906 0.474619647009403 90 12 13.5699261701497 -1.56992617014973 91 12 10.2345994239918 1.76540057600822 92 10 8.70936337421506 1.29063662578494 93 12 11.4454672608604 0.554532739139586 94 8 9.29247098996122 -1.29247098996122 95 16 14.0357440822437 1.96425591775626 96 11 7.72297792212706 3.27702207787294 97 12 11.4421523820269 0.557847617973136 98 9 10.3074555378289 -1.30745553782888 99 14 11.4472873308141 2.5527126691859 100 15 14.3144549553595 0.685545044640457 101 8 10.5692779610823 -2.56927796108232 102 12 12.373175300765 -0.373175300764953 103 10 10.4270650991506 -0.42706509915059 104 16 14.7883128061137 1.21168719388628 105 17 14.2054764572608 2.79452354273925 106 8 10.0343820261573 -2.0343820261573 107 9 10.7078936298132 -1.70789362981318 108 8 11.3302451431894 -3.33024514318938 109 11 12.3481588429061 -1.34815884290607 110 16 15.3080417520942 0.69195824790584 111 13 13.4300882747453 -0.430088274745342 112 5 7.9242294471067 -2.92422944710671 113 15 12.763995451971 2.23600454802897 114 15 13.8647885275956 1.13521147240442 115 12 11.4004404714328 0.599559528567214 116 12 11.4507509945712 0.549249005428758 117 16 15.7306935649448 0.269306435055161 118 12 12.4165342860829 -0.416534286082865 119 10 11.7091963037699 -1.70919630376992 120 12 10.6646637160973 1.33533628390266 121 4 6.22150795113951 -2.22150795113951 122 11 12.9228174382834 -1.92281743828336 123 16 14.6557390563799 1.34426094362011 124 7 8.76472456379762 -1.76472456379762 125 9 10.71615589177 -1.71615589177001 126 14 10.7909293947773 3.2090706052227 127 11 9.67850783494508 1.32149216505492 128 10 10.8792289215278 -0.879228921527816 129 6 8.48388602635172 -2.48388602635172 130 14 12.802020537476 1.19797946252395 131 11 10.8668887147792 0.133111285220822 132 11 9.15272311030956 1.84727688969044 133 9 13.8708137524157 -4.8708137524157 134 16 11.3572487923575 4.64275120764247 135 7 7.81813032982012 -0.818130329820122 136 8 8.78068323292848 -0.78068323292848 137 10 9.86739005992512 0.132609940074876 138 14 11.9469551594703 2.05304484052966 139 9 9.60739523239862 -0.60739523239862 140 13 12.2984550955551 0.701544904444911 141 13 9.52203060730148 3.47796939269852 142 12 11.7700311004715 0.229968899528461 143 11 12.56425405171 -1.56425405170999 144 10 14.9395072859872 -4.93950728598721 145 12 11.9287747754756 0.0712252245243581 146 14 13.2838800909699 0.71611990903015 147 11 13.3075729058824 -2.30757290588238 148 13 10.9874302433392 2.01256975666079 149 14 13.5223999685667 0.477600031433272 150 13 12.5557308965328 0.444269103467223 151 16 15.9668113575049 0.0331886424950527 152 13 12.0786978209033 0.921302179096672 153 12 11.256323242947 0.743676757053049 154 9 9.15504321665876 -0.155043216658764 155 14 11.2037893392544 2.7962106607456 156 15 14.5756124214192 0.424387578580833 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 14 0.999749261256894 0.000501477486212084 0.000250738743106042 15 0.99989713684738 0.000205726305240518 0.000102863152620259 16 0.999895147707139 0.000209704585722792 0.000104852292861396 17 0.999831570022249 0.000336859955502812 0.000168429977751406 18 0.999879645502272 0.000240708995456489 0.000120354497728245 19 0.999737854529744 0.000524290940512481 0.00026214547025624 20 0.999565437028515 0.00086912594296917 0.000434562971484585 21 0.99913746035658 0.00172507928683947 0.000862539643419735 22 0.99945292409578 0.0010941518084406 0.0005470759042203 23 0.99920832038281 0.00158335923438185 0.000791679617190925 24 0.9985830162137 0.00283396757259793 0.00141698378629897 25 0.997645021888355 0.00470995622328941 0.0023549781116447 26 0.996094335257995 0.00781132948401088 0.00390566474200544 27 0.99532081257678 0.00935837484643937 0.00467918742321969 28 0.992611219526769 0.0147775609464626 0.00738878047323132 29 0.995435437406731 0.0091291251865374 0.0045645625932687 30 0.994247663756647 0.0115046724867066 0.0057523362433533 31 0.991496442152955 0.0170071156940904 0.00850355784704518 32 0.995431158992026 0.00913768201594853 0.00456884100797426 33 0.993208901775156 0.0135821964496873 0.00679109822484363 34 0.98994189179894 0.0201162164021219 0.0100581082010609 35 0.988535632976139 0.0229287340477226 0.0114643670238613 36 0.983902334680705 0.0321953306385893 0.0160976653192947 37 0.980597121472098 0.0388057570558036 0.0194028785279018 38 0.982481820415247 0.0350363591695061 0.017518179584753 39 0.981212062109044 0.0375758757819113 0.0187879378909557 40 0.986739698724478 0.0265206025510432 0.0132603012755216 41 0.982369664659814 0.0352606706803719 0.017630335340186 42 0.97620518482119 0.0475896303576199 0.02379481517881 43 0.967815068479323 0.0643698630413536 0.0321849315206768 44 0.960266750527368 0.079466498945264 0.039733249472632 45 0.98920352982355 0.0215929403529001 0.01079647017645 46 0.985482740769604 0.029034518460793 0.0145172592303965 47 0.980581356335584 0.0388372873288316 0.0194186436644158 48 0.975122975524426 0.0497540489511477 0.0248770244755738 49 0.972052944540958 0.0558941109180839 0.027947055459042 50 0.967509099819537 0.0649818003609251 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0.916398728296698 0.167202543406604 0.083601271703302 69 0.900010507892477 0.199978984215045 0.0999894921075225 70 0.887382152220274 0.225235695559452 0.112617847779726 71 0.864926657720126 0.270146684559748 0.135073342279874 72 0.83925160567003 0.321496788659939 0.160748394329969 73 0.82382462603997 0.35235074792006 0.17617537396003 74 0.817228864291543 0.365542271416915 0.182771135708457 75 0.793604852935953 0.412790294128093 0.206395147064047 76 0.76097711008585 0.478045779828301 0.239022889914151 77 0.75101375257739 0.497972494845219 0.248986247422609 78 0.710736279267145 0.57852744146571 0.289263720732855 79 0.715234139465914 0.569531721068172 0.284765860534086 80 0.701133365086988 0.597733269826024 0.298866634913012 81 0.664457148929158 0.671085702141684 0.335542851070842 82 0.621793567250475 0.756412865499051 0.378206432749525 83 0.574654462548001 0.850691074903997 0.425345537451999 84 0.596875340109214 0.806249319781573 0.403124659890786 85 0.578059906190995 0.84388018761801 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0.997136418367282 0.498568209183641 104 0.50909542766829 0.98180914466342 0.49090457233171 105 0.566618183517896 0.866763632964208 0.433381816482104 106 0.566941290670047 0.866117418659907 0.433058709329953 107 0.526553304467624 0.946893391064752 0.473446695532376 108 0.611330904665016 0.777338190669967 0.388669095334984 109 0.59241920652089 0.81516158695822 0.40758079347911 110 0.549306197284816 0.901387605430368 0.450693802715184 111 0.493986853568009 0.987973707136018 0.506013146431991 112 0.645166851973817 0.709666296052366 0.354833148026183 113 0.659509930975526 0.680980138048948 0.340490069024474 114 0.651828986166507 0.696342027666987 0.348171013833493 115 0.598986485998671 0.802027028002658 0.401013514001329 116 0.561466366592205 0.877067266815589 0.438533633407795 117 0.518482211969976 0.963035576060047 0.481517788030024 118 0.505786421165175 0.98842715766965 0.494213578834825 119 0.526586649171406 0.946826701657188 0.473413350828594 120 0.471928099689009 0.943856199378018 0.528071900310991 121 0.44665603984859 0.89331207969718 0.55334396015141 122 0.406297163200944 0.812594326401888 0.593702836799056 123 0.451333397496003 0.902666794992006 0.548666602503997 124 0.410943106769869 0.821886213539738 0.589056893230131 125 0.444516545447413 0.889033090894825 0.555483454552587 126 0.464048662979816 0.928097325959632 0.535951337020184 127 0.452229736055376 0.904459472110752 0.547770263944624 128 0.382619801158783 0.765239602317566 0.617380198841217 129 0.606933859317905 0.78613228136419 0.393066140682095 130 0.71675839502137 0.566483209957261 0.283241604978631 131 0.771268256887777 0.457463486224446 0.228731743112223 132 0.82906451622962 0.341870967540758 0.170935483770379 133 0.806886769917045 0.386226460165909 0.193113230082955 134 0.862086889347525 0.27582622130495 0.137913110652475 135 0.802924978724534 0.394150042550932 0.197075021275466 136 0.747757640293216 0.504484719413568 0.252242359706784 137 0.807384602337044 0.385230795325911 0.192615397662956 138 0.74855138807117 0.502897223857661 0.25144861192883 139 0.64412932912165 0.7117413417567 0.35587067087835 140 0.513651020773494 0.972697958453011 0.486348979226505 141 0.481893920794529 0.963787841589058 0.518106079205471 142 0.336423223892389 0.672846447784778 0.663576776107611 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.124031007751938 NOK 5% type I error level 34 0.263565891472868 NOK 10% type I error level 46 0.356589147286822 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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Software written by Ed van Stee & Patrick Wessa

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