Interactie effecten 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: Wed, 29 Dec 2010 11:20:21 +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/29/t12936214822lqdet8brxvir1d.htm/, Retrieved Wed, 29 Dec 2010 12:18:05 +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/29/t12936214822lqdet8brxvir1d.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 «

13 13 26 14 28 13 26 3 6 2 12 12 12 8 8 13 13 5 5 1 15 10 0 12 0 16 0 6 0 0 12 9 27 7 21 12 36 6 18 3 10 10 30 10 30 11 33 5 15 3 12 12 12 7 7 12 12 3 3 1 15 13 39 16 48 18 54 8 24 3 9 12 12 11 11 11 11 4 4 1 12 12 48 14 56 14 56 4 16 4 11 6 0 6 0 9 0 4 0 0 11 5 15 16 48 14 42 6 18 3 11 12 24 11 22 12 24 6 12 2 15 11 44 16 64 11 44 5 20 4 7 14 42 12 36 12 36 4 12 3 11 14 14 7 7 13 13 6 6 1 11 12 12 13 13 11 11 4 4 1 10 12 24 11 22 12 24 6 12 2 14 11 33 15 45 16 48 6 18 3 10 11 11 7 7 9 9 4 4 1 6 7 7 9 9 11 11 4 4 1 11 9 18 7 14 13 26 2 4 2 15 11 33 14 42 15 45 7 21 3 11 11 44 15 60 10 40 5 20 4 12 12 24 7 14 11 22 4 8 2 14 12 12 15 15 13 13 6 6 1 15 11 22 17 34 16 32 6 12 2 9 11 22 15 30 15 30 7 14 2 13 8 32 14 56 14 56 5 20 4 13 9 18 14 28 14 28 6 12 2 16 12 36 8 24 14 42 4 12 3 13 10 30 8 24 8 24 4 12 3 12 10 30 14 42 13 39 7 21 3 14 12 48 14 56 15 60 7 28 4 11 8 16 8 16 13 26 4 8 2 9 12 24 11 22 11 22 4 8 2 16 11 44 16 64 15 60 6 24 4 12 12 36 10 30 15 45 6 18 3 10 7 28 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 8 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Popularity[t] = -0.956521145565252 + 0.122464404572676FindingFriends[t] -0.00611986826304842sum_friends[t] + 0.129901135405865KnowingPeople[t] + 0.0388655008006026sum_know[t] + 0.422882703355404Liked[t] -0.0255765571618555sum_liked[t] + 0.782432477255014Celebrity[t] -0.0824572683867577sum_celeb[t] + 0.579627939212371Sum[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) -0.956521145565252 2.451522 -0.3902 0.696976 0.348488 FindingFriends 0.122464404572676 0.177249 0.6909 0.490714 0.245357 sum_friends -0.00611986826304842 0.066175 -0.0925 0.926443 0.463221 KnowingPeople 0.129901135405865 0.111901 1.1609 0.247593 0.123797 sum_know 0.0388655008006026 0.045231 0.8593 0.391601 0.1958 Liked 0.422882703355404 0.169634 2.4929 0.013787 0.006893 sum_liked -0.0255765571618555 0.063828 -0.4007 0.689219 0.34461 Celebrity 0.782432477255014 0.291171 2.6872 0.008042 0.004021 sum_celeb -0.0824572683867577 0.117131 -0.704 0.48257 0.241285 Sum 0.579627939212371 0.887544 0.6531 0.514738 0.257369 Multiple Linear Regression - Regression Statistics Multiple R 0.716779838383906 R-squared 0.513773336713658 Adjusted R-squared 0.483800460209705 F-TEST (value) 17.1412756011558 F-TEST (DF numerator) 9 F-TEST (DF denominator) 146 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.10987944174862 Sum Squared Residuals 649.932323772166 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 11.2275438144205 1.77245618557947 2 12 11.0342302638718 0.9657697361282 3 15 13.2876546422484 1.71234535775157 4 12 10.8089897497838 1.19101025021616 5 10 10.7713737590258 -0.771373759025813 6 12 9.06820706373527 2.93179293626473 7 15 16.5909272245955 -1.59092722459553 8 9 10.0459426712359 -1.04594267123586 9 12 12.8313779907896 -0.831377990789559 10 11 7.49334633352468 3.50665366647532 11 11 13.3033553546585 -2.30335535465845 12 11 11.9752469677655 -0.975246967765519 13 15 12.7949933160697 2.20500668393034 14 7 11.4918805935504 -4.49188059355038 15 11 11.7981279091528 -0.798127909152844 16 11 10.3834759436488 0.616524056351211 17 10 11.9752469677655 -1.97524696776552 18 14 14.3737925792916 -0.373792579291638 19 10 8.45991929771326 1.54008070228674 20 6 9.12668671727478 -3.12668671727478 21 11 8.7157022427032 2.2842977572968 22 15 14.3162025817089 0.683797418291133 23 11 12.1890537027534 -1.18905370275341 24 12 9.53795294974255 2.46204705025745 25 14 12.9155719261853 1.08442807381467 26 15 14.5977334778881 0.402266522111864 27 9 14.4282575553238 -5.4282575553238 28 13 12.8920416684156 0.107958331584388 29 13 13.0109285527104 -0.0109285527103798 30 16 10.9899865064923 5.0100134935077 31 13 8.70485871570622 4.29514128429379 32 12 13.5197917181857 -1.51979171818566 33 14 14.5097646766215 -0.509764676621488 34 11 10.0481455926267 0.951854407373312 35 9 10.3684814977708 -1.36848149777083 36 16 14.5299026186096 1.47009738139043 37 12 12.899256158167 -0.899256158166965 38 10 9.47899553170556 0.521004468294436 39 13 12.8596482997718 0.140351700228155 40 16 15.450814104857 0.549185895142973 41 14 12.9464430078503 1.05355699214969 42 15 7.9843340361999 7.0156659638001 43 5 9.58600730887062 -4.58600730887062 44 8 10.1581376449133 -2.15813764491331 45 11 11.2421258886977 -0.242125888697728 46 16 13.7235519120314 2.27644808796864 47 17 15.0960283922259 1.90397160777411 48 9 8.17264327132336 0.827356728676637 49 9 11.3590384594999 -2.35903845949989 50 13 14.5003260089276 -1.50032600892764 51 10 11.1011837347548 -1.10118373475479 52 6 12.4348178203754 -6.43481782037539 53 12 12.1066029548912 -0.106602954891223 54 8 10.489044264813 -2.48904426481301 55 14 11.7338684040511 2.2661315959489 56 12 13.1770648027584 -1.17706480275844 57 11 10.6171692196699 0.382830780330143 58 16 14.6545092302766 1.34549076972341 59 8 10.6549086700045 -2.65490867000447 60 15 14.9418047041933 0.058195295806669 61 7 8.97415066458367 -1.97415066458367 62 16 14.3073110754221 1.69268892457793 63 14 12.8442987158888 1.15570128411123 64 16 13.4250730460563 2.57492695394366 65 9 10.0659685416539 -1.06596854165392 66 14 12.1447228903299 1.85527710967015 67 11 13.3807949873978 -2.38079498739784 68 13 10.4681574814718 2.53184251852821 69 15 13.1306451848893 1.86935481511073 70 5 5.67009245966134 -0.67009245966134 71 15 13.1770648027584 1.82293519724156 72 13 12.6780417811813 0.321958218818717 73 11 12.3299070471522 -1.32990704715223 74 11 14.1104046332066 -3.11040463320658 75 12 12.64931218324 -0.649312183240025 76 12 13.4394382834657 -1.43943828346566 77 12 12.4158317414769 -0.415831741476936 78 12 11.9949631366214 0.00503686337855331 79 14 10.8607738538447 3.1392261461553 80 6 7.802159072467 -1.802159072467 81 7 9.76818227260353 -2.76818227260353 82 14 12.4686570648375 1.53134293516251 83 14 13.9943896774927 0.00561032250727054 84 10 11.1372796911909 -1.13727969119086 85 13 9.02312685723486 3.97687314276514 86 12 12.2931037728192 -0.293103772819169 87 9 9.26363082967135 -0.263630829671345 88 12 11.9217944541462 0.078205545853795 89 16 14.8381433587982 1.16185664120177 90 10 10.4400732807768 -0.440073280776767 91 14 13.1443085188386 0.855691481161359 92 10 13.5346442718065 -3.53464427180645 93 16 15.401067428546 0.598932571453979 94 15 13.4777263217063 1.52227367829373 95 12 11.4681292284869 0.531870771513146 96 10 9.33370043848328 0.666299561516721 97 8 10.477416246366 -2.47741624636605 98 8 8.65893008827593 -0.658930088275935 99 11 12.654915533214 -1.654915533214 100 13 12.453447732235 0.546552267765037 101 16 15.5884456356338 0.411554364366185 102 16 14.7951278152298 1.20487218477024 103 14 15.4723250021853 -1.47232500218527 104 11 9.02818057721714 1.97181942278286 105 4 7.08784173263349 -3.08784173263349 106 14 14.4196353340098 -0.419635334009812 107 9 10.6400561163837 -1.64005611638368 108 14 15.0977608916113 -1.09776089161128 109 8 10.1364301921225 -2.13643019212252 110 8 10.715911302049 -2.71591130204897 111 11 11.873913882272 -0.873913882271967 112 12 13.1501689898151 -1.15016898981508 113 11 11.6144296642982 -0.614429664298249 114 14 13.3726509461595 0.627349053840505 115 15 14.2734651377469 0.726534862253111 116 16 13.4349888777443 2.56501112225571 117 16 12.9466203154947 3.05337968450529 118 11 12.8392689128711 -1.83926891287108 119 14 14.2583045582921 -0.258304558292105 120 14 10.837618555763 3.16238144423698 121 12 11.6298240258532 0.370175974146844 122 14 12.7006174175252 1.29938258247478 123 8 10.8907755528467 -2.8907755528467 124 13 14.1199328683343 -1.11993286833428 125 16 14.0219479368138 1.9780520631862 126 12 11.1249787549928 0.875021245007226 127 16 15.301521085457 0.698478914543025 128 12 12.6536387475452 -0.653638747545172 129 11 11.452657396154 -0.452657396154021 130 4 5.62386162589302 -1.62386162589302 131 16 15.2457914941984 0.754208505801594 132 15 12.5958405702591 2.40415942974089 133 10 11.5261734935842 -1.52617349358419 134 13 13.3974531743111 -0.397453174311143 135 15 13.133970419843 1.86602958015695 136 12 10.50302169121 1.49697830878999 137 14 13.681486515552 0.318513484448037 138 7 10.0808068809741 -3.08080688097408 139 19 13.9209642828889 5.07903571711112 140 12 13.0880115314566 -1.08801153145658 141 12 11.5680436083457 0.431956391654338 142 13 13.3416025568501 -0.341602556850117 143 15 12.2275207676955 2.77247923230451 144 8 9.35313962372138 -1.35313962372138 145 12 10.9043085388271 1.09569146117285 146 10 11.1754834642238 -1.17548346422381 147 8 11.4315364100654 -3.43153641006535 148 10 14.6364792060832 -4.63647920608323 149 15 13.9279841533596 1.07201584664036 150 16 14.6745657025484 1.32543429745159 151 13 13.1309012423538 -0.130901242353846 152 16 15.0129580511699 0.987041948830092 153 9 9.76401987267187 -0.764019872671873 154 14 13.0776185357745 0.92238146422549 155 14 13.2522110205335 0.74778897946646 156 12 10.1384097158563 1.86159028414367 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.18571882859656 0.37143765719312 0.81428117140344 14 0.776012001489515 0.44797599702097 0.223987998510485 15 0.660751206652744 0.678497586694513 0.339248793347256 16 0.614697812332198 0.770604375335604 0.385302187667802 17 0.512267517201389 0.975464965597222 0.487732482798611 18 0.416244566392152 0.832489132784304 0.583755433607848 19 0.346940566361714 0.693881132723428 0.653059433638286 20 0.680613165798704 0.638773668402592 0.319386834201296 21 0.610802512321098 0.778394975357805 0.389197487678902 22 0.557958758299772 0.884082483400456 0.442041241700228 23 0.530009137069659 0.939981725860682 0.469990862930341 24 0.524101023367654 0.951797953264692 0.475898976632346 25 0.584937256266562 0.830125487466877 0.415062743733438 26 0.522448988002732 0.955102023994537 0.477551011997268 27 0.737140268608154 0.525719462783692 0.262859731391846 28 0.68388354869418 0.63223290261164 0.31611645130582 29 0.628380757419477 0.743238485161047 0.371619242580523 30 0.819404555082694 0.361190889834612 0.180595444917306 31 0.884070567546612 0.231858864906776 0.115929432453388 32 0.855216637124941 0.289566725750118 0.144783362875059 33 0.8174043524608 0.365191295078401 0.182595647539201 34 0.784295099489387 0.431409801021227 0.215704900510613 35 0.769302278354945 0.46139544329011 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0.986611484102172 0.0267770317956549 0.0133885158978275 71 0.986237239955473 0.0275255200890533 0.0137627600445267 72 0.981602810480318 0.0367943790393643 0.0183971895196822 73 0.978114455195821 0.0437710896083577 0.0218855448041788 74 0.987611496070663 0.0247770078586739 0.0123885039293369 75 0.983945213904882 0.0321095721902355 0.0160547860951177 76 0.981148251972344 0.0377034960553115 0.0188517480276557 77 0.974897956227123 0.0502040875457542 0.0251020437728771 78 0.966983433991153 0.0660331320176941 0.0330165660088471 79 0.977884026845232 0.0442319463095352 0.0221159731547676 80 0.975880533595292 0.0482389328094166 0.0241194664047083 81 0.981293487987666 0.0374130240246679 0.0187065120123339 82 0.98194890255711 0.0361021948857811 0.0180510974428905 83 0.976281273395385 0.0474374532092292 0.0237187266046146 84 0.971431957932085 0.0571360841358296 0.0285680420679148 85 0.991875221487154 0.0162495570256919 0.00812477851284593 86 0.988886961293848 0.0222260774123034 0.0111130387061517 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0.941138907021939 0.117722185956123 0.0588610929780614 105 0.941820288348425 0.11635942330315 0.0581797116515749 106 0.92431493022409 0.15137013955182 0.0756850697759099 107 0.918215967755964 0.163568064488072 0.0817840322440362 108 0.925044887984505 0.14991022403099 0.0749551120154951 109 0.916566625749596 0.166866748500808 0.083433374250404 110 0.905209388773641 0.189581222452717 0.0947906112263587 111 0.884554505282002 0.230890989435996 0.115445494717998 112 0.883094717046193 0.233810565907614 0.116905282953807 113 0.852801707333106 0.294396585333789 0.147198292666894 114 0.820202540797502 0.359594918404995 0.179797459202498 115 0.780303992353167 0.439392015293666 0.219696007646833 116 0.773997986364079 0.452004027271842 0.226002013635921 117 0.783854093906539 0.432291812186923 0.216145906093461 118 0.766366044502706 0.467267910994588 0.233633955497294 119 0.717065646059348 0.565868707881303 0.282934353940652 120 0.790497126662922 0.419005746674157 0.209502873337078 121 0.756531998231144 0.486936003537711 0.243468001768856 122 0.715215508640921 0.569568982718158 0.284784491359079 123 0.713747013648639 0.572505972702722 0.286252986351361 124 0.65887117447617 0.68225765104766 0.34112882552383 125 0.658447507623792 0.683104984752416 0.341552492376208 126 0.638085781623485 0.72382843675303 0.361914218376515 127 0.57331141042449 0.85337717915102 0.42668858957551 128 0.607959819907074 0.784080360185852 0.392040180092926 129 0.601789364792553 0.796421270414894 0.398210635207447 130 0.573454118945876 0.853091762108247 0.426545881054124 131 0.496908003271027 0.993816006542054 0.503091996728973 132 0.475930254917935 0.95186050983587 0.524069745082065 133 0.472516560690427 0.945033121380853 0.527483439309573 134 0.397424929262939 0.794849858525878 0.602575070737061 135 0.358982351379124 0.717964702758248 0.641017648620876 136 0.36024844340139 0.72049688680278 0.63975155659861 137 0.276286312732384 0.552572625464768 0.723713687267616 138 0.338802972277992 0.677605944555983 0.661197027722008 139 0.629964957691366 0.740070084617268 0.370035042308634 140 0.791103058847175 0.417793882305651 0.208896941152825 141 0.723701383627538 0.552597232744925 0.276298616372462 142 0.684162606963401 0.631674786073198 0.315837393036599 143 0.731337446369691 0.537325107260618 0.268662553630309 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 35 0.267175572519084 NOK 10% type I error level 49 0.374045801526718 NOK

Charts produced by software:

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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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Software written by Ed van Stee & Patrick Wessa

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