Paper Multiple regression dummy

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 19 Dec 2010 14:13:46 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t1292767978vxsl29kqenlz9ig.htm/, Retrieved Sun, 19 Dec 2010 15:12:58 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t1292767978vxsl29kqenlz9ig.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 «

1 41 25 25 15 15 9 9 3 3 1 38 25 25 15 15 9 9 4 4 1 37 19 19 14 14 9 9 4 4 1 42 18 18 10 10 8 8 4 4 1 40 23 23 18 18 15 15 3 3 1 43 25 25 14 14 9 9 4 4 1 40 23 23 11 11 11 11 4 4 1 45 30 30 17 17 6 6 5 5 1 45 32 32 21 21 10 10 4 4 1 44 25 25 7 7 11 11 4 4 1 42 26 26 18 18 16 16 4 4 1 41 35 35 18 18 7 7 4 4 1 38 20 20 12 12 10 10 4 4 1 38 21 21 9 9 9 9 4 4 1 46 17 17 11 11 6 6 5 5 1 42 27 27 16 16 12 12 4 4 1 46 25 25 12 12 10 10 4 4 1 43 18 18 14 14 14 14 5 5 1 38 22 22 13 13 9 9 4 4 1 39 23 23 17 17 14 14 4 4 1 40 25 25 13 13 14 14 3 3 1 37 19 19 13 13 9 9 2 2 1 41 20 20 12 12 8 8 4 4 1 46 26 26 12 12 10 10 4 4 1 37 22 22 9 9 9 9 3 3 1 39 25 25 17 17 9 9 4 4 1 44 29 29 18 18 11 11 5 5 1 38 22 22 12 12 10 10 2 2 1 38 32 32 12 12 8 8 0 0 1 38 23 23 9 9 14 14 4 4 1 33 18 18 13 13 10 10 3 3 1 43 26 26 11 11 14 14 4 4 1 41 14 14 13 13 15 15 2 2 1 45 25 25 11 11 10 10 5 5 1 38 23 23 15 15 10 10 4 4 1 39 24 24 11 11 11 11 4 4 1 40 21 21 14 14 10 10 4 4 1 36 17 17 12 12 16 16 2 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation StudyForCareer[t] = + 32.7635896103937 + 0.167898304496122Gender[t] + 0.0764876579446363PersonalStandards[t] + 0.108955962720113PeGe[t] + 0.39341770621105ParentalExpectations[t] -0.368692551592275PaGe[t] + 0.112165660694827Doubts[t] -0.196556140775314DoGe[t] -0.173496812145797LeaderPreference[t] + 1.26512557081632LeGe[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) 32.7635896103937 3.529619 9.2825 0 0 Gender 0.167898304496122 4.514809 0.0372 0.970399 0.485199 PersonalStandards 0.0764876579446363 0.221133 0.3459 0.730052 0.365026 PeGe 0.108955962720113 0.154073 0.7072 0.480879 0.240439 ParentalExpectations 0.39341770621105 0.304603 1.2916 0.199072 0.099536 PaGe -0.368692551592275 0.197408 -1.8677 0.064333 0.032166 Doubts 0.112165660694827 0.327731 0.3422 0.732783 0.366391 DoGe -0.196556140775314 0.215415 -0.9125 0.363422 0.181711 LeaderPreference -0.173496812145797 0.972996 -0.1783 0.858789 0.429395 LeGe 1.26512557081632 0.706761 1.79 0.076057 0.038029 Multiple Linear Regression - Regression Statistics Multiple R 0.586854040562002 R-squared 0.344397664923948 Adjusted R-squared 0.293531966512875 F-TEST (value) 6.77072517791234 F-TEST (DF numerator) 9 F-TEST (DF denominator) 116 p-value 9.09873218990498e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.11721877493672 Sum Squared Residuals 1127.17813533488 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 41 40.4538277060774 0.54617229392257 2 38 41.5454564647479 -3.54545646474793 3 37 40.4080695861407 -3.40806958614066 4 42 40.2081158270813 1.7918841729187 5 40 39.6507730481213 0.349226951878687 6 43 41.5207313101292 1.47926868987084 7 40 40.9068876447824 -0.906887644782357 8 45 43.8669250762212 1.13307492377879 9 45 42.9075222570333 2.09247774296666 10 44 41.1788742676368 2.82112573236325 11 42 41.2143421887056 0.785657811294402 12 41 43.6428490954127 -2.64284909541272 13 38 40.4596724174874 -2.45967241748737 14 38 40.6553310543763 -2.65533105437628 15 46 41.3078070798668 4.69219292013318 16 42 41.6878974204547 0.312102579545257 17 46 41.3868905208111 4.61310947918888 18 43 40.892302323744 2.10769767625600 19 38 40.9396752935161 -2.93967529351613 20 39 40.8020671322535 -1.80206713225355 21 40 39.9824249964374 0.0175750035625806 22 37 38.2000869141808 -1.20008691418083 23 41 40.6284533776483 0.371546622351653 24 46 41.5723341414759 4.42766585852413 25 37 39.7491459163705 -2.74914591637051 26 39 41.5949067739855 -2.59490677398548 27 44 43.2842542097728 0.715745790227196 28 38 38.6473021414758 -0.647302141475819 29 38 38.4872617909432 -0.487261790943231 30 38 40.6042658953033 -2.60426589530335 31 33 39.0218815721061 -6.02188157210613 32 43 41.2100470665351 1.78995293346486 33 41 36.7665259303742 4.23347406962583 34 45 42.4537941248629 2.54620587513713 35 38 41.0901787433379 -3.09017874333795 36 39 41.0923312654471 -2.09233126544711 37 40 40.6945663473897 -0.694566347389673 38 36 37.2137411576692 -1.21374115766915 39 49 43.4589550639875 5.54104493601252 40 41 41.2777748861119 -0.277774886111855 41 42 40.0813256149125 1.91867438508748 42 41 42.6618103780372 -1.66181037803723 43 43 40.5854507126904 2.41454928730959 44 46 40.1673309448284 5.83266905517161 45 41 42.7462008581177 -1.74620085811772 46 39 41.1788742676368 -2.17887426763675 47 42 40.7940046381385 1.20599536186145 48 35 41.5926889912804 -6.59268899128045 49 36 39.4755444436807 -3.47554444368072 50 41 40.4596724174874 0.540327582512627 51 41 39.8131063640027 1.18689363599733 52 36 41.9227913503570 -5.92279135035704 53 46 41.5949067739855 4.40509322601452 54 44 40.8880724621694 3.11192753783058 55 43 39.0434522096979 3.95654779030213 56 40 41.1126861152517 -1.11268611525169 57 40 39.5862749282154 0.413725071784629 58 39 40.9498903097403 -1.94989030974029 59 44 41.8007804270986 2.19921957290145 60 38 37.6356935580716 0.364306441928412 61 39 39.7216653067729 -0.721665306772924 62 41 43.0079724647480 -2.00797246474795 63 39 40.0071501510562 -1.00715015105620 64 40 40.2043484551368 -0.204348455136752 65 44 40.3564667547939 3.64353324520605 66 42 39.6765101197058 2.32348988029417 67 46 43.125688265836 2.87431173416401 68 44 40.9482754599049 3.05172454009509 69 37 41.1417815746847 -4.14178157468466 70 39 38.5279714905524 0.472028509447604 71 40 39.4857594599049 0.514240540095116 72 42 40.0813256149125 1.91867438508748 73 37 39.6131526049433 -2.61315260494331 74 33 38.6553646355908 -5.65536463559082 75 35 40.1833906724625 -5.18339067246252 76 42 36.5063691735793 5.49363082642068 77 36 35.4033641085787 0.596635891421267 78 44 41.9328380814502 2.06716191854976 79 45 41.5393028708881 3.4606971291119 80 47 44.582434331914 2.41756566808601 81 40 41.2241989902996 -1.22419899029961 82 48 42.8765582651634 5.12344173483657 83 45 44.4444974795575 0.55550252044251 84 41 41.716869828039 -0.71686982803902 85 34 38.1386430236907 -4.1386430236907 86 38 38.0333057686589 -0.0333057686589387 87 37 38.6164112631620 -1.61641126316204 88 48 46.2128525711935 1.78714742880646 89 39 43.5958559180545 -4.59585591805453 90 34 39.7203302588433 -5.72033025884333 91 35 35.2504166312852 -0.250416631285214 92 41 40.7825456018878 0.217454398112158 93 43 40.2809867621743 2.71901323782571 94 41 38.4769167483976 2.52308325160241 95 39 37.037518428798 1.96248157120198 96 36 40.5723790474122 -4.57237904741219 97 46 42.1507639261883 3.84923607381166 98 42 42.6000685781936 -0.600068578193605 99 42 35.9787103128194 6.0212896871806 100 45 41.9362453085161 3.06375469148387 101 39 41.9751545603643 -2.97515456036430 102 45 44.2476758609419 0.752324139058051 103 48 46.2696717194568 1.73032828054325 104 35 37.3473285660388 -2.34732856603882 105 38 39.0623086881014 -1.06230868810141 106 42 40.1756495071425 1.82435049285748 107 36 38.5668434495734 -2.56684344957341 108 37 42.6361834292532 -5.63618342925318 109 38 39.3332095734941 -1.33320957349405 110 43 39.3133420857521 3.68665791424787 111 35 34.7000187607758 0.299981239224212 112 36 41.2045443999161 -5.20454439991606 113 33 33.629715273595 -0.629715273594992 114 39 38.572645148509 0.427354851491033 115 45 39.641062119408 5.35893788059203 116 35 41.191091437387 -6.191091437387 117 38 38.6525261142216 -0.652526114221623 118 36 38.7424528153974 -2.74245281539744 119 42 37.9015705441569 4.09842945584308 120 41 39.9412634017284 1.05873659827160 121 35 37.1279530855617 -2.12795308556171 122 43 38.0049502078618 4.99504979213819 123 40 43.1373423400478 -3.13734234004781 124 46 42.6496363917822 3.35036360821776 125 44 44.4444974795575 -0.44449747955749 126 35 38.4273489348089 -3.42734893480895 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.801974941369491 0.396050117261018 0.198025058630509 14 0.734870194870921 0.530259610258158 0.265129805129079 15 0.814203478953562 0.371593042092875 0.185796521046438 16 0.720179901603776 0.559640196792449 0.279820098396224 17 0.764822573468211 0.470354853063578 0.235177426531789 18 0.67949262879262 0.64101474241476 0.32050737120738 19 0.655796617556397 0.688406764887206 0.344203382443603 20 0.598671087134543 0.802657825730913 0.401328912865457 21 0.508297261690378 0.983405476619244 0.491702738309622 22 0.4378394935239 0.8756789870478 0.5621605064761 23 0.353743234163691 0.707486468327382 0.646256765836309 24 0.389276088291471 0.778552176582942 0.610723911708529 25 0.358241206893462 0.716482413786924 0.641758793106538 26 0.321599296358232 0.643198592716464 0.678400703641768 27 0.256116743142700 0.512233486285401 0.7438832568573 28 0.207109528805129 0.414219057610259 0.79289047119487 29 0.174677080309707 0.349354160619415 0.825322919690293 30 0.192919874263989 0.385839748527979 0.80708012573601 31 0.280896659366953 0.561793318733907 0.719103340633047 32 0.231613892859709 0.463227785719418 0.768386107140291 33 0.361672178439785 0.72334435687957 0.638327821560215 34 0.325389343742486 0.650778687484972 0.674610656257514 35 0.317596021065631 0.635192042131262 0.682403978934369 36 0.294090784997849 0.588181569995697 0.705909215002151 37 0.241540879973005 0.483081759946011 0.758459120026995 38 0.197178642426051 0.394357284852103 0.802821357573949 39 0.263444326299000 0.526888652597999 0.736555673701 40 0.220045375084777 0.440090750169554 0.779954624915223 41 0.198661605972355 0.39732321194471 0.801338394027645 42 0.169558183338855 0.339116366677711 0.830441816661145 43 0.15675585373346 0.31351170746692 0.84324414626654 44 0.295049606140981 0.590099212281962 0.704950393859019 45 0.259689642967422 0.519379285934845 0.740310357032578 46 0.248339280878589 0.496678561757178 0.751660719121411 47 0.208799654649373 0.417599309298747 0.791200345350627 48 0.370381192807272 0.740762385614543 0.629618807192728 49 0.384691474866155 0.76938294973231 0.615308525133845 50 0.333350343466970 0.666700686933939 0.66664965653303 51 0.292459344893823 0.584918689787646 0.707540655106177 52 0.43387770667841 0.86775541335682 0.56612229332159 53 0.478185103940082 0.956370207880164 0.521814896059918 54 0.474389517796023 0.948779035592046 0.525610482203977 55 0.513336496389994 0.973327007220012 0.486663503610006 56 0.480620077708199 0.961240155416397 0.519379922291801 57 0.427430326911956 0.854860653823911 0.572569673088044 58 0.388879874811631 0.777759749623262 0.611120125188369 59 0.441095400279551 0.882190800559102 0.558904599720449 60 0.388204429159243 0.776408858318487 0.611795570840757 61 0.348790841412323 0.697581682824646 0.651209158587677 62 0.310501399149466 0.621002798298932 0.689498600850534 63 0.270984812928728 0.541969625857456 0.729015187071272 64 0.315665876872972 0.631331753745945 0.684334123127028 65 0.308617558536839 0.617235117073678 0.691382441463161 66 0.275261496371547 0.550522992743094 0.724738503628453 67 0.306913127507006 0.613826255014012 0.693086872492994 68 0.307037165572224 0.614074331144449 0.692962834427776 69 0.302786490893235 0.60557298178647 0.697213509106765 70 0.256677353578237 0.513354707156473 0.743322646421763 71 0.224666457507741 0.449332915015481 0.77533354249226 72 0.192861440795214 0.385722881590428 0.807138559204786 73 0.169781893054549 0.339563786109099 0.83021810694545 74 0.183667354459377 0.367334708918754 0.816332645540623 75 0.186919801476545 0.373839602953091 0.813080198523455 76 0.205606761420397 0.411213522840793 0.794393238579603 77 0.168309217985880 0.336618435971760 0.83169078201412 78 0.142427366647545 0.284854733295090 0.857572633352455 79 0.1334933974033 0.2669867948066 0.8665066025967 80 0.119707627170770 0.239415254341541 0.88029237282923 81 0.0950522873879692 0.190104574775938 0.90494771261203 82 0.129009129252527 0.258018258505055 0.870990870747473 83 0.103578723650205 0.207157447300409 0.896421276349795 84 0.082545048182928 0.165090096365856 0.917454951817072 85 0.0860627866515274 0.172125573303055 0.913937213348473 86 0.0661816554560302 0.132363310912060 0.93381834454397 87 0.0525599564080308 0.105119912816062 0.94744004359197 88 0.0577787332814532 0.115557466562906 0.942221266718547 89 0.0682904456591995 0.136580891318399 0.9317095543408 90 0.120844113384600 0.241688226769200 0.8791558866154 91 0.0967795123986866 0.193559024797373 0.903220487601313 92 0.0727525143853268 0.145505028770654 0.927247485614673 93 0.0668634682209698 0.133726936441940 0.93313653177903 94 0.0581455341163439 0.116291068232688 0.941854465883656 95 0.0448303072104151 0.0896606144208302 0.955169692789585 96 0.0514683206806305 0.102936641361261 0.94853167931937 97 0.0561740251266458 0.112348050253292 0.943825974873354 98 0.0395140303267184 0.0790280606534368 0.960485969673282 99 0.0740581598155555 0.148116319631111 0.925941840184444 100 0.0770424449726486 0.154084889945297 0.922957555027351 101 0.0678821749170213 0.135764349834043 0.932117825082979 102 0.0505676140647885 0.101135228129577 0.949432385935212 103 0.0469775051148722 0.0939550102297445 0.953022494885128 104 0.0338695479823363 0.0677390959646726 0.966130452017664 105 0.022858730003791 0.045717460007582 0.977141269996209 106 0.0221402708892994 0.0442805417785987 0.9778597291107 107 0.0159853098376243 0.0319706196752486 0.984014690162376 108 0.0220771051717442 0.0441542103434884 0.977922894828256 109 0.0131968702732082 0.0263937405464164 0.986803129726792 110 0.00878436704605641 0.0175687340921128 0.991215632953944 111 0.00411015931840222 0.00822031863680445 0.995889840681598 112 0.00506336844733408 0.0101267368946682 0.994936631552666 113 0.00226820207107039 0.00453640414214077 0.99773179792893 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0198019801980198 NOK 5% type I error level 9 0.0891089108910891 NOK 10% type I error level 13 0.128712871287129 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 = 2 ; 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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