hl

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 04:33:13 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu.htm/, Retrieved Thu, 27 Nov 2008 11:36:28 +0000

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/2008/Nov/27/t12277857789bvsn4qu5teruiu.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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7,5 0 7,2 0 6,9 0 6,7 0 6,4 0 6,3 0 6,8 0 7,3 0 7,1 0 7,1 0 6,8 0 6,5 0 6,3 0 6,1 0 6,1 0 6,3 0 6,3 0 6,0 0 6,2 0 6,4 0 6,8 0 7,5 0 7,5 0 7,6 0 7,6 0 7,4 0 7,3 0 7,1 0 6,9 0 6,8 0 7,5 0 7,6 0 7,8 0 8,0 0 8,1 0 8,2 0 8,3 0 8,2 0 8,0 0 7,9 0 7,6 0 7,6 0 8,2 0 8,3 0 8,4 0 8,4 0 8,4 0 8,6 0 8,9 0 8,8 0 8,3 0 7,5 0 7,2 0 7,5 0 8,8 0 9,3 0 9,3 0 8,7 1 8,2 1 8,3 1 8,5 1 8,6 1 8,6 1 8,2 1 8,1 1 8,0 1 8,6 1 8,7 1 8,8 1 8,5 1 8,4 1 8,5 1 8,7 1 8,7 1 8,6 1 8,5 1 8,3 1 8,1 1 8,2 1 8,1 1 8,1 1 7,9 1 7,9 1 7,9 1 8,0 1 8,0 1 7,9 1 8,0 1 7,7 1 7,2 1 7,5 1 7,3 1 7,0 1 7,0 1 7,0 1 7,2 1 7,3 1 7,1 1 6,8 1 6,6 1 6,2 1 6,2 1 6,8 1 6,9 1

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation W[t] = + 7.36408459106366 -0.149919465489469D[t] + 0.093604024295134M1[t] -0.0278936635137787M2[t] -0.216058017989355M3[t] -0.415333483576042M4[t] -0.659053393607172M5[t] -0.780551081416083M6[t] -0.246493213669435M7[t] -0.112435345922788M8[t] + 0.0749197969072098M9[t] + 0.058273153395596M10[t] -0.0521134233022018M11[t] + 0.0103865766977978t + 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) 7.36408459106366 0.318857 23.0953 0 0 D -0.149919465489469 0.306007 -0.4899 0.625382 0.312691 M1 0.093604024295134 0.378383 0.2474 0.805178 0.402589 M2 -0.0278936635137787 0.378274 -0.0737 0.941381 0.470691 M3 -0.216058017989355 0.378233 -0.5712 0.569268 0.284634 M4 -0.415333483576042 0.37826 -1.098 0.275129 0.137565 M5 -0.659053393607172 0.378355 -1.7419 0.084944 0.042472 M6 -0.780551081416083 0.378518 -2.0621 0.042079 0.02104 M7 -0.246493213669435 0.378749 -0.6508 0.516828 0.258414 M8 -0.112435345922788 0.379048 -0.2966 0.767436 0.383718 M9 0.0749197969072098 0.389982 0.1921 0.848088 0.424044 M10 0.058273153395596 0.389232 0.1497 0.881326 0.440663 M11 -0.0521134233022018 0.389132 -0.1339 0.893763 0.446882 t 0.0103865766977978 0.005074 2.0471 0.043563 0.021781 Multiple Linear Regression - Regression Statistics Multiple R 0.454015651118839 R-squared 0.206130211460864 Adjusted R-squared 0.0914601308940997 F-TEST (value) 1.79759367432247 F-TEST (DF numerator) 13 F-TEST (DF denominator) 90 p-value 0.0553032149377491 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.778198484628689 Sum Squared Residuals 54.5033593330549 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.5 7.46807519205654 0.0319248079434558 2 7.2 7.35696408094547 -0.156964080945470 3 6.9 7.17918630316769 -0.279186303167692 4 6.7 6.9902974142788 -0.290297414278804 5 6.4 6.75696408094547 -0.356964080945468 6 6.3 6.64585296983436 -0.345852969834360 7 6.8 7.1902974142788 -0.390297414278804 8 7.3 7.33474185872325 -0.0347418587232457 9 7.1 7.53248357825104 -0.432483578251044 10 7.1 7.52622351143723 -0.426223511437227 11 6.8 7.42622351143723 -0.626223511437226 12 6.5 7.48872351143723 -0.988723511437227 13 6.3 7.59271411243016 -1.29271411243016 14 6.1 7.48160300131904 -1.38160300131904 15 6.1 7.30382522354127 -1.20382522354127 16 6.3 7.11493633465238 -0.814936334652376 17 6.3 6.88160300131904 -0.581603001319044 18 6 6.77049189020793 -0.770491890207932 19 6.2 7.31493633465238 -1.11493633465238 20 6.4 7.45938077909682 -1.05938077909682 21 6.8 7.65712249862462 -0.857122498624617 22 7.5 7.6508624318108 -0.150862431810801 23 7.5 7.5508624318108 -0.0508624318108007 24 7.6 7.6133624318108 -0.0133624318108011 25 7.6 7.71735303280373 -0.117353032803733 26 7.4 7.60624192169262 -0.206241921692616 27 7.3 7.42846414391484 -0.128464143914839 28 7.1 7.23957525502595 -0.139575255025950 29 6.9 7.00624192169262 -0.106241921692617 30 6.8 6.8951308105815 -0.0951308105815057 31 7.5 7.43957525502595 0.06042474497405 32 7.6 7.5840196994704 0.0159803005296048 33 7.8 7.78176141899819 0.0182385810018092 34 8 7.77550135218437 0.224498647815626 35 8.1 7.67550135218437 0.424498647815625 36 8.2 7.73800135218437 0.461998647815625 37 8.3 7.8419919531773 0.458008046822695 38 8.2 7.73088084206619 0.469119157933809 39 8 7.55310306428841 0.446896935711587 40 7.9 7.36421417539952 0.535785824600477 41 7.6 7.13088084206619 0.469119157933809 42 7.6 7.01976973095508 0.58023026904492 43 8.2 7.56421417539952 0.635785824600475 44 8.3 7.70865861984397 0.591341380156032 45 8.4 7.90640033937176 0.493599660628236 46 8.4 7.90014027255795 0.499859727442052 47 8.4 7.80014027255795 0.599859727442052 48 8.6 7.86264027255795 0.737359727442051 49 8.9 7.96663087355088 0.933369126449121 50 8.8 7.85551976243976 0.944480237560237 51 8.3 7.67774198466199 0.622258015338014 52 7.5 7.4888530957731 0.0111469042269028 53 7.2 7.25551976243976 -0.0555197624397643 54 7.5 7.14440865132865 0.355591348671347 55 8.8 7.6888530957731 1.11114690422690 56 9.3 7.83329754021754 1.46670245978246 57 9.3 8.03103925974534 1.26896074025466 58 8.7 7.87485972744205 0.825140272557948 59 8.2 7.77485972744205 0.425140272557947 60 8.3 7.83735972744205 0.462640272557949 61 8.5 7.94135032843498 0.558649671565017 62 8.6 7.83023921732387 0.769760782676132 63 8.6 7.65246143954609 0.94753856045391 64 8.2 7.4635725506572 0.736427449342798 65 8.1 7.23023921732387 0.86976078267613 66 8 7.11912810621276 0.880871893787243 67 8.6 7.6635725506572 0.936427449342799 68 8.7 7.80801699510165 0.891983004898353 69 8.8 8.00575871462944 0.794241285370559 70 8.5 7.99949864781563 0.500501352184375 71 8.4 7.89949864781563 0.500501352184375 72 8.5 7.96199864781563 0.538001352184375 73 8.7 8.06598924880856 0.634010751191442 74 8.7 7.95487813769744 0.745121862302558 75 8.6 7.77710035991966 0.822899640080336 76 8.5 7.58821147103077 0.911788528969225 77 8.3 7.35487813769744 0.945121862302559 78 8.1 7.24376702658633 0.856232973413669 79 8.2 7.78821147103077 0.411788528969224 80 8.1 7.93265591547522 0.167344084524780 81 8.1 8.13039763500302 -0.030397635003016 82 7.9 8.1241375681892 -0.224137568189199 83 7.9 8.0241375681892 -0.124137568189199 84 7.9 8.0866375681892 -0.186637568189199 85 8 8.19062816918213 -0.190628169182131 86 8 8.07951705807102 -0.0795170580710153 87 7.9 7.90173928029324 -0.00173928029323717 88 8 7.71285039140435 0.287149608595652 89 7.7 7.47951705807102 0.220482941928985 90 7.2 7.3684059469599 -0.168405946959904 91 7.5 7.91285039140435 -0.412850391404349 92 7.3 8.0572948358488 -0.757294835848794 93 7 8.25503655537659 -1.25503655537659 94 7 8.24877648856277 -1.24877648856277 95 7 8.14877648856277 -1.14877648856277 96 7.2 8.21127648856277 -1.01127648856277 97 7.3 8.3152670895557 -1.01526708955570 98 7.1 8.20415597844459 -1.10415597844459 99 6.8 8.02637820066681 -1.22637820066681 100 6.6 7.83748931177792 -1.23748931177792 101 6.2 7.60415597844459 -1.40415597844459 102 6.2 7.49304486733348 -1.29304486733348 103 6.8 8.03748931177792 -1.23748931177792 104 6.9 8.18193375622237 -1.28193375622237 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.153771139911528 0.307542279823056 0.846228860088472 18 0.089495490562453 0.178990981124906 0.910504509437547 19 0.0454155839658962 0.0908311679317925 0.954584416034104 20 0.0273410026299401 0.0546820052598803 0.97265899737006 21 0.0202581266627941 0.0405162533255882 0.979741873337206 22 0.0573193364967551 0.114638672993510 0.942680663503245 23 0.141033012439823 0.282066024879647 0.858966987560177 24 0.325427476299106 0.650854952598212 0.674572523700894 25 0.431880794499415 0.86376158899883 0.568119205500585 26 0.511496547215255 0.97700690556949 0.488503452784745 27 0.56987334548843 0.86025330902314 0.43012665451157 28 0.584613162407504 0.830773675184992 0.415386837592496 29 0.595282874222313 0.809434251555374 0.404717125777687 30 0.632462516656005 0.735074966687991 0.367537483343996 31 0.715747414716242 0.568505170567516 0.284252585283758 32 0.771062818503423 0.457874362993153 0.228937181496577 33 0.825582617229824 0.348834765540352 0.174417382770176 34 0.81175293471859 0.376494130562821 0.188247065281411 35 0.802576807483889 0.394846385032223 0.197423192516111 36 0.813634993731726 0.372730012536549 0.186365006268274 37 0.815285202547843 0.369429594904315 0.184714797452157 38 0.83308823277884 0.333823534442321 0.166911767221161 39 0.84526062159198 0.309478756816040 0.154739378408020 40 0.841732611838927 0.316534776322147 0.158267388161073 41 0.838932879764275 0.322134240471451 0.161067120235725 42 0.839075595154154 0.321848809691691 0.160924404845846 43 0.849481070314649 0.301037859370702 0.150518929685351 44 0.85308727703419 0.29382544593162 0.14691272296581 45 0.850392419830017 0.299215160339965 0.149607580169983 46 0.812897959962653 0.374204080074694 0.187102040037347 47 0.765835230462572 0.468329539074855 0.234164769537428 48 0.71782714967999 0.56434570064002 0.28217285032001 49 0.673585673476923 0.652828653046154 0.326414326523077 50 0.630515787446623 0.738968425106755 0.369484212553377 51 0.575123852595322 0.849752294809357 0.424876147404679 52 0.708887713066541 0.582224573866917 0.291112286933459 53 0.896942807761554 0.206114384476892 0.103057192238446 54 0.959269656842351 0.0814606863152973 0.0407303431576487 55 0.96069357983131 0.0786128403373785 0.0393064201686893 56 0.958705259583362 0.0825894808332752 0.0412947404166376 57 0.95072373190164 0.0985525361967185 0.0492762680983592 58 0.934173840684879 0.131652318630243 0.0658261593151215 59 0.9500588164874 0.0998823670251997 0.0499411835125999 60 0.96609089914026 0.0678182017194797 0.0339091008597399 61 0.978466559592906 0.0430668808141872 0.0215334404070936 62 0.983526763389852 0.0329464732202969 0.0164732366101484 63 0.984840389713626 0.0303192205727475 0.0151596102863738 64 0.99633770708845 0.00732458582310005 0.00366229291155003 65 0.999078615335264 0.00184276932947272 0.000921384664736361 66 0.99979536570568 0.000409268588639746 0.000204634294319873 67 0.999876214037282 0.000247571925436614 0.000123785962718307 68 0.99986932129758 0.000261357404837781 0.000130678702418891 69 0.999718517583964 0.000562964832072063 0.000281482416036032 70 0.999567728276749 0.00086454344650219 0.000432271723251095 71 0.999480427376019 0.00103914524796281 0.000519572623981407 72 0.999395584941986 0.00120883011602892 0.000604415058014458 73 0.999101950416826 0.00179609916634793 0.000898049583173964 74 0.998320683878694 0.00335863224261227 0.00167931612130614 75 0.996568092182652 0.00686381563469548 0.00343190781734774 76 0.993424765268278 0.0131504694634437 0.00657523473172186 77 0.987322849818614 0.025354300362773 0.0126771501813865 78 0.977542046576886 0.0449159068462281 0.0224579534231140 79 0.97270736348479 0.0545852730304179 0.0272926365152090 80 0.97840247055098 0.04319505889804 0.02159752944902 81 0.970463409057442 0.0590731818851162 0.0295365909425581 82 0.959025889914145 0.08194822017171 0.040974110085855 83 0.9354266948172 0.129146610365601 0.0645733051828005 84 0.909827654682048 0.180344690635903 0.0901723453179517 85 0.875299563925902 0.249400872148195 0.124700436074097 86 0.790484145504251 0.419031708991498 0.209515854495749 87 0.652872997020316 0.694254005959368 0.347127002979684 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.169014084507042 NOK 5% type I error level 20 0.281690140845070 NOK 10% type I error level 31 0.436619718309859 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/10etwd1227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/10etwd1227785587.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/1jr6z1227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/1jr6z1227785587.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/2owx21227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/2owx21227785587.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/3adr11227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/3adr11227785587.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/4h7e81227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/4h7e81227785587.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/5if2a1227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/5if2a1227785587.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/6vd2j1227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/6vd2j1227785587.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/7u7pm1227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/7u7pm1227785587.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/82ehf1227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/82ehf1227785587.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/9xunk1227785587.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277857789bvsn4qu5teruiu/9xunk1227785587.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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