hk

*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 04:32:49 -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/t1227785755696rkmtj0n6l25f.htm/, Retrieved Thu, 27 Nov 2008 11:36:06 +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/t1227785755696rkmtj0n6l25f.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:

» Textbox « » Textfile « » CSV «

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 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 6.8 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation w[t] = + 7.23222965083163 -0.0486480761562996d[t] + 0.00802353896103895t + 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) 7.23222965083163 0.180285 40.1155 0 0 d -0.0486480761562996 0.307403 -0.1583 0.874569 0.437284 t 0.00802353896103895 0.005052 1.5881 0.115367 0.057684 Multiple Linear Regression - Regression Statistics Multiple R 0.273902779092826 R-squared 0.0750227323947734 Adjusted R-squared 0.0568859232260436 F-TEST (value) 4.1364901453626 F-TEST (DF numerator) 2 F-TEST (DF denominator) 102 p-value 0.0187363089238227 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.793083278944422 Sum Squared Residuals 64.1560709088061 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.5 7.24025318979265 0.259746810207350 2 7.2 7.2482767287537 -0.0482767287537039 3 6.9 7.25630026771474 -0.356300267714739 4 6.7 7.26432380667578 -0.56432380667578 5 6.4 7.27234734563682 -0.872347345636819 6 6.3 7.28037088459786 -0.98037088459786 7 6.8 7.2883944235589 -0.488394423558898 8 7.3 7.29641796251994 0.00358203748006342 9 7.1 7.30444150148098 -0.204441501480976 10 7.1 7.31246504044201 -0.212465040442015 11 6.8 7.32048857940305 -0.520488579403053 12 6.5 7.32851211836409 -0.828512118364092 13 6.3 7.33653565732513 -1.03653565732513 14 6.1 7.34455919628617 -1.24455919628617 15 6.1 7.35258273524721 -1.25258273524721 16 6.3 7.36060627420825 -1.06060627420825 17 6.3 7.36862981316929 -1.06862981316929 18 6 7.37665335213033 -1.37665335213033 19 6.2 7.38467689109136 -1.18467689109136 20 6.4 7.3927004300524 -0.992700430052404 21 6.8 7.40072396901344 -0.600723969013443 22 7.5 7.40874750797448 0.091252492025518 23 7.5 7.41677104693552 0.0832289530644791 24 7.6 7.42479458589656 0.175205414103440 25 7.6 7.4328181248576 0.167181875142401 26 7.4 7.44084166381864 -0.0408416638186374 27 7.3 7.44886520277968 -0.148865202779677 28 7.1 7.45688874174072 -0.356888741740716 29 6.9 7.46491228070175 -0.564912280701754 30 6.8 7.4729358196628 -0.672935819662794 31 7.5 7.48095935862383 0.0190406413761674 32 7.6 7.48898289758487 0.111017102415128 33 7.8 7.49700643654591 0.302993563454089 34 8 7.50502997550695 0.494970024493050 35 8.1 7.51305351446799 0.586946485532011 36 8.2 7.52107705342903 0.678922946570972 37 8.3 7.52910059239007 0.770899407609934 38 8.2 7.5371241313511 0.662875868648894 39 8 7.54514767031214 0.454852329687856 40 7.9 7.55317120927318 0.346828790726817 41 7.6 7.56119474823422 0.0388052517657774 42 7.6 7.56921828719526 0.0307817128047384 43 8.2 7.5772418261563 0.622758173843699 44 8.3 7.58526536511734 0.714734634882662 45 8.4 7.59328890407838 0.806711095921622 46 8.4 7.60131244303942 0.798687556960583 47 8.4 7.60933598200046 0.790664017999544 48 8.6 7.6173595209615 0.982640479038504 49 8.9 7.62538305992253 1.27461694007747 50 8.8 7.63340659888357 1.16659340111643 51 8.3 7.64143013784461 0.658569862155389 52 7.5 7.64945367680565 -0.149453676805651 53 7.2 7.65747721576669 -0.45747721576669 54 7.5 7.66550075472773 -0.165500754727729 55 8.8 7.67352429368877 1.12647570631123 56 9.3 7.6815478326498 1.61845216735019 57 9.3 7.68957137161085 1.61042862838915 58 8.7 7.64894683441558 1.05105316558442 59 8.2 7.65697037337662 0.543029626623376 60 8.3 7.66499391233766 0.635006087662339 61 8.5 7.6730174512987 0.826982548701299 62 8.6 7.68104099025974 0.91895900974026 63 8.6 7.68906452922078 0.91093547077922 64 8.2 7.69708806818182 0.502911931818181 65 8.1 7.70511160714286 0.394888392857143 66 8 7.7131351461039 0.286864853896104 67 8.6 7.72115868506493 0.878841314935065 68 8.7 7.72918222402597 0.970817775974025 69 8.8 7.73720576298701 1.06279423701299 70 8.5 7.74522930194805 0.754770698051948 71 8.4 7.75325284090909 0.64674715909091 72 8.5 7.76127637987013 0.73872362012987 73 8.7 7.76929991883117 0.93070008116883 74 8.7 7.77732345779221 0.922676542207792 75 8.6 7.78534699675325 0.814653003246753 76 8.5 7.79337053571429 0.706629464285714 77 8.3 7.80139407467532 0.498605925324676 78 8.1 7.80941761363636 0.290582386363636 79 8.2 7.8174411525974 0.382558847402597 80 8.1 7.82546469155844 0.274535308441558 81 8.1 7.83348823051948 0.266511769480519 82 7.9 7.84151176948052 0.058488230519481 83 7.9 7.84953530844156 0.050464691558442 84 7.9 7.8575588474026 0.0424411525974030 85 8 7.86558238636364 0.134417613636364 86 8 7.87360592532468 0.126394074675325 87 7.9 7.88162946428571 0.0183705357142861 88 8 7.88965300324675 0.110346996753247 89 7.7 7.89767654220779 -0.197676542207792 90 7.2 7.90570008116883 -0.705700081168831 91 7.5 7.91372362012987 -0.41372362012987 92 7.3 7.92174715909091 -0.621747159090909 93 7 7.92977069805195 -0.929770698051948 94 7 7.93779423701299 -0.937794237012987 95 7 7.94581777597403 -0.945817775974026 96 7.2 7.95384131493507 -0.753841314935065 97 7.3 7.9618648538961 -0.661864853896104 98 7.1 7.96988839285714 -0.869888392857143 99 6.8 7.97791193181818 -1.17791193181818 100 6.6 7.98593547077922 -1.38593547077922 101 6.2 7.99395900974026 -1.79395900974026 102 6.2 8.0019825487013 -1.8019825487013 103 6.8 8.01000608766234 -1.21000608766234 104 6.9 8.01802962662338 -1.11802962662338 105 6.8 8.02605316558441 -1.22605316558442 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.00112368489713084 0.00224736979426168 0.99887631510287 7 0.0368421616481369 0.0736843232962737 0.963157838351863 8 0.109322892042008 0.218645784084015 0.890677107957992 9 0.0735783961913903 0.147156792382781 0.92642160380861 10 0.0424300428350906 0.0848600856701811 0.95756995716491 11 0.0206025470324353 0.0412050940648707 0.979397452967565 12 0.0120260664647210 0.0240521329294421 0.98797393353528 13 0.00825067205641532 0.0165013441128306 0.991749327943585 14 0.00675791251932789 0.0135158250386558 0.993242087480672 15 0.00466859165360438 0.00933718330720877 0.995331408346396 16 0.00274501846840607 0.00549003693681213 0.997254981531594 17 0.00172871086660804 0.00345742173321608 0.998271289133392 18 0.00144325759114174 0.00288651518228349 0.998556742408858 19 0.00122090334659374 0.00244180669318749 0.998779096653406 20 0.00146848495853463 0.00293696991706926 0.998531515041465 21 0.00442460060786005 0.0088492012157201 0.99557539939214 22 0.0494182535691818 0.0988365071383637 0.950581746430818 23 0.122949825321167 0.245899650642334 0.877050174678833 24 0.205780665696800 0.411561331393601 0.7942193343032 25 0.262645482674547 0.525290965349095 0.737354517325453 26 0.275818152839461 0.551636305678922 0.72418184716054 27 0.280238363980979 0.560476727961959 0.71976163601902 28 0.296370132573084 0.592740265146168 0.703629867426916 29 0.357042502870794 0.714085005741588 0.642957497129206 30 0.481356657534551 0.962713315069102 0.518643342465449 31 0.542548708877599 0.914902582244802 0.457451291122401 32 0.602714764004183 0.794570471991634 0.397285235995817 33 0.659785489073576 0.680429021852849 0.340214510926424 34 0.711294542312556 0.577410915374888 0.288705457687444 35 0.747594281653219 0.504811436693561 0.252405718346781 36 0.771175063640516 0.457649872718968 0.228824936359484 37 0.784584081952327 0.430831836095347 0.215415918047673 38 0.778395559443088 0.443208881113824 0.221604440556912 39 0.767920173961355 0.464159652077289 0.232079826038645 40 0.764951951352355 0.47009609729529 0.235048048647645 41 0.814207356572179 0.371585286855643 0.185792643427821 42 0.868159759492875 0.263680481014251 0.131840240507125 43 0.860491671789204 0.279016656421593 0.139508328210796 44 0.847351876209052 0.305296247581897 0.152648123790949 45 0.82890952385165 0.342180952296702 0.171090476148351 46 0.804276367586755 0.391447264826489 0.195723632413245 47 0.773567858105317 0.452864283789366 0.226432141894683 48 0.741940074961438 0.516119850077124 0.258059925038562 49 0.741784396552166 0.516431206895668 0.258215603447834 50 0.722028869652633 0.555942260694733 0.277971130347367 51 0.676108147793977 0.647783704412046 0.323891852206023 52 0.814039204454312 0.371921591091375 0.185960795545688 53 0.976506497660424 0.046987004679152 0.023493502339576 54 0.999244131426808 0.00151173714638385 0.000755868573191924 55 0.999302261303071 0.00139547739385773 0.000697738696928867 56 0.999303550388195 0.00139289922360926 0.000696449611804629 57 0.999206635793674 0.00158672841265144 0.00079336420632572 58 0.998822438749152 0.00235512250169633 0.00117756125084816 59 0.999435149827347 0.00112970034530656 0.00056485017265328 60 0.999649691315818 0.000700617368364674 0.000350308684182337 61 0.999627124606463 0.000745750787073208 0.000372875393536604 62 0.999503224406252 0.00099355118749624 0.00049677559374812 63 0.99931406386457 0.00137187227086124 0.000685936135430622 64 0.999715036487097 0.000569927025805686 0.000284963512902843 65 0.9999531751566 9.3649686801446e-05 4.6824843400723e-05 66 0.999999043360861 1.91327827747949e-06 9.56639138739747e-07 67 0.999998901473158 2.19705368359351e-06 1.09852684179675e-06 68 0.999998021592843 3.95681431412567e-06 1.97840715706284e-06 69 0.999995673038673 8.6539226534627e-06 4.32696132673135e-06 70 0.999994446362696 1.11072746084123e-05 5.55363730420614e-06 71 0.99999498991086 1.00201782791256e-05 5.01008913956281e-06 72 0.999992217424153 1.55651516948136e-05 7.7825758474068e-06 73 0.999983046083276 3.39078334475568e-05 1.69539167237784e-05 74 0.999966710107078 6.65797858434153e-05 3.32898929217077e-05 75 0.999935600052623 0.000128799894754135 6.43999473770677e-05 76 0.999879221412224 0.000241557175552472 0.000120778587776236 77 0.999793111479393 0.000413777041214755 0.000206888520607377 78 0.999747779014298 0.000504441971403617 0.000252220985701808 79 0.999587604811718 0.000824790376563922 0.000412395188281961 80 0.999383047680541 0.00123390463891715 0.000616952319458577 81 0.999029646954073 0.00194070609185362 0.000970353045926812 82 0.998823666122312 0.00235266775537656 0.00117633387768828 83 0.998414030649219 0.00317193870156145 0.00158596935078072 84 0.99767476558161 0.00465046883677816 0.00232523441838908 85 0.996374136379544 0.00725172724091241 0.00362586362045620 86 0.994919787054967 0.0101604258900653 0.00508021294503266 87 0.99316637518927 0.0136672496214602 0.00683362481073009 88 0.994792680136 0.0104146397280003 0.00520731986400017 89 0.994200847003175 0.0115983059936508 0.0057991529968254 90 0.992766442821125 0.0144671143577497 0.00723355717887486 91 0.990299951203668 0.0194000975926649 0.00970004879633243 92 0.98529674345099 0.0294065130980187 0.0147032565490093 93 0.978748781076452 0.0425024378470954 0.0212512189235477 94 0.967079109361367 0.0658417812772655 0.0329208906386328 95 0.94641612541251 0.107167749174979 0.0535838745874896 96 0.911977165909062 0.176045668181877 0.0880228340909384 97 0.902713951880386 0.194572096239228 0.097286048119614 98 0.90897194718383 0.182056105632339 0.0910280528161696 99 0.89919474690474 0.201610506190521 0.100805253095260 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 40 0.425531914893617 NOK 5% type I error level 53 0.563829787234043 NOK 10% type I error level 57 0.606382978723404 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/10p5951227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/10p5951227785563.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/1j5ur1227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/1j5ur1227785563.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/2k2z61227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/2k2z61227785563.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/3b5v71227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/3b5v71227785563.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/4mvww1227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/4mvww1227785563.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/5l0tu1227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/5l0tu1227785563.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/6lroo1227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/6lroo1227785563.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/7fxg01227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/7fxg01227785563.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/8z1xt1227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/8z1xt1227785563.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/9rzyj1227785563.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227785755696rkmtj0n6l25f/9rzyj1227785563.ps (open in new window)

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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