Paper model

*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: Thu, 02 Dec 2010 12:04:19 +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/02/t129129141976wihzz02st81s8.htm/, Retrieved Thu, 02 Dec 2010 13:03:54 +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/02/t129129141976wihzz02st81s8.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 «

66 4818 4488 5 73 68 0 4964 1 54 3132 2916 12 58 54 1 3132 1 82 5576 3362 11 68 41 1 2788 1 61 3782 2989 6 62 49 1 3038 1 65 4225 3185 12 65 49 1 3185 1 77 6237 5544 11 81 72 1 5832 1 66 4818 5148 12 73 78 1 5694 1 66 4224 3828 7 64 58 0 3712 1 66 4488 3828 8 68 58 1 3944 1 48 2448 1104 13 51 23 1 1173 1 57 3876 2223 12 68 39 1 2652 1 80 4880 5040 13 61 63 1 3843 1 60 4140 2760 12 69 46 1 3174 1 70 5110 4060 12 73 58 1 4234 1 85 5185 3315 11 61 39 0 2379 1 59 3658 2596 12 62 44 0 2728 1 72 4536 3528 12 63 49 1 3087 1 70 4830 3990 12 69 57 1 3933 1 74 3478 5624 11 47 76 0 3572 1 70 4620 4410 13 66 63 0 4158 1 51 2958 918 9 58 18 1 1044 1 70 4410 2800 11 63 40 0 2520 1 71 4899 4189 11 69 59 1 4071 1 72 4248 4464 11 59 62 0 3658 1 50 2950 3500 9 59 70 1 4130 1 69 4347 4485 11 63 65 0 4095 1 73 4745 4088 12 65 56 0 3640 1 66 4290 2970 12 65 45 1 2925 1 73 5183 4161 10 71 57 0 4047 1 58 3480 2900 12 60 50 1 3000 1 78 6318 3120 12 81 40 0 3240 1 83 5561 4814 12 67 58 1 3886 1 76 5016 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 11 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Vrienden_vinden[t] = + 17.6345996892029 -0.181159874985208Groepsgevoel[t] + 0.00145155830344216InteractieGR_NV[t] + 0.00217752371159492InteractieGR_U[t] + 0.0389399054811559NVC[t] -0.00437109904214467Uitingsangst[t] -0.146541157879546Geslacht[t] -0.00243327707071304InteractieNV_U[t] -2.36746598370249Leeftijd[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) 17.6345996892029 8.850847 1.9924 0.048312 0.024156 Groepsgevoel -0.181159874985208 0.135777 -1.3342 0.184337 0.092168 InteractieGR_NV 0.00145155830344216 0.001962 0.74 0.460563 0.230281 InteractieGR_U 0.00217752371159492 0.001092 1.9945 0.048083 0.024042 NVC 0.0389399054811559 0.145522 0.2676 0.78942 0.39471 Uitingsangst -0.00437109904214467 0.101495 -0.0431 0.965711 0.482855 Geslacht -0.146541157879546 0.313971 -0.4667 0.641431 0.320715 InteractieNV_U -0.00243327707071304 0.001576 -1.544 0.124902 0.062451 Leeftijd -2.36746598370249 1.300442 -1.8205 0.070864 0.035432 Multiple Linear Regression - Regression Statistics Multiple R 0.350079775474221 R-squared 0.122555849196081 Adjusted R-squared 0.0713182345505966 F-TEST (value) 2.39191168527362 F-TEST (DF numerator) 8 F-TEST (DF denominator) 137 p-value 0.0191049602741371 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.73130022144076 Sum Squared Residuals 410.643862576234 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5 10.5435072663380 -5.54350726633796 2 12 10.6353504319692 1.36464956803080 3 11 11.3649286560521 -0.364928656052079 4 6 10.8760465970390 -4.87604659703897 5 12 10.9803700600443 1.01962993995574 6 11 10.9453841059516 0.0546158940484499 7 12 10.0141285060691 1.98587149393088 8 7 10.9838307180645 -3.98383071806446 9 8 10.8117402938128 -2.81174029381285 10 13 11.4134853503813 1.58651464961872 11 12 11.2857447870253 0.714255212974731 12 13 11.4349977879855 1.5650022120145 13 12 11.0329783685788 0.967021631421218 14 12 10.9842047366021 1.01579526339795 15 11 11.0294604595230 -0.0294604595229894 16 12 11.1253188437371 0.874681156262936 17 12 11.0711575425629 0.92884245743706 18 12 11.0063696272288 0.99363037277125 19 11 11.962522423801 -0.962522423800994 20 13 11.0721098486485 1.92789015135146 21 9 11.8136086255253 -2.81360862552530 22 11 11.2308928326117 -0.230892832611686 23 11 11.0141600599458 -0.0141600599457506 24 11 11.2358269862543 -0.23582698625434 25 9 9.90807197249024 -0.908071972490241 26 11 11.0480431260908 -0.0480431260908234 27 12 11.2610076869328 0.738992313067167 28 12 11.0755303113439 0.924469688656142 29 10 11.2946740208515 -1.29467402085151 30 12 10.7975696227058 1.20243037729425 31 12 12.1969534711554 -0.196953471155447 32 12 11.5387730229105 0.46122697708947 33 9 11.3757298206751 -2.37572982067514 34 9 11.1246130241807 -2.12461302418067 35 12 10.8742162507450 1.12578374925498 36 14 11.3736191426869 2.62638085731313 37 12 11.9409468579365 0.0590531420635451 38 11 10.5635743373781 0.436425662621931 39 9 11.1161007889729 -2.11610078897293 40 11 11.1837247804429 -0.183724780442889 41 7 9.44390614943709 -2.44390614943709 42 15 11.1260150507178 3.87398494928216 43 11 10.8005614051278 0.199438594872224 44 12 11.6091530221065 0.390846977893515 45 12 10.8580316896343 1.14196831036571 46 9 11.7529551632901 -2.75295516329012 47 12 10.6708435865344 1.32915641346557 48 11 11.3356938666300 -0.335693866629967 49 11 10.5963447210727 0.403655278927346 50 8 11.2144594572438 -3.21445945724382 51 7 10.4830697611230 -3.48306976112299 52 12 11.6974651796283 0.302534820371736 53 8 11.6294592252584 -3.62945922525844 54 10 10.4163907913601 -0.416390791360069 55 12 10.3517334957703 1.64826650422973 56 15 11.0717980868526 3.92820191314744 57 12 11.5778019786564 0.422198021343571 58 12 11.2584789415087 0.741521058491282 59 12 10.9263026206602 1.07369737933984 60 12 10.6385532881705 1.36144671182951 61 8 9.69360106617084 -1.69360106617084 62 10 11.1769303169177 -1.17693031691771 63 14 12.0840956909697 1.91590430903026 64 10 10.9295821259906 -0.929582125990552 65 12 10.9916008609180 1.00839913908201 66 14 11.0375705512732 2.96242944872684 67 6 11.2833989583816 -5.28339895838157 68 11 10.7598661501961 0.240133849803944 69 10 11.1999714110604 -1.19997141106044 70 14 12.6438121762883 1.35618782371166 71 12 11.0306928284514 0.969307171548573 72 13 11.2166114310221 1.78338856897793 73 11 10.9310975951456 0.0689024048544474 74 11 10.9268411154336 0.073158884566371 75 12 11.1907903939268 0.809209606073198 76 13 10.9235553420421 2.07644465795791 77 12 10.3683384839127 1.63166151608731 78 8 10.2308629658036 -2.23086296580362 79 12 11.3785986526042 0.62140134739584 80 11 11.2255177481915 -0.225517748191538 81 10 11.1395103063947 -1.13951030639467 82 12 11.0058843593364 0.994115640663582 83 11 11.122227310061 -0.122227310060990 84 12 11.0383316469368 0.961668353063216 85 12 11.0089436069678 0.991056393032151 86 10 10.6969990986762 -0.6969990986762 87 12 11.3535183256724 0.646481674327636 88 12 11.0212197572287 0.978780242771261 89 11 11.2985559580985 -0.298555958098510 90 10 10.9543690738741 -0.95436907387409 91 12 11.3195996967482 0.680400303251784 92 11 11.0203031384541 -0.0203031384540535 93 12 10.5037628061629 1.49623719383712 94 12 9.78832916762296 2.21167083237704 95 10 9.78565106371063 0.214348936289374 96 11 11.0497925283835 -0.0497925283834792 97 10 10.9373494642369 -0.937349464236905 98 11 11.0853205359066 -0.0853205359065512 99 11 10.7145439191498 0.285456080850211 100 12 11.2123017785307 0.787698221469252 101 11 10.8794518187594 0.120548181240594 102 11 10.6763871443369 0.323612855663097 103 7 6.76098341371467 0.239016586285329 104 12 10.6916420637751 1.30835793622489 105 8 8.2390165862852 -0.239016586285199 106 10 11.0130575946915 -1.01305759469147 107 12 11.2154278408711 0.784572159128858 108 11 11.0269899566807 -0.0269899566807348 109 13 11.2411440426891 1.7588559573109 110 9 11.3928956093908 -2.39289560939079 111 11 11.2565965548330 -0.25659655483305 112 13 11.1936190378623 1.80638096213773 113 8 10.7837760268410 -2.78377602684097 114 12 11.1060762019295 0.893923798070503 115 11 10.5840327612704 0.415967238729583 116 11 10.8223003396708 0.177699660329158 117 12 10.8453436698240 1.15465633017597 118 13 11.2891512843394 1.71084871566059 119 11 11.2386203775146 -0.238620377514557 120 10 10.7900008924281 -0.790000892428113 121 10 10.8637324932235 -0.863732493223543 122 10 10.8773544333450 -0.877354433344983 123 12 11.0027709651446 0.99722903485538 124 12 11.1781810349083 0.821818965091681 125 13 10.9281488035798 2.07185119642024 126 11 10.9923563298497 0.00764367015026635 127 11 10.2436462284920 0.756353771507987 128 12 11.0346566094724 0.965343390527581 129 9 10.7078332365051 -1.70783323650509 130 11 11.8009655208978 -0.800965520897843 131 12 10.8651988980684 1.13480110193162 132 12 10.8647762933772 1.13522370662279 133 13 10.8966823060069 2.10331769399306 134 6 10.8447872605399 -4.84478726053989 135 11 11.8123422676052 -0.812342267605178 136 10 11.7635305294252 -1.76353052942524 137 12 11.0948321947216 0.905167805278443 138 11 11.0648559834634 -0.0648559834634111 139 12 12.1150880599938 -0.115088059993787 140 12 11.0813804009708 0.918619599029181 141 7 11.0928525819036 -4.0928525819036 142 12 11.0779090544061 0.922090945593855 143 12 12.1345107291208 -0.134510729120844 144 9 10.9876059933981 -1.98760599339813 145 12 11.1350870680793 0.864912931920652 146 12 11.2122614649071 0.787738535092876 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.997939620240663 0.00412075951867471 0.00206037975933735 13 0.996192961301993 0.00761407739601485 0.00380703869800742 14 0.992776655290362 0.0144466894192755 0.00722334470963775 15 0.993632873485729 0.0127342530285420 0.00636712651427102 16 0.998554746025826 0.00289050794834868 0.00144525397417434 17 0.99711024026335 0.00577951947329889 0.00288975973664945 18 0.995185283931181 0.00962943213763788 0.00481471606881894 19 0.991964626379097 0.0160707472418069 0.00803537362090344 20 0.997345635275974 0.00530872944805231 0.00265436472402615 21 0.998782318967343 0.0024353620653129 0.00121768103265645 22 0.998044921391485 0.00391015721702953 0.00195507860851476 23 0.996569820978755 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0.000846568770925875 74 0.998717001871973 0.00256599625605427 0.00128299812802714 75 0.998238896878846 0.00352220624230708 0.00176110312115354 76 0.9984916645082 0.00301667098359811 0.00150833549179905 77 0.998476715813625 0.00304656837275031 0.00152328418637516 78 0.998880856457052 0.00223828708589647 0.00111914354294824 79 0.998355086588833 0.00328982682233384 0.00164491341116692 80 0.997540681475431 0.00491863704913752 0.00245931852456876 81 0.997188888269206 0.00562222346158775 0.00281111173079387 82 0.996366657833405 0.00726668433319006 0.00363334216659503 83 0.994798989377067 0.0104020212458659 0.00520101062293293 84 0.993312703127561 0.0133745937448774 0.00668729687243872 85 0.991566736287316 0.0168665274253678 0.00843326371268388 86 0.990164778472198 0.0196704430556037 0.00983522152780186 87 0.987487025999432 0.0250259480011367 0.0125129740005683 88 0.983530102325515 0.0329397953489711 0.0164698976744855 89 0.977800423753176 0.0443991524936484 0.0221995762468242 90 0.97317358889332 0.0536528222133605 0.0268264111066802 91 0.96663330738079 0.0667333852384205 0.0333666926192102 92 0.955296078566628 0.0894078428667435 0.0447039214333717 93 0.949596223902418 0.100807552195164 0.050403776097582 94 0.961997113798207 0.0760057724035854 0.0380028862017927 95 0.949424067616764 0.101151864766471 0.0505759323832355 96 0.933339299998874 0.133321400002253 0.0666607000011263 97 0.919695959425608 0.160608081148784 0.080304040574392 98 0.901606007822567 0.196787984354865 0.0983939921774327 99 0.878795254520672 0.242409490958655 0.121204745479328 100 0.868137894730757 0.263724210538486 0.131862105269243 101 0.835396335650876 0.329207328698249 0.164603664349124 102 0.79873607972216 0.402527840555681 0.201263920277840 103 0.761615758741757 0.476768482516487 0.238384241258243 104 0.722884457903941 0.554231084192118 0.277115542096059 105 0.672791793510919 0.654416412978162 0.327208206489081 106 0.631852928524099 0.736294142951802 0.368147071475901 107 0.613044919976284 0.773910160047432 0.386955080023716 108 0.555783375533878 0.888433248932244 0.444216624466122 109 0.527292369961271 0.94541526007746 0.47270763003873 110 0.515780237221275 0.96843952555745 0.484219762778725 111 0.460333088317568 0.920666176635136 0.539666911682432 112 0.447018702789825 0.89403740557965 0.552981297210175 113 0.538787795303521 0.922424409392959 0.461212204696479 114 0.492563426763099 0.985126853526197 0.507436573236901 115 0.428548337022673 0.857096674045346 0.571451662977327 116 0.367893812264555 0.735787624529109 0.632106187735445 117 0.342745786010716 0.685491572021432 0.657254213989284 118 0.379660501244645 0.759321002489289 0.620339498755355 119 0.318495030788681 0.636990061577361 0.68150496921132 120 0.271510900362703 0.543021800725405 0.728489099637297 121 0.230911885541331 0.461823771082661 0.76908811445867 122 0.190298133198622 0.380596266397243 0.809701866801378 123 0.152370300090581 0.304740600181163 0.847629699909419 124 0.114119474030615 0.22823894806123 0.885880525969385 125 0.154834436991504 0.309668873983008 0.845165563008496 126 0.112140923972026 0.224281847944051 0.887859076027974 127 0.091430470532251 0.182860941064502 0.908569529467749 128 0.0766120622774263 0.153224124554853 0.923387937722574 129 0.0768943362496098 0.153788672499220 0.92310566375039 130 0.0495037131369904 0.0990074262739808 0.95049628686301 131 0.0386551719929272 0.0773103439858545 0.961344828007073 132 0.0268097840379371 0.0536195680758742 0.973190215962063 133 0.0137999642365009 0.0275999284730018 0.9862000357635 134 0.220997260609031 0.441994521218063 0.779002739390969 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.219512195121951 NOK 5% type I error level 57 0.463414634146341 NOK 10% type I error level 74 0.601626016260163 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/10zpu41291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/10zpu41291291447.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/1s6fb1291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/1s6fb1291291447.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/2s6fb1291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/2s6fb1291291447.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/3lgww1291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/3lgww1291291447.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/4lgww1291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/4lgww1291291447.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/5lgww1291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/5lgww1291291447.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/6epvh1291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/6epvh1291291447.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/7epvh1291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/7epvh1291291447.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/8oyck1291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/8oyck1291291447.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/9oyck1291291447.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t129129141976wihzz02st81s8/9oyck1291291447.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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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