Hypothese testing paper: connected goed

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 23 Nov 2010 09:44:30 +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/Nov/23/t1290505386fa9taacrq2idgme.htm/, Retrieved Tue, 23 Nov 2010 10:43:06 +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/Nov/23/t1290505386fa9taacrq2idgme.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:

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14 12 41 18 11 39 11 14 30 12 12 31 16 21 34 18 12 35 14 22 39 14 11 34 15 10 36 15 13 37 17 10 38 19 8 36 10 15 38 16 14 39 18 10 33 14 14 32 14 14 36 17 11 38 14 10 39 16 13 32 18 7 32 11 14 31 14 12 39 12 14 37 17 11 39 9 9 41 16 11 36 14 15 33 15 14 33 11 13 34 16 9 31 13 15 27 17 10 37 15 11 34 14 13 34 16 8 32 9 20 29 15 12 36 17 10 29 13 10 35 15 9 37 16 14 34 16 8 38 12 14 35 12 11 38 11 13 37 15 9 38 15 11 33 17 15 36 13 11 38 16 10 32 14 14 32 11 18 32 12 14 34 12 11 32 15 12 37 16 13 39 15 9 29 12 10 37 12 15 35 8 20 30 13 12 38 11 12 34 14 14 31 15 13 34 10 11 35 11 17 36 12 12 30 15 13 39 15 14 35 14 13 38 16 15 31 15 13 34 15 10 38 13 11 34 12 19 39 17 13 37 13 17 34 15 13 28 13 9 37 15 11 33 16 10 37 15 9 35 16 12 37 15 12 32 14 13 33 15 13 38 14 12 33 13 15 29 7 22 33 17 13 31 13 15 36 15 13 35 14 15 32 13 10 29 16 11 39 12 16 37 14 11 35 17 11 37 15 10 32 17 10 38 12 16 37 16 12 36 11 11 32 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Conn[t] = + 33.2324706807634 + 0.158472808251303Happ[t] -0.0645138156938056Depr[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) 33.2324706807634 2.822206 11.7754 0 0 Happ 0.158472808251303 0.134913 1.1746 0.241898 0.120949 Depr -0.0645138156938056 0.099608 -0.6477 0.518127 0.259064 Multiple Linear Regression - Regression Statistics Multiple R 0.151458819815008 R-squared 0.0229397740997551 Adjusted R-squared 0.0106497083651609 F-TEST (value) 1.8665298131957 F-TEST (DF numerator) 2 F-TEST (DF denominator) 159 p-value 0.158032381913386 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.35710886629123 Sum Squared Residuals 1791.95861048086 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 41 34.6769242079561 6.3230757920439 2 39 35.375329256655 3.624670743345 3 30 34.0724781518145 -4.07247815181446 4 31 34.3599785914534 -3.35997859145338 5 34 34.4132454832143 -0.413245483214339 6 35 35.3108154409612 -0.310815440961195 7 39 34.0317860510179 4.96821394898207 8 34 34.7414380236498 -0.741438023649788 9 36 34.9644246475949 1.03557535240510 10 37 34.7708832005135 2.22911679948652 11 38 35.2813702640975 2.71862973590250 12 36 35.7273435119877 0.272656488012280 13 38 33.8494915278694 4.15050847213065 14 39 34.864842193071 4.13515780692902 15 33 35.4398430723488 -2.43984307234881 16 32 34.5478965765684 -2.54789657656837 17 36 34.5478965765684 1.45210342343163 18 38 35.2168564484037 2.7831435515963 19 39 34.8059518393436 4.19404816065641 20 32 34.9293560087648 -2.92935600876478 21 32 35.6333845194302 -3.63338451943022 22 31 34.0724781518145 -3.07247815181446 23 39 34.676924207956 4.32307579204402 24 37 34.2309509600658 2.76904903993424 25 39 35.2168564484037 3.7831435515963 26 41 34.0781016137809 6.92189838621912 27 36 35.0583836401524 0.941616359847606 28 33 34.4833827608746 -1.48338276087457 29 33 34.7063693848197 -1.70636938481967 30 34 34.1369919675083 -0.136991967508267 31 31 35.18741127154 -4.18741127154001 32 27 34.3249099526233 -7.32490995262326 33 37 35.2813702640975 1.71862973590250 34 34 34.8999108319011 -0.899910831901091 35 34 34.6124103922622 -0.612410392262177 36 32 35.2519250872338 -3.25192508723381 37 29 33.368449641149 -4.36844964114902 38 36 34.8353970162073 1.16460298379271 39 29 35.2813702640975 -6.2813702640975 40 35 34.6474790310923 0.35252096890771 41 37 35.0289384632887 1.97106153671130 42 34 34.864842193071 -0.864842193070978 43 38 35.2519250872338 2.74807491276619 44 35 34.2309509600658 0.769049039934235 45 38 34.4244924071472 3.57550759285282 46 37 34.1369919675083 2.86300803249173 47 38 35.0289384632887 2.9710615367113 48 33 34.8999108319011 -1.89991083190109 49 36 34.9588011856285 1.04119881437152 50 38 34.5829652153985 3.41703478460152 51 32 35.1228974558462 -3.1228974558462 52 32 34.5478965765684 -2.54789657656837 53 32 33.8144228890392 -1.81442288903924 54 34 34.2309509600658 -0.230950960065765 55 32 34.4244924071472 -2.42449240714718 56 37 34.8353970162073 2.16460298379271 57 39 34.9293560087648 4.07064399123522 58 29 35.0289384632887 -6.0289384632887 59 37 34.489006222841 2.51099377715901 60 35 34.1664371443720 0.833562855628041 61 30 33.2099768328977 -3.20997683289772 62 38 34.5184513997047 3.48154860029532 63 34 34.2015057832021 -0.201505783202073 64 31 34.5478965765684 -3.54789657656837 65 34 34.7708832005135 -0.77088320051348 66 35 34.1075467906446 0.892453209355425 67 36 33.8789367047330 2.12106329526696 68 30 34.3599785914534 -4.35997859145338 69 39 34.7708832005135 4.22911679948652 70 35 34.7063693848197 0.293630615180326 71 38 34.6124103922622 3.38758960773782 72 31 34.8003283773772 -3.80032837737717 73 34 34.7708832005135 -0.77088320051348 74 38 34.9644246475949 3.03557535240510 75 34 34.5829652153985 -0.582965215398485 76 39 33.9083818815967 5.09161811840326 77 37 35.0878288170161 1.91217118298391 78 34 34.1958823212357 -0.195882321235651 79 28 34.7708832005135 -6.77088320051348 80 37 34.7119928467861 2.28800715321390 81 33 34.8999108319011 -1.89991083190109 82 37 35.1228974558462 1.8771025441538 83 35 35.0289384632887 -0.0289384632887022 84 37 34.9938698244586 2.00613017554141 85 32 34.8353970162073 -2.83539701620729 86 33 34.6124103922622 -1.61241039226218 87 38 34.7708832005135 3.22911679948652 88 33 34.676924207956 -1.67692420795598 89 29 34.3249099526233 -5.32490995262326 90 33 32.9224763932588 0.0775236067411961 91 31 35.0878288170161 -4.08782881701609 92 36 34.3249099526233 1.67509004737674 93 35 34.7708832005135 0.22911679948652 94 32 34.4833827608746 -2.48338276087457 95 29 34.6474790310923 -5.64747903109229 96 39 35.0583836401524 3.94161635984761 97 37 34.1019233286782 2.89807667132185 98 35 34.7414380236498 0.258561976350212 99 37 35.2168564484037 1.78314355159630 100 32 34.9644246475949 -2.96442464759490 101 38 35.2813702640975 2.71862973590250 102 37 34.1019233286782 2.89807667132185 103 36 34.9938698244586 1.00613017554141 104 32 34.2660195988959 -2.26601959889588 105 33 34.5773417534321 -1.57734175343206 106 40 33.4329634568428 6.56703654315717 107 38 35.0583836401524 2.94161635984761 108 41 34.5773417534321 6.42265824656794 109 36 33.8494915278694 2.15050847213065 110 43 33.2688671866251 9.7311328133749 111 30 34.7063693848197 -4.70636938481967 112 31 34.0079643361207 -3.00796433612066 113 32 34.5829652153985 -2.58296521539848 114 32 34.4833827608746 -2.48338276087457 115 37 35.3108154409612 1.68918455903880 116 37 35.1228974558462 1.8771025441538 117 33 34.5478965765684 -1.54789657656837 118 34 34.6124103922622 -0.612410392262177 119 33 34.8704656550374 -1.8704656550374 120 38 34.4833827608746 3.51661723912543 121 33 34.1664371443720 -1.16643714437196 122 31 34.5478965765684 -3.54789657656837 123 38 34.8999108319011 3.10008916809891 124 37 35.0934522789825 1.90654772101749 125 33 34.8999108319011 -1.89991083190109 126 31 34.5829652153985 -3.58296521539848 127 39 35.4103978954851 3.58960210451489 128 44 35.2813702640975 8.7186297359025 129 33 35.5338020649063 -2.53380206490630 130 35 34.7708832005135 0.22911679948652 131 32 34.5829652153985 -2.58296521539848 132 28 33.368449641149 -5.36844964114902 133 40 34.9644246475949 5.0355753524051 134 27 34.6418555691259 -7.64185556912587 135 37 34.8353970162073 2.16460298379271 136 32 34.864842193071 -2.86484219307098 137 28 33.4918538105702 -5.49185381057021 138 34 34.5478965765684 -0.547896576568371 139 30 33.9434505204269 -3.94345052042685 140 35 34.8999108319011 0.100089168098909 141 31 34.5184513997047 -3.51845139970468 142 32 34.9644246475949 -2.96442464759490 143 30 34.864842193071 -4.86484219307098 144 30 34.676924207956 -4.67692420795598 145 31 34.8353970162073 -3.83539701620729 146 40 35.0583836401524 4.94161635984761 147 32 34.9938698244586 -2.99386982445859 148 36 34.1369919675083 1.86300803249173 149 32 34.4244924071472 -2.42449240714718 150 35 33.4329634568428 1.56703654315717 151 38 34.9938698244586 3.00613017554141 152 42 34.1958823212357 7.80411767876435 153 34 35.18741127154 -1.18741127154001 154 35 34.3599785914534 0.640021408546624 155 35 33.4329634568428 1.56703654315717 156 33 34.1313685055418 -1.13136850554185 157 36 34.3249099526233 1.67509004737674 158 32 34.5478965765684 -2.54789657656837 159 33 35.5338020649063 -2.53380206490630 160 34 34.7119928467861 -0.711992846786096 161 32 33.9728956972905 -1.97289569729054 162 34 34.2603961369295 -0.260396136929457 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.824448587133092 0.351102825733817 0.175551412866908 7 0.875152960676748 0.249694078646503 0.124847039323252 8 0.795971857734682 0.408056284530636 0.204028142265318 9 0.701732516621468 0.596534966757065 0.298267483378532 10 0.611017537271304 0.777964925457392 0.388982462728696 11 0.511749282205377 0.976501435589246 0.488250717794623 12 0.454415908618023 0.908831817236046 0.545584091381977 13 0.530935591564321 0.938128816871359 0.469064408435679 14 0.491242875238055 0.98248575047611 0.508757124761945 15 0.51058527216198 0.97882945567604 0.48941472783802 16 0.51955046964345 0.9608990607131 0.48044953035655 17 0.440164302730596 0.880328605461192 0.559835697269404 18 0.386944697269373 0.773889394538746 0.613055302730627 19 0.407591554891178 0.815183109782357 0.592408445108822 20 0.446229342495595 0.892458684991191 0.553770657504404 21 0.471045273675119 0.942090547350239 0.528954726324881 22 0.479442074056507 0.958884148113015 0.520557925943493 23 0.504783754310585 0.99043249137883 0.495216245689415 24 0.463869075301882 0.927738150603763 0.536130924698118 25 0.453511366525574 0.907022733051149 0.546488633474426 26 0.601659888169335 0.796680223661329 0.398340111830665 27 0.540267140200858 0.919465719598284 0.459732859799142 28 0.515595421990577 0.968809156018846 0.484404578009423 29 0.490069597003825 0.98013919400765 0.509930402996175 30 0.441744612437798 0.883489224875596 0.558255387562202 31 0.500542668047808 0.998914663904384 0.499457331952192 32 0.74574884769068 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0.135726894205412 0.271453788410825 0.864273105794588 86 0.117403655365431 0.234807310730863 0.882596344634569 87 0.115468656692939 0.230937313385878 0.884531343307061 88 0.0994486905604619 0.198897381120924 0.900551309439538 89 0.131295504176331 0.262591008352661 0.86870449582367 90 0.108615780810935 0.217231561621869 0.891384219189065 91 0.119664056457202 0.239328112914403 0.880335943542798 92 0.103413088916294 0.206826177832588 0.896586911083706 93 0.084362898009782 0.168725796019564 0.915637101990218 94 0.0765553897289924 0.153110779457985 0.923444610271008 95 0.106657132852256 0.213314265704511 0.893342867147744 96 0.113992690389063 0.227985380778126 0.886007309610937 97 0.108188436653545 0.21637687330709 0.891811563346455 98 0.0885780746693391 0.177156149338678 0.91142192533066 99 0.0758357275801614 0.151671455160323 0.924164272419839 100 0.0709462342507464 0.141892468501493 0.929053765749254 101 0.0656544055826058 0.131308811165212 0.934345594417394 102 0.0617573996853415 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0.434913081506413 0.782543459246793 120 0.239518909783081 0.479037819566163 0.760481090216919 121 0.203417822179800 0.406835644359599 0.7965821778202 122 0.195810086375088 0.391620172750175 0.804189913624912 123 0.190777575762711 0.381555151525422 0.809222424237289 124 0.163552714740272 0.327105429480545 0.836447285259728 125 0.140160080264687 0.280320160529373 0.859839919735313 126 0.143685255257322 0.287370510514644 0.856314744742678 127 0.140649913467957 0.281299826935914 0.859350086532043 128 0.414768472246691 0.829536944493382 0.585231527753309 129 0.370559650443899 0.741119300887799 0.6294403495561 130 0.324507768206085 0.64901553641217 0.675492231793915 131 0.300534883631935 0.601069767263871 0.699465116368065 132 0.3480235160605 0.696047032121 0.6519764839395 133 0.446126655933017 0.892253311866035 0.553873344066983 134 0.613052195694028 0.773895608611944 0.386947804305972 135 0.604770789139637 0.790458421720725 0.395229210860363 136 0.558269803994179 0.883460392011643 0.441730196005821 137 0.675725578579409 0.648548842841183 0.324274421420592 138 0.612983289171732 0.774033421656537 0.387016710828268 139 0.65252256144601 0.69495487710798 0.34747743855399 140 0.596363863701752 0.807272272596496 0.403636136298248 141 0.58160937950634 0.83678124098732 0.41839062049366 142 0.532347019344282 0.935305961311435 0.467652980655718 143 0.585518054101218 0.828963891797564 0.414481945898782 144 0.641731820909464 0.716536358181072 0.358268179090536 145 0.663147260634487 0.673705478731026 0.336852739365513 146 0.798024853193309 0.403950293613382 0.201975146806691 147 0.77275050423974 0.45449899152052 0.22724949576026 148 0.719975142590934 0.560049714818133 0.280024857409066 149 0.676319807792869 0.647360384414263 0.323680192207131 150 0.583028051891931 0.833943896216138 0.416971948108069 151 0.60508698773666 0.78982602452668 0.39491301226334 152 0.994749219653908 0.0105015606921844 0.0052507803460922 153 0.985440148371776 0.029119703256448 0.014559851628224 154 0.963903083515065 0.0721938329698691 0.0360969164849346 155 0.923832694747377 0.152334610505245 0.0761673052526227 156 0.823890226235867 0.352219547528265 0.176109773764133 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 2 0.0132450331125828 OK 10% type I error level 6 0.0397350993377483 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/10f1xl1290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/10f1xl1290505458.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/18z0r1290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/18z0r1290505458.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/2j9zc1290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/2j9zc1290505458.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/3j9zc1290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/3j9zc1290505458.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/4j9zc1290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/4j9zc1290505458.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/5j9zc1290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/5j9zc1290505458.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/6uihf1290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/6uihf1290505458.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/74ry01290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/74ry01290505458.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/84ry01290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/84ry01290505458.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/9f1xl1290505458.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290505386fa9taacrq2idgme/9f1xl1290505458.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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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