Mini-tutorial Interaction effect Gender

*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: Tue, 23 Nov 2010 11:55:50 +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/t1290513279f66e4263rxosqgx.htm/, Retrieved Tue, 23 Nov 2010 12:54:43 +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/t1290513279f66e4263rxosqgx.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 «

0 69 26 0 9 0 15 0 6 0 25 0 25 0 1 53 20 20 9 9 15 15 6 6 25 25 24 24 1 43 21 21 9 9 14 14 13 13 19 19 21 21 0 60 31 0 14 0 10 0 8 0 18 0 23 0 1 49 21 21 8 8 10 10 7 7 18 18 17 17 1 62 18 18 8 8 12 12 9 9 22 22 19 19 1 45 26 26 11 11 18 18 5 5 29 29 18 18 1 50 22 22 10 10 12 12 8 8 26 26 27 27 1 75 22 22 9 9 14 14 9 9 25 25 23 23 1 82 29 29 15 15 18 18 11 11 23 23 23 23 0 60 15 0 14 0 9 0 8 0 23 0 29 0 1 59 16 16 11 11 11 11 11 11 23 23 21 21 1 21 24 24 14 14 11 11 12 12 24 24 26 26 1 62 17 17 6 6 17 17 8 8 30 30 25 25 0 54 19 0 20 0 8 0 7 0 19 0 25 0 1 47 22 22 9 9 16 16 9 9 24 24 23 23 1 59 31 31 10 10 21 21 12 12 32 32 26 26 0 37 28 0 8 0 24 0 20 0 30 0 20 0 0 43 38 0 11 0 21 0 7 0 29 0 29 0 1 48 26 26 14 14 14 14 8 8 17 17 24 24 0 79 25 0 11 0 7 0 8 0 25 0 23 0 0 62 25 0 16 0 18 0 16 0 26 0 24 0 1 16 29 29 14 14 18 18 10 10 26 26 30 30 0 38 28 0 11 0 13 0 6 0 25 0 22 0 1 58 15 15 11 11 11 11 8 8 23 23 22 22 0 60 18 0 12 0 13 0 9 0 21 0 13 0 0 67 21 0 9 0 13 0 9 0 19 0 24 0 0 55 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 10 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Anxiety[t] = + 72.6760102555913 -18.2843507953355Gender[t] -0.0886231903464793Concern[t] -0.397485256901703Concern_G[t] -0.603213979753444Doubts[t] + 1.07713437867055Doubts_G[t] -0.0391215489632878Expectations[t] + 1.21866622210472Expectations_G[t] -0.139015564284004Criticism[t] -1.39668931547673Criticism_G[t] -0.615310967424411Perstandards[t] + 1.19279724420671Perstandards_G[t] + 0.248985111510631Organization[t] -0.877901085869994Organization_G[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) 72.6760102555913 14.773011 4.9195 2e-06 1e-06 Gender -18.2843507953355 18.840008 -0.9705 0.333492 0.166746 Concern -0.0886231903464793 0.391181 -0.2266 0.821107 0.410553 Concern_G -0.397485256901703 0.500598 -0.794 0.428546 0.214273 Doubts -0.603213979753444 0.724333 -0.8328 0.406405 0.203202 Doubts_G 1.07713437867055 0.919125 1.1719 0.243251 0.121626 Expectations -0.0391215489632878 0.680796 -0.0575 0.954258 0.477129 Expectations_G 1.21866622210472 0.84632 1.44 0.152144 0.076072 Criticism -0.139015564284004 0.845179 -0.1645 0.869593 0.434797 Criticism_G -1.39668931547673 1.054501 -1.3245 0.187526 0.093763 Perstandards -0.615310967424411 0.521018 -1.181 0.239642 0.119821 Perstandards_G 1.19279724420671 0.651105 1.832 0.069114 0.034557 Organization 0.248985111510631 0.464372 0.5362 0.5927 0.29635 Organization_G -0.877901085869994 0.6355 -1.3814 0.169378 0.084689 Multiple Linear Regression - Regression Statistics Multiple R 0.347149841668879 R-squared 0.120513012570728 Adjusted R-squared 0.0376627891172459 F-TEST (value) 1.45458886587545 F-TEST (DF numerator) 13 F-TEST (DF denominator) 138 p-value 0.14242126291324 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.1720315014651 Sum Squared Residuals 23943.3331148311 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 69 54.3638184708039 14.6361815291961 2 53 56.756888459036 -3.75688845903594 3 43 42.7631314427054 0.236868557294558 4 60 54.6314157855024 5.36858421449755 5 49 48.7234392504422 0.276560749557806 6 62 50.5215573373586 11.4784426626414 7 45 65.9458584258515 -20.9458584258515 8 50 48.3392865382151 1.66071346178488 9 75 50.6269282264753 24.3730717735247 10 82 50.5594878687203 31.4405121312797 11 60 54.5058642119511 5.49413578804887 12 59 47.984235324007 11.015764675993 13 21 41.4143304679976 -20.4143304679976 14 62 58.3396476003427 3.66035239965732 15 54 52.175528108947 1.82447189105304 16 47 52.4085312959759 -5.40853129597586 17 59 52.5312266872646 6.46877331273542 18 37 48.1699938345437 -11.1699938345437 19 43 50.2548639454205 -7.25486394542053 20 48 47.3389951232111 0.661004876788916 21 79 52.7829847417606 26.2170152582394 22 62 47.8581274342114 14.1418725657886 23 16 48.9513193593953 -32.9513193593953 24 38 52.3114318939989 -14.3114318939989 25 58 52.448542436178 5.551457563822 26 60 52.3977809909602 7.60221900903984 27 67 57.9110115206468 9.08898847935319 28 55 46.7014365860183 8.29856341398165 29 47 43.3355531005602 3.66444689943983 30 59 55.9464219714914 3.0535780285086 31 49 46.948994854905 2.05100514509504 32 47 57.1243962700847 -10.1243962700847 33 57 46.1833733211462 10.8166266788538 34 39 51.792204054069 -12.792204054069 35 49 47.3457317387206 1.65426826127944 36 26 42.6400335417166 -16.6400335417166 37 53 51.4152831145428 1.58471688545724 38 75 57.4198911602466 17.5801088397534 39 65 46.874705028133 18.125294971867 40 49 59.9868724580845 -10.9868724580845 41 48 49.6619347117771 -1.66193471177712 42 45 49.2481068182347 -4.2481068182347 43 31 55.4207386664032 -24.4207386664032 44 61 57.8539364308592 3.14606356914077 45 49 54.7525815985101 -5.75258159851009 46 69 49.4680779838661 19.5319220161339 47 54 50.1170080782535 3.88299192174652 48 80 59.0828824449033 20.9171175550967 49 57 52.5578303916966 4.4421696083034 50 34 51.6337692068881 -17.6337692068881 51 69 59.3519568036842 9.6480431963158 52 44 48.5058719132579 -4.50587191325787 53 70 57.0977346550605 12.9022653449395 54 51 59.6312421533678 -8.63124215336784 55 66 50.0130019927011 15.9869980072989 56 18 45.3696024007475 -27.3696024007475 57 74 48.9037047927905 25.0962952072095 58 59 55.373893696294 3.62610630370595 59 48 57.467058825437 -9.46705882543698 60 55 52.1915748878096 2.80842511219037 61 44 49.3027241198105 -5.30272411981049 62 56 50.8183255513281 5.18167444867189 63 65 56.7285753247021 8.27142467529785 64 77 55.3186778611088 21.6813221388912 65 46 44.9660624897309 1.03393751026914 66 70 55.4770561053846 14.5229438946154 67 39 54.0082059736741 -15.0082059736741 68 55 56.1357794838725 -1.13577948387251 69 44 59.3705363221604 -15.3705363221604 70 45 59.994862852241 -14.994862852241 71 45 53.8081247876397 -8.80812478763971 72 49 54.7268609575506 -5.72686095755061 73 65 48.1788015862795 16.8211984137205 74 45 50.1788692358464 -5.17886923584638 75 71 54.3201852845208 16.6798147154792 76 48 49.3032560816701 -1.30325608167011 77 41 42.1853056644682 -1.18530566446815 78 40 53.7050932937603 -13.7050932937603 79 64 52.507566700967 11.492433299033 80 56 54.4318807995962 1.56811920040381 81 52 56.801893851142 -4.80189385114196 82 41 46.0016179378135 -5.0016179378135 83 42 52.6101509812274 -10.6101509812274 84 54 61.3719929802723 -7.37199298027229 85 40 47.6858441527095 -7.68584415270948 86 40 49.2862248304236 -9.2862248304236 87 51 56.2286862622773 -5.22868626227735 88 48 60.3782975014772 -12.3782975014772 89 80 54.7633509762218 25.2366490237782 90 38 56.3554706277557 -18.3554706277557 91 57 56.4178544845084 0.582145515491567 92 28 41.4137278579547 -13.4137278579547 93 51 53.1227861404026 -2.12278614040263 94 46 53.7161123103079 -7.71611231030791 95 58 57.3261414433805 0.673858556619482 96 67 51.3196932142173 15.6803067857827 97 72 44.9981539706822 27.0018460293178 98 26 49.1717129113081 -23.1717129113081 99 54 53.1816787688382 0.818321231161827 100 53 57.9551873231052 -4.95518732310525 101 64 49.7722165380226 14.2277834619774 102 47 52.7929689401532 -5.79296894015319 103 43 57.0417970305472 -14.0417970305472 104 66 49.6600438087346 16.3399561912654 105 54 52.3062501181034 1.6937498818966 106 62 58.1121029769897 3.88789702301025 107 52 50.0602522771618 1.93974772283823 108 64 52.6199354856922 11.3800645143078 109 55 52.136149087221 2.86385091277905 110 57 52.8483706237815 4.15162937621852 111 74 55.1368991665201 18.8631008334799 112 32 47.616619486964 -15.616619486964 113 38 54.5538279595515 -16.5538279595515 114 66 54.7906960849073 11.2093039150927 115 37 56.1293465565965 -19.1293465565965 116 26 47.2855996360277 -21.2855996360277 117 64 52.9498859500895 11.0501140499105 118 28 45.4038094436904 -17.4038094436904 119 66 58.6679728782566 7.3320271217434 120 65 54.1547378598272 10.8452621401728 121 48 48.3190725548548 -0.31907255485481 122 44 55.9584786217907 -11.9584786217907 123 64 58.8418847891997 5.15811521080032 124 39 45.5953916958758 -6.59539169587577 125 50 51.4220760158187 -1.42207601581875 126 66 49.9094361148359 16.0905638851641 127 48 52.6106421932279 -4.6106421932279 128 70 55.7424149226492 14.2575850773508 129 66 55.2961290735578 10.7038709264422 130 61 52.4407843589768 8.55921564102318 131 31 48.6628685228663 -17.6628685228663 132 61 52.6162728602939 8.38372713970613 133 54 40.3170119392343 13.6829880607657 134 34 40.77515564456 -6.77515564455995 135 62 60.2394803000263 1.76051969997375 136 47 53.9750292190912 -6.97502921909117 137 52 50.7425227201104 1.25747727988958 138 37 55.2124921360615 -18.2124921360615 139 46 48.2158750040394 -2.21587500403941 140 38 51.7674637360396 -13.7674637360396 141 63 52.448542436178 10.551457563822 142 34 54.21631513015 -20.21631513015 143 46 47.6041337594669 -1.60413375946688 144 40 47.3195699213459 -7.31956992134593 145 30 47.6858441527095 -17.6858441527095 146 35 48.8001238315276 -13.8001238315276 147 51 48.3190725548548 2.68092744514519 148 56 55.0940722754602 0.905927724539765 149 68 50.8080556066677 17.1919443933323 150 39 48.8113551555407 -9.81135515554065 151 44 56.3746342520892 -12.3746342520892 152 58 48.8113551555407 9.18864484445935 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.993011189927822 0.0139776201443569 0.00698881007217847 18 0.98409250052893 0.0318149989421398 0.0159074994710699 19 0.967611931080054 0.0647761378398914 0.0323880689199457 20 0.946504245355266 0.106991509289469 0.0534957546447343 21 0.924863205718669 0.150273588562663 0.0751367942813315 22 0.947476654604437 0.105046690791126 0.052523345395563 23 0.986785250249528 0.026429499500944 0.013214749750472 24 0.988967568736801 0.0220648625263985 0.0110324312631992 25 0.982004059488908 0.0359918810221842 0.0179959405110921 26 0.978016852696771 0.0439662946064571 0.0219831473032286 27 0.966708522497636 0.0665829550047288 0.0332914775023644 28 0.951227384683958 0.0975452306320846 0.0487726153160423 29 0.930148832090908 0.139702335818183 0.0698511679090917 30 0.903415122008511 0.193169755982978 0.0965848779914889 31 0.875502607226864 0.248994785546271 0.124497392773136 32 0.874062694154808 0.251874611690385 0.125937305845192 33 0.864689551472098 0.270620897055804 0.135310448527902 34 0.899669505928725 0.200660988142549 0.100330494071275 35 0.867545579731752 0.264908840536497 0.132454420268248 36 0.858205122074806 0.283589755850388 0.141794877925194 37 0.819415736489875 0.36116852702025 0.180584263510125 38 0.829587624476929 0.340824751046142 0.170412375523071 39 0.857827692829858 0.284344614340283 0.142172307170142 40 0.837334583935858 0.325330832128284 0.162665416064142 41 0.79853024261408 0.402939514771839 0.20146975738592 42 0.760336248466846 0.479327503066307 0.239663751533154 43 0.858842159920858 0.282315680158284 0.141157840079142 44 0.823436783953887 0.353126432092227 0.176563216046114 45 0.787486694615511 0.425026610768977 0.212513305384488 46 0.801153996295177 0.397692007409647 0.198846003704823 47 0.764631825723023 0.470736348553955 0.235368174276977 48 0.792697992771212 0.414604014457575 0.207302007228788 49 0.752061918576068 0.495876162847864 0.247938081423932 50 0.77248527112654 0.455029457746921 0.22751472887346 51 0.758844211689047 0.482311576621906 0.241155788310953 52 0.761560891487553 0.476878217024893 0.238439108512447 53 0.748002448828933 0.503995102342135 0.251997551171068 54 0.742337441401521 0.515325117196958 0.257662558598479 55 0.76908732312433 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0.249753281929241 108 0.722289379879221 0.555421240241558 0.277710620120779 109 0.676155056211538 0.647689887576923 0.323844943788462 110 0.625793272674862 0.748413454650275 0.374206727325138 111 0.667074436112503 0.665851127774995 0.332925563887498 112 0.689763140094874 0.620473719810253 0.310236859905126 113 0.708621907715262 0.582756184569475 0.291378092284737 114 0.667938539895975 0.66412292020805 0.332061460104025 115 0.646886884055638 0.706226231888724 0.353113115944362 116 0.74725266692761 0.505494666144779 0.252747333072389 117 0.722139331172905 0.55572133765419 0.277860668827095 118 0.764163997707593 0.471672004584814 0.235836002292407 119 0.730826707737409 0.538346584525182 0.269173292262591 120 0.693171687906981 0.613656624186037 0.306828312093019 121 0.624198189317981 0.751603621364038 0.375801810682019 122 0.614192461153408 0.771615077693184 0.385807538846592 123 0.594430251244949 0.8111394975101 0.40556974875505 124 0.537478720406488 0.925042559187023 0.462521279593512 125 0.455831809298588 0.911663618597177 0.544168190701412 126 0.455880597656718 0.911761195313436 0.544119402343282 127 0.631488340992976 0.737023318014048 0.368511659007024 128 0.545898253825565 0.90820349234887 0.454101746174435 129 0.450936686695437 0.901873373390873 0.549063313304563 130 0.451379583797849 0.902759167595698 0.548620416202151 131 0.537093601376273 0.925812797247454 0.462906398623727 132 0.419605588911661 0.839211177823322 0.580394411088339 133 0.387109928355542 0.774219856711083 0.612890071644458 134 0.268599426161481 0.537198852322962 0.731400573838519 135 0.157151935272355 0.31430387054471 0.842848064727645 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 6 0.0504201680672269 NOK 10% type I error level 9 0.0756302521008403 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/106g0t1290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/106g0t1290513336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/1zflz1290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/1zflz1290513336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/2ap221290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/2ap221290513336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/3ap221290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/3ap221290513336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/4ap221290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/4ap221290513336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/5ap221290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/5ap221290513336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/6lg151290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/6lg151290513336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/7vpiq1290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/7vpiq1290513336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/8vpiq1290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/8vpiq1290513336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/9vpiq1290513336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290513279f66e4263rxosqgx/9vpiq1290513336.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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