Multiple Regression - Gender - Model 3

*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: Sun, 19 Dec 2010 14:39:35 +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/19/t1292769489ql79mw8mkfsq3im.htm/, Retrieved Sun, 19 Dec 2010 15:38:20 +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/19/t1292769489ql79mw8mkfsq3im.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:

elly.decuyper@student.lessius.eu

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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1 1 4 4 5 5 3 3 1 1 2 2 7 7 4 4 1 2 2 2 7 7 3 3 1 1 4 4 7 7 4 4 1 1 3 3 3 3 1 1 1 1 1 1 2 2 4 4 1 1 2 2 1 1 2 2 1 1 3 3 7 7 6 6 1 1 5 5 5 5 2 2 1 1 2 2 5 5 4 4 1 2 4 4 6 6 2 2 1 1 3 3 2 2 2 2 1 1 2 2 6 6 2 2 1 2 4 4 6 6 6 6 1 1 2 2 6 6 2 2 1 1 5 5 6 6 4 4 1 1 5 5 6 6 3 3 1 1 1 1 1 1 1 1 1 1 2 2 7 7 4 4 1 1 1 1 4 4 1 1 1 1 3 3 3 3 4 4 1 2 2 2 7 7 1 1 1 1 4 4 5 5 4 4 1 1 6 6 2 2 3 3 1 2 3 3 7 7 2 2 1 1 2 2 2 2 4 4 1 1 6 6 7 7 5 5 1 1 2 2 3 3 5 5 1 2 2 2 3 3 2 2 1 1 1 1 2 2 3 3 1 1 3 3 5 5 2 2 1 1 4 4 2 2 2 2 1 1 3 3 5 5 2 2 1 1 2 2 2 2 2 2 1 1 2 2 5 5 2 2 1 1 3 3 2 2 2 2 1 1 7 7 2 2 1 1 1 1 2 2 5 5 3 3 1 1 4 4 3 3 2 2 1 1 2 2 5 5 2 2 1 2 4 4 5 5 4 4 1 1 5 5 5 5 3 3 1 2 6 6 6 6 3 3 1 2 5 5 5 5 4 4 1 2 1 1 5 5 2 2 1 1 4 4 3 3 5 5 1 1 1 1 3 3 1 1 1 1 3 3 2 2 3 3 1 2 5 5 4 4 3 3 1 2 6 6 5 5 2 2 1 1 2 2 5 5 4 4 1 1 2 2 5 5 4 4 1 4 2 2 6 6 4 4 1 1 5 5 5 5 4 4 1 1 6 6 5 5 4 4 1 1 5 5 7 7 2 2 1 1 2 2 3 3 3 3 1 1 6 6 6 6 3 3 1 1 2 2 4 4 3 3 1 1 5 5 5 5 2 2 0 1 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Depressed[t] = + 0.662537841777522 + 0.25238470945307Gender[t] + 0.0853508546307408Cannotdo[t] -0.100066691536636Cannotdo_G[t] + 0.0272494690723533Worrytoomuch[t] + 0.0661112827823411Worrytoomuch_G[t] + 0.0878761060617949Limitactivity[t] -0.100397334494416Limitactivity_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) 0.662537841777522 0.178105 3.7199 0.000285 0.000142 Gender 0.25238470945307 0.308852 0.8172 0.415181 0.207591 Cannotdo 0.0853508546307408 0.038783 2.2007 0.029349 0.014675 Cannotdo_G -0.100066691536636 0.059751 -1.6747 0.096155 0.048078 Worrytoomuch 0.0272494690723533 0.035504 0.7675 0.444035 0.222017 Worrytoomuch_G 0.0661112827823411 0.055424 1.1928 0.234896 0.117448 Limitactivity 0.0878761060617949 0.044924 1.9561 0.052388 0.026194 Limitactivity_G -0.100397334494416 0.07554 -1.3291 0.185931 0.092966 Multiple Linear Regression - Regression Statistics Multiple R 0.365770122455319 R-squared 0.133787782480979 Adjusted R-squared 0.0916802441293597 F-TEST (value) 3.17728814645453 F-TEST (DF numerator) 7 F-TEST (DF denominator) 144 p-value 0.00371872395290329 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.550583886731518 Sum Squared Residuals 43.6525367512875 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1 1.28529927758259 -0.285299277582593 2 1 1.48893122667116 -0.488931226671161 3 2 1.50145245510380 0.498547544896202 4 1 1.45949955285938 -0.459499552859382 5 1 1.13833606764436 -0.138336067644365 6 1 1.03684330430360 -0.0368433043035968 7 1 0.95380917240825 0.0461908275917495 8 1 1.44917293290004 -0.449172932900035 9 1 1.28310466910934 -0.28310466910934 10 1 1.30220972296178 -0.302209722961785 11 2 1.39118125786993 0.60881874213007 12 1 1.03245408735705 -0.0324540873570483 13 1 1.42061293168172 -0.420612931681722 14 2 1.34109634413944 0.658903655860555 15 1 1.42061293168172 -0.420612931681722 16 1 1.35142296409879 -0.351422964098792 17 1 1.36394419253141 -0.363944192531413 18 1 0.981046237746767 0.0189537622532331 19 1 1.48893122667117 -0.488931226671174 20 1 1.26112849331085 -0.26112849331085 21 1 1.1007723823465 -0.1007723823465 22 2 1.52649491196904 0.473505088030963 23 1 1.27277804914999 -0.272778049149993 24 1 0.97578534820674 0.0242146517932606 25 2 1.49925784663052 0.500742153369479 26 1 1.02212746739770 -0.0221274673977014 27 1 1.41754665061497 -0.417546650614969 28 1 1.10296699081977 -0.102966990819774 29 2 1.14053067611764 0.859469323882361 30 1 1.04936453273622 -0.0493645327362187 31 1 1.31253634292113 -0.312536342921132 32 1 1.01773825045115 -0.0177382504511524 33 1 1.31253634292113 -0.312536342921132 34 1 1.04716992426294 -0.0471699242629441 35 1 1.32725217982703 -0.327252179827027 36 1 1.03245408735705 -0.0324540873570483 37 1 0.986111968166086 0.0138880318339138 38 1 1.31473095139441 -0.314730951394406 39 1 1.11109900230585 -0.111099002305847 40 1 1.32725217982703 -0.327252179827027 41 2 1.27277804914999 0.727221950850007 42 1 1.27058344067672 -0.270583440676719 43 2 1.34922835562552 0.650771644374483 44 2 1.25806221224410 0.741937787755903 45 2 1.34196801673292 0.658031983267077 46 1 1.07353531700798 -0.0735353170079827 47 1 1.16776774145616 -0.167767741456156 48 1 1.01993285892443 -0.0199328589244269 49 2 1.17722268882202 0.822777311177976 50 2 1.26838883220344 0.731611167796556 51 1 1.30220972296178 -0.302209722961785 52 1 1.30220972296178 -0.302209722961785 53 4 1.39557047481648 2.60442952518352 54 1 1.25806221224410 -0.258062212244097 55 1 1.2433463753382 -0.243346375338201 56 1 1.46982617281873 -0.469826172818729 57 1 1.12800944768502 -0.128009447685017 58 1 1.34922835562552 -0.349228355625517 59 1 1.22137019953971 -0.221370199539712 60 1 1.28310466910934 -0.28310466910934 61 1 1.28869134871349 -0.288691348713487 62 1 1.0634907013073 -0.0634907013072994 63 1 1.57704388454017 -0.577043884540171 64 2 0.950890377604205 1.04910962239579 65 1 1.03011353339021 -0.0301135333902110 66 4 1.60429335361252 2.39570664638748 67 1 1.14776435995541 -0.147764359955413 68 1 1.11546438802095 -0.115464388020952 69 2 1.57704388454017 0.422956115459829 70 1 1.23924291343089 -0.239242913430889 71 1 1.32099132064795 -0.320991320647949 72 1 0.972012147831824 0.0279878521681764 73 1 1.68964420824326 -0.689644208243265 74 2 1.34571553828925 0.654284461710752 75 2 1.75279609666376 0.247203903336240 76 1 1.63767052152961 -0.637670521529612 77 2 1.37909270620634 0.620907293793664 78 1 1.36791450449949 -0.367914504499493 79 2 1.51894249898178 0.481057501018217 80 1 0.86301427154241 0.136985728457589 81 1 1.37151695191317 -0.371516951913174 82 1 1.0634907013073 -0.0634907013072994 83 1 0.86301427154241 0.136985728457589 84 1 1.038766483666 -0.0387664836660002 85 1 1.32206851663058 -0.322068516630575 86 1 1.52254494639546 -0.522544946395464 87 1 1.31846606921689 -0.318466069216895 88 1 1.17609102501039 -0.176091025010393 89 1 1.32711901949268 -0.327119019492684 90 2 1.45579061056129 0.544209389438712 91 1 1.29481904755822 -0.294819047558222 92 1 1.0634907013073 -0.0634907013072994 93 2 1.31846606921689 0.681533930783105 94 1 1.11798963945201 -0.117989639452006 95 1 1.57704388454017 -0.577043884540171 96 1 1.42854114148893 -0.428541141488934 97 1 1.34679273427187 -0.346792734271875 98 2 1.46444356083708 0.535556439162923 99 1 1.66491999060197 -0.664919990601965 100 1 1.68964420824326 -0.689644208243265 101 1 1.51389199611967 -0.513891996119675 102 3 1.61042105245726 1.38957894754274 103 2 1.11798963945201 0.882010360547994 104 2 1.43106639291999 0.568933607080011 105 1 1.12159208686569 -0.121592086865687 106 1 1.2058657455138 -0.205865745513801 107 1 1.23311521458615 -0.233115214586154 108 1 1.43466884033367 -0.434668840333669 109 1 0.890263740614764 0.109736259385236 110 3 1.63154282268488 1.36845717731512 111 1 0.948365126173151 0.0516348738268488 112 1 0.972012147831824 0.0279878521681764 113 1 1.05736300246256 -0.0573630024625643 114 1 1.15136680736909 -0.151366807369094 115 1 1.00178686833523 -0.00178686833523106 116 1 1.40741937126132 -0.407419371261316 117 1 1.49421828134048 -0.494218281340484 118 1 1.17861627644145 -0.178616276441447 119 1 1.23311521458615 -0.233115214586154 120 1 1.26288993508956 -0.262889935089561 121 1 1.31594081778584 -0.315940817785840 122 1 1.15136680736909 -0.151366807369094 123 3 1.72662382357403 1.27337617642597 124 2 1.40129167241658 0.598708327583419 125 2 1.25783943222745 0.742160567772547 126 1 1.51894249898178 -0.518942498981784 127 1 0.914987958256063 0.085012041743937 128 1 1.8381469512945 -0.838146951294501 129 1 1.22806471172405 -0.228064711724046 130 1 1.0634907013073 -0.0634907013072994 131 2 1.42961833747156 0.570381662528439 132 1 1.43719409176472 -0.437194091764724 133 1 1.34319028685819 -0.343190286858194 134 1 1.49169302990943 -0.49169302990943 135 1 0.975614595245505 0.0243854047544955 136 1 1.54979441546782 -0.549794415467818 137 1 0.975614595245505 0.0243854047544955 138 1 1.2305899631551 -0.2305899631551 139 1 1.57956913597123 -0.579569135971225 140 1 1.03624123223495 -0.0362412322349461 141 1 1.32099132064795 -0.320991320647949 142 2 1.32099132064795 0.679008679352051 143 1 1.25783943222745 -0.257839432227453 144 1 1.31594081778584 -0.315940817785840 145 1 0.86301427154241 0.136985728457589 146 1 1.14523910852436 -0.145239108524359 147 1 1.05736300246256 -0.0573630024625643 148 1 1.0634907013073 -0.0634907013072994 149 2 1.29626710300665 0.70373289699335 150 1 1.49421828134048 -0.494218281340484 151 3 1.66239473917091 1.33760526082909 152 1 0.972012147831824 0.0279878521681764 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.560084776363711 0.879830447272578 0.439915223636289 12 0.390916677926849 0.781833355853697 0.609083322073151 13 0.423990510647899 0.847981021295797 0.576009489352101 14 0.609390007531117 0.781219984937766 0.390609992468883 15 0.51208483127525 0.9758303374495 0.48791516872475 16 0.440372675130584 0.880745350261167 0.559627324869416 17 0.360677073677177 0.721354147354354 0.639322926322823 18 0.271404723268144 0.542809446536288 0.728595276731856 19 0.220928181667198 0.441856363334396 0.779071818332802 20 0.159238714975946 0.318477429951893 0.840761285024054 21 0.110626898365765 0.221253796731529 0.889373101634235 22 0.142353664687467 0.284707329374935 0.857646335312533 23 0.103332034064049 0.206664068128099 0.896667965935951 24 0.0713812501577186 0.142762500315437 0.928618749842281 25 0.0801688210775593 0.160337642155119 0.91983117892244 26 0.0559425722566111 0.111885144513222 0.944057427743389 27 0.0432371308563278 0.0864742617126556 0.956762869143672 28 0.0292192815153899 0.0584385630307799 0.97078071848461 29 0.0665341907683226 0.133068381536645 0.933465809231677 30 0.0463963760659297 0.0927927521318594 0.95360362393407 31 0.0362637000795064 0.0725274001590127 0.963736299920494 32 0.0244417188268333 0.0488834376536667 0.975558281173167 33 0.0185693547773487 0.0371387095546974 0.981430645222651 34 0.0121751251746524 0.0243502503493048 0.987824874825348 35 0.00929064136786889 0.0185812827357378 0.99070935863213 36 0.00589058526471341 0.0117811705294268 0.994109414735287 37 0.00385242159408569 0.00770484318817139 0.996147578405914 38 0.00282448773349448 0.00564897546698897 0.997175512266506 39 0.00173438674241608 0.00346877348483215 0.998265613257584 40 0.00129046336930661 0.00258092673861322 0.998709536630693 41 0.00350005799116092 0.00700011598232185 0.996499942008839 42 0.00240813656864604 0.00481627313729207 0.997591863431354 43 0.00395451721914205 0.00790903443828411 0.996045482780858 44 0.00702066293754553 0.0140413258750911 0.992979337062454 45 0.0101484629276111 0.0202969258552221 0.989851537072389 46 0.00687509500996451 0.0137501900199290 0.993124904990035 47 0.00472025445539556 0.00944050891079113 0.995279745544604 48 0.00310230012693526 0.00620460025387051 0.996897699873065 49 0.00670469791905024 0.0134093958381005 0.99329530208095 50 0.011949517172557 0.023899034345114 0.988050482827443 51 0.0116982736552099 0.0233965473104198 0.98830172634479 52 0.0160422710617196 0.0320845421234393 0.98395772893828 53 0.633947040489985 0.73210591902003 0.366052959510015 54 0.595321640204161 0.809356719591678 0.404678359795839 55 0.554840675870675 0.89031864825865 0.445159324129325 56 0.533363966766258 0.933272066467484 0.466636033233742 57 0.484286474280104 0.968572948560208 0.515713525719896 58 0.448154309062465 0.89630861812493 0.551845690937535 59 0.403516620669732 0.807033241339464 0.596483379330268 60 0.363462154663192 0.726924309326385 0.636537845336808 61 0.321673547479864 0.643347094959728 0.678326452520136 62 0.278279522995065 0.55655904599013 0.721720477004935 63 0.253527436032192 0.507054872064384 0.746472563967808 64 0.293488002004158 0.586976004008315 0.706511997995843 65 0.252189710146042 0.504379420292085 0.747810289853958 66 0.82675024336041 0.346499513279179 0.173249756639589 67 0.903460279262305 0.193079441475390 0.0965397207376949 68 0.881444112023458 0.237111775953085 0.118555887976542 69 0.868484876826194 0.263030246347612 0.131515123173806 70 0.866217224330238 0.267565551339524 0.133782775669762 71 0.85596070650421 0.288078586991579 0.144039293495790 72 0.827453599870758 0.345092800258483 0.172546400129242 73 0.86509088410232 0.269818231795362 0.134909115897681 74 0.869898486084972 0.260203027830056 0.130101513915028 75 0.847103275332534 0.305793449334933 0.152896724667466 76 0.859008796533036 0.281982406933927 0.140991203466964 77 0.8621507142547 0.275698571490601 0.137849285745300 78 0.846775926284212 0.306448147431576 0.153224073715788 79 0.836317047931979 0.327365904136043 0.163682952068021 80 0.806091488893204 0.387817022213592 0.193908511106796 81 0.783601277717204 0.432797444565592 0.216398722282796 82 0.746679328958022 0.506641342083957 0.253320671041978 83 0.708242695012003 0.583514609975995 0.291757304987997 84 0.667086652629474 0.665826694741052 0.332913347370526 85 0.633362415296216 0.733275169407568 0.366637584703784 86 0.621263743278232 0.757472513443536 0.378736256721768 87 0.594567816286804 0.810864367426393 0.405432183713197 88 0.550049573107996 0.899900853784007 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0.659776323181706 0.680447353636587 0.340223676818294 107 0.619836354260944 0.760327291478111 0.380163645739056 108 0.608710566596661 0.782578866806678 0.391289433403339 109 0.557718155621369 0.884563688757262 0.442281844378631 110 0.819765416101227 0.360469167797547 0.180234583898773 111 0.781383087969218 0.437233824061563 0.218616912030782 112 0.740221397128901 0.519557205742198 0.259778602871099 113 0.691444302094123 0.617111395811755 0.308555697905877 114 0.641792744743687 0.716414510512627 0.358207255256313 115 0.605115428385838 0.789769143228325 0.394884571614162 116 0.60891408480908 0.78217183038184 0.39108591519092 117 0.582833363688873 0.834333272622253 0.417166636311127 118 0.527079191210928 0.945841617578145 0.472920808789072 119 0.47079080403903 0.94158160807806 0.52920919596097 120 0.420603649895876 0.841207299791751 0.579396350104124 121 0.377533414982577 0.755066829965155 0.622466585017422 122 0.324103752205037 0.648207504410075 0.675896247794963 123 0.517883788444448 0.964232423111103 0.482116211555552 124 0.528818840790681 0.942362318418639 0.471181159209319 125 0.61187822556989 0.77624354886022 0.38812177443011 126 0.56968095612006 0.860638087759879 0.430319043879939 127 0.496650595663715 0.99330119132743 0.503349404336285 128 0.549520566833864 0.900958866332272 0.450479433166136 129 0.479787615432710 0.959575230865419 0.520212384567290 130 0.402843039522964 0.805686079045929 0.597156960477036 131 0.403277559134694 0.806555118269389 0.596722440865306 132 0.366680468071539 0.733360936143077 0.633319531928461 133 0.296434654361861 0.592869308723723 0.703565345638139 134 0.268471794269801 0.536943588539602 0.731528205730199 135 0.19702021980513 0.39404043961026 0.80297978019487 136 0.214832852935545 0.42966570587109 0.785167147064455 137 0.146864457577361 0.293728915154722 0.853135542422639 138 0.0977303840801633 0.195460768160327 0.902269615919837 139 0.148607329508854 0.297214659017707 0.851392670491146 140 0.094376688960924 0.188753377921848 0.905623311039076 141 0.0665794589234304 0.133158917846861 0.93342054107657 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.0687022900763359 NOK 5% type I error level 21 0.160305343511450 NOK 10% type I error level 25 0.190839694656489 NOK

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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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Software written by Ed van Stee & Patrick Wessa

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