p_Stress_MR1v2

*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: Sat, 04 Dec 2010 14:53:22 +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/04/t1291474310srg5ax2h4j980en.htm/, Retrieved Sat, 04 Dec 2010 15:51:50 +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/04/t1291474310srg5ax2h4j980en.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 «

23 10 53 7 6 7 15 11 12 2 4 2 6 21 6 86 4 6 5 15 8 11 4 3 1 6 21 13 66 6 5 7 14 12 14 7 5 4 11 21 12 67 5 4 3 10 10 12 3 3 1 7 24 8 76 4 4 7 10 7 21 7 6 5 12 22 6 78 3 6 7 12 6 12 2 5 1 8 21 10 53 5 7 7 18 8 22 7 6 1 7 22 10 80 6 5 1 12 16 11 2 6 1 11 21 9 74 5 4 4 14 8 10 1 5 1 8 20 9 76 6 6 5 18 16 13 2 5 1 9 22 7 79 7 1 6 9 7 10 6 3 2 9 21 5 54 6 4 4 11 11 8 1 5 1 6 21 14 67 7 6 7 11 16 15 1 7 3 9 23 6 87 6 6 6 17 16 10 1 5 1 5 22 10 58 4 5 2 8 12 14 2 5 1 9 23 10 75 6 3 2 16 13 14 2 3 1 4 22 7 88 4 7 6 21 19 11 2 5 1 9 24 10 64 5 2 7 24 7 10 1 6 1 6 23 8 57 3 5 5 21 8 13 7 5 2 8 21 6 66 3 5 2 14 12 7 1 2 4 12 23 10 54 4 3 7 7 13 12 2 5 1 7 23 12 56 5 5 4 18 11 14 4 4 2 8 21 7 86 3 5 5 18 8 11 2 6 1 3 20 15 80 7 6 5 13 16 9 1 3 2 9 32 8 76 7 4 5 11 15 11 1 5 3 7 22 10 69 4 4 3 13 11 15 5 4 1 9 21 13 67 4 4 5 13 12 13 2 5 1 9 21 8 80 5 2 1 18 7 9 1 2 1 7 21 11 54 6 3 1 14 9 15 3 2 1 5 22 7 71 5 6 3 12 15 10 1 5 1 8 21 9 84 4 6 2 9 6 11 2 2 2 7 21 10 74 6 5 3 12 14 13 5 2 1 6 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 PStress[t] = + 9.03106352044203 -0.102588137617922AGE[t] -0.0294508517465285BelInSprt[t] + 0.191265388962981KunnenRekRel[t] -0.129384440040129ExtraCurAct[t] -0.0135175731533391VerandVorigJr[t] -0.0454055853017774VerwOuders[t] + 0.0273918110157730KenStudenten[t] + 0.403815279363892Depressie[t] -0.205750464844944Slaapgebrek[t] + 0.220708269386516Toekomstzorgen[t] + 0.0795949547996137Rookgedrag[t] -0.0417224932886921MateAlcCon[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) 9.03106352044203 2.358632 3.8289 2e-04 1e-04 AGE -0.102588137617922 0.071025 -1.4444 0.15105 0.075525 BelInSprt -0.0294508517465285 0.018222 -1.6162 0.10849 0.054245 KunnenRekRel 0.191265388962981 0.111911 1.7091 0.08984 0.04492 ExtraCurAct -0.129384440040129 0.130236 -0.9935 0.322346 0.161173 VerandVorigJr -0.0135175731533391 0.102393 -0.132 0.895177 0.447588 VerwOuders -0.0454055853017774 0.049648 -0.9146 0.362131 0.181066 KenStudenten 0.0273918110157730 0.050368 0.5438 0.587497 0.293748 Depressie 0.403815279363892 0.064958 6.2165 0 0 Slaapgebrek -0.205750464844944 0.095972 -2.1439 0.03392 0.01696 Toekomstzorgen 0.220708269386516 0.118626 1.8605 0.065086 0.032543 Rookgedrag 0.0795949547996137 0.185396 0.4293 0.668403 0.334202 MateAlcCon -0.0417224932886921 0.083603 -0.4991 0.618589 0.309295 Multiple Linear Regression - Regression Statistics Multiple R 0.626515070345575 R-squared 0.392521133370121 Adjusted R-squared 0.336011471358040 F-TEST (value) 6.94608885266736 F-TEST (DF numerator) 12 F-TEST (DF denominator) 129 p-value 1.33963962145600e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.94457498966075 Sum Squared Residuals 487.796973863418 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 10.4247658748273 -0.424765874827350 2 6 7.91350815555825 -1.91350815555825 3 13 10.2081614717984 2.79183852820164 4 12 9.79979855355279 2.20020144644721 5 8 12.4826931761664 -4.4826931761664 6 6 9.08294719176611 -3.08294719176611 7 10 13.2291343032434 -3.22913430324337 8 10 9.77397515443644 0.226024845563561 9 9 9.34728347138154 -0.347283471381539 10 9 10.3114338681129 -1.31143386811290 11 7 8.60842974077693 -1.60842974077693 12 5 9.62177251207734 -4.62177251207734 13 14 12.5699602079275 1.43003979207253 14 6 8.8727926981038 -2.87279269810379 15 10 11.1720831942073 -1.17208319420733 16 10 10.6414732911766 -0.641473291176622 17 7 8.36574269921349 -1.36574269921349 18 10 9.37494932854365 0.62505067145635 19 8 8.85603779142643 -0.856037791426432 20 6 7.40590170275029 -1.40590170275029 21 10 10.7270913020562 -0.727091302056234 22 12 10.3002875877171 1.69971241228288 23 7 8.91420366844558 -1.91420366844558 24 15 8.84059154001461 6.15940845998539 25 8 9.46161257413152 -1.46161257413152 26 10 10.2754265698002 -0.275426569800188 27 13 10.4676021808138 2.53239781918617 28 8 8.23666821398501 -0.236668213985009 29 11 11.3955130046271 -0.395513004627105 30 7 9.37035042979025 -2.37035042979025 31 9 8.45927759010474 0.540722409895263 32 10 9.48760818728566 0.512391812714345 33 8 8.43677914571834 -0.436779145718339 34 15 13.2420704691314 1.75792953086859 35 9 10.2176807444013 -1.21768074440125 36 7 8.14583771860678 -1.14583771860678 37 11 9.7362732708379 1.26372672916211 38 9 7.99263316799098 1.00736683200902 39 8 9.6582818475053 -1.65828184750529 40 8 7.79042302341365 0.209576976586346 41 12 8.57898151223873 3.42101848776127 42 13 9.79079980902355 3.20920019097645 43 9 9.50193185889484 -0.501931858894844 44 11 9.36601174030825 1.63398825969175 45 8 8.88537124220957 -0.885371242209573 46 10 10.9487154062928 -0.948715406292811 47 13 12.7578565615497 0.242143438450346 48 12 10.9275654900900 1.07243450991001 49 12 10.2494937383191 1.75050626168085 50 9 8.82899519986074 0.171004800139258 51 8 9.93839745930042 -1.93839745930042 52 9 8.59333746251159 0.40666253748841 53 12 8.44807795730324 3.55192204269676 54 12 11.3651905101776 0.634809489822446 55 16 14.4164239498831 1.58357605011687 56 11 8.76665440101559 2.23334559898441 57 13 9.61702652821569 3.38297347178432 58 10 10.7214928560551 -0.72149285605511 59 9 10.8771651132598 -1.87716511325975 60 14 10.1901720972235 3.80982790277648 61 13 11.4883638746019 1.51163612539806 62 12 10.4117040208311 1.58829597916886 63 9 10.8657877426678 -1.86578774266781 64 9 10.3441235701389 -1.34412357013886 65 10 11.0102634529880 -1.01026345298804 66 8 10.3331288962957 -2.33312889629573 67 9 10.3141598582602 -1.31415985826020 68 9 9.08969020282346 -0.0896902028234594 69 11 8.50665354944695 2.49334645055305 70 7 9.32726047094701 -2.32726047094701 71 11 11.5593415332079 -0.559341533207881 72 9 9.33671947029637 -0.336719470296374 73 11 8.8455469572209 2.15445304277909 74 9 9.25524438317568 -0.255244383175676 75 8 10.1319816575392 -2.13198165753924 76 9 8.13131515508228 0.868684844917718 77 8 9.51906852881131 -1.51906852881131 78 9 9.59943339282161 -0.599433392821611 79 10 9.83943663343977 0.160563366560231 80 9 10.0267641671335 -1.02676416713346 81 17 13.9029826605469 3.09701733945312 82 7 9.51842722573087 -2.51842722573087 83 11 10.8894228951342 0.110577104865755 84 9 10.0297766902440 -1.02977669024402 85 10 9.99830778373437 0.00169221626563169 86 11 8.51715626672106 2.48284373327894 87 8 8.51481346373896 -0.514813463738956 88 12 12.0324766384710 -0.0324766384710358 89 10 10.0239161641402 -0.0239161641402335 90 7 9.06971963900235 -2.06971963900235 91 9 8.58322790666704 0.416772093332962 92 7 8.17642665826965 -1.17642665826965 93 12 10.4325990635608 1.56740093643918 94 8 9.0840520704905 -1.08405207049050 95 13 10.2819997140517 2.7180002859483 96 9 11.2153418678249 -2.2153418678249 97 15 12.6686411321848 2.33135886781522 98 8 8.92057423762213 -0.920574237622128 99 14 11.8142679409044 2.18573205909562 100 14 13.6288813546085 0.371118645391509 101 9 10.3608639198152 -1.36086391981524 102 13 11.7191007142649 1.28089928573514 103 11 9.0564614173383 1.94353858266170 104 10 11.7712044055388 -1.7712044055388 105 6 9.99277295390467 -3.99277295390467 106 8 8.20780348894397 -0.207803488943973 107 10 11.3019777801403 -1.30197778014031 108 10 8.01974839684535 1.98025160315465 109 10 9.44721055330907 0.552789446690932 110 12 12.0400376341852 -0.0400376341852216 111 10 9.44081997674662 0.559180023253384 112 9 9.13081009035105 -0.13081009035105 113 9 7.39799678395639 1.60200321604361 114 11 9.15654277847064 1.84345722152936 115 7 8.25027586129514 -1.25027586129513 116 7 8.85378852128818 -1.85378852128818 117 5 8.15097735507334 -3.15097735507334 118 9 9.03804857828693 -0.0380485782869272 119 11 11.8422283612372 -0.842228361237202 120 15 12.0679664897954 2.93203351020464 121 9 7.90795535506115 1.09204464493885 122 9 9.43131779994382 -0.431317799943817 123 8 9.1443371486864 -1.14433714868641 124 13 15.5314894864242 -2.53148948642417 125 10 10.3307215339714 -0.330721533971427 126 13 11.4918088462398 1.50819115376021 127 9 7.82944321783343 1.17055678216657 128 11 9.42155203339282 1.57844796660718 129 8 10.3276509846251 -2.32765098462513 130 10 9.25336056303942 0.746639436960576 131 9 8.75852800126935 0.241471998730649 132 8 8.52576021286184 -0.525760212861844 133 8 7.99017394298731 0.00982605701268877 134 13 10.7986157566273 2.20138424337274 135 11 10.9064078477011 0.0935921522988669 136 8 9.46161257413152 -1.46161257413152 137 12 9.77421229954979 2.22578770045021 138 15 11.8667335231489 3.13326647685111 139 11 10.8894228951342 0.110577104865755 140 10 10.5451734972668 -0.545173497266828 141 5 8.15097735507334 -3.15097735507334 142 11 7.18839265570911 3.81160734429089 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.950895764019616 0.0982084719607676 0.0491042359803838 17 0.914089569856942 0.171820860286116 0.0859104301430578 18 0.970108157245957 0.0597836855080868 0.0298918427540434 19 0.947204744684067 0.105590510631867 0.0527952553159333 20 0.969461078269832 0.0610778434603367 0.0305389217301683 21 0.951364846288903 0.0972703074221938 0.0486351537110969 22 0.944230441619078 0.111539116761845 0.0557695583809223 23 0.931139967082988 0.137720065834023 0.0688600329170116 24 0.993063713610274 0.0138725727794519 0.00693628638972594 25 0.99007843911294 0.0198431217741193 0.00992156088705967 26 0.984566190586903 0.0308676188261935 0.0154338094130967 27 0.988869386613195 0.0222612267736102 0.0111306133868051 28 0.98316275168104 0.0336744966379213 0.0168372483189607 29 0.975116505692978 0.0497669886140444 0.0248834943070222 30 0.971949755094557 0.0561004898108862 0.0280502449054431 31 0.972421731492904 0.055156537014193 0.0275782685070965 32 0.960021067701077 0.0799578645978452 0.0399789322989226 33 0.944702946270962 0.110594107458076 0.0552970537290378 34 0.965347789998172 0.0693044200036552 0.0346522100018276 35 0.954522822188356 0.0909543556232882 0.0454771778116441 36 0.943763790956007 0.112472418087986 0.0562362090439932 37 0.933165708903517 0.133668582192965 0.0668342910964825 38 0.913259781708737 0.173480436582526 0.086740218291263 39 0.898924652307098 0.202150695385805 0.101075347692902 40 0.871267021589843 0.257465956820314 0.128732978410157 41 0.93302807133179 0.133943857336419 0.0669719286682093 42 0.951924279580303 0.0961514408393933 0.0480757204196966 43 0.936544981031595 0.126910037936809 0.0634550189684046 44 0.926815793470354 0.146368413059292 0.0731842065296461 45 0.909889535494697 0.180220929010606 0.0901104645053028 46 0.892825192506466 0.214349614987068 0.107174807493534 47 0.87163301563886 0.256733968722279 0.128366984361140 48 0.847181171603523 0.305637656792953 0.152818828396477 49 0.841574267569075 0.316851464861849 0.158425732430925 50 0.806717466396818 0.386565067206364 0.193282533603182 51 0.793005369519362 0.413989260961276 0.206994630480638 52 0.755300929051312 0.489398141897375 0.244699070948688 53 0.839023926838375 0.321952146323250 0.160976073161625 54 0.80904018211361 0.381919635772781 0.190959817886391 55 0.809640553982699 0.380718892034603 0.190359446017301 56 0.858298976540067 0.283402046919866 0.141701023459933 57 0.897989198066744 0.204021603866512 0.102010801933256 58 0.873661829873237 0.252676340253526 0.126338170126763 59 0.864564802491755 0.270870395016489 0.135435197508245 60 0.93632397351541 0.127352052969181 0.0636760264845907 61 0.929339483145094 0.141321033709812 0.0706605168549058 62 0.924077531468144 0.151844937063711 0.0759224685318556 63 0.931532742754418 0.136934514491164 0.0684672572455818 64 0.922412899400762 0.155174201198476 0.0775871005992382 65 0.924065296280562 0.151869407438877 0.0759347037194383 66 0.926914148292574 0.146171703414851 0.0730858517074256 67 0.918861611056355 0.162276777887291 0.0811383889436454 68 0.900243153058135 0.199513693883731 0.0997568469418653 69 0.915798951069501 0.168402097860998 0.0842010489304991 70 0.931172184038995 0.13765563192201 0.068827815961005 71 0.91442038935256 0.171159221294879 0.0855796106474397 72 0.894490309446028 0.211019381107944 0.105509690553972 73 0.908809566089641 0.182380867820718 0.091190433910359 74 0.88579839387898 0.228403212242040 0.114201606121020 75 0.886306549124609 0.227386901750782 0.113693450875391 76 0.87042962584386 0.25914074831228 0.12957037415614 77 0.861077294053642 0.277845411892716 0.138922705946358 78 0.844584007053408 0.310831985893184 0.155415992946592 79 0.811330208752763 0.377339582494474 0.188669791247237 80 0.783831996116408 0.432336007767185 0.216168003883592 81 0.837127651514177 0.325744696971646 0.162872348485823 82 0.853641107588742 0.292717784822516 0.146358892411258 83 0.823428327548715 0.35314334490257 0.176571672451285 84 0.808336802318305 0.38332639536339 0.191663197681695 85 0.770955800483876 0.458088399032248 0.229044199516124 86 0.857880316361721 0.284239367276558 0.142119683638279 87 0.828823005802176 0.342353988395647 0.171176994197824 88 0.792157652706985 0.415684694586030 0.207842347293015 89 0.75033915675517 0.49932168648966 0.24966084324483 90 0.76606183324356 0.46787633351288 0.23393816675644 91 0.723923687597485 0.552152624805029 0.276076312402515 92 0.703661319023036 0.592677361953929 0.296338680976964 93 0.700866756440782 0.598266487118436 0.299133243559218 94 0.66606136992481 0.667877260150379 0.333938630075189 95 0.70554063635298 0.588918727294039 0.294459363647020 96 0.691820390708519 0.616359218582962 0.308179609291481 97 0.684687841322744 0.630624317354511 0.315312158677256 98 0.637630255923169 0.724739488153662 0.362369744076831 99 0.625453809339972 0.749092381320057 0.374546190660028 100 0.62133284308508 0.75733431382984 0.37866715691492 101 0.634722340012076 0.730555319975847 0.365277659987924 102 0.644144744726693 0.711710510546613 0.355855255273307 103 0.670578116858655 0.65884376628269 0.329421883141345 104 0.6615063655975 0.676987268805 0.3384936344025 105 0.81571569813936 0.368568603721279 0.184284301860640 106 0.832372620400836 0.335254759198327 0.167627379599163 107 0.8324568011346 0.335086397730802 0.167543198865401 108 0.810945623604569 0.378108752790862 0.189054376395431 109 0.76329875649852 0.473402487002961 0.236701243501481 110 0.728918938533766 0.542162122932469 0.271081061466234 111 0.688273961750929 0.623452076498142 0.311726038249071 112 0.618626838646333 0.762746322707334 0.381373161353667 113 0.577002732418886 0.845994535162229 0.422997267581114 114 0.548644176617011 0.902711646765978 0.451355823382989 115 0.47506880250116 0.95013760500232 0.52493119749884 116 0.402265697030975 0.804531394061949 0.597734302969025 117 0.446354008959035 0.89270801791807 0.553645991040965 118 0.36042794695303 0.72085589390606 0.63957205304697 119 0.286121211371553 0.572242422743107 0.713878788628447 120 0.256687044294929 0.513374088589859 0.74331295570507 121 0.18630629984903 0.37261259969806 0.81369370015097 122 0.125566765741963 0.251133531483927 0.874433234258037 123 0.0902753505768808 0.180550701153762 0.90972464942312 124 0.145956008305050 0.291912016610101 0.85404399169495 125 0.296707225562953 0.593414451125906 0.703292774437047 126 0.918342476502274 0.163315046995453 0.0816575234977264 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 0.0540540540540541 NOK 10% type I error level 16 0.144144144144144 NOK

Charts produced by software:

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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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