Workshop 7 mini-tutorial Concern over mistakes - interaction 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: Wed, 24 Nov 2010 15:50:55 +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/24/t1290613816s49mu49x42cazqp.htm/, Retrieved Wed, 24 Nov 2010 16:50:16 +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/24/t1290613816s49mu49x42cazqp.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 24 14 0 11 0 12 0 24 0 26 0 0 25 11 0 7 0 8 0 25 0 23 0 0 17 6 0 17 0 8 0 30 0 25 0 1 18 12 12 10 10 8 8 19 19 23 23 1 18 8 8 12 12 9 9 22 22 19 19 1 16 10 10 12 12 7 7 22 22 29 29 1 20 10 10 11 11 4 4 25 25 25 25 1 16 11 11 11 11 11 11 23 23 21 21 1 18 16 16 12 12 7 7 17 17 22 22 1 17 11 11 13 13 7 7 21 21 25 25 0 23 13 0 14 0 12 0 19 0 24 0 0 30 12 0 16 0 10 0 19 0 18 0 1 23 8 8 11 11 10 10 15 15 22 22 1 18 12 12 10 10 8 8 16 16 15 15 1 15 11 11 11 11 8 8 23 23 22 22 1 12 4 4 15 15 4 4 27 27 28 28 0 21 9 0 9 0 9 0 22 0 20 0 1 15 8 8 11 11 8 8 14 14 12 12 1 20 8 8 17 17 7 7 22 22 24 24 0 31 14 0 17 0 11 0 23 0 20 0 0 27 15 0 11 0 9 0 23 0 21 0 1 34 16 16 18 18 11 11 21 21 20 20 1 21 9 9 14 14 13 13 19 19 21 21 1 31 14 14 10 10 8 8 18 18 23 23 1 19 11 11 11 11 8 8 20 20 28 28 0 16 8 0 15 0 9 0 23 0 24 0 1 20 9 9 15 15 6 6 25 25 24 24 1 21 9 9 13 13 9 9 19 19 24 24 1 22 9 9 16 16 9 9 24 24 23 23 1 17 9 9 13 13 6 6 22 22 23 23 1 24 10 10 9 9 6 6 25 25 29 29 0 25 16 0 18 0 16 0 2 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 9 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Concernovermistakes[t] = + 17.7079010523209 + 0.0365943321020006Gender[t] + 0.695145667491059DoubtsaboutactionsFemale[t] -0.0783765564644862DoubtsaboutactionsMale[t] -0.223159928154307ParentalexpectationsFemale[t] -0.173559424326886ParentalexpectationsMale[t] + 0.323644568786631ParentalcritismFemale[t] -0.511763726976633ParentalcritismMale[t] + 0.320852405582859PersonalstandardsFemale[t] + 0.182877097980758PersonalstandarsMale[t] -0.529139795925977OrganizationFemale[t] + 0.187133930397755OrganizationMale[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 17.7079010523209 1.128795 15.6874 0 0 Gender 0.0365943321020006 0.074049 0.4942 0.62191 0.310955 DoubtsaboutactionsFemale 0.695145667491059 0.117085 5.9371 0 0 DoubtsaboutactionsMale -0.0783765564644862 0.121858 -0.6432 0.521109 0.260554 ParentalexpectationsFemale -0.223159928154307 0.138236 -1.6143 0.108598 0.054299 ParentalexpectationsMale -0.173559424326886 0.16425 -1.0567 0.29239 0.146195 ParentalcritismFemale 0.323644568786631 0.183802 1.7608 0.080347 0.040173 ParentalcritismMale -0.511763726976633 0.098292 -5.2065 1e-06 0 PersonalstandardsFemale 0.320852405582859 0.089897 3.5691 0.000484 0.000242 PersonalstandarsMale 0.182877097980758 0.087225 2.0966 0.037741 0.01887 OrganizationFemale -0.529139795925977 0.094701 -5.5875 0 0 OrganizationMale 0.187133930397755 0.088454 2.1156 0.036064 0.018032 Multiple Linear Regression - Regression Statistics Multiple R 0.844823945222742 R-squared 0.713727498421718 Adjusted R-squared 0.692305746602935 F-TEST (value) 33.3178866256824 F-TEST (DF numerator) 11 F-TEST (DF denominator) 147 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.68941928829607 Sum Squared Residuals 2000.93675867311 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 22.8117390528511 1.18826094714887 2 25 22.2326352812094 2.76736471879058 3 17 17.0712900982734 -0.0712900982734 4 18 21.3783035869694 -3.37830358696943 5 18 20.8088812525145 -2.80888125251449 6 16 18.9985991356654 -2.99859913566543 7 20 22.8388879355204 -2.83888793552036 8 16 22.4993873942026 -6.49938739420257 9 18 22.5746073427043 -4.57460734270433 10 17 20.0829428527601 -3.08294285276008 11 23 20.9011311648348 2.09886883516521 12 30 22.2872152790177 7.71278472098229 13 23 16.4653573252757 6.5346426747243 14 18 22.6031620005044 -4.60316200050435 15 15 22.7217390032444 -7.72173900324436 16 12 17.5328372699787 -5.53283726997872 17 21 21.3445308297347 -0.344530829734676 18 15 19.7579247933743 -4.7579247933743 19 20 17.4914934788474 2.50850652115257 20 31 24.0031212851116 6.99687871488836 21 27 24.8607975880293 2.1392024119707 22 34 22.1407443403681 11.8592556596319 23 21 17.6845347840714 3.31546521592862 24 31 22.1081123054590 8.89188769454104 25 19 19.1585152993842 -0.158515299384177 26 16 17.5147188151967 -1.51471881519673 27 20 20.6010089637172 -0.601008963717237 28 21 17.8077131727279 3.19228682727209 29 22 19.4782084986306 2.52179150136936 30 17 20.225265023517 -3.22526502351699 31 24 21.8880648619899 2.11193513801014 32 25 25.6344735689173 -0.634473568917275 33 26 22.7360509671135 3.26394903288652 34 25 24.1703794113613 0.829620588638668 35 17 20.2692303807695 -3.26923038076951 36 32 28.6296151389884 3.37038486101157 37 33 26.4100635424048 6.58993645759517 38 13 22.9508456590843 -9.95084565908433 39 32 22.3276435319607 9.67235646803934 40 25 25.8102790758332 -0.81027907583322 41 29 20.1939141390408 8.8060858609592 42 22 20.7753767071566 1.22462329284335 43 18 17.6945562778616 0.305443722138433 44 17 20.7344146349543 -3.73441463495427 45 20 21.1888622348383 -1.18886223483830 46 15 20.8013020069954 -5.80130200699542 47 20 19.8029907136260 0.197009286374033 48 33 29.5912771586826 3.40872284131743 49 29 20.4216899729468 8.57831002705317 50 23 23.7293142522824 -0.729314252282378 51 26 23.6892605927351 2.31073940726488 52 18 19.4000106905263 -1.40001069052634 53 20 16.1717289654027 3.82827103459735 54 6 12.3826532627888 -6.3826532627888 55 8 7.5930752791294 0.4069247208706 56 13 10.6550007507472 2.34499924925282 57 10 8.00294096086173 1.99705903913828 58 8 8.37051905183334 -0.370519051833335 59 7 10.9530501253590 -3.95305012535898 60 15 17.9022858335098 -2.90228583350983 61 9 9.26733895850331 -0.267338958503315 62 10 9.70333273434957 0.296667265650434 63 12 9.48393327318776 2.51606672681224 64 13 13.4497906665922 -0.449790666592187 65 10 10.8265675715985 -0.826567571598502 66 11 7.00472188700895 3.99527811299105 67 8 9.77691669093432 -1.77691669093432 68 9 6.20575509323005 2.79424490676995 69 13 12.8625411144788 0.137458885521235 70 11 10.7279533085567 0.27204669144329 71 8 10.2898601042278 -2.28986010422777 72 9 9.94202337083844 -0.942023370838442 73 9 7.00523176860224 1.99476823139776 74 15 7.88481253241727 7.11518746758273 75 9 8.2201830691474 0.779816930852598 76 10 10.0128550669390 -0.0128550669389756 77 14 12.4176320137339 1.58236798626610 78 12 11.6241656608797 0.375834339120325 79 12 10.6848129795712 1.31518702042877 80 11 11.1562528010513 -0.156252801051293 81 14 11.9277452311274 2.07225476887265 82 6 8.17293024970463 -2.17293024970463 83 12 10.5559306712279 1.44406932877212 84 8 7.78126686985428 0.218733130145723 85 14 13.8304698602852 0.169530139714766 86 11 10.4075993780191 0.592400621980913 87 10 9.77449791633986 0.225502083660140 88 14 12.2302634707641 1.76973652923586 89 12 11.7656475275764 0.23435247242361 90 10 12.4569330428162 -2.45693304281617 91 14 14.0019001188809 -0.00190011888091950 92 5 8.10017184343734 -3.10017184343734 93 11 12.4135480614905 -1.4135480614905 94 10 10.9037275317824 -0.903727531782403 95 9 9.67235782007674 -0.672357820076738 96 10 6.15054514773932 3.84945485226068 97 16 10.1950418514455 5.80495814855448 98 13 12.8987409649225 0.101259035077522 99 9 11.0635646168111 -2.06356461681112 100 10 12.9168787734598 -2.91687877345979 101 10 10.8163544919866 -0.816354491986637 102 7 10.3918490553556 -3.39184905535562 103 9 8.09293183682401 0.90706816317599 104 8 9.4657853717346 -1.4657853717346 105 14 12.7928494114434 1.20715058855663 106 14 17.5360240887630 -3.53602408876303 107 8 12.9370428116627 -4.93704281166275 108 9 13.7892315294793 -4.78923152947933 109 14 14.1982562224062 -0.198256222406150 110 14 15.3960645469042 -1.39606454690421 111 8 12.1123350752938 -4.11233507529381 112 8 9.52343713494843 -1.52343713494843 113 8 7.42418579326049 0.575814206739514 114 7 7.14369677937642 -0.143696779376418 115 6 5.05324302356007 0.946756976439927 116 8 8.55271855857902 -0.55271855857902 117 6 9.21530140868406 -3.21530140868406 118 11 13.9532057872090 -2.95320578720898 119 14 12.859297839651 1.14070216034900 120 11 17.478310416642 -6.47831041664202 121 11 12.5109844305528 -1.51098443055279 122 11 14.7004205395233 -3.70042053952332 123 14 14.5098011424281 -0.509801142428058 124 8 10.4344794563191 -2.43447945631911 125 20 11.5968155076155 8.40318449238453 126 11 10.1877711519117 0.812228848088272 127 8 10.0961950603485 -2.09619506034849 128 11 10.5726019320053 0.427398067994736 129 10 9.6224772550949 0.377522744905109 130 14 13.0982053069621 0.90179469303788 131 11 13.2630746439127 -2.26307464391267 132 9 11.9930008448726 -2.99300084487263 133 9 8.81318348873168 0.186816511268320 134 8 10.3406420843232 -2.34064208432323 135 10 12.1653415762053 -2.16534157620526 136 13 6.26080149899628 6.73919850100372 137 13 14.3201306238853 -1.32013062388528 138 12 15.8157201479836 -3.81572014798365 139 8 9.4911899531388 -1.49118995313879 140 13 16.6756834890563 -3.67568348905627 141 14 17.9360701355344 -3.93607013553442 142 12 5.26377042879681 6.73622957120319 143 14 16.7984008276639 -2.79840082766390 144 15 15.4109287992392 -0.410928799239195 145 13 12.2164862379305 0.78351376206949 146 16 14.1973004011682 1.80269959883181 147 9 10.1586420511149 -1.15864205111490 148 9 11.4954016740183 -2.49540167401828 149 9 13.0506218734206 -4.05062187342059 150 8 9.28951167527347 -1.28951167527347 151 7 8.9453325554941 -1.9453325554941 152 16 13.5259278990730 2.47407210092696 153 11 5.88537128799132 5.11462871200868 154 9 9.03249631571158 -0.0324963157115840 155 11 10.4996415500084 0.500358449991589 156 9 10.5737950934929 -1.57379509349289 157 14 13.6433189871849 0.356681012815118 158 13 12.7098457858064 0.290154214193631 159 16 9.53377858643988 6.46622141356012 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.299197157899065 0.59839431579813 0.700802842100935 16 0.360950803501772 0.721901607003544 0.639049196498228 17 0.226825081819398 0.453650163638795 0.773174918180602 18 0.264718522878353 0.529437045756707 0.735281477121647 19 0.401486521201952 0.802973042403905 0.598513478798048 20 0.334343178118498 0.668686356236996 0.665656821881502 21 0.395798537782903 0.791597075565807 0.604201462217096 22 0.775997672194069 0.448004655611862 0.224002327805931 23 0.730507026319836 0.538985947360328 0.269492973680164 24 0.98011514849604 0.0397697030079209 0.0198848515039604 25 0.969339714859622 0.0613205702807555 0.0306602851403778 26 0.956573117247739 0.086853765504522 0.043426882752261 27 0.94200695150303 0.115986096993940 0.0579930484969698 28 0.924345076827567 0.151309846344866 0.0756549231724328 29 0.899890875966247 0.200218248067505 0.100109124033753 30 0.873685275557068 0.252629448885863 0.126314724442932 31 0.944773200708683 0.110453598582635 0.0552267992913174 32 0.942309576771944 0.115380846456112 0.0576904232280559 33 0.943995082215729 0.112009835568542 0.0560049177842712 34 0.964622607782794 0.0707547844344125 0.0353773922172063 35 0.969573061708357 0.0608538765832864 0.0304269382916432 36 0.972839133059945 0.0543217338801105 0.0271608669400553 37 0.99514610670107 0.00970778659786018 0.00485389329893009 38 0.99946627906408 0.00106744187184138 0.00053372093592069 39 0.999848197095487 0.000303605809025679 0.000151802904512840 40 0.999761317974885 0.000477364050230377 0.000238682025115189 41 0.999872869097483 0.000254261805034778 0.000127130902517389 42 0.99982810281389 0.000343794372218686 0.000171897186109343 43 0.999818842601438 0.000362314797123236 0.000181157398561618 44 0.999747751900233 0.000504496199533586 0.000252248099766793 45 0.999633004053636 0.000733991892728717 0.000366995946364359 46 0.999857107956848 0.000285784086304912 0.000142892043152456 47 0.999803644297014 0.000392711405971399 0.000196355702985699 48 0.999818477771806 0.000363044456387448 0.000181522228193724 49 0.999999888797058 2.22405883035724e-07 1.11202941517862e-07 50 0.999999806447451 3.87105097687282e-07 1.93552548843641e-07 51 0.999999989348302 2.1303396840489e-08 1.06516984202445e-08 52 0.99999998122387 3.75522618160504e-08 1.87761309080252e-08 53 1 2.74828117060051e-20 1.37414058530026e-20 54 1 1.01788784506055e-21 5.08943922530274e-22 55 1 3.3425615886261e-21 1.67128079431305e-21 56 1 3.40769362733598e-21 1.70384681366799e-21 57 1 1.07793536907343e-21 5.38967684536715e-22 58 1 2.71366330445582e-22 1.35683165222791e-22 59 1 5.24240722160804e-23 2.62120361080402e-23 60 1 1.24869286440906e-22 6.2434643220453e-23 61 1 2.61993146770140e-22 1.30996573385070e-22 62 1 7.7774554357039e-22 3.88872771785195e-22 63 1 2.66417157228663e-21 1.33208578614332e-21 64 1 2.92207973755596e-21 1.46103986877798e-21 65 1 5.81844174948085e-21 2.90922087474042e-21 66 1 7.45790655643018e-21 3.72895327821509e-21 67 1 2.42777939755999e-20 1.21388969877999e-20 68 1 3.33302339836264e-20 1.66651169918132e-20 69 1 8.88025583734823e-20 4.44012791867411e-20 70 1 2.37510068848755e-19 1.18755034424377e-19 71 1 7.545135987206e-19 3.772567993603e-19 72 1 2.05377253670218e-18 1.02688626835109e-18 73 1 6.09396464965254e-19 3.04698232482627e-19 74 1 4.07170550909177e-20 2.03585275454589e-20 75 1 6.4354097842404e-20 3.2177048921202e-20 76 1 1.75349953790275e-19 8.76749768951375e-20 77 1 4.48521124842468e-19 2.24260562421234e-19 78 1 1.46522656784663e-18 7.32613283923317e-19 79 1 4.61760723273371e-18 2.30880361636686e-18 80 1 1.15910106462230e-17 5.79550532311148e-18 81 1 3.66931106294930e-17 1.83465553147465e-17 82 1 1.14824313964726e-16 5.74121569823631e-17 83 1 3.15203312976219e-16 1.57601656488109e-16 84 1 9.54140649788794e-16 4.77070324894397e-16 85 0.999999999999999 2.83006484819312e-15 1.41503242409656e-15 86 0.999999999999996 8.27291935542848e-15 4.13645967771424e-15 87 0.999999999999988 2.32952545756093e-14 1.16476272878047e-14 88 0.99999999999997 5.80764493293656e-14 2.90382246646828e-14 89 0.99999999999993 1.38276515680587e-13 6.91382578402937e-14 90 0.999999999999828 3.43925654901153e-13 1.71962827450577e-13 91 0.999999999999548 9.03420139449399e-13 4.51710069724699e-13 92 0.999999999999025 1.94945155771051e-12 9.74725778855254e-13 93 0.999999999997395 5.20944245973802e-12 2.60472122986901e-12 94 0.999999999993436 1.31273950513604e-11 6.56369752568022e-12 95 0.999999999982888 3.42248059097686e-11 1.71124029548843e-11 96 0.999999999958774 8.24518333837646e-11 4.12259166918823e-11 97 0.999999999961513 7.69745851801696e-11 3.84872925900848e-11 98 0.999999999904977 1.90046252294448e-10 9.50231261472241e-11 99 0.999999999871297 2.57405809342815e-10 1.28702904671407e-10 100 0.999999999740833 5.18334593187106e-10 2.59167296593553e-10 101 0.999999999394054 1.21189227739079e-09 6.05946138695397e-10 102 0.999999999525378 9.49243041686502e-10 4.74621520843251e-10 103 0.999999998986317 2.02736603236428e-09 1.01368301618214e-09 104 0.999999997510719 4.97856209707563e-09 2.48928104853781e-09 105 0.99999999398873 1.20225387934871e-08 6.01126939674357e-09 106 0.999999990215115 1.95697692571343e-08 9.78488462856713e-09 107 0.999999984402011 3.11959778579485e-08 1.55979889289743e-08 108 0.99999997651703 4.69659405075203e-08 2.34829702537601e-08 109 0.99999994493399 1.10132019573979e-07 5.50660097869893e-08 110 0.99999987270321 2.54593579443313e-07 1.27296789721657e-07 111 0.999999733376903 5.33246194055068e-07 2.66623097027534e-07 112 0.999999494166589 1.01166682213634e-06 5.05833411068172e-07 113 0.99999913734545 1.72530909959696e-06 8.62654549798478e-07 114 0.9999984910356 3.01792880118361e-06 1.50896440059180e-06 115 0.999999820312659 3.59374682333375e-07 1.79687341166688e-07 116 0.9999995613981 8.77203798736342e-07 4.38601899368171e-07 117 0.999999046184704 1.90763059101826e-06 9.5381529550913e-07 118 0.999997986618959 4.02676208274442e-06 2.01338104137221e-06 119 0.999995363978086 9.272043828396e-06 4.636021914198e-06 120 0.999992996734184 1.40065316323677e-05 7.00326581618384e-06 121 0.999984615439634 3.07691207314870e-05 1.53845603657435e-05 122 0.999969270114656 6.14597706887002e-05 3.07298853443501e-05 123 0.99993519829215 0.000129603415698171 6.48017078490857e-05 124 0.999868396916523 0.000263206166954893 0.000131603083477446 125 0.999999999999821 3.58009495111676e-13 1.79004747555838e-13 126 0.999999999998965 2.07084826342284e-12 1.03542413171142e-12 127 0.999999999997468 5.0630867341022e-12 2.5315433670511e-12 128 0.999999999985192 2.96165388901382e-11 1.48082694450691e-11 129 0.999999999915927 1.68145768138335e-10 8.40728840691674e-11 130 0.999999999555379 8.89242404685853e-10 4.44621202342927e-10 131 0.999999997738778 4.52244452085647e-09 2.26122226042824e-09 132 0.999999988639976 2.27200479962714e-08 1.13600239981357e-08 133 0.999999946930512 1.06138975131047e-07 5.30694875655237e-08 134 0.999999744555546 5.1088890768623e-07 2.55444453843115e-07 135 0.999999348533744 1.30293251296214e-06 6.5146625648107e-07 136 0.999998252894217 3.49421156526312e-06 1.74710578263156e-06 137 0.99999277504072 1.44499185601954e-05 7.22495928009768e-06 138 0.99997652335206 4.69532958798631e-05 2.34766479399315e-05 139 0.999893456461803 0.000213087076394795 0.000106543538197397 140 0.999559134262038 0.00088173147592411 0.000440865737962055 141 0.998559811786628 0.00288037642674359 0.00144018821337180 142 0.999959809124748 8.0381750504162e-05 4.0190875252081e-05 143 0.999630915452091 0.00073816909581702 0.00036908454790851 144 0.997451538016132 0.00509692396773639 0.00254846198386820 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 108 0.83076923076923 NOK 5% type I error level 109 0.838461538461538 NOK 10% type I error level 114 0.876923076923077 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/10ud4e1290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/10ud4e1290613842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/15cp21290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/15cp21290613842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/2y3o51290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/2y3o51290613842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/3y3o51290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/3y3o51290613842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/4y3o51290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/4y3o51290613842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/5y3o51290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/5y3o51290613842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/69voq1290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/69voq1290613842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/7j35b1290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/7j35b1290613842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/8j35b1290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/8j35b1290613842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/9j35b1290613842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290613816s49mu49x42cazqp/9j35b1290613842.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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