Multiple Regression Minitutorial 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 23:09:01 +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/t12905538855tpnkmiiwv11coy.htm/, Retrieved Wed, 24 Nov 2010 00:11:36 +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/t12905538855tpnkmiiwv11coy.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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7 7 1 7 7 1 7 7 1 5 6 1 5 5 1 5 5 1 6 6 2 5 6 1 4 5 1 4 5 2 5 6 2 5 6 2 5 6 2 5 6 2 5 6 1 6 7 1 7 5 1 6 7 1 7 7 1 7 7 1 7 6 2 6 7 1 5 6 1 5 7 1 6 7 1 3 7 2 7 7 1 6 6 1 6 6 1 5 6 1 5 4 1 7 7 1 4 7 2 5 6 1 6 7 1 6 7 2 4 6 1 5 6 1 4 5 1 6 7 1 3 6 1 6 6 1 6 6 1 7 7 1 7 7 1 5 6 2 5 6 3 6 6 2 3 4 1 7 7 1 4 7 1 7 7 1 7 7 1 6 7 2 3 7 1 7 7 1 6 7 2 5 6 2 6 7 2 6 6 1 3 3 1 5 5 1 4 4 2 5 7 1 7 7 NA 5 7 1 2 5 1 4 5 1 2 6 1 6 7 1 7 6 1 6 7 2 3 6 1 7 7 2 5 7 1 6 5 1 7 6 1 6 5 1 6 5 1 7 6 1 6 5 1 5 6 1 3 6 1 5 7 1 5 5 1 7 6 1 5 6 2 7 6 1 5 6 1 5 6 1 6 6 1 7 6 1 6 6 2 5 5 1 5 5 1 6 6 1 5 4 4 5 3 6 5 1 1 4 5 3 4 3 3 4 5 2 4 4 1 5 5 1 6 7 2 6 6 2 6 6 2 5 5 2 5 6 1 7 7 1 5 7 1 5 7 1 5 7 1 5 5 2 7 7 1 7 7 1 7 7 2 5 7 1 7 6 1 5 6 1 5 7 1 6 7 1 5 7 1 6 5 1 6 7 1 7 6 1 5 6 2 7 6 2 5 6 1 6 6 1 7 6 2 7 5 2 7 3 1 6 5 1 6 6 2 5 6 4 6 6 4 3 6 1 5 5 1 4 6 2 4 5 2 5 4 3 7 7 3 6 7 2 6 6 2 5 6 2 5 6 1 2 6 3 6 7 2 4 7 2 4 6 2 5 6 2 4 5 1 4 5 1 3 5 1 6 5 1 6 6 2 7 7 1 5 7 1 3 5 1 6 4 1 4 3 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 11 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Q1_2[t] = + 1.47989704422343 + 0.211536296316238Q1_3[t] -0.226959234717235Q1_5[t] + 0.142272613689836Q1_7[t] -0.173251587312293Q1_8[t] + 0.0994300625037737Q1_12[t] + 0.53567045852253Q1_16[t] + 0.000956764440532458Q1_22[t] -0.0478595993824866GENDER[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) 1.47989704422343 0.784861 1.8856 0.061251 0.030625 Q1_3 0.211536296316238 0.080826 2.6172 0.009756 0.004878 Q1_5 -0.226959234717235 0.118389 -1.9171 0.057094 0.028547 Q1_7 0.142272613689836 0.074375 1.9129 0.057629 0.028814 Q1_8 -0.173251587312293 0.115132 -1.5048 0.134434 0.067217 Q1_12 0.0994300625037737 0.098505 1.0094 0.31438 0.15719 Q1_16 0.53567045852253 0.086879 6.1657 0 0 Q1_22 0.000956764440532458 0.100131 0.0096 0.992389 0.496194 GENDER -0.0478595993824866 0.165571 -0.2891 0.772929 0.386464 Multiple Linear Regression - Regression Statistics Multiple R 0.589970343960876 R-squared 0.348065006753315 Adjusted R-squared 0.313976902531266 F-TEST (value) 10.2107469657459 F-TEST (DF numerator) 8 F-TEST (DF denominator) 153 p-value 2.23783214181594e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.990510676830705 Sum Squared Residuals 150.110044340090 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7 6.32480009222537 0.675199907774634 2 5 5.10196729722794 -0.101967297227940 3 6 4.16608601667588 1.83391398332412 4 4 4.54274740644399 -0.542747406443987 5 5 4.80214330214271 0.197856697857290 6 6 6.13563280832744 -0.13563280832744 7 7 6.27598372840237 0.724016271597629 8 6 5.14216553511294 0.857834464887056 9 6 5.85513969996981 0.144860300030186 10 6 5.07194508804601 0.928054911953991 11 5 4.03532022832659 0.96467977167341 12 5 5.38746112431429 -0.387461124314292 13 4 4.39304525139311 -0.39304525139311 14 6 5.39233400181527 0.60766599818473 15 6 6.11326379590915 -0.113263795909145 16 5 5.38938422378653 -0.38938422378653 17 3 4.08317982770908 -1.08317982770908 18 7 5.74127003432037 1.25872996567963 19 3 5.74127003432037 -2.74127003432037 20 5 5.30683478704278 -0.306834787042785 21 3 3.88287158593367 -0.882871585933669 22 5 6.1421037760948 -1.1421037760948 23 2 1.91452162163266 0.0854783783673403 24 6 8.14135294136786 -2.14135294136786 25 3 2.53739509950160 0.462604900498395 26 6 5.5373950995016 0.462604900498395 27 6 5.64608401141703 0.353915988582967 28 5 4.95482180603712 0.0451781939628805 29 5 2.92967247435617 2.07032752564383 30 7 6.70202856087589 0.297971439124111 31 6 6.42705822387476 -0.427058223874759 32 5 4.83784342982508 0.162156570174922 33 5 5.25607315951451 -0.256073159514507 34 4 5.16861909261657 -1.16861909261657 35 4 2.89559955200953 1.10440044799047 36 6 6.04192287886408 -0.0419228788640837 37 5 4.9191408195371 0.0808591804629012 38 5 4.2769404928429 0.723059507157103 39 7 7.42575399805208 -0.425753998052083 40 5 5.11118656149049 -0.111186561490486 41 5 4.75849812146254 0.241501878537462 42 6 6.08668852952238 -0.0866885295223836 43 5 5.33617231746166 -0.336172317461661 44 6 4.09839864554963 1.90160135445037 45 7 5.61801665436056 1.38198334563944 46 5 4.09080680450832 0.909193195491678 47 5 4.85160280094473 0.148397199055271 48 5 3.80214330214271 1.19785669785729 49 6 7.96163180033853 -1.96163180033853 50 2 2.26551607917965 -0.265516079179649 51 4 5.14155623205455 -1.14155623205455 52 4 2.81496364414685 1.18503635585315 53 6 7.66837121451023 -1.66837121451023 54 3 2.59804065619 0.401959343810002 55 6 5.20235412417224 0.79764587582776 56 6 5.92871570991564 0.0712842900843596 57 5 4.78817286926232 0.211827130737680 58 6 10.1057043809311 -4.10570438093112 59 1 0.668165614338178 0.331834385661822 60 5 2.47657767254177 2.52342232745823 61 7 7.19495402597906 -0.194954025979062 62 4 4.00172464097907 -0.00172464097907366 63 5 3.74137198083982 1.25862801916018 64 6 6.35696090937731 -0.356960909377314 65 4 3.37789439866960 0.622105601330403 66 6 5.65918600968983 0.340813990310173 67 6 6.24518851062446 -0.245188510624456 68 5 4.89869350073372 0.101306499266285 69 5 6.85680771398819 -1.85680771398819 70 3 2.96891394780065 0.0310860521993497 71 5 4.38746112431429 0.612538875685708 72 6 6.65918600968983 -0.659186009689827 73 5 4.97877049444785 0.0212295055521520 74 6 5.88855969620663 0.111440303793373 75 6 6.36950398386554 -0.369503983865540 76 4 4.01569507385947 -0.015695073859465 77 4 3.30407287386108 0.695927126138923 78 6 4.74127003432037 1.25872996567963 79 7 7.64288062754604 -0.642880627546036 80 4 5.44732178683359 -1.44732178683359 81 5 4.07098832360548 0.929011676394524 82 6 5.3960792502523 0.603920749747699 83 6 6.96142445657461 -0.961424456574614 84 5 7.06980790376235 -2.06980790376235 85 3 1.49049226455965 1.50950773544035 86 7 6.67702339989039 0.322976600109612 87 6 5.87465649212607 0.125343507873927 88 4 4.86555077201281 -0.865550772012814 89 4 3.94537268027308 0.0546273197269198 90 5 5.85955397380869 -0.859553973808687 91 3 1.74066073126198 1.25933926873802 92 7 6.84476038058003 0.155239619419968 93 6 5.64590025557248 0.354099744427515 94 6 6.52085169112248 -0.520851691122482 95 4 4.25345917518032 -0.253459175180322 96 5 4.55879918274552 0.44120081725448 97 6 5.8057443798843 0.194255620115704 98 5 3.66166661745328 1.33833338254672 99 6 5.88321061246772 0.116789387532276 100 6 7.39703601469283 -1.39703601469283 101 4 4.09143564240886 -0.0914356424088597 102 5 4.12627746434900 0.873722535650996 103 6 6.60761554656854 -0.60761554656854 104 5 4.98484401521904 0.0151559847809555 105 5 6.00460450489184 -1.00460450489184 106 4 4.71504222931575 -0.715042229315748 107 4 2.78658726692125 1.21341273307875 108 6 6.89798225115588 -0.897982251155876 109 5 4.60761554656854 0.392384453431461 110 6 7.32480009222538 -1.32480009222538 111 5 4.67702339989039 0.322976600109612 112 6 6.06368258822399 -0.0636825882239886 113 5 5.22818813461624 -0.228188134616236 114 4 3.78912963370285 0.210870366297147 115 6 5.9887324287035 0.0112675712965032 116 4 2.63151649328932 1.36848350671068 117 5 5.21349648790703 -0.213496487907030 118 5 4.01501039509642 0.984989604903584 119 6 6.89580952781846 -0.895809527818462 120 3 3.73287961951631 -0.732879619516309 121 5 4.70175647519841 0.298243524801594 122 4 2.32976676934099 1.67023323065901 123 5 4.32480009222538 0.675199907774616 124 5 5.54528977322559 -0.545289773225589 125 7 5.36510027662984 1.63489972337016 126 5 4.27815053358415 0.721849466415853 127 7 6.88478511481473 0.115214885185267 128 5 4.55975594718605 0.440244052813947 129 4 3.97024992627654 0.0297500737234563 130 6 7.04743889134406 -1.04743889134406 131 4 3.44360726891870 0.556392731081304 132 4 4.63929760503499 -0.63929760503499 133 4 3.26674492929766 0.733255070702336 134 4 3.29497120947918 0.705028790520819 135 6 5.54448236131893 0.455517638681069 136 6 7.54514671215415 -1.54514671215415 137 5 4.59804065619 0.401959343810002 138 3 3.30683478704278 -0.306834787042785 139 6 6.60324146023097 -0.603241460230972 140 5 5.1240164740858 -0.124016474085796 141 4 5.74396433323544 -1.74396433323544 142 5 5.20594703717998 -0.205947037179979 143 2 0.902304656338277 1.09769534366172 144 5 5.85945202728924 -0.859452027289238 145 7 8.21708940091477 -1.21708940091477 146 4 3.40543600008806 0.594563999911937 147 4 3.19733707597582 0.802662924024185 148 7 7.12999649282165 -0.129996492821651 149 6 5.85680771398819 0.143192286011814 150 5 5.74628708251679 -0.746287082516791 151 5 4.45785344165267 0.542146558347333 152 5 5.2769404928429 -0.276940492842897 153 7 6.77129224350229 0.228707756497709 154 6 5.97037784125858 0.0296221587414193 155 6 9.48008637435508 -3.48008637435508 156 5 4.70059231106869 0.299407688931307 157 2 1.84476038058003 0.155239619419968 158 4 4.46438616843817 -0.464386168438171 159 6 5.09536464669044 0.904635353309561 160 5 4.7171794135994 0.282820586400598 161 5 5.96160821241916 -0.961608212419163 162 5 4.96727357743301 0.0327264225669863 163 4 NA NA 164 4 NA NA Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.118972714956854 0.237945429913708 0.881027285043146 13 0.329179826092859 0.658359652185719 0.67082017390714 14 0.201883786134971 0.403767572269943 0.798116213865029 15 0.188312890032631 0.376625780065261 0.81168710996737 16 0.137447537492015 0.27489507498403 0.862552462507985 17 0.175920632099529 0.351841264199059 0.82407936790047 18 0.117805540031868 0.235611080063737 0.882194459968132 19 0.860780931793674 0.278438136412651 0.139219068206326 20 0.821411950194735 0.357176099610530 0.178588049805265 21 0.773772383246228 0.452455233507543 0.226227616753772 22 0.761378431949241 0.477243136101519 0.238621568050759 23 0.697198452586272 0.605603094827456 0.302801547413728 24 0.689914669359845 0.62017066128031 0.310085330640155 25 0.640220557572272 0.719558884855456 0.359779442427728 26 0.579361540826491 0.841276918347017 0.420638459173509 27 0.508798328377329 0.982403343245342 0.491201671622671 28 0.447572688001334 0.895145376002669 0.552427311998666 29 0.630714090087362 0.738571819825275 0.369285909912638 30 0.570037907413934 0.859924185172131 0.429962092586066 31 0.539535746625076 0.920928506749849 0.460464253374924 32 0.538750949673242 0.922498100653516 0.461249050326758 33 0.482903880887967 0.965807761775935 0.517096119112033 34 0.452422661377208 0.904845322754416 0.547577338622792 35 0.426406220229132 0.852812440458264 0.573593779770868 36 0.36713888344114 0.73427776688228 0.63286111655886 37 0.330823856006182 0.661647712012365 0.669176143993818 38 0.295863797252517 0.591727594505033 0.704136202747483 39 0.264788166864044 0.529576333728088 0.735211833135956 40 0.220050260115008 0.440100520230015 0.779949739884992 41 0.181543588639501 0.363087177279003 0.818456411360499 42 0.148553520183578 0.297107040367156 0.851446479816422 43 0.118043260690735 0.23608652138147 0.881956739309265 44 0.249045743079582 0.498091486159164 0.750954256920418 45 0.264069232389105 0.52813846477821 0.735930767610895 46 0.33419944024689 0.66839888049378 0.66580055975311 47 0.289980404397062 0.579960808794123 0.710019595602938 48 0.284738141457005 0.569476282914011 0.715261858542995 49 0.542979001275319 0.914041997449363 0.457020998724681 50 0.50415277035308 0.99169445929384 0.49584722964692 51 0.572930762335005 0.85413847532999 0.427069237664995 52 0.561639052828678 0.876721894342645 0.438360947171322 53 0.65669325817182 0.68661348365636 0.34330674182818 54 0.613891676947714 0.772216646104571 0.386108323052286 55 0.618029363472767 0.763941273054466 0.381970636527233 56 0.569832608181947 0.860334783636105 0.430167391818053 57 0.521896274197407 0.956207451605186 0.478103725802593 58 0.967944006369273 0.0641119872614532 0.0320559936307266 59 0.960161028173764 0.0796779436524719 0.0398389718262359 60 0.991411564956954 0.0171768700860928 0.0085884350430464 61 0.989378964172012 0.0212420716559754 0.0106210358279877 62 0.98548932237316 0.0290213552536821 0.0145106776268411 63 0.988709444257548 0.0225811114849047 0.0112905557424523 64 0.98546680150216 0.0290663969956781 0.0145331984978390 65 0.982824327072803 0.0343513458543944 0.0171756729271972 66 0.97799058758096 0.0440188248380815 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0.146800239640603 101 0.823043126164934 0.353913747670131 0.176956873835066 102 0.816638317321823 0.366723365356355 0.183361682678177 103 0.793396852126059 0.413206295747882 0.206603147873941 104 0.755888199045457 0.488223601909085 0.244111800954543 105 0.77243272961202 0.455134540775961 0.227567270387981 106 0.761118084691807 0.477763830616385 0.238881915308193 107 0.789208446146094 0.421583107707811 0.210791553853906 108 0.770219972083209 0.459560055833582 0.229780027916791 109 0.737149483934959 0.525701032130083 0.262850516065041 110 0.754448557222804 0.491102885554391 0.245551442777196 111 0.719759166686728 0.560481666626545 0.280240833313272 112 0.681038895169742 0.637922209660515 0.318961104830258 113 0.640535229501936 0.718929540996128 0.359464770498064 114 0.597469778719117 0.805060442561766 0.402530221280883 115 0.550882581019462 0.898234837961075 0.449117418980538 116 0.653802761003758 0.692394477992484 0.346197238996242 117 0.605970965386221 0.788058069227559 0.394029034613779 118 0.586988781878482 0.826022436243036 0.413011218121518 119 0.54905920045506 0.901881599089879 0.450940799544940 120 0.521022062403636 0.957955875192727 0.478977937596364 121 0.468905726461781 0.937811452923562 0.531094273538219 122 0.506077567628906 0.987844864742188 0.493922432371094 123 0.489167303362523 0.978334606725046 0.510832696637477 124 0.44180369420841 0.88360738841682 0.55819630579159 125 0.634721492565375 0.73055701486925 0.365278507434625 126 0.609471873600668 0.781056252798665 0.390528126399332 127 0.558709344727744 0.882581310544512 0.441290655272256 128 0.512779114184209 0.974441771631583 0.487220885815791 129 0.461467399734777 0.922934799469553 0.538532600265223 130 0.452797005359385 0.90559401071877 0.547202994640615 131 0.390324406409727 0.780648812819453 0.609675593590273 132 0.336103047450635 0.67220609490127 0.663896952549365 133 0.339776430686065 0.67955286137213 0.660223569313935 134 0.298872238959641 0.597744477919282 0.701127761040359 135 0.279843785230844 0.559687570461687 0.720156214769156 136 0.3750495006461 0.7500990012922 0.6249504993539 137 0.33152865297438 0.66305730594876 0.66847134702562 138 0.269292588184308 0.538585176368616 0.730707411815692 139 0.212618932218121 0.425237864436241 0.78738106778188 140 0.190027788842290 0.380055577684581 0.80997221115771 141 0.228733267466930 0.457466534933859 0.77126673253307 142 0.170539287919054 0.341078575838108 0.829460712080946 143 0.165847441641709 0.331694883283417 0.834152558358291 144 0.156351492902047 0.312702985804094 0.843648507097953 145 0.159549521311012 0.319099042622025 0.840450478688988 146 0.109916534333784 0.219833068667567 0.890083465666216 147 0.082414354932107 0.164828709864214 0.917585645067893 148 0.0610985634475787 0.122197126895157 0.938901436552421 149 0.0369741433360638 0.0739482866721276 0.963025856663936 150 0.0176388803344763 0.0352777606689526 0.982361119665524 151 0.57973473654083 0.84053052691834 0.42026526345917 152 0.506214540453736 0.987570919092527 0.493785459546264 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 9 0.0638297872340425 NOK 10% type I error level 23 0.163120567375887 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/102mna1290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/102mna1290553729.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/1lcrd1290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/1lcrd1290553729.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/2elqg1290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/2elqg1290553729.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/3elqg1290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/3elqg1290553729.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/4pcpj1290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/4pcpj1290553729.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/5pcpj1290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/5pcpj1290553729.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/6pcpj1290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/6pcpj1290553729.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/7hmo41290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/7hmo41290553729.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/8sv671290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/8sv671290553729.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/9sv671290553729.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t12905538855tpnkmiiwv11coy/9sv671290553729.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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