Multiple regression interactie 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: Sun, 19 Dec 2010 14:24:36 +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/t12927685768bw5mlbswt1umij.htm/, Retrieved Sun, 19 Dec 2010 15:22:58 +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/t12927685768bw5mlbswt1umij.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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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 8 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Concernovermistakes[t] = -1.14208682459579 -0.524489276020748Gender[t] + 1.06323858761745Doubtsaboutactions[t] -0.380848998061055DoubtsaboutactionsMale[t] + 0.439180646830957Parentalexpectations[t] -0.316205593815534ParentalexpectationsMale[t] + 0.0492100023570672Parentalcritism[t] + 0.193886948718829ParentalcritismMale[t] + 0.434261540435126Personalstandards[t] + 0.211023390416551PersonalstandarsMale[t] -0.178643594475865Organization[t] + 0.0804598211776829OrganizationMale[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) -1.14208682459579 6.203746 -0.1841 0.854192 0.427096 Gender -0.524489276020748 7.159094 -0.0733 0.941697 0.470849 Doubtsaboutactions 1.06323858761745 0.245987 4.3223 2.8e-05 1.4e-05 DoubtsaboutactionsMale -0.380848998061055 0.291892 -1.3048 0.194013 0.097006 Parentalexpectations 0.439180646830957 0.235734 1.863 0.064452 0.032226 ParentalexpectationsMale -0.316205593815534 0.288464 -1.0962 0.274797 0.137398 Parentalcritism 0.0492100023570672 0.309117 0.1592 0.873734 0.436867 ParentalcritismMale 0.193886948718829 0.371429 0.522 0.602454 0.301227 Personalstandards 0.434261540435126 0.188518 2.3036 0.02265 0.011325 PersonalstandarsMale 0.211023390416551 0.220129 0.9586 0.339316 0.169658 Organization -0.178643594475865 0.241571 -0.7395 0.460779 0.230389 OrganizationMale 0.0804598211776829 0.26771 0.3005 0.764184 0.382092 Multiple Linear Regression - Regression Statistics Multiple R 0.653897516090643 R-squared 0.427581961549513 Adjusted R-squared 0.384747958672265 F-TEST (value) 9.98230220917869 F-TEST (DF numerator) 11 F-TEST (DF denominator) 147 p-value 2.08166817117217e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.4888649398094 Sum Squared Residuals 2962.03654183396 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 24.9423040595443 -0.942304059544293 2 25 20.7692180238026 4.23078197619738 3 17 21.6588520672489 -4.65885206724886 4 18 19.6988120131452 -1.69881201314523 5 18 19.7868905977742 -1.78689059777417 6 16 19.6836381417533 -3.68363814175333 7 20 21.159962121258 -1.15996212125798 8 16 22.646195599835 -6.64619559983502 9 18 21.2388374379206 -3.23883743792058 10 17 20.2364529466662 -3.2364529466662 11 23 23.3825868991959 -0.382586899195856 12 30 24.1711511673814 5.82884883261862 13 23 15.0954666599784 7.90453334002165 14 18 18.5484274069757 -0.548427406975659 15 15 21.8187209733092 -6.81872097330915 16 12 18.5535433377901 -6.55354333779013 17 21 18.8034583067089 2.19654169329108 18 15 14.9458255599567 0.0541744400432935 19 20 19.4246530942086 0.575346905791424 20 31 28.1657779645931 2.83422203540693 21 27 26.3168690720348 0.68313092796522 22 34 25.7265828303198 8.27341716968023 23 21 19.5553957585136 1.44460424148641 24 31 20.4183062614063 10.5816937385937 25 19 19.293763540965 -0.293763540965028 26 16 20.0949907626089 -4.09499076260888 27 20 21.5538504192133 -1.55385041921326 28 21 18.1654815813 2.83451841869996 29 22 21.8590151679029 0.140984832097126 30 17 19.4702292939256 -2.47022929392556 31 24 21.0074708241862 2.9925291758138 32 25 31.5656960418462 -6.56569604184618 33 26 28.0828392659918 -2.08283926599177 34 25 25.1644433643501 -0.164443364350052 35 17 22.8236426983663 -5.82364269836634 36 32 27.9774994048258 4.02250059517419 37 33 24.3130016193849 8.6869983806151 38 13 20.7526435357688 -7.75264353576879 39 32 27.5607483925355 4.43925160746453 40 25 26.1545144766169 -1.15451447661695 41 29 26.3632936214077 2.63670637859235 42 22 22.2583499927237 -0.258349992723706 43 18 16.3115860846403 1.68841391535973 44 17 21.531912294451 -4.53191229445097 45 20 21.4577829171132 -1.45778291711317 46 15 20.270704450416 -5.27070445041597 47 20 21.2982166023216 -1.29821660232164 48 33 28.7449425648644 4.25505743513558 49 29 21.90188267034 7.09811732966004 50 23 25.7493967439965 -2.7493967439965 51 26 23.9630429834536 2.03695701654645 52 18 19.0899794389121 -1.08997943891207 53 20 18.837356995785 1.162643004215 54 11 12.2463331491222 -1.24633314912217 55 28 29.0007533693093 -1.00075336930933 56 26 23.2644737232018 2.73552627679824 57 22 21.6215698328716 0.378430167128377 58 17 20.2995683923861 -3.29956839238613 59 12 14.1234210702199 -2.12342107021986 60 14 19.5757676996896 -5.57576769968957 61 17 21.1707474092448 -4.17074740924482 62 21 21.3450049718871 -0.345004971887127 63 19 22.6831210618985 -3.6831210618985 64 18 23.5065893287069 -5.50658932870688 65 10 18.709502773827 -8.70950277382704 66 29 22.855013391673 6.14498660832696 67 31 18.9298997271919 12.0701002728081 68 19 22.5036349993754 -3.50363499937545 69 9 19.6986401539535 -10.6986401539535 70 20 22.814739515118 -2.81473951511796 71 28 17.7155755140245 10.2844244859755 72 19 17.7348459781321 1.26515402186793 73 30 22.5379883230151 7.4620116769849 74 29 29.1322664156139 -0.132266415613881 75 26 21.7004788295495 4.29952117045048 76 23 20.0878621571161 2.91213784288394 77 13 22.0405247642595 -9.04052476425948 78 21 22.2714801955397 -1.27148019553967 79 19 21.6908490967628 -2.69084909676285 80 28 22.8690470388916 5.13095296110844 81 23 25.3976847453734 -2.39768474537341 82 18 14.1423283472244 3.8576716527756 83 21 22.0406148442709 -1.04061484427091 84 20 21.9668236869759 -1.96682368697587 85 23 19.6956151398068 3.30438486019325 86 21 20.058276948159 0.941723051841026 87 21 22.1465277764837 -1.14652777648374 88 15 22.9326532228602 -7.9326532228602 89 28 27.1557072578047 0.844292742195257 90 19 17.2426836738804 1.75731632611959 91 26 20.1667377693182 5.83326223068183 92 10 14.0917571047697 -4.09175710476967 93 16 18.0258569869123 -2.02585698691232 94 22 20.6931203094073 1.30687969059265 95 19 19.1586469681539 -0.158646968153896 96 31 28.7532990026829 2.24670099731706 97 31 27.1145521413714 3.88544785862862 98 29 23.8243272394283 5.17567276057169 99 19 18.1068904862713 0.893109513728712 100 22 18.4504081554198 3.54959184458023 101 23 22.2126092712644 0.787390728735574 102 15 16.3654648491145 -1.36546484911445 103 20 23.0905260744088 -3.09052607440877 104 18 19.4590764571501 -1.45907645715011 105 23 21.7289424384878 1.27105756151219 106 25 19.9521757167279 5.04782428327214 107 21 16.6699744127812 4.33002558721882 108 24 19.2922324920596 4.70776750794042 109 25 24.5399796497142 0.460020350285787 110 17 18.4385752193267 -1.43857521932672 111 13 14.138417248809 -1.13841724880896 112 28 17.9360230731659 10.0639769268341 113 21 18.7071628046759 2.29283719532414 114 25 28.9136168761303 -3.91361687613027 115 9 17.776881901358 -8.77688190135805 116 16 17.8012740491434 -1.80127404914342 117 19 21.2206633917085 -2.22066339170854 118 17 19.445273492466 -2.44527349246603 119 25 23.7592344276679 1.24076557233213 120 20 14.9082126611262 5.09178733887383 121 29 22.2902877494989 6.7097122505011 122 14 18.4159921299277 -4.41599212992771 123 22 26.5492269358502 -4.54922693585019 124 15 16.4400769249615 -1.44007692496149 125 19 27.765479525271 -8.76547952527104 126 20 21.1678176564002 -1.16781765640015 127 15 17.2187759762336 -2.21877597623364 128 20 22.0712708109681 -2.0712708109681 129 18 20.0650485389788 -2.06504853897884 130 33 25.5071932146388 7.49280678536118 131 22 23.8319819566121 -1.83198195661209 132 16 16.1581061045482 -0.158106104548236 133 17 19.331915847776 -2.33191584777595 134 16 15.228605866749 0.77139413325102 135 21 17.9935288064238 3.00647119357618 136 26 29.3163687474651 -3.31636874746508 137 18 20.4355091264752 -2.43550912647522 138 18 22.5832417179526 -4.58324171795257 139 17 18.6506868004316 -1.65068680043158 140 22 24.5639614430524 -2.56396144305244 141 30 24.6485067734088 5.35149322659116 142 30 27.3600115469982 2.63998845300177 143 24 28.2693642619644 -4.26936426196441 144 21 21.8401508579456 -0.840150857945623 145 21 24.6506413237422 -3.65064132374216 146 29 27.1292471957497 1.87075280425034 147 31 23.0200102449627 7.9799897550373 148 20 19.1166425280135 0.883357471986517 149 16 16.1603669233113 -0.160366923311333 150 22 19.1168098747331 2.88319012526692 151 20 21.2681143587145 -1.26811435871451 152 28 26.2944615704235 1.70553842957646 153 38 25.9897977244103 12.0102022755897 154 22 20.978095668294 1.02190433170595 155 20 24.9826336832755 -4.98263368327548 156 17 18.9311226711803 -1.9311226711803 157 28 24.3148071262336 3.68519287376636 158 22 23.5163093357837 -1.51630933578374 159 31 28.8110972026645 2.18890279733554 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.167330673591397 0.334661347182794 0.832669326408603 16 0.181300682747078 0.362601365494156 0.818699317252922 17 0.0920854777082034 0.184170955416407 0.907914522291797 18 0.0948946936694731 0.189789387338946 0.905105306330527 19 0.147114767373459 0.294229534746918 0.852885232626541 20 0.0908462519180936 0.181692503836187 0.909153748081906 21 0.101801150974099 0.203602301948199 0.8981988490259 22 0.279382627212769 0.558765254425537 0.720617372787231 23 0.217789254129114 0.435578508258229 0.782210745870886 24 0.637056522442035 0.72588695511593 0.362943477557965 25 0.557923686943383 0.884152626113233 0.442076313056617 26 0.496203527656505 0.99240705531301 0.503796472343495 27 0.430764419207606 0.861528838415213 0.569235580792394 28 0.362505320678984 0.725010641357968 0.637494679321016 29 0.296148029866199 0.592296059732397 0.703851970133802 30 0.2377298038437 0.4754596076874 0.7622701961563 31 0.358583838696648 0.717167677393296 0.641416161303352 32 0.34306440697545 0.6861288139509 0.65693559302455 33 0.305373759858965 0.61074751971793 0.694626240141035 34 0.360718468625089 0.721436937250177 0.639281531374911 35 0.376991391074125 0.75398278214825 0.623008608925875 36 0.386588653065091 0.773177306130183 0.613411346934909 37 0.605488784051666 0.789022431896668 0.394511215948334 38 0.716847948644151 0.566304102711698 0.283152051355849 39 0.685511493221379 0.628977013557241 0.314488506778621 40 0.63750434594241 0.724991308115182 0.362495654057591 41 0.585718485923958 0.828563028152083 0.414281514076042 42 0.527988956529989 0.944022086940021 0.472011043470011 43 0.498231477093479 0.996462954186957 0.501768522906521 44 0.469293457587486 0.938586915174971 0.530706542412514 45 0.418137991525621 0.836275983051243 0.581862008474379 46 0.45846008070331 0.91692016140662 0.54153991929669 47 0.420178513644055 0.840357027288109 0.579821486355946 48 0.396669269389944 0.793338538779889 0.603330730610056 49 0.479735665725134 0.959471331450269 0.520264334274866 50 0.442082518928325 0.88416503785665 0.557917481071675 51 0.424286684314683 0.848573368629366 0.575713315685317 52 0.374178963347351 0.748357926694703 0.625821036652649 53 0.329127145217039 0.658254290434078 0.670872854782961 54 0.288624362980398 0.577248725960797 0.711375637019602 55 0.274988383174718 0.549976766349437 0.725011616825282 56 0.243775373836244 0.487550747672488 0.756224626163756 57 0.213519104236147 0.427038208472293 0.786480895763853 58 0.192171199575519 0.384342399151039 0.80782880042448 59 0.179937416305192 0.359874832610385 0.820062583694807 60 0.198051665925635 0.396103331851269 0.801948334074365 61 0.200144188367927 0.400288376735855 0.799855811632073 62 0.16913767780553 0.338275355611059 0.83086232219447 63 0.158306056677427 0.316612113354855 0.841693943322573 64 0.204915346470809 0.409830692941618 0.795084653529191 65 0.364611917689138 0.729223835378277 0.635388082310862 66 0.4404317073274 0.8808634146548 0.5595682926726 67 0.739019368040553 0.521961263918894 0.260980631959447 68 0.720101837912761 0.559796324174478 0.279898162087239 69 0.874288752607414 0.251422494785172 0.125711247392586 70 0.858860919464806 0.282278161070388 0.141139080535194 71 0.946257591362679 0.107484817274643 0.0537424086373213 72 0.933677699753693 0.132644600492615 0.0663223002463073 73 0.955898592262975 0.0882028154740507 0.0441014077370254 74 0.943679979735106 0.112640040529787 0.0563200202648936 75 0.94342596415181 0.113148071696381 0.0565740358481903 76 0.936999815662873 0.126000368674253 0.0630001843371267 77 0.975434294941854 0.0491314101162924 0.0245657050581462 78 0.968593691261831 0.0628126174763375 0.0314063087381688 79 0.963043770239 0.0739124595220002 0.0369562297610001 80 0.965186642081173 0.0696267158376543 0.0348133579188272 81 0.958448084230597 0.0831038315388054 0.0415519157694027 82 0.956594156454717 0.0868116870905656 0.0434058435452828 83 0.94437284206 0.111254315879999 0.0556271579399993 84 0.932601410355255 0.13479717928949 0.0673985896447448 85 0.92411216897796 0.15177566204408 0.0758878310220399 86 0.909699307960478 0.180601384079044 0.0903006920395218 87 0.892652243153568 0.214695513692863 0.107347756846432 88 0.94177853702644 0.116442925947119 0.0582214629735597 89 0.927566951305406 0.144866097389187 0.0724330486945936 90 0.911286184585729 0.177427630828543 0.0887138154142713 91 0.919352687462659 0.161294625074682 0.0806473125373408 92 0.916851551511985 0.16629689697603 0.0831484484880148 93 0.897878159200597 0.204243681598807 0.102121840799403 94 0.876068712371942 0.247862575256116 0.123931287628058 95 0.848180265074817 0.303639469850367 0.151819734925183 96 0.82082155579845 0.3583568884031 0.17917844420155 97 0.832066703365875 0.335866593268251 0.167933296634125 98 0.841461041619406 0.317077916761189 0.158538958380594 99 0.812480737020043 0.375038525959914 0.187519262979957 100 0.797086800177257 0.405826399645487 0.202913199822743 101 0.759391737364118 0.481216525271764 0.240608262635882 102 0.718202958158406 0.563594083683188 0.281797041841594 103 0.723398714353863 0.553202571292274 0.276601285646137 104 0.68246238054813 0.63507523890374 0.31753761945187 105 0.636769808399979 0.726460383200042 0.363230191600021 106 0.636617284107878 0.726765431784244 0.363382715892122 107 0.638499016824035 0.72300196635193 0.361500983175965 108 0.676735033954783 0.646529932090434 0.323264966045217 109 0.627297138384994 0.745405723230012 0.372702861615006 110 0.585480251115504 0.829039497768991 0.414519748884496 111 0.532558908897338 0.934882182205323 0.467441091102662 112 0.854746082251163 0.290507835497673 0.145253917748837 113 0.866709001964756 0.266581996070488 0.133290998035244 114 0.893904485560258 0.212191028879483 0.106095514439742 115 0.951814777165337 0.0963704456693262 0.0481852228346631 116 0.943592247765607 0.112815504468785 0.0564077522343927 117 0.95735254378382 0.0852949124323591 0.0426474562161796 118 0.951239559985181 0.0975208800296374 0.0487604400148187 119 0.937648607456986 0.124702785086028 0.0623513925430142 120 0.964513765208541 0.070972469582917 0.0354862347914585 121 0.959376525366174 0.0812469492676512 0.0406234746338256 122 0.95125542646218 0.0974891470756395 0.0487445735378197 123 0.96158138643817 0.0768372271236585 0.0384186135618292 124 0.945938287267787 0.108123425464426 0.0540617127322129 125 0.947218353308485 0.10556329338303 0.0527816466915148 126 0.928518353478138 0.142963293043725 0.0714816465218624 127 0.910631852169153 0.178736295661693 0.0893681478308465 128 0.890174586305547 0.219650827388905 0.109825413694453 129 0.851640532389012 0.296718935221977 0.148359467610988 130 0.894701598146356 0.210596803707289 0.105298401853644 131 0.859552095659584 0.280895808680832 0.140447904340416 132 0.873818050020376 0.252363899959248 0.126181949979624 133 0.880383454139704 0.239233091720592 0.119616545860296 134 0.848192380084286 0.303615239831428 0.151807619915714 135 0.819521957758839 0.360956084482323 0.180478042241161 136 0.772720369133352 0.454559261733296 0.227279630866648 137 0.700243873967861 0.599512252064277 0.299756126032139 138 0.865332895382019 0.269334209235962 0.134667104617981 139 0.838556767018054 0.322886465963893 0.161443232981946 140 0.77525851934405 0.449482961311901 0.22474148065595 141 0.720665020351559 0.558669959296883 0.279334979648442 142 0.596867063720007 0.806265872559986 0.403132936279993 143 0.645677144065565 0.70864571186887 0.354322855934435 144 0.497912368382193 0.995824736764385 0.502087631617807 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 1 0.00769230769230769 OK 10% type I error level 14 0.107692307692308 NOK

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http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/10q74v1292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/10q74v1292768664.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/169eq1292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/169eq1292768664.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/2uf641292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/2uf641292768664.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/3uf641292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/3uf641292768664.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/4uf641292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/4uf641292768664.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/5uf641292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/5uf641292768664.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/6576p1292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/6576p1292768664.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/7yy5a1292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/7yy5a1292768664.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/8yy5a1292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/8yy5a1292768664.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/9yy5a1292768664.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927685768bw5mlbswt1umij/9yy5a1292768664.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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Software written by Ed van Stee & Patrick Wessa

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