MR organisatie

*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, 12 Dec 2010 19:33:58 +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/12/t1292182315gixel0n6resfdwz.htm/, Retrieved Sun, 12 Dec 2010 20:32:05 +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/12/t1292182315gixel0n6resfdwz.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 «

1 26 9 15 6 25 25 13 1 20 9 15 6 25 24 16 1 21 9 14 13 19 21 19 0 31 14 10 8 18 23 15 1 21 8 10 7 18 17 14 1 18 8 12 9 22 19 13 1 26 11 18 5 29 18 19 1 22 10 12 8 26 27 15 1 22 9 14 9 25 23 14 1 29 15 18 11 23 23 15 0 15 14 9 8 23 29 16 1 16 11 11 11 23 21 16 0 24 14 11 12 24 26 16 1 17 6 17 8 30 25 17 0 19 20 8 7 19 25 15 0 22 9 16 9 24 23 15 1 31 10 21 12 32 26 20 0 28 8 24 20 30 20 18 1 38 11 21 7 29 29 16 0 26 14 14 8 17 24 16 1 25 11 7 8 25 23 19 1 25 16 18 16 26 24 16 0 29 14 18 10 26 30 17 1 28 11 13 6 25 22 17 0 15 11 11 8 23 22 16 1 18 12 13 9 21 13 15 0 21 9 13 9 19 24 14 1 25 7 18 11 35 17 15 0 23 13 14 12 19 24 12 1 23 10 12 8 20 21 14 1 19 9 9 7 21 23 16 0 18 9 12 8 21 24 14 0 18 13 8 9 24 24 7 0 26 16 5 4 23 24 10 0 18 12 10 8 19 23 14 1 18 6 11 8 17 26 16 0 28 14 11 8 24 24 16 0 17 14 12 6 15 21 16 1 29 10 12 8 25 23 14 0 12 4 15 4 27 28 20 1 28 12 16 14 27 22 14 1 20 14 14 10 18 24 11 1 17 9 17 9 25 21 15 1 17 9 13 6 22 23 16 0 20 10 10 8 26 23 14 1 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Organization[t] = + 10.4980892782650 -2.15563229975179Gender[t] -0.0564877448316168Concern_mistakes[t] + 0.244749750576054Doubts_actions[t] -0.0878682090368594Parental_expectations[t] -0.265570849993746Parental_criticism[t] + 0.392456084218977Personal_standards[t] + 0.422739980796405PLC[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) 10.4980892782650 2.786009 3.7681 0.000244 0.000122 Gender -2.15563229975179 0.576467 -3.7394 0.000271 0.000135 Concern_mistakes -0.0564877448316168 0.060567 -0.9326 0.352655 0.176327 Doubts_actions 0.244749750576054 0.116078 2.1085 0.036822 0.018411 Parental_expectations -0.0878682090368594 0.100194 -0.877 0.382045 0.191022 Parental_criticism -0.265570849993746 0.123447 -2.1513 0.033221 0.01661 Personal_standards 0.392456084218977 0.074806 5.2463 1e-06 0 PLC 0.422739980796405 0.127707 3.3102 0.001194 0.000597 Multiple Linear Regression - Regression Statistics Multiple R 0.573275404225135 R-squared 0.328644689089493 Adjusted R-squared 0.29408963632204 F-TEST (value) 9.51075639505441 F-TEST (DF numerator) 7 F-TEST (DF denominator) 136 p-value 1.39365097240329e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.21599473075328 Sum Squared Residuals 1406.59660671967 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25 21.472096988388 3.52790301161201 2 24 23.0792433997669 0.920756600233053 3 21 20.1651113510913 0.834888648908694 4 23 22.5755260341188 0.424473965881232 5 17 19.3591035484242 -2.35910354842417 6 19 19.9687730209373 -0.96877302093731 7 18 26.0698269340776 -8.06982693407763 8 27 22.9131966912254 4.08680330877458 9 23 21.4119436075665 1.58805639243349 10 23 21.2402411734250 1.75975882657496 11 29 25.9522185623528 3.04778143764721 12 21 22.0334002979863 -1.03340029798626 13 26 24.5982651250385 1.40173487496149 14 25 24.1925996663637 0.807400333636295 15 25 25.5556408278409 -0.555640827840934 16 23 23.422123385822 -0.422123385822011 17 26 25.0201461157300 0.979853884269965 18 20 22.8371785917333 -2.83717859173326 19 29 23.3290077266109 5.67099227338905 20 24 22.5367758187068 1.46322418129316 21 23 24.7263280914576 -1.72632809145756 22 24 21.9831958868122 2.01680411318783 23 30 25.4395427868443 4.56045721315574 24 22 23.7153173411362 -1.71531734113623 25 22 25.0422328925509 -3.04223289255091 26 13 21.3129276915785 -8.3129276915785 27 24 21.3571953558729 2.64280464412709 28 17 24.2176671587708 -7.2176671587708 29 24 20.493158147903 3.50684185209701 30 21 20.0792324602835 0.920767539716471 31 23 21.8275452119501 1.17245478804995 32 24 22.6650098178363 1.33499018216368 33 24 21.9480991933763 2.05190080662367 34 24 24.6976692217011 -0.697669221701099 35 23 22.7900833192002 0.20991668079975 36 26 19.1386521001101 6.86134789988987 37 24 25.4345975456870 -1.43459754568703 38 21 22.9671314718146 -1.96713147181465 39 23 21.7025864123887 1.29741358761129 40 28 27.4700426969025 0.529957303097546 41 22 21.0885878907005 0.911412109299517 42 24 18.4676844681942 5.53231553180579 43 21 21.8535176854104 -0.853517685410424 44 23 22.2470747996786 0.752925200321428 45 23 24.9348009179177 -1.93480091791775 46 20 21.3931241230190 -1.39312412301902 47 23 20.7780159128226 2.22198408717736 48 21 24.0299104076889 -3.0299104076889 49 27 23.2469819362511 3.75301806374891 50 12 17.8166990172771 -5.81669901727711 51 15 18.7665289716325 -3.76652897163253 52 22 20.4183450465599 1.58165495344011 53 21 19.5055525482457 1.49444745175431 54 21 22.1194646511379 -1.11946465113793 55 20 21.5758841065183 -1.57588410651831 56 24 24.3546187203705 -0.35461872037052 57 24 23.3714504282183 0.628549571781727 58 29 22.3706096541294 6.62939034587064 59 25 24.4518013300897 0.54819866991034 60 14 22.683699240133 -8.683699240133 61 30 28.7862420875115 1.21375791248855 62 19 20.9538997508260 -1.95389975082595 63 29 25.9943798344302 3.00562016556984 64 25 22.0904787670295 2.90952123297051 65 25 24.4787749269239 0.521225073076063 66 25 22.3834045500627 2.61659544993727 67 16 19.3687662022976 -3.36876620229763 68 25 22.7909457951786 2.20905420482138 69 28 26.0994772382378 1.90052276176221 70 24 25.2064180391179 -1.20641803911790 71 25 24.2066076864779 0.793392313522083 72 21 20.1229986346376 0.877001365362361 73 22 25.4263920823487 -3.42639208234869 74 20 23.7405371241445 -3.74053712414450 75 25 24.1096143904700 0.890385609529972 76 27 24.7016884668325 2.29831153316749 77 21 21.3073343474352 -0.307334347435201 78 13 21.7383813724922 -8.7383813724922 79 26 22.5738779720636 3.42612202793639 80 26 21.1582999621859 4.84170003781407 81 25 23.2026101209902 1.79738987900978 82 22 21.3411122660312 0.658887733968845 83 19 17.2199647905347 1.78003520946534 84 23 24.2461468721482 -1.24614687214816 85 25 23.389074037079 1.61092596292101 86 15 19.8798227475879 -4.87982274758793 87 21 22.4240017076957 -1.42400170769567 88 23 23.4264092752187 -0.426409275218671 89 25 22.0785473666180 2.92145263338204 90 24 21.4788199100982 2.52118008990184 91 24 23.0117236392979 0.9882763607021 92 21 22.0912030827286 -1.09120308272860 93 24 23.3291237019031 0.67087629809695 94 22 24.7657798718929 -2.76577987189294 95 24 23.3753019633647 0.624698036635341 96 28 23.2161736797711 4.7838263202289 97 21 21.5153309258635 -0.51533092586346 98 17 19.4762223709726 -2.47622237097255 99 28 21.5169156371676 6.4830843628324 100 24 28.9986856133420 -4.99868561334197 101 10 16.3074511320150 -6.30745113201503 102 20 22.0336494080228 -2.03364940802282 103 22 22.4559782390996 -0.455978239099575 104 19 21.9081692193606 -2.9081692193606 105 22 23.4648450283357 -1.46484502833571 106 22 18.6244014788176 3.3755985211824 107 26 26.0819504133096 -0.0819504133096118 108 24 24.5750069933073 -0.575006993307343 109 22 20.8601352981832 1.13986470181681 110 20 20.8118660711419 -0.811866071141882 111 20 19.7848225474946 0.215177452505362 112 15 20.2297284984893 -5.22972849848927 113 20 20.8779498215596 -0.87794982155955 114 20 20.3271011066555 -0.327101106655502 115 24 23.0566569236910 0.943343076309048 116 29 23.0593227759856 5.94067722401444 117 23 23.8840700984419 -0.884070098441859 118 24 23.3327868466602 0.667213153339774 119 22 22.1142202882036 -0.114220288203600 120 16 19.0750945500881 -3.07509455008814 121 23 23.6034576104604 -0.603457610460381 122 27 22.2786331410636 4.72136685893639 123 16 20.2507785906528 -4.25077859065278 124 21 21.5490183366986 -0.549018336698598 125 26 22.0798255891111 3.92017441088892 126 22 21.9342754767984 0.0657245232015839 127 23 23.0249327649373 -0.0249327649373062 128 19 20.8363307180701 -1.83633071807008 129 18 18.0527671656676 -0.0527671656676029 130 24 20.8201850792315 3.1798149207685 131 29 23.1933302521853 5.80666974781469 132 22 18.4523989894751 3.54760101052488 133 24 23.3521679757137 0.647832024286278 134 22 21.2197162553014 0.780283744698575 135 12 21.5448795754195 -9.5448795754195 136 26 27.0990846642795 -1.09908466427955 137 18 21.4884535143454 -3.48845351434542 138 22 25.0422328925509 -3.04223289255091 139 24 21.8707923130622 2.12920768693783 140 21 22.0766958455374 -1.07669584553738 141 15 18.6360674521269 -3.63606745212691 142 23 24.2461468721482 -1.24614687214816 143 22 22.1142202882036 -0.114220288203600 144 24 21.9319275597089 2.06807244029110 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.835932502227607 0.328134995544786 0.164067497772393 12 0.782283779125978 0.435432441748044 0.217716220874022 13 0.751600654190144 0.496798691619712 0.248399345809856 14 0.64447937299062 0.711041254018761 0.355520627009380 15 0.530391448837033 0.939217102325935 0.469608551162967 16 0.474898928292455 0.94979785658491 0.525101071707545 17 0.377348300053574 0.754696600107147 0.622651699946426 18 0.449829473411108 0.899658946822216 0.550170526588892 19 0.540409061059599 0.919181877880802 0.459590938940401 20 0.524522723562992 0.950954552874015 0.475477276437008 21 0.465650507472302 0.931301014944604 0.534349492527698 22 0.385988172294766 0.771976344589531 0.614011827705234 23 0.420071012169619 0.840142024339238 0.579928987830381 24 0.373883729486149 0.747767458972299 0.62611627051385 25 0.337490525430194 0.674981050860388 0.662509474569806 26 0.67554028916968 0.648919421660641 0.324459710830320 27 0.643864320786672 0.712271358426656 0.356135679213328 28 0.871989509614126 0.256020980771747 0.128010490385874 29 0.847762128477124 0.304475743045752 0.152237871522876 30 0.806902886782026 0.386194226435949 0.193097113217974 31 0.778585890422773 0.442828219154455 0.221414109577227 32 0.733879556562722 0.532240886874555 0.266120443437278 33 0.687771766282127 0.624456467435746 0.312228233717873 34 0.669926201636844 0.660147596726312 0.330073798363156 35 0.61334230557235 0.773315388855301 0.386657694427651 36 0.761966091108882 0.476067817782236 0.238033908891118 37 0.726203075991101 0.547593848017798 0.273796924008899 38 0.706349605069491 0.587300789861017 0.293650394930509 39 0.657718095048295 0.68456380990341 0.342281904951705 40 0.636969527719061 0.726060944561878 0.363030472280939 41 0.584200170633455 0.83159965873309 0.415799829366545 42 0.629989584653955 0.74002083069209 0.370010415346045 43 0.580840141931712 0.838319716136577 0.419159858068288 44 0.528327569561863 0.943344860876274 0.471672430438137 45 0.485441362494334 0.970882724988668 0.514558637505666 46 0.460289414455365 0.92057882891073 0.539710585544635 47 0.423194179981029 0.846388359962058 0.576805820018971 48 0.419317374885659 0.838634749771318 0.580682625114341 49 0.453646655414215 0.907293310828431 0.546353344585785 50 0.648932338128152 0.702135323743696 0.351067661871848 51 0.686682165579313 0.626635668841373 0.313317834420687 52 0.64757973780507 0.70484052438986 0.35242026219493 53 0.606677160655094 0.786645678689812 0.393322839344906 54 0.562183109873139 0.875633780253722 0.437816890126861 55 0.519647174010968 0.960705651978064 0.480352825989032 56 0.4683662714956 0.9367325429912 0.5316337285044 57 0.418774445166567 0.837548890333134 0.581225554833433 58 0.577686354841707 0.844627290316585 0.422313645158293 59 0.533380030255386 0.933239939489227 0.466619969744614 60 0.7947266071237 0.410546785752599 0.205273392876299 61 0.773159688245722 0.453680623508556 0.226840311754278 62 0.74933885477968 0.501322290440641 0.250661145220320 63 0.747206929257402 0.505586141485195 0.252793070742598 64 0.736163757458787 0.527672485082426 0.263836242541213 65 0.695425615148002 0.609148769703997 0.304574384851998 66 0.673934145868465 0.65213170826307 0.326065854131535 67 0.687313053512212 0.625373892975577 0.312686946487788 68 0.666944204580404 0.666111590839192 0.333055795419596 69 0.637207435700847 0.725585128598306 0.362792564299153 70 0.596512136531554 0.806975726936892 0.403487863468446 71 0.549684717898977 0.900630564202046 0.450315282101023 72 0.502902972452088 0.994194055095824 0.497097027547912 73 0.505045842923073 0.989908314153853 0.494954157076927 74 0.516838520108713 0.966322959782574 0.483161479891287 75 0.470156761505566 0.940313523011131 0.529843238494434 76 0.446702295487636 0.893404590975272 0.553297704512364 77 0.398064373290314 0.796128746580628 0.601935626709686 78 0.697544879163227 0.604910241673545 0.302455120836773 79 0.700333093511373 0.599333812977255 0.299666906488627 80 0.753226463231044 0.493547073537913 0.246773536768956 81 0.723263905119706 0.553472189760589 0.276736094880294 82 0.687660813298927 0.624678373402145 0.312339186701072 83 0.655138656330301 0.689722687339398 0.344861343669699 84 0.614482482218531 0.771035035562939 0.385517517781469 85 0.578267529498262 0.843464941003477 0.421732470501738 86 0.634885014027263 0.730229971945475 0.365114985972737 87 0.596028589557074 0.80794282088585 0.403971410442926 88 0.54806329609506 0.90387340780988 0.45193670390494 89 0.548532860140392 0.902934279719216 0.451467139859608 90 0.54049430324104 0.91901139351792 0.45950569675896 91 0.495582022540904 0.991164045081808 0.504417977459096 92 0.451063869602721 0.902127739205443 0.548936130397279 93 0.415658110334496 0.831316220668991 0.584341889665504 94 0.391352910410673 0.782705820821345 0.608647089589327 95 0.349678274492409 0.699356548984817 0.650321725507591 96 0.393903281013575 0.78780656202715 0.606096718986425 97 0.345946108413104 0.691892216826208 0.654053891586896 98 0.332137367946913 0.664274735893826 0.667862632053087 99 0.531612518702001 0.936774962595998 0.468387481297999 100 0.673407729971518 0.653184540056964 0.326592270028482 101 0.768932459125814 0.462135081748372 0.231067540874186 102 0.730932056815566 0.538135886368868 0.269067943184434 103 0.685294218130572 0.629411563738856 0.314705781869428 104 0.698222962463022 0.603554075073955 0.301777037536978 105 0.65001071655655 0.699978566886899 0.349989283443450 106 0.667474650237553 0.665050699524893 0.332525349762446 107 0.611978881029944 0.776042237940113 0.388021118970056 108 0.55311976134628 0.89376047730744 0.44688023865372 109 0.514418567550734 0.971162864898532 0.485581432449266 110 0.470230246302234 0.940460492604468 0.529769753697766 111 0.408174117524988 0.816348235049976 0.591825882475012 112 0.457167144330914 0.914334288661828 0.542832855669086 113 0.396658211277157 0.793316422554314 0.603341788722843 114 0.335353670822361 0.670707341644722 0.664646329177639 115 0.27769679218416 0.55539358436832 0.72230320781584 116 0.450521066674806 0.901042133349613 0.549478933325194 117 0.384143259448017 0.768286518896035 0.615856740551983 118 0.385766468555411 0.771532937110823 0.614233531444589 119 0.318314425886883 0.636628851773767 0.681685574113116 120 0.264046872786571 0.528093745573142 0.735953127213429 121 0.206385799309297 0.412771598618594 0.793614200690703 122 0.251996261174219 0.503992522348439 0.748003738825781 123 0.207068465755578 0.414136931511155 0.792931534244423 124 0.163055228926365 0.326110457852731 0.836944771073635 125 0.167363114487302 0.334726228974605 0.832636885512698 126 0.128728470968656 0.257456941937312 0.871271529031344 127 0.0870540175363354 0.174108035072671 0.912945982463665 128 0.0561018529315769 0.112203705863154 0.943898147068423 129 0.0334882006833902 0.0669764013667804 0.96651179931661 130 0.0195227293141082 0.0390454586282165 0.980477270685892 131 0.0323597946594215 0.064719589318843 0.967640205340578 132 0.215598900552752 0.431197801105505 0.784401099447248 133 0.242860754031431 0.485721508062863 0.757139245968569 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 0.008130081300813 OK 10% type I error level 3 0.024390243902439 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/10c9x31292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/10c9x31292182426.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/1npir1292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/1npir1292182426.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/2npir1292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/2npir1292182426.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/3gzic1292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/3gzic1292182426.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/4gzic1292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/4gzic1292182426.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/5gzic1292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/5gzic1292182426.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/6qqzf1292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/6qqzf1292182426.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/7jzyi1292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/7jzyi1292182426.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/8jzyi1292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/8jzyi1292182426.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/9jzyi1292182426.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182315gixel0n6resfdwz/9jzyi1292182426.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = quantiles ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;

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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by