*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, 28 Dec 2010 09:04:46 +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/28/t1293526948wv56qkoy8rt5fhy.htm/, Retrieved Tue, 28 Dec 2010 10:02:40 +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/28/t1293526948wv56qkoy8rt5fhy.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 22 27 27 5 5 26 26 49 49 35 35 1 23 36 36 4 4 25 25 45 45 34 34 1 27 25 25 4 4 17 17 54 54 13 13 1 19 27 27 3 3 37 37 36 36 35 35 1 25 50 50 4 4 27 27 46 46 35 35 1 25 41 41 4 4 36 36 42 42 36 36 1 21 48 48 5 5 25 25 41 41 27 27 1 22 44 44 2 2 29 29 45 45 29 29 1 21 28 28 3 3 26 26 42 42 15 15 1 20 56 56 3 3 24 24 45 45 33 33 1 21 50 50 5 5 29 29 43 43 32 32 1 23 47 47 4 4 26 26 45 45 21 21 1 19 52 52 2 2 21 21 42 42 25 25 1 23 45 45 4 4 21 21 47 47 22 22 1 27 3 3 30 30 41 41 26 26 1 18 52 52 4 4 21 21 44 44 34 34 1 30 46 46 2 2 29 29 51 51 34 34 1 26 58 58 3 3 28 28 46 46 36 36 1 20 54 54 5 5 19 19 47 47 36 36 1 22 29 29 3 3 26 26 46 46 26 26 1 21 43 43 2 2 34 34 50 50 34 34 1 27 45 45 3 3 24 24 51 51 33 33 1 24 46 46 5 5 20 20 47 47 37 37 1 21 25 25 4 4 21 21 46 46 29 29 1 23 47 47 2 2 33 33 43 43 35 35 1 22 41 41 3 3 22 22 55 55 28 28 1 22 29 29 4 4 18 18 52 52 25 25 1 27 45 45 5 5 20 20 56 56 32 32 1 25 54 54 2 2 26 26 46 46 27 27 1 20 28 28 4 4 23 23 51 51 27 27 1 22 37 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 18 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Behoefte_affiliatie[t] = -3.40706072545636 + 0.673472126654755geslacht[t] + 0.168252980774944leeftijd[t] -0.145276934162708leeftijd_man[t] -0.501503813733889opleiding[t] + 0.495292574820152opleiding_man[t] + 0.207046157582967Neuroticisme[t] -0.136851415221506Neuroticisme_man[t] + 0.299625022585822Extraversie[t] + 0.0998485589946164Extraversie_man[t] -0.0326076307539497Openheid[t] + 0.103846821229733Openheid_man[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) -3.40706072545636 2.650164 -1.2856 0.200206 0.100103 geslacht 0.673472126654755 0.040228 16.7414 0 0 leeftijd 0.168252980774944 0.042493 3.9595 0.000107 5.4e-05 leeftijd_man -0.145276934162708 0.065882 -2.2051 0.028692 0.014346 opleiding -0.501503813733889 0.074938 -6.6923 0 0 opleiding_man 0.495292574820152 0.080994 6.1152 0 0 Neuroticisme 0.207046157582967 0.054992 3.765 0.000224 0.000112 Neuroticisme_man -0.136851415221506 0.075238 -1.8189 0.07056 0.03528 Extraversie 0.299625022585822 0.048214 6.2144 0 0 Extraversie_man 0.0998485589946164 0.075381 1.3246 0.18696 0.09348 Openheid -0.0326076307539497 0.069771 -0.4674 0.640802 0.320401 Openheid_man 0.103846821229733 0.083369 1.2456 0.214492 0.107246 Multiple Linear Regression - Regression Statistics Multiple R 0.936114779057777 R-squared 0.87631087957039 Adjusted R-squared 0.868876014407954 F-TEST (value) 117.865066874100 F-TEST (DF numerator) 11 F-TEST (DF denominator) 183 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.64178670153387 Sum Squared Residuals 3942.95163220421 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 22 21.748348930652 0.251651069347999 2 23 20.2220163299175 2.77798367008253 3 27 21.5069611125231 5.49303888747693 4 19 17.3397570139098 1.66024298609017 5 25 21.1547832392673 3.84521676073269 6 25 20.0530963651644 4.94690363483561 7 21 18.3949489906978 2.60505100930225 8 22 20.3428301977092 1.65716980229084 9 21 17.562648574513 3.437351425487 10 20 20.5463145682381 -0.546314568238074 11 21 19.8768231689078 1.12317683109217 12 23 19.6188381088277 3.38116189117226 13 19 18.4817031250709 0.518296874929094 14 23 20.0920986574326 2.90790134256737 15 27 12.2499350829156 14.7500649170844 16 52 51.3284254736285 0.671574526371483 17 46 47.2679266207769 -1.26792662077694 18 58 55.2036719501351 2.79632804986490 19 54 52.8895250717851 1.11047492821487 20 29 31.7945137604001 -2.79451376040011 21 43 45.2501061251123 -2.25010612511227 22 45 46.0413195112632 -1.04131951126319 23 46 47.7911635799841 -1.79116357998408 24 25 30.5607718821628 -5.5607718821628 25 47 47.3390821495466 -0.339082149546559 26 41 41.4355709015558 -0.435570901555839 27 29 32.5119555182890 -3.51195551828896 28 45 46.1674635115992 -1.16746351159921 29 54 48.9039676405074 5.09603235949262 30 28 32.0169455117163 -4.01694551171628 31 37 38.5347587524705 -1.53475875247047 32 56 45.1696380656059 10.8303619343941 33 43 39.7272501441681 3.27274985583187 34 34 34.4515062192942 -0.451506219294211 35 42 41.9569898254178 0.0430101745821530 36 46 41.2427260101216 4.75727398987844 37 25 29.2203415414979 -4.22034154149790 38 25 29.5797919346738 -4.57979193467378 39 25 29.4100803347221 -4.41008033472211 40 48 46.7846991137238 1.21530088627617 41 27 31.5384056125168 -4.53840561251677 42 28 32.2250573111248 -4.22505731112479 43 25 24.8801146119112 0.119885388088787 44 26 29.6725517431804 -3.67255174318038 45 51 49.258484281479 1.74151571852099 46 29 30.5008827464202 -1.50088274642018 47 29 30.9439613347676 -1.94396133476755 48 43 37.501979002716 5.49802099728398 49 44 41.4536434062448 2.5463565937552 50 25 27.5400787928293 -2.54007879282926 51 51 45.1511839880869 5.84881601191314 52 42 41.4683607602728 0.531639239727157 53 25 30.8665663784858 -5.86656637848579 54 51 46.301955967415 4.69804403258495 55 46 46.4480132259165 -0.448013225916463 56 29 23.9082100805726 5.09178991942736 57 3 4.92725384568103 -1.92725384568103 58 1 4.94552151325091 -3.94552151325091 59 4 6.69832203934784 -2.69832203934784 60 5 8.92031191402827 -3.92031191402827 61 4 7.30864305454018 -3.30864305454018 62 4 5.61266300832699 -1.61266300832699 63 3 7.1595973397394 -4.15959733973940 64 5 7.33154021159766 -2.33154021159766 65 3 3.2494488429953 -0.249448842995301 66 26 33.0320054432439 -7.03200544324394 67 30 19.1005444708337 10.8994555291663 68 35 43.4343832915794 -8.43438329157936 69 29 27.8350693093801 1.16493069061994 70 25 25.3502114803408 -0.350211480340851 71 27 30.2148056939411 -3.21480569394109 72 24 23.9243034345596 0.0756965654403998 73 35 31.3181359403666 3.6818640596334 74 32 35.2974595728038 -3.29745957280375 75 24 31.4607351091177 -7.46073510911774 76 38 29.6439445835121 8.35605541648788 77 36 34.3374357230930 1.66256427690696 78 24 25.3284257175568 -1.32842571755682 79 18 18.9776754681178 -0.97767546811778 80 34 29.5371217132533 4.4628782867467 81 23 24.8483120573274 -1.84831205732739 82 34 30.4912882752038 3.50871172479621 83 32 29.8416266712258 2.15837332877420 84 24 25.1111628203216 -1.11116282032158 85 34 27.9599724598287 6.04002754017131 86 33 37.9094208330562 -4.9094208330562 87 33 14.3714156160230 18.6285843839770 88 0 -7.10522473100612 7.10522473100612 89 0 2.06938867088764 -2.06938867088764 90 0 -3.33522560844348 3.33522560844348 91 0 0.204133599192590 -0.204133599192590 92 0 -2.40570771347691 2.40570771347691 93 0 -0.389328862553655 0.389328862553655 94 0 -2.48761938304939 2.48761938304939 95 0 -4.36630618084046 4.36630618084046 96 0 15.1741635010604 -15.1741635010604 97 0 4.45505107693719 -4.45505107693719 98 0 15.1490327660078 -15.1490327660078 99 0 7.93488129258886 -7.93488129258886 100 0 1.23641341591376 -1.23641341591376 101 0 6.21966115192624 -6.21966115192624 102 0 -4.94480671805528 4.94480671805528 103 0 5.72321924406316 -5.72321924406316 104 0 3.75774906424738 -3.75774906424738 105 0 12.3186109965140 -12.3186109965140 106 2 -2.75158451321112 4.75158451321112 107 2 0.742304488413337 1.25769551158666 108 2 14.9384006508831 -12.9384006508831 109 22 25.4270371949954 -3.42703719499536 110 19 28.3545858820960 -9.35458588209604 111 19 22.5924530319061 -3.5924530319061 112 24 22.2786651714277 1.7213348285723 113 22 20.2378685327953 1.76213146720469 114 26 21.5011884969175 4.49881150308245 115 23 17.3466109290971 5.6533890709029 116 21 23.0021649890824 -2.00216498908235 117 16 18.3557139769996 -2.35571397699964 118 21 23.1295240715956 -2.12952407159562 119 18 21.6059475930155 -3.60594759301551 120 20 21.7168958012726 -1.71689580127257 121 24 19.5640064052214 4.43599359477857 122 24 24.038289835222 -0.0382898352219761 123 23 21.5332682569219 1.46673174307814 124 23 22.8448500840493 0.155149915950730 125 22 22.7234868752787 -0.723486875278708 126 17 18.9176817112553 -1.91768171125532 127 25 23.1449155368164 1.8550844631836 128 25 18.1067400060772 6.89325999392276 129 23 26.0016715322332 -3.00167153223319 130 27 22.6765642962009 4.32343570379908 131 23 19.9378859920083 3.06211400799173 132 19 21.5732170295688 -2.57321702956885 133 19 21.1834971450390 -2.18349714503904 134 26 23.2875493467651 2.71245065323492 135 19 22.4872427258826 -3.48724272588259 136 22 24.8723522021251 -2.87235220212512 137 20 22.8344398790570 -2.83443987905705 138 21 23.4077106878281 -2.40771068782806 139 21 25.2379795171793 -4.23797951717931 140 28 23.9302269612611 4.06977303873889 141 24 21.4942597736537 2.50574022634631 142 29 21.7641625404963 7.23583745950366 143 24 21.8087141280025 2.19128587199747 144 25 22.7861573783320 2.21384262166796 145 19 14.6642338938153 4.33576610618471 146 23 20.9155102228600 2.08448977714004 147 22 22.8307942959363 -0.830794295936259 148 24 20.0822944536113 3.91770554638869 149 19 20.5856618559172 -1.58566185591719 150 21 25.2376536173498 -4.23765361734983 151 18 21.0030919447211 -3.00309194472112 152 24 22.6399765132228 1.36002348677723 153 24 23.6666314844636 0.333368515536428 154 23 22.6461205829295 0.353879417070497 155 24 24.8194716820622 -0.819471682062188 156 20 21.2190315864365 -1.21903158643651 157 20 21.3324076453930 -1.33240764539297 158 22 16.0270566281677 5.97294337183228 159 18 22.5811304858573 -4.58113048585735 160 14 21.6767510419284 -7.67675104192838 161 29 21.4237458739071 7.57625412609288 162 25 21.0307597508793 3.96924024912074 163 20 19.5619444525489 0.438055547451104 164 25 21.5216561842221 3.47834381577794 165 21 21.3327154548758 -0.332715454875766 166 21 23.1232970491942 -2.12329704919421 167 13 14.9617834989334 -1.96178349893337 168 23 19.7535832619979 3.24641673800206 169 24 24.1611919214367 -0.161191921436717 170 16 21.7489376917492 -5.74893769174915 171 20 20.8191020866479 -0.819102086647907 172 24 24.3112616244492 -0.311261624449218 173 27 22.5421061603906 4.45789383960938 174 27 22.9436875491107 4.05631245088932 175 19 20.8996468692792 -1.89964686927921 176 22 23.3344280313776 -1.33442803137760 177 18 17.3836463784110 0.616353621588978 178 24 24.2907879529218 -0.290787952921754 179 20 23.073566985346 -3.073566985346 180 20 23.3474608443502 -3.34746084435016 181 27 22.3481110760967 4.6518889239033 182 19 22.6993152268611 -3.69931522686106 183 23 23.7670757960041 -0.767075796004081 184 30 27.1547488627135 2.84525113728653 185 22 26.7075806352532 -4.70758063525322 186 23 23.6901962057048 -0.690196205704802 187 22 21.8367652157659 0.163234784234105 188 22 18.9079040268737 3.09209597312629 189 23 17.3126604075962 5.68733959240384 190 27 21.361481538327 5.63851846167302 191 23 18.7763423127515 4.22365768724854 192 18 19.4388515278364 -1.43885152783636 193 24 23.2488557166491 0.751144283350882 194 19 17.7564019245008 1.24359807549921 195 22 21.7483489306520 0.251651069347983 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.215362582593849 0.430725165187699 0.78463741740615 16 0.100356498905236 0.200712997810472 0.899643501094764 17 0.0721989204815564 0.144397840963113 0.927801079518444 18 0.17281349040834 0.34562698081668 0.82718650959166 19 0.115499037549060 0.230998075098119 0.88450096245094 20 0.368362513048028 0.736725026096057 0.631637486951972 21 0.408962715236971 0.817925430473943 0.591037284763029 22 0.326635606087403 0.653271212174806 0.673364393912597 23 0.255113859316252 0.510227718632504 0.744886140683748 24 0.384367411552011 0.768734823104023 0.615632588447989 25 0.336424878323777 0.672849756647555 0.663575121676223 26 0.262909234818869 0.525818469637738 0.737090765181131 27 0.205635834882543 0.411271669765087 0.794364165117457 28 0.157738837034984 0.315477674069968 0.842261162965016 29 0.152835588841729 0.305671177683459 0.847164411158271 30 0.162601584610264 0.325203169220528 0.837398415389736 31 0.191331121834652 0.382662243669304 0.808668878165348 32 0.202613248653597 0.405226497307195 0.797386751346403 33 0.161612196684764 0.323224393369528 0.838387803315236 34 0.150200379559721 0.300400759119442 0.84979962044028 35 0.184024522621623 0.368049045243245 0.815975477378378 36 0.146938776260958 0.293877552521917 0.853061223739042 37 0.179241638490663 0.358483276981325 0.820758361509337 38 0.197341284953868 0.394682569907736 0.802658715046132 39 0.210406783939034 0.420813567878068 0.789593216060966 40 0.170270639679787 0.340541279359574 0.829729360320213 41 0.168778316708247 0.337556633416493 0.831221683291753 42 0.193080158995717 0.386160317991434 0.806919841004283 43 0.158891296795040 0.317782593590081 0.84110870320496 44 0.136969588763247 0.273939177526493 0.863030411236753 45 0.109986983352110 0.219973966704221 0.89001301664789 46 0.0939365251220418 0.187873050244084 0.906063474877958 47 0.0885670560975504 0.177134112195101 0.91143294390245 48 0.079148076027895 0.15829615205579 0.920851923972105 49 0.0640539746801228 0.128107949360246 0.935946025319877 50 0.0567359354958267 0.113471870991653 0.943264064504173 51 0.0521359364743199 0.104271872948640 0.94786406352568 52 0.0397030748288348 0.0794061496576697 0.960296925171165 53 0.0353387324244644 0.0706774648489288 0.964661267575536 54 0.0300589352230090 0.0601178704460179 0.96994106477699 55 0.0226722066131327 0.0453444132262653 0.977327793386867 56 0.0207421434963337 0.0414842869926675 0.979257856503666 57 0.0246755477701217 0.0493510955402435 0.975324452229878 58 0.0296679135963044 0.0593358271926088 0.970332086403696 59 0.0236718317795020 0.0473436635590041 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0.00867185865823972 0.99566407067088 77 0.00436936500363724 0.00873873000727448 0.995630634996363 78 0.00317646055050405 0.0063529211010081 0.996823539449496 79 0.00406206630988613 0.00812413261977225 0.995937933690114 80 0.00583632590487032 0.0116726518097406 0.99416367409513 81 0.00537329451228376 0.0107465890245675 0.994626705487716 82 0.00421126779588991 0.00842253559177981 0.99578873220411 83 0.00317660367704165 0.0063532073540833 0.996823396322958 84 0.00277572501410627 0.00555145002821255 0.997224274985894 85 0.00232396741208654 0.00464793482417308 0.997676032587913 86 0.00184901901876031 0.00369803803752062 0.99815098098124 87 0.737138116801499 0.525723766397002 0.262861883198501 88 0.896233852522632 0.207532294954737 0.103766147477368 89 0.975432806638962 0.0491343867220757 0.0245671933610378 90 0.972138083051578 0.0557238338968447 0.0278619169484223 91 0.976165982105744 0.0476680357885115 0.0238340178942557 92 0.971086877120515 0.0578262457589691 0.0289131228794845 93 0.967804074562653 0.0643918508746931 0.0321959254373465 94 0.96077794049026 0.0784441190194793 0.0392220595097397 95 0.95142744331061 0.0971451133787814 0.0485725566893907 96 0.99727033220816 0.0054593355836818 0.0027296677918409 97 0.997644021351574 0.00471195729685181 0.00235597864842591 98 0.999661739158167 0.000676521683665654 0.000338260841832827 99 0.99967904153082 0.00064191693835867 0.000320958469179335 100 0.999534122678169 0.000931754643662572 0.000465877321831286 101 0.999864183150462 0.00027163369907631 0.000135816849538155 102 0.999994110521967 1.17789560667192e-05 5.88947803335959e-06 103 0.999999491338301 1.01732339758802e-06 5.08661698794012e-07 104 0.999999285951468 1.42809706335037e-06 7.14048531675183e-07 105 0.999999373926369 1.25214726274043e-06 6.26073631370214e-07 106 0.99999942506103 1.14987793765528e-06 5.7493896882764e-07 107 0.999998991325606 2.01734878796480e-06 1.00867439398240e-06 108 0.999999135591822 1.72881635581287e-06 8.64408177906435e-07 109 0.999998567666243 2.86466751377055e-06 1.43233375688527e-06 110 0.99999904507988 1.90984024179249e-06 9.54920120896244e-07 111 0.999998551973913 2.89605217371246e-06 1.44802608685623e-06 112 0.999998109925921 3.78014815759783e-06 1.89007407879891e-06 113 0.999997127735896 5.7445282076537e-06 2.87226410382685e-06 114 0.999997790704483 4.41859103322388e-06 2.20929551661194e-06 115 0.999997921640625 4.15671875069295e-06 2.07835937534647e-06 116 0.999996614339943 6.77132011428564e-06 3.38566005714282e-06 117 0.999995652096956 8.69580608833472e-06 4.34790304416736e-06 118 0.999992944637003 1.41107259943242e-05 7.05536299716211e-06 119 0.999991672623885 1.66547522292634e-05 8.32737611463171e-06 120 0.999991597490765 1.68050184700253e-05 8.40250923501267e-06 121 0.999990599317925 1.88013641510149e-05 9.40068207550743e-06 122 0.9999841014168 3.17971664004673e-05 1.58985832002336e-05 123 0.999975028257426 4.99434851488382e-05 2.49717425744191e-05 124 0.99996009983146 7.98003370784096e-05 3.99001685392048e-05 125 0.999935024253963 0.000129951492073994 6.49757460369972e-05 126 0.999930512265327 0.000138975469345870 6.94877346729348e-05 127 0.9998962640513 0.000207471897400076 0.000103735948700038 128 0.999911556247676 0.000176887504648553 8.84437523242764e-05 129 0.999877106254677 0.000245787490645389 0.000122893745322694 130 0.999856492182706 0.000287015634587106 0.000143507817293553 131 0.999800130855119 0.00039973828976256 0.00019986914488128 132 0.999786816076767 0.000426367846466064 0.000213183923233032 133 0.999687133375283 0.000625733249433282 0.000312866624716641 134 0.999635659601637 0.000728680796725657 0.000364340398362829 135 0.9995247743565 0.000950451287000153 0.000475225643500076 136 0.999361937099412 0.00127612580117658 0.000638062900588291 137 0.999256205613828 0.00148758877234492 0.000743794386172461 138 0.998928288229604 0.00214342354079205 0.00107171177039602 139 0.998841284985926 0.00231743002814873 0.00115871501407437 140 0.998877038320283 0.00224592335943491 0.00112296167971746 141 0.998493455987545 0.00301308802490964 0.00150654401245482 142 0.9994534580788 0.00109308384240035 0.000546541921200173 143 0.999215411280411 0.00156917743917792 0.000784588719588961 144 0.999124483294262 0.00175103341147561 0.000875516705737805 145 0.998734234376014 0.00253153124797116 0.00126576562398558 146 0.998200953317273 0.00359809336545327 0.00179904668272663 147 0.997423964876703 0.00515207024659377 0.00257603512329689 148 0.997074924080549 0.00585015183890287 0.00292507591945143 149 0.99557568700423 0.00884862599154143 0.00442431299577071 150 0.994288365525502 0.0114232689489956 0.0057116344744978 151 0.992301282801644 0.0153974343967124 0.00769871719835622 152 0.989507209913568 0.0209855801728640 0.0104927900864320 153 0.984527502636402 0.0309449947271953 0.0154724973635976 154 0.977398030738307 0.0452039385233870 0.0226019692616935 155 0.967648132250461 0.0647037354990773 0.0323518677495387 156 0.96404114038991 0.071917719220182 0.035958859610091 157 0.950141857653475 0.099716284693049 0.0498581423465245 158 0.942998591808453 0.114002816383093 0.0570014081915467 159 0.944767555763339 0.110464888473323 0.0552324442366614 160 0.981107276543447 0.0377854469131061 0.0188927234565530 161 0.987443239343292 0.0251135213134153 0.0125567606567076 162 0.984660291580593 0.0306794168388135 0.0153397084194067 163 0.978447297552106 0.0431054048957873 0.0215527024478936 164 0.97178863225288 0.0564227354942414 0.0282113677471207 165 0.95679302644441 0.0864139471111784 0.0432069735555892 166 0.937982003680166 0.124035992639667 0.0620179963198336 167 0.925784824927336 0.148430350145328 0.0742151750726642 168 0.894638902054241 0.210722195891518 0.105361097945759 169 0.853141873854548 0.293716252290904 0.146858126145452 170 0.886912942132982 0.226174115734036 0.113087057867018 171 0.853895552975753 0.292208894048493 0.146104447024247 172 0.7953910290545 0.409217941891 0.2046089709455 173 0.784218204099605 0.43156359180079 0.215781795900395 174 0.807656773205263 0.384686453589474 0.192343226794737 175 0.738212897722801 0.523574204554398 0.261787102277199 176 0.636445775600833 0.727108448798334 0.363554224399167 177 0.518655859688863 0.962688280622273 0.481344140311137 178 0.391480725727014 0.782961451454028 0.608519274272986 179 0.325961687099964 0.651923374199928 0.674038312900036 180 0.230434975599165 0.46086995119833 0.769565024400835 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 67 0.403614457831325 NOK 5% type I error level 96 0.578313253012048 NOK 10% type I error level 110 0.662650602409639 NOK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

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• 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.

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