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Maanden Beursspel

*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: Thu, 02 Dec 2010 20:12:20 +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/02/t1291320678k7l13rwk7dmcaek.htm/, Retrieved Thu, 02 Dec 2010 21:11:18 +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/02/t1291320678k7l13rwk7dmcaek.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 «
6282929 1 162556 1081 213118 4324047 1 29790 309 81767 4108272 1 87550 458 153198 -1212617 0 84738 588 -26007 1485329 1 54660 299 126942 1779876 1 42634 156 157214 1367203 0 40949 481 129352 2519076 1 42312 323 234817 912684 1 37704 452 60448 1443586 1 16275 109 47818 1220017 0 25830 115 245546 984885 0 12679 110 48020 1457425 1 18014 239 -1710 -572920 0 43556 247 32648 929144 1 24524 497 95350 1151176 0 6532 103 151352 790090 0 7123 109 288170 774497 1 20813 502 114337 990576 1 37597 248 37884 454195 0 17821 373 122844 876607 1 12988 119 82340 711969 1 22330 84 79801 702380 0 13326 102 165548 264449 0 16189 295 116384 450033 0 7146 105 134028 541063 0 15824 64 63838 588864 1 26088 267 74996 -37216 0 11326 129 31080 783310 0 8568 37 32168 467359 0 14416 361 49857 688779 1 3369 28 87161 608419 1 11819 85 106113 696348 1 6620 44 80570 597793 1 4519 49 102129 821730 0 2220 22 301670 377934 0 18562 155 102313 651939 0 10327 91 88577 697458 1 5336 81 112477 700368 1 2365 79 19 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time18 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Multiple Linear Regression - Estimated Regression Equation
Wealth[t] = + 72181.4498121034 + 78702.905101078Group[t] + 26.2814924515352Costs[t] -466.247947109262Trades[t] + 3.51295882947257Dividends[t] + 45311.4814373008M1[t] + 30258.9310141814M2[t] + 13636.4022027213M3[t] -134925.814792821M4[t] -39765.0881503512M5[t] -27177.4771094474M6[t] -13411.2690128503M7[t] -34722.6176943456M8[t] + 15125.0384876965M9[t] -13111.3793857872M10[t] -63422.6623984366M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)72181.449812103459421.5001421.21470.2251570.112579
Group78702.90510107833277.4822912.3650.0184870.009243
Costs26.28149245153521.93248513.599800
Trades-466.247947109262208.738468-2.23360.0260390.013019
Dividends3.512958829472570.4921327.138200
M145311.481437300871522.8270310.63350.526740.26337
M230258.931014181471174.8074110.42510.6709580.335479
M313636.402202721371377.2319180.1910.8485820.424291
M4-134925.81479282171436.775994-1.88870.0596230.029812
M5-39765.088150351271430.548405-0.55670.5780350.289018
M6-27177.477109447471365.838508-0.38080.7035320.351766
M7-13411.269012850371194.969472-0.18840.8506760.425338
M8-34722.617694345671418.931248-0.48620.6270950.313547
M915125.038487696571151.840150.21260.8317640.415882
M10-13111.379385787271568.252049-0.18320.854730.427365
M11-63422.662398436671663.078643-0.8850.3766630.188331


Multiple Linear Regression - Regression Statistics
Multiple R0.753023913205486
R-squared0.567045013859303
Adjusted R-squared0.551396038456627
F-TEST (value)36.2352805387082
F-TEST (DF numerator)15
F-TEST (DF denominator)415
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation299670.437651961
Sum Squared Residuals37267984049045


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
162829294713070.852296661569858.14770334
243240471107242.435011323216804.56498868
341082722790102.128229311318169.87177069
4-12126171798781.42919914-3011398.42919914
514853291854199.52770898-368870.527708983
617798761723743.6566501456132.3433498574
713672031365114.003147552088.99685244994
825190761902488.61237216616587.387627837
99126841158534.04802413-245850.048024134
101443586682665.90424894760920.09575106
1112200171496584.21226093-276567.212260928
12984885522809.501414373462075.498585627
131457425552190.222414927905234.777585073
14-5729201246684.90297399-1819604.90297399
15929144912283.4726742616860.5273257409
161151176592596.149918789558579.850081211
177900901181127.75204824-391037.752048238
18774497838308.284434093-63811.2844340926
199905761143033.79901334-152457.799013343
20454195763456.739272541-309261.739272541
21876607741126.941674187135480.058325813
22711969965811.501963738-253842.501963738
23702380892991.973519205-190611.973519205
24264449768961.58712211-504512.587122111
25450033727179.287858152-277146.287858152
26541063712739.114520255-171676.114520255
27588864989121.990688506-400257.990688506
28-37216283956.593768283-321172.593768283
29783310353349.874569937429960.125430063
30467359430708.04733855836650.9526614417
31688779519153.495986152169625.504013848
32608419759922.221271066-151503.221271066
33696348602517.05664783893830.9433521617
34597793592467.8628027325325.137197268
358217301116600.53590666-294870.535906661
36377934847171.437615391-469237.437615391
37651939657640.694847657-5701.6948476573
38697458678742.3161954918715.6838045093
39700368863551.117340742-163183.117340742
40225986256937.241900965-30951.2419009655
41348695305279.88369028543415.1163097146
42373683715910.214564122-342227.214564122
43501709401876.07884740899832.921152592
44413743581873.290960497-168130.290960497
45379825422982.582332224-43157.5823322241
46336260580180.421891901-243920.421891901
47636765848302.048645476-211537.048645476
48481231678835.920715971-197604.920715971
49469107635433.638686496-166326.638686496
50211928415481.720779178-203553.720779178
51563925576945.266687962-13020.2666879617
52511939528492.360628737-16553.3606287366
53521016912464.498353672-391448.498353672
54543856591628.711022236-47772.7110222358
55329304736382.558462359-407078.558462359
56423262270400.857523103152861.142476897
57509665358490.767548114151174.232451886
58455881477612.350395634-21731.3503956343
59367772342142.73585859525629.2641414050
60406339656020.140264278-249681.140264278
61493408652003.918684024-158595.918684024
62232942336803.833648841-103861.833648841
63416002563290.958734049-147288.958734049
64337430458651.024350342-121221.024350342
65361517273210.0573869688306.94261304
66360962270854.32377254690107.6762274542
67235561331211.002862437-95650.002862437
68408247498188.364557171-89941.3645571707
69450296541509.136407641-91213.136407641
70418799566712.327266292-147913.327266292
71247405236455.62090681810949.3790931822
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73326638514731.921234012-188093.921234012
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75386225539029.180064311-152804.180064311
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78269236306254.967626114-37018.9676261135
79365732281887.16641474783844.8335852534
80420383468005.756506977-47622.7565069769
81345811325679.75659697420131.2434030261
82431809541468.484864285-109659.484864285
83418876492472.147412398-73596.1474123976
84297476224383.11523753773092.884762463
85416776484876.594920424-68100.594920424
86357257522836.651016123-165579.651016123
87458343458524.413937773-181.413937772597
88388386310127.3695955578258.6304044498
89358934432261.399471432-73327.399471432
90407560417823.581736317-10263.5817363173
91392558422781.283060027-30223.2830600270
92373177465784.518466698-92607.518466698
93428370323759.734871941104610.265128059
94369419720330.974728443-350911.974728443
95358649169254.175028317189394.824971683
96376641422770.883226157-46129.8832261572
97467427517858.296555601-50431.2965556005
98364885372541.725213903-7656.72521390284
99436230600122.605891717-163892.605891717
100329118304399.47521544324718.5247845570
101317365396259.394836086-78894.3948360856
102286849377946.939726859-91097.9397268594
103376685491016.109484734-114331.109484735
104407198443842.218121504-36644.2181215043
105377772349901.39467189327870.6053281068
106271483319343.207807535-47860.207807535
107153661353561.179319663-199900.179319663
108513294539188.587851456-25894.5878514556
109324881590426.611500722-265545.611500722
110264512471418.601693137-206906.601693137
111420968335096.80045172185871.1995482794
112129302253073.685071084-123771.685071084
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114268673440205.356508094-171532.356508094
115353179371882.368851087-18703.368851087
116354624256780.65679954697843.343200454
117363713384315.57255735-20602.5725573499
118456657419336.14783898437320.8521610161
119211742409915.001133009-198173.001133009
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121418530740141.004280433-321611.004280433
122351483354277.457036511-2794.45703651061
123372928401509.949497529-28581.9494975292
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126219060465949.105112142-246889.105112142
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129322046288865.60538078233180.3946192183
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136331514153915.929087222177598.070912778
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140325560250765.69224333274794.3077566678
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192325560285488.30993767840071.6900623222
193331824334904.786662809-3080.78666280942
194325560315747.2409518599812.75904814078
195325685299525.30103583426159.6989641661
196325560229265.40024593596294.5997540647
197404480540052.339905284-135572.339905284
198325560337013.737929309-11453.7379293086
199325560350779.946025906-25219.9460259056
200318314400436.432218907-82122.4322189072
201325560379316.253526453-53756.2535264526
202325560351079.835652969-25519.8356529687
203325560300768.55264031924791.4473596806
204311807322103.163681417-10296.1636814174
205337724508302.713613194-170578.713613194
206326431397068.594547621-70637.5945476205
207327556395070.738942307-67514.7389423066
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299325560222065.647539241103494.352460759
300325560285488.30993767840071.6900623222
301324047408700.160249865-84653.1602498647
302311464315656.367327704-4192.36732770391
303325560299124.71214039926435.2878596009
304325560229265.40024593596294.5997540647
305353417234453.206034264118963.793965736
306325590254219.85867159271370.1413284078
307325560350779.946025906-25219.9460259056
308328576340478.918256302-11902.9182563024
309326126279240.25423683446885.7457631658
310325560351079.835652969-25519.8356529687
311325560300768.55264031924791.4473596806
312369376349377.52120621719998.4787937826
313325560409502.696476057-83942.6964760567
314332013403906.347161612-71893.3471616122
315325871297482.59614132528388.403858675
316342165166043.755412702176121.244587298
317324967243526.49573549381440.5042645074
318314832261626.49954631253205.5004536878
319325557269302.32177579456254.6782242061
320325560250765.69224333274794.3077566678
321325560300613.34842537424946.6515746257
322325560272376.93055189153183.0694481095
323325560222065.647539241103494.352460759
324325560364191.215038756-38631.215038756
325325560330799.791374978-5239.79137497848
326322649315399.9638092337249.03619076658
327325560377827.617241477-52267.6172414773
328325560229265.40024593596294.5997540647
329325560245723.22178732779836.7782126734
330325560258310.83282823067249.1671717696
331325560272077.04092482753482.9590751726
332325560250765.69224333274794.3077566678
333325560300613.34842537424946.6515746257
334324598271370.79513084153227.2048691585
335325567221184.023849281104382.976150719
336325560285488.30993767840071.6900623222
337324005330385.01319383-6380.01319382993
338325560315747.2409518599812.75904814078
339325748299960.06375101725787.936248983
340323385144322.680983019179062.319016981
341315409254619.72069343860789.2793065618
342325560258310.83282823067249.1671717696
343325560272077.04092482753482.9590751726
344325560250765.69224333274794.3077566678
345325560300613.34842537424946.6515746257
346325560272376.93055189153183.0694481095
347312275216767.89741350395507.1025864967
348325560285488.30993767840071.6900623222
349325560330799.791374978-5239.79137497848
350325560315747.2409518599812.75904814078
351320576293342.56788960827233.4321103923
352325246148896.140587963176349.859412037
353332961251603.4038079181357.59619209
354323010265651.2680406157358.7319593902
355325560272077.04092482753482.9590751726
356325560250765.69224333274794.3077566678
357345253353321.178756056-8068.17875605636
358325560272376.93055189153183.0694481095
359325560222065.647539241103494.352460759
360325560285488.30993767840071.6900623222
361325559328968.106996652-3409.10699665196
362325560315747.2409518599812.75904814078
363325560299124.71214039926435.2878596009
364319634232482.38673673287151.6132632677
365319951241513.60268042378437.3973195767
366325560258310.83282823067249.1671717696
367325560272077.04092482753482.9590751726
368325560250765.69224333274794.3077566678
369325560300613.34842537424946.6515746257
370325560272376.93055189153183.0694481095
371325560222065.647539241103494.352460759
372318519275313.83233072043205.1676692796
373343222388509.815038015-45287.8150380148
374317234334257.103892155-17023.1038921547
375325560299124.71214039926435.2878596009
376325560150562.495144857174997.504855143
377314025221757.47263987692267.5273601243
378320249268137.2910205452111.7089794598
379325560272077.04092482753482.9590751726
380325560250765.69224333274794.3077566678
381325560300613.34842537424946.6515746257
382349365302991.49032373346373.5096762666
383289197132172.677204786157024.322795214
384325560285488.30993767840071.6900623222
385329245332345.439114806-3100.43911480576
386240869387397.758127344-146528.758127344
387327182296049.87656848331132.1234315167
388322876163713.115558858159162.884441142
389323117245085.57265941978031.4273405808
390306351258366.14746168947984.8525383109
391335137284839.10202764550297.8979723553
392308271253961.80270253554309.1972974653
393301731314504.681631243-12773.6816312431
394382409312671.42054471169737.5794552894
395279230465634.446116438-186404.446116438
396298731286243.94659309412487.0534069057
397243650412659.48459829-169009.484598290
398532682589699.700527263-57017.7005272635
399319771319897.210436116-126.210436116513
40017149381266.42474990226.575251
401347262305334.78139459641927.2186054042
402343945286839.52493808757105.4750619129
403311874279030.22222674832843.7777732516
404302211388238.303804899-86027.3038048994
405316708325379.260171546-8671.260171546
406333463332418.6826823441044.31731765580
407344282256518.39227060487763.6077293959
408319635300063.78224928819571.2177507116
409301186328751.256459815-27565.2564598149
410300381365958.859391262-65577.859391262
411318765337047.354608952-18282.3546089523
412286146393631.743783211-107485.743783211
413306844335147.508980928-28303.5089809276
414307705459592.328304931-151887.328304931
415312448370406.154094997-57958.1540949968
416299715319589.072870101-19874.0728701006
417373399265938.461951695107460.538048305
418299446378480.895052728-79034.8950527283
419325586273131.66495475452454.3350452456
420291221319729.018520072-28508.0185200722
421261173388282.724740892-127109.724740892
422255027401278.719504685-146251.719504685
423-78375383760.459624874-462135.459624874
424-58143292774.699582614-350917.699582614
425227033274551.700298725-47518.7002987253
426235098432623.042611944-197525.042611944
42721267329928.596627793-308661.596627793
4282386751422248.56578767-1183573.56578767
429197687587398.311531523-389711.311531523
430418341689576.105932429-271235.105932429
431-297706495667.138713925-793373.138713925


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
1912.9418053273632e-691.4709026636816e-69
2012.80149488330959e-1061.40074744165479e-106
2111.40111559383510e-1107.00557796917548e-111
2212.97569707018121e-1201.48784853509061e-120
2312.95400880129629e-1231.47700440064815e-123
2411.56891054480042e-1357.84455272400209e-136
2518.413709227962e-1374.206854613981e-137
2611.09916738845737e-1375.49583694228685e-138
2713.86919268621552e-1431.93459634310776e-143
2812.34484087191614e-1471.17242043595807e-147
2911.27889335061701e-1916.39446675308507e-192
3011.57706629924364e-2027.88533149621818e-203
3111.94366234250802e-2079.71831171254009e-208
3212.37551685592746e-2101.18775842796373e-210
3312.3307955275795e-2161.16539776378975e-216
3412.25667228880877e-2181.12833614440439e-218
3513.22151009825753e-2181.61075504912876e-218
3614.89857500851782e-2182.44928750425891e-218
3714.74291449053863e-2252.37145724526931e-225
3811.94874412359098e-2369.74372061795488e-237
3911.09205152762556e-2375.4602576381278e-238
4012.27528900128768e-2381.13764450064384e-238
4113.01527898680810e-2381.50763949340405e-238
4211.05112318876393e-2385.25561594381965e-239
4311.29481358228960e-2396.47406791144801e-240
4416.67902510828146e-2393.33951255414073e-239
4512.20144114339006e-2391.10072057169503e-239
4614.10736383801031e-2392.05368191900516e-239
4711.10158872001092e-2385.5079436000546e-239
4812.79367705110182e-2391.39683852555091e-239
4915.2566787750373e-2392.62833938751865e-239
5014.47559685999905e-2392.23779842999953e-239
5116.48553748185871e-2403.24276874092935e-240
5213.92177301129812e-2391.96088650564906e-239
5311.86902878983449e-2399.34514394917244e-240
5411.15307853054983e-2385.76539265274917e-239
5512.10260222221903e-2411.05130111110951e-241
5614.32871499673229e-2422.16435749836614e-242
5714.34000238849895e-2472.17000119424947e-247
5812.9258333414832e-2471.4629166707416e-247
5916.75872352441193e-2473.37936176220596e-247
6018.01279257245456e-2474.00639628622728e-247
6114.34418543940213e-2492.17209271970107e-249
6212.00369110571711e-2481.00184555285856e-248
6311.62841392925917e-2478.14206964629583e-248
6419.00259601423678e-2474.50129800711839e-247
6516.58627038541416e-2473.29313519270708e-247
6615.5017967219386e-2462.7508983609693e-246
6712.02265597621424e-2461.01132798810712e-246
6812.49442227148807e-2451.24721113574404e-245
6913.00656708905182e-2441.50328354452591e-244
7012.15790176430505e-2431.07895088215253e-243
7111.23378638358505e-2426.16893191792526e-243
7211.93449717210557e-2449.67248586052785e-245
7312.38117936828960e-2441.19058968414480e-244
7412.65489076910002e-2431.32744538455001e-243
7511.52905661872742e-2427.64528309363712e-243
7617.37884606848766e-2423.68942303424383e-242
7712.11842521708076e-2411.05921260854038e-241
7815.50491383176792e-2412.75245691588396e-241
7913.28503430334971e-2401.64251715167486e-240
8011.82852178972406e-2399.14260894862032e-240
8111.74293267034982e-2388.71466335174908e-239
8214.38638377996939e-2382.19319188998469e-238
8316.64418380086342e-2383.32209190043171e-238
8413.53264788006649e-2371.76632394003325e-237
8514.48194882166395e-2362.24097441083198e-236
8611.34384467899993e-2366.71922339499965e-237
8711.26099394219312e-2366.30496971096558e-237
8812.0492419992376e-2361.0246209996188e-236
8919.50289354532182e-2364.75144677266091e-236
9011.00428318288947e-2345.02141591444733e-235
9111.32140495849353e-2336.60702479246766e-234
9211.48725557098661e-2327.43627785493305e-233
9314.27486063757484e-2332.13743031878742e-233
9419.84527451618576e-2334.92263725809288e-233
9511.36655757780666e-2346.83278788903329e-235
9612.55156363640521e-2341.27578181820261e-234
9713.34856068964486e-2331.67428034482243e-233
9811.62922630363888e-2328.14613151819439e-233
9917.75950288145302e-2323.87975144072651e-232
10017.58076959035407e-2313.79038479517703e-231
10118.4402787202183e-2304.22013936010915e-230
10214.10145227613173e-2302.05072613806587e-230
10313.6085078309587e-2291.80425391547935e-229
10416.44798862432582e-2293.22399431216291e-229
10514.95268277249842e-2282.47634138624921e-228
10615.54384456951759e-2282.77192228475880e-228
10713.80779427463898e-2281.90389713731949e-228
10814.14583839724894e-2282.07291919862447e-228
10911.60616690752361e-2278.03083453761803e-228
11013.87749657849514e-2271.93874828924757e-227
11114.17577466574e-2272.08788733287e-227
11213.52873579138945e-2271.76436789569473e-227
11318.10749664132244e-2294.05374832066122e-229
11413.52188562117069e-2281.76094281058535e-228
11511.11376674194023e-2275.56883370970116e-228
11613.04705824532719e-2271.52352912266360e-227
11712.71374707297862e-2261.35687353648931e-226
11819.42015888456502e-2264.71007944228251e-226
11919.62158638658067e-2274.81079319329034e-227
12019.0924701438917e-2264.54623507194585e-226
12115.91160211085552e-2262.95580105542776e-226
12216.78346588683998e-2253.39173294341999e-225
12316.42690195978822e-2243.21345097989411e-224
12413.50081105903856e-2231.75040552951928e-223
12514.50570732594431e-2222.25285366297216e-222
12611.21272710414001e-2226.06363552070004e-223
12711.32072477737777e-2216.60362388688887e-222
12811.45048863258765e-2207.25244316293825e-221
12911.86563942188868e-2199.3281971094434e-220
13011.93337832390122e-2189.6668916195061e-219
13111.44155161833095e-2177.20775809165476e-218
13211.10200661642484e-2175.51003308212422e-218
13311.49450088098226e-2167.47250440491128e-217
13411.99175842687288e-2159.9587921343644e-216
13512.46368413978618e-2141.23184206989309e-214
13617.97267986993828e-2143.98633993496914e-214
13717.33970573299366e-2133.66985286649683e-213
13817.35122916730636e-2123.67561458365318e-212
13918.3842240275937e-2114.19211201379685e-211
14019.74584082990108e-2104.87292041495054e-210
14111.24615187689978e-2086.2307593844989e-209
14211.35992539495176e-2076.79962697475882e-208
14317.53641479431165e-2073.76820739715583e-207
14418.59228657347416e-2064.29614328673708e-206
14518.97604424914146e-2054.48802212457073e-205
14619.1828898884234e-2044.5914449442117e-204
14711.12726121365226e-2025.63630606826131e-203
14815.46702134644102e-2022.73351067322051e-202
14915.40952559980396e-2012.70476279990198e-201
15015.70069589359541e-2002.85034794679771e-200
15116.14255423416572e-1993.07127711708286e-199
15215.33850732430989e-1982.66925366215494e-198
15316.58617659523803e-1973.29308829761901e-197
15416.70256924535455e-1963.35128462267728e-196
15516.18577565187218e-1953.09288782593609e-195
15616.92706291129988e-1943.46353145564994e-194
15718.35091450878985e-1934.17545725439493e-193
15819.97135322577074e-1924.98567661288537e-192
15911.00464484425933e-1905.02322422129665e-191
16015.77049145440279e-1902.88524572720139e-190
16115.81047303118032e-1892.90523651559016e-189
16216.12335562673081e-1883.06167781336541e-188
16316.68597771706383e-1873.34298885853192e-187
16416.97667759866506e-1863.48833879933253e-186
16518.16782427494169e-1854.08391213747084e-185
16618.58586086080833e-1844.29293043040417e-184
16717.97100488707681e-1833.98550244353841e-183
16817.12649789748426e-1843.56324894874213e-184
16918.2427430036667e-1834.12137150183335e-183
17019.4667269896191e-1824.73336349480955e-182
17111.09038963831459e-1805.45194819157294e-181
17217.23191720885767e-1803.61595860442883e-180
17317.35943648623712e-1793.67971824311856e-179
17417.753882338533e-1783.8769411692665e-178
17518.2658490077444e-1774.1329245038722e-177
17619.09659635243547e-1764.54829817621774e-176
17711.02688383081100e-1745.13441915405502e-175
17811.10457931925927e-1735.52289659629637e-174
17911.02882787849794e-1725.14413939248972e-173
18011.10554419275911e-1715.52772096379555e-172
18115.5354078306495e-1712.76770391532475e-171
18215.5431786515194e-1702.7715893257597e-170
18313.88707423249804e-1691.94353711624902e-169
18412.19913345990943e-1681.09956672995472e-168
18512.39313301116024e-1671.19656650558012e-167
18612.50645339792080e-1661.25322669896040e-166
18711.74812555527549e-1658.74062777637743e-166
18811.84781025373155e-1649.23905126865777e-165
18911.38129243461722e-1636.90646217308611e-164
19011.35447171945424e-1626.77235859727118e-163
19111.02689348210579e-1615.13446741052897e-162
19211.06202046753593e-1605.31010233767965e-161
19311.04076087281271e-1595.20380436406355e-160
19411.07706033120474e-1585.38530165602371e-159
19511.12306060058459e-1575.61530300292297e-158
19611.02001551501605e-1565.10007757508027e-157
19713.06949808035283e-1561.53474904017642e-156
19813.20121390136885e-1551.60060695068443e-155
19913.18462623656994e-1541.59231311828497e-154
20017.87146379600887e-1543.93573189800444e-154
20117.69379608910892e-1533.84689804455446e-153
20217.31939755514989e-1523.65969877757495e-152
20317.18223665502648e-1513.59111832751324e-151
20416.13024086465364e-1503.06512043232682e-150
20511.35770049387600e-1496.78850246938001e-150
20611.30797837326706e-1486.53989186633531e-149
20711.19550485612004e-1475.97752428060021e-148
20811.06357152158425e-1465.31785760792126e-147
20911.15951676143859e-1465.79758380719293e-147
21011.17145699752541e-1455.85728498762707e-146
21111.08369616023467e-1445.41848080117334e-145
21211.05978779446667e-1435.29893897233334e-144
21311.01263274890435e-1425.06316374452173e-143
21418.28605618465898e-1424.14302809232949e-142
21517.17780739822034e-1413.58890369911017e-141
21616.86989523743812e-1403.43494761871906e-140
21716.54034485048774e-1393.27017242524387e-139
21815.61260939690146e-1382.80630469845073e-138
21915.39851502515111e-1372.69925751257555e-137
22011.83484214685856e-1369.17421073429278e-137
22111.43686346218480e-1357.18431731092398e-136
22211.31119702439777e-1346.55598512198883e-135
22311.15411751668446e-1335.77058758342231e-134
22411.0802319598472e-1325.401159799236e-133
22511.0184876836066e-1315.092438418033e-132
22619.17505154516769e-1314.58752577258384e-131
22717.4951931054474e-1303.7475965527237e-130
22816.85296505553796e-1293.42648252776898e-129
22916.10100993033153e-1283.05050496516577e-128
23015.39779277467524e-1272.69889638733762e-127
23114.86874357971989e-1262.43437178985994e-126
23213.36614388341656e-1251.68307194170828e-125
23312.89296659067398e-1241.44648329533699e-124
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3800.99999999999992.01803961927168e-131.00901980963584e-13
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3820.9999999999992221.55634984291052e-127.78174921455261e-13
3830.9999999999985962.80785994517140e-121.40392997258570e-12
3840.999999999995059.89895282988084e-124.94947641494042e-12
3850.9999999999829163.41687528890022e-111.70843764445011e-11
3860.999999999942111.15780069512128e-105.7890034756064e-11
3870.9999999998042233.91553715830146e-101.95776857915073e-10
3880.9999999995851578.29685551727838e-104.14842775863919e-10
3890.9999999986471842.70563115210744e-091.35281557605372e-09
3900.9999999958920528.21589617148677e-094.10794808574339e-09
3910.999999990762091.84758196940447e-089.23790984702233e-09
3920.9999999723399745.53200529113086e-082.76600264556543e-08
3930.9999999192649361.6147012790237e-078.0735063951185e-08
3940.9999997545845944.90830811844994e-072.45415405922497e-07
3950.999999289335241.42132952025278e-067.10664760126388e-07
3960.9999979135659274.17286814614685e-062.08643407307343e-06
3970.9999946400102031.07199795932357e-055.35998979661786e-06
3980.9999852338421492.95323157022297e-051.47661578511149e-05
3990.9999598446382648.03107234715386e-054.01553617357693e-05
4000.9999400203515390.0001199592969223695.99796484611846e-05
4010.9998402201000950.0003195597998107250.000159779899905362
4020.9996486183592120.0007027632815751030.000351381640787551
4030.999405926569810.001188146860379750.000594073430189877
4040.9985228061440760.002954387711847360.00147719385592368
4050.9967882459363840.006423508127231050.00321175406361553
4060.9932632782018680.01347344359626500.00673672179813249
4070.986464558378250.02707088324350110.0135354416217505
4080.971011095043290.05797780991342120.0289889049567106
4090.942291070572220.1154178588555620.0577089294277809
4100.8841625915382040.2316748169235920.115837408461796
4110.8113008732357580.3773982535284840.188699126764242
4120.8270984707626350.345803058474730.172901529237365


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3870.982233502538071NOK
5% type I error level3890.98730964467005NOK
10% type I error level3900.98984771573604NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/10xhzz1291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/10xhzz1291320718.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/18y2n1291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/18y2n1291320718.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/28y2n1291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/28y2n1291320718.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/38y2n1291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/38y2n1291320718.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/4jp281291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/4jp281291320718.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/5jp281291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/5jp281291320718.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/6cg1b1291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/6cg1b1291320718.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/74q0e1291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/74q0e1291320718.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/84q0e1291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/84q0e1291320718.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/94q0e1291320718.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291320678k7l13rwk7dmcaek/94q0e1291320718.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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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