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Juiste met dummies én lineair én orders

*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: Fri, 03 Dec 2010 19:18:29 +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/03/t1291403793nhy1my0z9nnr43z.htm/, Retrieved Fri, 03 Dec 2010 20:16:33 +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/03/t1291403793nhy1my0z9nnr43z.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 «
350 1 1595 17 10 60720 319369 143 7022 75 0 61 0 5565 47 43 94355 493408 38 6243 53 39 287 0 601 6 5 60720 319210 144 19868 198 0 268 1 188 11 9 77655 381180 67 16471 964 0 25 1 7146 105 83 134028 297978 367 933 14 305 418 0 1135 17 20 62285 290476 384 5322 80 -9 392 1 450 8 6 59325 292136 380 11517 205 -1 131 1 34 8 7 60630 314353 309 14294 3340 0 316 1 133 14 4 65990 339445 109 9960 1052 0 347 1 119 6 3 59118 303677 354 17280 872 0 68 0 2053 53 44 100423 397144 60 3720 96 33 70 0 4036 63 85 100269 424898 53 3570 56 32 147 1 0 0 0 60720 315380 203 0 169 1 4655 393 55 60720 341570 105 360 30 0 248 1 131 11 15 61808 308989 338 9908 834 0 152 1 1766 73 46 60438 305959 349 1451 60 0 351 1 312 14 3 58598 318690 147 8478 381 0 372 1 448 40 7 59781 323361 132 3084 275 0 137 1 115 13 10 60945 318903 145 9146 1037 0 352 1 60 10 7 61124 314049 314 11405 1916 0 338 1 0 0 0 60720 315380 180 0 258 1 364 35 26 47705 298466 365 2813 271 0 343 1 0 0 0 60720 315380 183 0 398 0 1442 118 239 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 time22 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
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
CCScore [t] = + 115876.867770394 -0.196791064768874Rank[t] + 0.00883614982164447group[t] -0.270862558545223Costs[t] -0.139237133115806Trades[t] -0.0754360553532818Orders[t] -0.341807012010589Dividends[t] -0.122373231272038Wealth[t] -0.212536089750411WRank[t] -0.0703682133142447`Profit/Trades`[t] -0.00479391593631428`Profit/Cost`[t] -15.8919679785877t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)115876.86777039415618.2553597.419300
Rank-0.1967910647688740.071301-2.760.0060340.003017
group0.008836149821644470.0603190.14650.8836040.441802
Costs-0.2708625585452230.06387-4.24082.7e-051.4e-05
Trades-0.1392371331158060.040859-3.40780.0007180.000359
Orders-0.07543605535328180.029027-2.59880.0096850.004843
Dividends-0.3418070120105890.076533-4.46611e-055e-06
Wealth-0.1223732312720380.041417-2.95460.0033070.001653
WRank-0.2125360897504110.054432-3.90460.000115.5e-05
`Profit/Trades`-0.07036821331424470.026708-2.63480.0087320.004366
`Profit/Cost`-0.004793915936314280.019732-0.24290.8081630.404082
t-15.891967978587753.40236-0.29760.7661640.383082


Multiple Linear Regression - Regression Statistics
Multiple R0.363825191842531
R-squared0.132368770219255
Adjusted R-squared0.109590862038853
F-TEST (value)5.81127859375372
F-TEST (DF numerator)11
F-TEST (DF denominator)419
p-value8.79713768497936e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation128914.789735116
Sum Squared Residuals6963370642216.23


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1054995.344527376-54995.344527376
23921237.170427137-21198.170427137
3054361.800852072-54361.800852072
4041340.2881268704-41340.2881268704
530531416.0697385784-31111.0697385784
6-958095.5190680773-58104.5190680773
7-158645.5316105338-58646.5316105338
8055433.4298764531-55433.4298764531
9050809.4838842252-50809.4838842252
10056952.1125662732-56952.1125662732
113331921.8324807563-31888.8324807563
123228032.4675878407-28000.4675878407
13156248.8062904308-56247.8062904308
14180418.2117551791-80417.2117551791
15182187.8743297468-82186.8743297468
16183456.4056768653-83455.4056768653
17182174.397141702-82173.397141702
18182302.672245965-82301.672245965
19181616.2292392084-81615.2292392084
20181432.2314023345-81431.2314023345
213583179.4596558028-83144.4596558028
22034671.7689111159-34671.7689111159
2312117330145.803987424491027.1960125756
245753028299.045584473729230.9544155263
256204135610.351154263626430.6488457364
266072046959.388226689413760.6117733106
2713699648544.555824684688451.4441753154
286072030312.726492673930407.2735073261
2911545620.1796550427-45505.1796550427
30387105968.793336408-105581.793336408
31219108988.901920017-108769.901920017
3243037517.9399011763-37087.9399011763
33288-14754.717788037315042.7177880373
342226710.5698491453-26688.5698491453
351841915.4319069036-41897.4319069036
361245840796.2576020225-28338.2576020225
373250213.0440337195-50181.0440337195
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394212993.7730133385-12951.7730133385
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444017623.59330980641-7222.59330980641
453711380.38656393889-1009.38656393889
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47368950987.4885821997-47298.4885821997
4856732545.2292487221-31978.2292487221
492766427491.3536764948172.646323505159
5016862484.74723157613-798.747231576134
51116443699.3696596906-42535.3696596906
521582460521.6633475101-44697.6633475101
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70111579190.7075564648-78075.7075564648
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105249-159094.675873069159343.675873069
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107267552702.2753352425-50027.2753352425
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1101153658.6782150813-53647.6782150813
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2241951897.3615181402-51878.3615181402
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3211618935469.8983635211-19280.8983635211
322028958.8206018263-28958.8206018263
323050332.7170988491-50332.7170988491
32437519599634.7877930643275560.212206936
325288985110332.12750727178652.87249273
326315380110290.834597996205089.165402004
327300458868.1784397724-55864.1784397724
328075505.4997144782-75505.4997144782
329049796.9150205058-49796.9150205058
330046856.8357100846-46856.8357100846
331-5886971.5183593274-87029.5183593274
332151158.7377753197-51157.7377753197
333178320.155876754-78319.155876754
334078195.6888285015-78195.6888285015
3356079825529.079340761835268.9206592382
3366072040593.022089893120126.9779101069
33739641852.7272620702-41456.7272620702
338228104093.791150671-103865.791150671
33924936013.6330746266-35764.6330746266
3400-1954.684276518141954.68427651814
34113649784.579917358-49648.579917358
34211487834.2260976941-87720.2260976941
3431581443.1889138961-81428.1889138961
34411104289.015784370-104278.015784370
34512100783.452922872-100771.452922872
346894930.2843869228-94922.2843869228
34712299428.5119795813-99306.5119795813
348113107907.222729216-107794.222729216
3496100904.910771332-100898.910771332
350098291.1901524818-98291.1901524818
351215362100055.977367733115306.022632267
352314073109577.309638911204495.690361089
353298096109838.753502649188257.246497351
354325176109848.148858527215327.851141473
355207393109126.23541149898266.7645885019
356314806109801.946435026205004.053564974
357341340108333.519426538233006.480573462
358426280109110.548128882317169.451871118
359929118106550.266209220822567.73379078
360307322109694.914866495197627.085133505
361315380110055.009772176205324.990227824
362302467734.7510302215-64710.7510302215
3631835775522.5179679701-57165.5179679701
364235375964.484038727-73611.484038727
365865273319.9490557237-64667.9490557237
3663861174793.9148049147-36182.9148049147
367950575263.9256278603-65758.9256278603
368410870424.3150937017-66316.3150937017
369074830.363700141-74830.363700141
370050570.3366908334-50570.3366908334
371077604.33791391-77604.33791391
3726072024970.089098654635749.9109013454
3739035072.0754055809-34982.0754055809
374168104123.891051111-103955.891051111
375208101307.826525832-101099.826525832
37622235424.7455122624-35202.7455122624
3770-2543.952032689442543.95203268944
378349431.7032815306-49428.7032815306
3791597900.9362439205-97885.9362439205
3805591006.8182115203-90951.8182115203
3818590177.2002734609-90092.2002734609
3821795671.1564724764-95654.1564724764
38327799701.4125456717-99424.4125456717
3841089126.6732676803-89116.6732676803
385498422.4377629795-98418.4377629795
386797223.2873515545-97216.2873515545
3872699472.4728602314-99446.4728602314
3884795707.4858527481-95660.4858527481
38920391631.0538866953-91428.0538866953
390586881.1698030895-86876.1698030895
391097801.574770726-97801.574770726
392315380100095.848844360215284.15115564
3931062078228.4058187004-67608.4058187004
394566972061.9053213598-66392.9053213598
3951058174522.396192864-63941.396192864
396253334126.6090952229-31593.6090952229
39782773894.3868899417-73067.3868899417
3985769474345.8218336052-16651.8218336052
399051162.9175625659-51162.9175625659
400048723.9114554973-48723.9114554973
4011451981.0805543261-51967.0805543261
4022854-219183.447027556222037.447027556
403050583.3759894607-50583.3759894607
404-1051391.1810206905-51401.1810206905
405-2894222.627851959-94250.627851959
406149956.9667505419-49955.9667505419
407171635.2925638576-71634.2925638576
408169398.4978283299-69397.4978283299
4097276987.763750467-76915.763750467
4101724543.6575449279-24526.6575449279
4114722759.8263174007-22712.8263174007
4126-24054.109056350024060.1090563500
4131121742.5719640953-21731.5719640953
4141055113.6238468491-5008.6238468491
4151729480.7032377040-29463.7032377040
416830566.4375805127-30558.4375805127
417829624.5057485055-29616.5057485055
4181422291.1134823493-22277.1134823493
419616657.0105616377-16651.0105616377
4205325794.1221003904-25741.1221003904
421632133.54925252653-2070.54925252653
4220-5380.294046502875380.29404650287
4236072023163.03585134937556.964148651
4246180841706.410815977720101.5891840223
4256043845086.890513918915351.1094860811
4265859848311.192610564410286.8073894356
4275978143802.190771453715978.8092285463
4286094544495.731700316616449.2682996834
4296112443547.608107431717576.3918925683
4306072043772.440020496516947.5599795035
43136542007.0921715803-41642.0921715803


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
158.16036964948506e-121.63207392989701e-110.99999999999184
162.07001173694960e-154.14002347389920e-150.999999999999998
176.35233528526388e-191.27046705705278e-181
181.17117506071354e-222.34235012142709e-221
192.13899258811305e-264.2779851762261e-261
203.54024371705856e-307.08048743411712e-301
217.92681309948912e-341.58536261989782e-331
222.79073996956703e-375.58147993913406e-371
233.50442602852995e-207.00885205705989e-201
247.49815802853514e-211.49963160570703e-201
256.97471569632074e-141.39494313926415e-130.99999999999993
262.65254329378230e-135.30508658756459e-130.999999999999735
276.14648531082502e-141.22929706216500e-130.999999999999938
287.02409525082439e-151.40481905016488e-140.999999999999993
296.06805277210958e-161.21361055442192e-151
301.41561501133162e-162.83123002266324e-161
311.36076640137659e-172.72153280275319e-171
321.04172628035481e-132.08345256070963e-130.999999999999896
332.82745026672546e-145.65490053345091e-140.999999999999972
344.62297651344623e-159.24595302689247e-150.999999999999995
358.84032780656138e-161.76806556131228e-151
361.29919502517294e-162.59839005034588e-161
373.05014220559966e-176.10028441119933e-171
381.16546330513631e-172.33092661027262e-171
391.67705386047928e-183.35410772095856e-181
402.38507747691438e-194.77015495382876e-191
413.30068245199534e-206.60136490399068e-201
424.53056180384473e-219.06112360768947e-211
436.04963467486927e-221.20992693497385e-211
448.04849134157286e-231.60969826831457e-221
451.04848784792665e-232.09697569585331e-231
461.33628593583399e-242.67257187166798e-241
472.58859555597789e-255.17719111195577e-251
486.17126168082205e-261.23425233616441e-251
493.41169843305596e-266.82339686611192e-261
501.14897943607257e-252.29795887214515e-251
511.65146975582232e-263.30293951164464e-261
522.67600430119675e-275.35200860239351e-271
534.19385503277168e-288.38771006554337e-281
541.02875272959979e-282.05750545919958e-281
555.01561045571497e-291.00312209114299e-281
566.99792013619728e-301.39958402723946e-291
579.64448421812315e-311.92889684362463e-301
581.34336747087279e-312.68673494174559e-311
591.82898243474558e-323.65796486949115e-321
602.92835638943469e-335.85671277886939e-331
614.88702087551648e-219.77404175103297e-211
622.02932474192427e-154.05864948384853e-150.999999999999998
631.90871500130494e-153.81743000260989e-150.999999999999998
641.39158441426122e-132.78316882852244e-130.99999999999986
657.097967500507e-141.4195935001014e-130.99999999999993
664.03368079393939e-138.06736158787878e-130.999999999999597
677.17822980648635e-131.43564596129727e-120.999999999999282
688.78934027360984e-131.75786805472197e-120.999999999999121
699.53578026572906e-111.90715605314581e-100.999999999904642
709.52514379049837e-101.90502875809967e-090.999999999047486
719.43983826506652e-091.88796765301330e-080.999999990560162
727.36502909901258e-081.47300581980252e-070.99999992634971
736.9193353721426e-071.38386707442852e-060.999999308066463
749.06886668527779e-071.81377333705556e-060.999999093113331
757.81393087058523e-071.56278617411705e-060.999999218606913
765.9322028629627e-071.18644057259254e-060.999999406779714
775.15981596181448e-071.03196319236290e-060.999999484018404
786.12666398185788e-071.22533279637158e-060.999999387333602
795.2958112901592e-071.05916225803184e-060.99999947041887
804.35708520911471e-078.71417041822941e-070.99999956429148
813.25970866729265e-076.5194173345853e-070.999999674029133
822.74915371431551e-075.49830742863102e-070.999999725084629
831.60962096171059e-073.21924192342118e-070.999999839037904
842.02951468923416e-074.05902937846831e-070.999999797048531
852.38362488822157e-074.76724977644314e-070.999999761637511
862.99298295292305e-075.9859659058461e-070.999999700701705
872.39616903465969e-074.79233806931938e-070.999999760383097
881.83308553489263e-073.66617106978527e-070.999999816691447
891.38355538690572e-072.76711077381144e-070.999999861644461
901.16369271549283e-072.32738543098565e-070.999999883630728
911.01897407300850e-072.03794814601700e-070.999999898102593
926.86314462835691e-081.37262892567138e-070.999999931368554
934.61964192628998e-089.23928385257995e-080.99999995380358
943.15333103221238e-086.30666206442476e-080.99999996846669
951.81972364465999e-083.63944728931999e-080.999999981802764
961.04243723774565e-082.0848744754913e-080.999999989575628
975.99391500558993e-091.19878300111799e-080.999999994006085
983.63680760482165e-097.2736152096433e-090.999999996363192
992.04037016660334e-094.08074033320667e-090.99999999795963
1001.13701445485585e-092.27402890971169e-090.999999998862986
1016.29272744913333e-101.25854548982667e-090.999999999370727
1023.48455382340584e-106.96910764681168e-100.999999999651545
1033.27225475840646e-106.54450951681292e-100.999999999672775
1041.80635983319086e-103.61271966638172e-100.999999999819364
1051.71923636711573e-103.43847273423146e-100.999999999828076
1061.15694151572510e-102.31388303145021e-100.999999999884306
1077.33752565788588e-111.46750513157718e-100.999999999926625
1084.55632069192648e-119.11264138385296e-110.999999999954437
1092.930331729345e-115.86066345869e-110.999999999970697
1101.80473168516409e-113.60946337032819e-110.999999999981953
1111.16719476326522e-092.33438952653044e-090.999999998832805
1121.11994591263988e-092.23989182527975e-090.999999998880054
1139.66173173820511e-081.93234634764102e-070.999999903382683
1140.0448569497534410.0897138995068820.955143050246559
1150.437888246837750.87577649367550.56211175316225
1160.668763559844520.6624728803109590.331236440155479
1170.6482550208504550.7034899582990890.351744979149545
1180.6557333664859350.6885332670281290.344266633514065
1190.6655218340056320.6689563319887350.334478165994368
1200.668970644994370.662058710011260.33102935500563
1210.6696146278979910.6607707442040180.330385372102009
1220.6708803167776960.6582393664446080.329119683222304
1230.6577565617171560.6844868765656890.342243438282844
1240.6446545679981830.7106908640036350.355345432001817
1250.6217384291061320.7565231417877370.378261570893868
1260.6008223838908330.7983552322183340.399177616109167
1270.5782233363144270.8435533273711460.421776663685573
1280.5563960390106080.8872079219787840.443603960989392
1290.5268933295426040.9462133409147910.473106670457396
1300.5150049144980990.9699901710038020.484995085501901
1310.5214027074320470.9571945851359050.478597292567953
1320.5143588722047660.9712822555904690.485641127795234
1330.4948594292672740.9897188585345480.505140570732726
1340.4754015020615720.9508030041231430.524598497938428
1350.4523812935155910.9047625870311820.547618706484409
1360.4236462076593050.847292415318610.576353792340695
1370.4057560490275350.811512098055070.594243950972465
1380.3773235326825610.7546470653651210.62267646731744
1390.3570349323199420.7140698646398840.642965067680058
1400.357656041839630.715312083679260.64234395816037
1410.3374155973482360.6748311946964720.662584402651764
1420.3157435159868860.6314870319737730.684256484013114
1430.2962123975563370.5924247951126750.703787602443663
1440.2761423511046410.5522847022092820.723857648895359
1450.2515916703098970.5031833406197950.748408329690103
1460.2350965596190520.4701931192381040.764903440380948
1470.2190841974622150.4381683949244290.780915802537785
1480.2503322918258170.5006645836516340.749667708174183
1490.2585611870553120.5171223741106240.741438812944688
1500.2611604766295540.5223209532591070.738839523370446
1510.2596024431665300.5192048863330610.74039755683347
1520.2915516958393740.5831033916787470.708448304160626
1530.470983682918110.941967365836220.52901631708189
1540.4995100954218880.9990201908437760.500489904578112
1550.526105611240010.947788777519980.47389438875999
1560.5106272126293330.9787455747413340.489372787370667
1570.4926533780303960.9853067560607920.507346621969604
1580.4771471255965120.9542942511930240.522852874403488
1590.4481899306136260.8963798612272520.551810069386374
1600.4262529016676130.8525058033352250.573747098332387
1610.4041718118464150.8083436236928310.595828188153585
1620.3824247256853320.7648494513706630.617575274314668
1630.3614766416559530.7229532833119060.638523358344047
1640.3395063775071340.6790127550142690.660493622492866
1650.3205162192897680.6410324385795370.679483780710232
1660.304825705307270.609651410614540.69517429469273
1670.2884678919162200.5769357838324390.71153210808378
1680.271646094860330.543292189720660.72835390513967
1690.2546597609752290.5093195219504580.745340239024771
1700.2399003116561330.4798006233122660.760099688343867
1710.2198886430782460.4397772861564930.780111356921754
1720.2011289521749640.4022579043499290.798871047825036
1730.1903874310398750.380774862079750.809612568960125
1740.1801124907918670.3602249815837350.819887509208133
1750.1619837460250250.3239674920500490.838016253974975
1760.1493021679424520.2986043358849030.850697832057548
1770.1359216290795200.2718432581590400.86407837092048
1780.1209767525678720.2419535051357430.879023247432128
1790.1071242843434710.2142485686869420.89287571565653
1800.09447442735893310.1889488547178660.905525572641067
1810.08598020235751450.1719604047150290.914019797642485
1820.07529829677666190.1505965935533240.924701703223338
1830.06726365280547820.1345273056109560.932736347194522
1840.06083041973941110.1216608394788220.939169580260589
1850.05497553580575520.1099510716115100.945024464194245
1860.05538468612009540.1107693722401910.944615313879905
1870.05447630883151920.1089526176630380.94552369116848
1880.06423533661092350.1284706732218470.935764663389076
1890.05611054993344950.1122210998668990.94388945006655
1900.06453070531856730.1290614106371350.935469294681433
1910.05947256084252270.1189451216850450.940527439157477
1920.05180423521021150.1036084704204230.948195764789789
1930.04676814273939860.09353628547879720.953231857260601
1940.04273544207181890.08547088414363780.957264557928181
1950.03724758757951970.07449517515903940.96275241242048
1960.03269416316273970.06538832632547930.96730583683726
1970.02897957923778350.0579591584755670.971020420762217
1980.02469532878710880.04939065757421750.975304671212891
1990.02175315263559660.04350630527119320.978246847364403
2000.02230810203108070.04461620406216150.97769189796892
2010.02126542999283590.04253085998567180.978734570007164
2020.02096920206214630.04193840412429250.979030797937854
2030.02073040300426130.04146080600852250.979269596995739
2040.02023692146096890.04047384292193790.979763078539031
2050.01822884252921290.03645768505842580.981771157470787
2060.01790713118883300.03581426237766610.982092868811167
2070.01766692925728740.03533385851457480.982333070742713
2080.01684918921777520.03369837843555030.983150810782225
2090.01429933894356690.02859867788713380.985700661056433
2100.01196151167054140.02392302334108280.988038488329459
2110.01007476569563920.02014953139127850.98992523430436
2120.008487295561363370.01697459112272670.991512704438637
2130.007030094728578960.01406018945715790.992969905271421
2140.00579886066433920.01159772132867840.99420113933566
2150.004762757018696280.009525514037392560.995237242981304
2160.003930156310006410.007860312620012830.996069843689994
2170.003196036075867950.00639207215173590.996803963924132
2180.00274689884109770.00549379768219540.997253101158902
2190.002343119374028720.004686238748057440.997656880625971
2200.00189173565236890.00378347130473780.998108264347631
2210.001664735701662430.003329471403324850.998335264298338
2220.001391692619985070.002783385239970150.998608307380015
2230.001103378559755960.002206757119511920.998896621440244
2240.000931308847448940.001862617694897880.999068691152551
2250.0009425068787067980.001885013757413600.999057493121293
2260.0009333781347790850.001866756269558170.99906662186522
2270.0009338521880157840.001867704376031570.999066147811984
2280.001316307964821660.002632615929643320.998683692035178
2290.001064111185914280.002128222371828570.998935888814086
2300.0008664306370728050.001732861274145610.999133569362927
2310.000703056972034840.001406113944069680.999296943027965
2320.0005839366320180040.001167873264036010.999416063367982
2330.000505785895203520.001011571790407040.999494214104796
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3500.880516834160390.2389663316792210.119483165839610
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3520.8543432689091370.2913134621817260.145656731090863
3530.8393929003449970.3212141993100060.160607099655003
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3600.999999999999637.3898789949409e-133.69493949747045e-13
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36417.57490819758988e-173.78745409879494e-17
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36615.41215075570296e-162.70607537785148e-16
36711.58118938083417e-157.90594690417086e-16
3680.9999999999999984.60068422281157e-152.30034211140578e-15
3690.9999999999999931.31693557326648e-146.58467786633242e-15
3700.9999999999999813.7763035474985e-141.88815177374925e-14
3710.9999999999999471.05922912246443e-135.29614561232217e-14
3720.9999999999999549.21182826864343e-144.60591413432172e-14
3730.9999999999998662.67875908755517e-131.33937954377758e-13
3740.999999999999637.38885844014718e-133.69442922007359e-13
3750.999999999998992.01869748102755e-121.00934874051378e-12
3760.9999999999971635.67396273350473e-122.83698136675236e-12
3770.9999999999959578.08625106454611e-124.04312553227306e-12
3780.9999999999958938.21339257226624e-124.10669628613312e-12
3790.999999999990611.87812941254138e-119.3906470627069e-12
3800.9999999999748045.03915127320849e-112.51957563660425e-11
3810.9999999999345981.30804822064456e-106.54024110322281e-11
3820.9999999998591022.81795733151132e-101.40897866575566e-10
3830.9999999996633776.73246340343427e-103.36623170171713e-10
3840.999999999541169.17681711021315e-104.58840855510657e-10
3850.9999999988069132.38617451971947e-091.19308725985974e-09
3860.9999999980927433.81451443663941e-091.90725721831970e-09
3870.9999999962646057.47079040418553e-093.73539520209277e-09
3880.9999999965890126.82197564031621e-093.41098782015810e-09
3890.9999999989518552.09629065447800e-091.04814532723900e-09
3900.9999999999997634.74496670865677e-132.37248335432839e-13
39111.66571705278838e-178.3285852639419e-18
39212.85002698169264e-221.42501349084632e-22
39311.83680712635701e-219.18403563178507e-22
39411.40156688691997e-207.00783443459986e-21
39511.21437802948209e-196.07189014741045e-20
39611.17648999732829e-185.88244998664145e-19
39711.34133498738172e-186.70667493690862e-19
39817.21585763535535e-213.60792881767767e-21
39914.51387167497096e-272.25693583748548e-27
40011.36992337246192e-256.84961686230959e-26
40113.4753119683561e-241.73765598417805e-24
40219.9070323516322e-234.9535161758161e-23
40312.61817756189142e-211.30908878094571e-21
40414.11377574378049e-202.05688787189025e-20
40511.23754764785196e-186.18773823925978e-19
40613.22008384513144e-171.61004192256572e-17
40717.60181562143371e-163.80090781071685e-16
4080.9999999999999951.02989521558040e-145.14947607790201e-15
4090.99999999999991.98905958449255e-139.94529792246277e-14
4100.9999999999971685.6645888341519e-122.83229441707595e-12
4110.9999999999240971.51806226300606e-107.59031131503029e-11
4120.9999999982601593.47968307102787e-091.73984153551394e-09
4130.9999999812033513.75932972839914e-081.87966486419957e-08
4140.9999997245191955.50961609470388e-072.75480804735194e-07
4150.9999926576354961.46847290082893e-057.34236450414467e-06
4160.999934061233890.0001318775322184476.59387661092236e-05


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1910.475124378109453NOK
5% type I error level2240.557213930348259NOK
10% type I error level2380.592039800995025NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/10yrjj1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/10yrjj1291403883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/1984p1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/1984p1291403883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/2984p1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/2984p1291403883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/3jh3s1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/3jh3s1291403883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/4uqkd1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/4uqkd1291403883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/5uqkd1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/5uqkd1291403883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/6uqkd1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/6uqkd1291403883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/75zky1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/75zky1291403883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/8yrjj1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/8yrjj1291403883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/9yrjj1291403883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403793nhy1my0z9nnr43z/9yrjj1291403883.ps (open in new window)


 
Parameters (Session):
par1 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = 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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