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tweede regressie model

*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 19:22:19 +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/t1291404030rt8l4s6sg09awr8.htm/, Retrieved Fri, 03 Dec 2010 20:20:41 +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/t1291404030rt8l4s6sg09awr8.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 «
0 162556 1081 807 213118 6282154 1 5626 37 56289 0 29790 309 444 81767 4321023 2 13337 138 28328 0 87550 458 412 153198 4111912 3 8541 45 17936 1 84738 588 428 -26007 223193 415 39 0 4145 0 54660 302 315 126942 1491348 6 4276 24 3040 0 42634 156 168 157214 1629616 5 9164 34 2964 1 40949 481 263 129352 1398893 9 2493 29 2865 0 45187 353 267 234817 1926517 4 4891 38 2854 0 37704 452 228 60448 983660 14 1734 21 2167 0 16275 109 129 47818 1443586 7 11409 76 1974 1 25830 115 104 245546 1073089 10 7592 34 1910 1 12679 110 122 48020 984885 13 7135 62 1871 0 18014 239 393 -1710 1405225 8 5043 67 943 1 43556 247 190 32648 227132 414 110 1 929 0 24811 505 280 95350 929118 15 1444 29 822 1 6575 159 63 151352 1071292 11 5480 133 819 1 7123 109 102 288170 638830 28 4026 62 769 0 21950 519 265 114337 856956 17 1266 30 745 0 37597 248 234 37884 992426 12 3195 21 652 1 17821 373 277 122844 444477 46 655 14 643 0 12988 119 73 82340 857217 16 5523 51 601 0 22330 84 67 79801 711969 20 6095 23 446 1 13326 1 etc...
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time32 seconds
R Server'George Udny Yule' @ 72.249.76.132
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] = + 58577.3831072006 + 0.0169275248220059groep[t] + 0.385335299976859Costs[t] -0.12554949669882Trades[t] -0.138863231558318Orders[t] -0.212772848710286Dividends[t] -0.0261156324204527Wealth[t] -0.151598280144555WRank[t] -0.140462505851361`Profit/Trades`[t] -0.0906241036551652`Profit/Cost`[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)58577.38310720065623.15299510.417200
groep0.01692752482200590.0065142.59850.0096920.004846
Costs0.3853352999768590.0571066.747700
Trades-0.125549496698820.041584-3.01920.0026890.001344
Orders-0.1388632315583180.051162-2.71420.0069160.003458
Dividends-0.2127728487102860.035085-6.064500
Wealth-0.02611563242045270.006503-4.01577e-053.5e-05
WRank-0.1515982801445550.035641-4.25342.6e-051.3e-05
`Profit/Trades`-0.1404625058513610.048554-2.89290.0040150.002007
`Profit/Cost`-0.09062410365516520.039266-2.3080.0214850.010742


Multiple Linear Regression - Regression Statistics
Multiple R0.523202905924197
R-squared0.273741280767524
Adjusted R-squared0.258215559928825
F-TEST (value)17.6314699724088
F-TEST (DF numerator)9
F-TEST (DF denominator)421
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation61934.7824433416
Sum Squared Residuals1614921173324.01


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
156289-89233.7288957103145522.728895710
228328-62174.132008000490502.1320080004
317936-48987.005056664366923.0050566643
4414590733.051922601-86588.051922601
5304012997.1433548443-9957.14335484426
62964-2337.507152147365301.50715214736
728659849.8504751336-6984.85047513361
82854-25057.810212440927911.8102124409
9216734219.4724554383-32052.4724554383
10197415332.0980902759-13358.0980902759
111910-12839.17746967114749.177469671
12187126484.2782614416-24613.2782614416
1394328384.0986063398-27441.0986063398
1492962347.0626454941-61418.0626454941
1582223275.5278148478-22453.5278148478
16819117.749474606026701.250525393974
17769-17279.272335127618048.2723351276
1874520042.6554707737-19297.6554707737
1965238570.1848962904-37918.1848962904
2064327513.2543602204-26870.2543602204
2160122867.7383163352-22266.7383163352
2244630727.828269609-30281.828269609
234369419.64670179544-8983.64670179544
2437930520.1847129397-30141.1847129397
2530524818.93763444-24513.93763444
2628434926.5331040324-34642.5331040324
2724735795.8347116123-35548.8347116123
2823850983.5871589194-50745.5871589194
2922332244.1376366476-32021.1376366476
3022041871.8651052037-41651.8651052037
3121720866.5176959157-20649.5176959157
3219924907.8809865029-24708.8809865029
3319523052.0337925165-22857.0337925165
3416321812.0627355327-21649.0627355327
35154-24440.202935549124594.2029355491
3614333868.7473918376-33725.7473918376
3713526279.5942639701-26144.5942639701
3813217591.3017867927-17459.3017867927
391203640.99340419161-3520.99340419161
4011937641.5937232588-37522.5937232588
4110826523.2579197568-26415.2579197568
4210433944.2752997517-33840.2752997517
4310127017.184694946-26916.184694946
449026016.8863695082-25926.8863695082
458535075.0434562491-34990.0434562491
467427468.4138830885-27394.4138830885
47735814.5214332622-5741.5214332622
487020889.5734709871-20819.5734709871
496721793.7946982049-21726.7946982049
506638071.3900918079-38005.3900918079
516622466.1201865471-22400.1201865471
526617617.9752259274-17551.9752259274
53597973.5792080337-7914.5792080337
545813796.1364917470-13738.1364917470
555820816.5474388527-20758.5474388527
565435502.9760991007-35448.9760991007
575325171.4803029765-25118.4803029765
584929779.7905672067-29730.7905672067
594928757.5831191775-28708.5831191775
604433149.1848314602-33105.1848314602
613926860.586768731-26821.586768731
623842631.8217262323-42593.8217262323
633827905.0399612129-27867.0399612129
643734137.0917711109-34100.0917711109
653637462.6184336962-37426.6184336962
663534823.2364176421-34788.2364176421
673434710.1753356098-34676.1753356098
683327076.4415319916-27043.4415319916
693221130.1598794693-21098.1598794693
703227167.3218288739-27135.3218288739
713148575.7855764792-48544.7855764792
723141821.3066706425-41790.3066706425
733027746.9248300690-27716.9248300690
743031766.5474330618-31736.5474330618
753022933.115693939-22903.115693939
762835889.8158436138-35861.8158436138
772624395.5588663015-24369.5588663015
782636764.8726352644-36738.8726352644
792635918.2641135788-35892.2641135788
802524706.0852613602-24681.0852613602
812536233.5432434179-36208.5432434179
822429615.0052566633-29591.0052566633
832419724.5470112939-19700.5470112939
842341711.1647882616-41688.1647882616
852325350.6803068257-25327.6803068257
862237429.2419977631-37407.2419977631
872228346.1366298908-28324.1366298908
882225823.6608923579-25801.6608923579
892028333.3151718081-28313.3151718081
902024299.9608244107-24279.9608244107
911928193.4394213612-28174.4394213612
921831550.8117593336-31532.8117593336
931733701.4624963946-33684.4624963946
941624226.443885249-24210.443885249
951441426.3815555678-41412.3815555678
961436496.2281535446-36482.2281535446
971419324.2338111880-19310.2338111880
981335409.7235777641-35396.7235777641
991320014.7129334247-20001.7129334247
1001333690.348572381-33677.348572381
1011334311.2341102064-34298.2341102064
1021333263.7073577922-33250.7073577922
1031228728.6305231595-28716.6305231595
1041225433.0105736104-25421.0105736104
1051134374.1246386141-34363.1246386141
1061036095.4182248796-36085.4182248796
1071043900.07030296-43890.07030296
1081022989.2731941124-22979.2731941124
1091030458.0099949014-30448.0099949014
1101037007.5477781239-36997.5477781239
111932790.7528242271-32781.7528242271
112945595.0872787363-45586.0872787363
113934346.2548983606-34337.2548983606
114635227.3718636988-35221.3718636988
115634195.7068903708-34189.7068903708
116637643.1046274757-37637.1046274757
117531190.0300308838-31185.0300308838
118527377.9765070889-27372.9765070889
119537225.6192295550-37220.6192295550
120437335.9891475928-37331.9891475928
121429079.8186376122-29075.8186376122
122334547.4095125188-34544.4095125188
123229829.1350743997-29827.1350743997
124222395.2264632662-22393.2264632662
125136381.6076266409-36380.6076266409
126137864.8889174455-37863.8889174455
1279437392.4386126633-37298.4386126633
1284226595.08983362549-6173.08983362549
12906962.16814590149-6962.16814590149
13077111.76313128189-7104.76313128189
13125177775.937848791-177750.937848791
1320201473.605820156-201473.605820156
133312846175585.232030489137260.767969511
13431207567228.1044003481244846.895599652
13531500961704.7944605287253304.205539471
13631890357931.2667481869260971.733251813
13731488756470.4368397929258416.563160207
13831491372074.3055897373242838.694410263
13931538061919.6946642221253460.305335778
14089653847.554153808345117.44584619166
14105058.65825211286-5058.65825211286
142038100.9019004061-38100.9019004061
143031646.2322768013-31646.2322768013
144035400.5288972083-35400.5288972083
145036970.3993075009-36970.3993075009
146937390.6194333016-37381.6194333016
1472714749.2257208214-4478.2257208214
148144056.47518656167-4042.47518656167
1495202773.62098783047-2253.62098783047
150176610369.6965705811-8603.69657058105
15108400.59026672278-8400.59026672278
15277051.52133165402-7044.52133165402
1532172980.152893141-172978.152893141
1540174504.795273314-174504.795273314
155315380175602.116733521139777.883266479
156576184939.2555884474252678.7444115526
157194892050.2003506395617438.7996493604
15805059.54653371079-5059.54653371079
159037389.2550487803-37389.2550487803
16007115.13612082298-7115.13612082298
161306268175574.332291058130693.667708942
16230218754316.1894335492247870.810566451
16331488253822.6584990982261059.341500902
16431538067530.3312568234247849.668743177
1659616-2218.1492135560511834.1492135561
166360657.548361570064-297.548361570064
16705056.25288976717-5056.25288976717
1683637389.4066470604-37353.4066470604
16906186.64010365541-6186.64010365541
17097089.63652647903-7080.63652647903
1710177075.974859246-177075.974859246
172315547175576.758907003139970.241092997
17331326757364.9262172166255902.073782783
17431617653327.3602938209262848.639706179
17531538097637.2255754023217742.774424598
17605059.46416491763-5059.46416491763
177033631.0469531185-33631.0469531185
178033048.6284321452-33048.6284321452
179039103.4120329869-39103.4120329869
180035312.1308670642-35312.1308670642
181033331.2474624758-33331.2474624758
182035900.1316998499-35900.1316998499
183035700.6713255286-35700.6713255286
184033948.9540695546-33948.9540695546
185034381.036624342-34381.036624342
18627537393.4998006243-37118.4998006243
18705976.2293849729-5976.2293849729
18877100.74480263989-7093.74480263989
1890178260.766795180-178260.766795180
190315688175589.146848511140098.853151489
19131538074846.0532819343240533.946718066
1922304-16885.304975404019189.3049754040
19305050.50975461683-5050.50975461683
19447237381.900429632-36909.900429632
195010901.9479074374-10901.9479074374
19607107.62789654955-7107.62789654955
197315380175598.186372978139781.813627022
19837398538.92570340028-4799.92570340028
1997381-464.3632034037257845.36320340373
200580774810.3540601909353266.6459398091
201296434045.4483297881225597.5516702119
20205084.61809522603-5084.61809522603
203039052.2320258737-39052.2320258737
204137165.8190357266-37164.8190357266
2050-16964.318084584516964.3180845845
2065764042304.541905462215335.4580945378
2076197763557.6318537969-1580.63185379690
2086262062950.8313400443-330.831340044265
2096072061965.4368479693-1245.43684796927
21016426835.5725820218-26671.5725820218
21115224384.8130558963-24232.8130558963
21219921652.6527073178-21453.6527073178
213667599.11683055963-7533.11683055963
2143359745.38674824833-9410.38674824833
21519153.15007378242-9152.15007378242
2161-30828.458357629830829.4583576298
2171-17112.097286874217113.0972868742
2181-26608.291360236426609.2913602364
2191-17680.225678320917681.2256783209
2201-18288.628683207318289.6286832073
2214-16981.848490648416985.8484906484
222038479.9817982748-38479.9817982748
2236391542299.444219651721615.5557803483
2246072062239.2978017443-1519.29780174434
2256072062524.3254545562-1804.32545455617
22635527773.8472081071-27418.8472081071
22720421678.3942279212-21474.3942279212
22852257-1326.3153976356053583.3153976356
22919152.58822375902-9151.58822375902
2301-20231.142575194120232.1425751941
2310-16963.926350098216963.9263500982
2326072042352.176670366918367.8233296331
23332126998.4970134190-26677.4970134190
23423722566.6992694080-22329.6992694080
23547727826.37456935443-3054.37456935443
236534373.26603303781-4320.26603303781
23709147.4271632601-9147.4271632601
23811-16976.79050170916987.790501709
239041424.1582108441-41424.1582108441
2406072042343.037812716518376.9621872835
24137229612.2683620199-29240.2683620199
24224021830.0784510416-21590.0784510416
2436425377.58613104464-4735.58613104464
24416810252.9169077136-10084.9169077136
2452317548.3641099125-17525.3641099125
24619147.53157354837-9146.53157354837
2471-17019.705846295417020.7058462954
2480-12010.207856798712010.2078567987
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25419140.12311295582-9139.12311295582
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26009140.54450047338-9140.54450047338
26112-16977.082457324516989.0824573245
262041036.5446441776-41036.5446441776
2636245242291.626900106520160.3730998935
2646417563429.9226016742745.077398325809
2656072063546.9925673993-2826.99256739933
26638127795.6001149068-27414.6001149068
26733120852.9848600923-20521.9848600923
26825621776.743422922-21520.743422922
26919145.1437109489-9144.1437109489
2701-16284.558857995316285.5588579953
27113-16995.331611339117008.3316113391
272042089.8302861676-42089.8302861676
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2946072063283.8728216011-2563.87282160111
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2981-21509.235154763221510.2351547632
2991-26736.53651480526737.536514805
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31219142.05353582017-9141.05353582017
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3219447993.67881465865-7049.67881465865
32231411450.7995791444-11136.7995791444
32319154.6951613468-9153.69516134679
3240-16963.612962509116963.6129625091
3256072042251.780183547518468.2198164525
3266072063619.4911030456-2899.49110304564
32735428135.3429073873-27781.3429073873
32817822095.8510884125-21917.8510884125
32919156.24024891116-9155.24024891116
3301-18853.94197210218854.941972102
3311-18700.609689441318701.6096894413
3321-18733.908120014218734.9081200142
3331-20606.111774356820607.1117743568
3341-25677.703307564725678.7033075647
3350-16965.937253794616965.9372537946
3367878042255.177000146736524.8229998533
3376072063757.2007686906-3037.2007686906
33825126913.4424049662-26662.4424049662
33919143.03677336113-9142.03677336113
3401-45532.281389880545533.2813898805
3410-16965.832791264916965.8327912649
3426160042065.834436063719534.1655639363
3435963563322.8868069316-3687.88680693159
3446072062575.5881019113-1855.58810191131
34524826916.5250873660-26668.5250873660
34619140.08706073825-9139.08706073825
34721-16986.215621039817007.2156210398
348041149.0370835278-41149.0370835278
3495978142264.676901672917516.3230983271
3507664463640.229227765113003.7707722349
3516482062301.59148004782518.40851995216
3526072050340.993115163510379.0068848365
35328026908.4330460665-26628.4330460665
35478811899.1011774619-11111.1011774619
3552017027.28575312117-6826.28575312117
356547423.53841001968-7369.53841001968
35719142.89631085528-9141.89631085528
35860-17013.759103672417073.7591036724
3598046543.1364282048-46463.1364282048
360039084.5284143939-39084.5284143939
3616270042326.880099274420373.1199007256
3626780462492.25855883495311.7414411651
3635966162622.7350013266-2961.73500132662
3645862061226.0917853172-2606.09178531722
3656039862199.2295662758-1801.22956627580
3665858058926.0918295846-346.0918295846
3676271062986.1497166421-276.149716642119
3685932562437.6701193461-3112.67011934609
3696095062098.7136932398-1148.71369323980
3706806061931.87441032626128.12558967375
3718362061646.948503609821973.0514963902
3725845662564.2105286708-4108.21052867076
3735281161946.6784428164-9135.6784428164
37412117362470.324827832658702.6751721674
3756387065660.735894079-1790.73589407894
3762100162023.5543767516-41022.5543767516
3777041562449.34545739817965.65454260189
3786423061113.51970236273116.48029763732
3795919062286.5854339282-3096.58543392816
3806935161967.43228686457383.56771313552
3816427062956.1632496241313.83675037595
3827069461837.23180253168856.76819746845
3836800562822.75779675085182.24220324919
3845893062908.2730655699-3978.27306556986
3855832062591.8223131017-4271.82231310167
3866998062503.97162830147476.02837169859
3876986361111.29984923378751.70015076629
3886325562578.0837499437676.91625005631
3895732063026.7481164115-5706.74811641153
3907523062264.330870957112965.6691290429
3917942064934.136784012314485.8632159877
3927349061687.01781300111802.9821869991
3933525061513.4451378177-26263.4451378177
3946228564913.9919714982-2628.99197149816
3956920662766.64010321556439.35989678448
3966592062918.62698182233001.37301817766
3976977060797.86403448928972.1359655108
3987268359214.763787312613468.2362126874
399-1454560973.047047876-75518.047047876
4005583056856.0609832522-1026.06098325221
4015517458861.6104456114-3687.61044561137
4026703862171.72699925214866.27300074786
4035125261757.571456862-10505.571456862
40415727854687.9142971832102590.085702817
4057951062437.906945814517072.0930541855
4067744060580.070442207616859.9295577924
4072728461935.0482955381-34651.0482955381
40821311833977.0477791391179140.952220861
40981767147631.083142278-65864.0831422783
410153198110627.23346002542570.7665399750
411-26007110320.630399485-136327.630399485
41212694253271.234153893873670.7658461062
41315721476137.135505718781076.8644942813
41412935278079.538718992651272.4612810074
41523481774409.979580057160407.020419943
4166044884163.482781696-23715.4827816961
4174781872057.746217659-24239.7462176590
41824554677212.069098492168333.930901508
4194802073433.0401482-25413.0401482000
420-171071151.477341023-72861.477341023
4213264874869.3984886549-42221.3984886549
4229535058512.465961797736837.5340382023
42315135272925.783691141178426.2163088589
42428817074730.7879655036213439.212034496
42511433765299.791385739349037.2086142607
4263788467012.7691563093-29128.7691563093
42712284472059.393755146350784.6062448537
4288234063905.722973729418434.2770262706
4297980168862.681879129510938.3181208705
43016554867729.761750856997818.238249143
43111638467236.684894723349147.3151052767


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.0007342693562871920.001468538712574380.999265730643713
140.0001671035817845870.0003342071635691740.999832896418215
151.41386881642916e-052.82773763285833e-050.999985861311836
161.13260212457153e-062.26520424914305e-060.999998867397875
171.18962464269252e-072.37924928538503e-070.999999881037536
188.47342254961136e-091.69468450992227e-080.999999991526577
198.82756188652081e-101.76551237730416e-090.999999999117244
206.08036665707574e-111.21607333141515e-100.999999999939196
219.12006764708774e-121.82401352941755e-110.99999999999088
221.41421546819198e-122.82843093638397e-120.999999999998586
231.10832711190984e-132.21665422381968e-130.99999999999989
248.08326146058511e-151.61665229211702e-140.999999999999992
255.24355044999372e-161.04871008999874e-151
263.72420822201437e-177.44841644402874e-171
272.89722066889842e-185.79444133779684e-181
281.81847174135212e-193.63694348270423e-191
291.13915007757251e-202.27830015514502e-201
306.55235237724866e-221.31047047544973e-211
316.9804134940275e-231.3960826988055e-221
329.7595276280284e-241.95190552560568e-231
337.2930518094008e-251.45861036188016e-241
346.69685356524851e-261.33937071304970e-251
354.27718393718302e-278.55436787436604e-271
363.34215691242172e-286.68431382484345e-281
371.97060510030437e-293.94121020060874e-291
381.39137356023290e-302.78274712046581e-301
399.08790223834296e-321.81758044766859e-311
404.95334155088442e-339.90668310176884e-331
413.50197729700859e-347.00395459401718e-341
422.38641497783958e-354.77282995567916e-351
431.24688226748708e-362.49376453497417e-361
447.88190008138577e-381.57638001627715e-371
454.5348012754822e-399.0696025509644e-391
466.17487774880415e-401.23497554976083e-391
475.26381456783736e-411.05276291356747e-401
483.68681921133125e-427.3736384226625e-421
491.95002224963621e-433.90004449927242e-431
509.91170481162444e-451.98234096232489e-441
515.1294473179256e-461.02588946358512e-451
522.82226249058252e-475.64452498116504e-471
531.87253342725225e-483.7450668545045e-481
541.44252627406811e-492.88505254813623e-491
556.99231541681753e-511.39846308336351e-501
563.43152216503772e-526.86304433007543e-521
573.1951376889541e-536.3902753779082e-531
581.68508954617329e-543.37017909234659e-541
597.98873085962395e-561.59774617192479e-551
606.05121511727404e-571.21024302345481e-561
613.83776834203311e-587.67553668406621e-581
621.86314639445651e-593.72629278891302e-591
631.22245334308756e-602.44490668617512e-601
648.00996832689112e-621.60199366537822e-611
653.91044056274279e-637.82088112548557e-631
661.73701309597335e-643.47402619194671e-641
678.72324216729043e-661.74464843345809e-651
684.8782289962409e-679.7564579924818e-671
692.18272071517422e-684.36544143034845e-681
701.28232950079350e-692.56465900158701e-691
715.8490793514437e-711.16981587028874e-701
722.82393902790081e-725.64787805580161e-721
731.25083724332546e-732.50167448665092e-731
745.21923140680715e-751.04384628136143e-741
752.34813425335298e-764.69626850670596e-761
769.80946959446617e-781.96189391889323e-771
774.0157751188425e-798.031550237685e-791
781.77233121100685e-803.54466242201369e-801
797.86773769889792e-821.57354753977958e-811
803.13737087004288e-836.27474174008575e-831
811.31503537429623e-842.63007074859245e-841
826.9156417594312e-861.38312835188624e-851
832.71475141535368e-875.42950283070735e-871
841.21656251927359e-882.43312503854718e-881
854.94074372966939e-909.88148745933877e-901
862.64444215476786e-915.28888430953572e-911
871.08344069777294e-922.16688139554588e-921
884.11043536180153e-948.22087072360306e-941
891.59239812841406e-953.18479625682812e-951
905.99566730670164e-971.19913346134033e-961
912.36688678689116e-984.73377357378233e-981
921.20459459898434e-992.40918919796868e-991
934.50227546215292e-1019.00455092430584e-1011
942.19600055006152e-1024.39200110012305e-1021
958.4512165786581e-1041.69024331573162e-1031
963.89550493999025e-1057.79100987998051e-1051
972.23768785357414e-1064.47537570714829e-1061
988.52962916675359e-1081.70592583335072e-1071
993.59214591465091e-1097.18429182930182e-1091
1001.68897886263751e-1103.37795772527502e-1101
1015.95212535273263e-1121.19042507054653e-1111
1022.19898224159356e-1134.39796448318711e-1131
1037.97865852901852e-1151.59573170580370e-1141
1042.96805040717264e-1165.93610081434528e-1161
1051.06991195635643e-1172.13982391271286e-1171
1063.95585336373740e-1197.91170672747479e-1191
1071.62223713306107e-1203.24447426612213e-1201
1086.99853961905192e-1221.39970792381038e-1211
1092.52628963137466e-1235.05257926274931e-1231
1108.57350591905867e-1251.71470118381173e-1241
1112.89274772480195e-1265.7854954496039e-1261
1121.07898953046054e-1272.15797906092109e-1271
1133.80735018445346e-1297.61470036890692e-1291
1141.24375696110271e-1302.48751392220542e-1301
1156.39995671236733e-1321.27999134247347e-1311
1162.18265505650820e-1334.36531011301641e-1331
1177.07519286690386e-1351.41503857338077e-1341
1182.55125084193896e-1365.10250168387791e-1361
1198.39973377803047e-1381.67994675560609e-1371
1202.65305846649453e-1395.30611693298905e-1391
1219.31886755325098e-1411.86377351065020e-1401
1223.09681633563542e-1426.19363267127084e-1421
1231.06800423615789e-1432.13600847231579e-1431
1244.07245132323785e-1458.1449026464757e-1451
1251.51208271067530e-1463.02416542135060e-1461
1264.77480917284259e-1489.54961834568519e-1481
1271.4859747107756e-1492.9719494215512e-1491
1281.03416847159144e-1502.06833694318288e-1501
1293.21014748843925e-1526.4202949768785e-1521
1309.39903175812552e-1521.87980635162510e-1511
1311.55388757890575e-1523.10777515781149e-1521
1327.99030583377092e-1531.59806116675418e-1521
1334.7391319809627e-1549.4782639619254e-1541
1341.81821295958838e-1543.63642591917677e-1541
1352.26037215377277e-1544.52074430754553e-1541
1366.39764528570972e-1541.27952905714194e-1531
1376.3540079040172e-1541.27080158080344e-1531
1383.76103973142721e-1537.52207946285443e-1531
1395.87364156080311e-1531.17472831216062e-1521
1401.23784546084949e-1512.47569092169899e-1511
1416.00816165833206e-1501.20163233166641e-1491
1422.58276572175028e-1515.16553144350056e-1511
1439.06409011865549e-1481.81281802373110e-1471
1444.00153939657192e-1498.00307879314384e-1491
1451.73661157726554e-1503.47322315453109e-1501
1467.64565351535197e-1521.52913070307039e-1511
1475.92576429101823e-1511.18515285820365e-1501
1482.97818859156968e-1525.95637718313937e-1521
1492.93818462147161e-1535.87636924294323e-1531
1504.10184221482852e-1548.20368442965704e-1541
1512.2255609105862e-1554.4511218211724e-1551
1521.11661377229146e-1562.23322754458292e-1561
1532.59404142790273e-1565.18808285580546e-1561
1541.32262758934779e-1552.64525517869558e-1551
1551.16470245249490e-1562.32940490498980e-1561
1561.13421213228303e-1232.26842426456606e-1231
1579.1066847422274e-1251.82133694844548e-1241
1581.76232790823429e-1213.52465581646858e-1211
1591.49886928662391e-1222.99773857324782e-1221
1601.35080678080024e-1232.70161356160048e-1231
1611.24087877979733e-1232.48175755959467e-1231
1621.26519298249308e-1172.53038596498616e-1171
1631.15820423071016e-1152.31640846142031e-1151
1647.42754490097619e-1141.48550898019524e-1131
1651.57537039443425e-1143.15074078886851e-1141
1661.26064320622332e-1132.52128641244664e-1131
1677.34016812981309e-1131.46803362596262e-1121
1687.92451338601439e-1141.58490267720288e-1131
1698.47232803482332e-1151.69446560696466e-1141
1708.95457897265118e-1161.79091579453024e-1151
1712.53892801459855e-1135.07785602919711e-1131
1725.9542627440542e-1141.19085254881084e-1131
1735.51633803451819e-1111.10326760690364e-1101
1747.85329579545856e-1071.57065915909171e-1061
1751.89027856553972e-1043.78055713107944e-1041
1764.88542984740409e-1049.77085969480819e-1041
1776.48492053345152e-1051.29698410669030e-1041
1788.44431835218521e-1061.68886367043704e-1051
1791.09150405024536e-1062.18300810049073e-1061
1801.38910293726087e-1072.77820587452174e-1071
1814.48700921373324e-1048.97401842746647e-1041
1826.09563610789195e-1051.21912722157839e-1041
1838.48788200401695e-1061.69757640080339e-1051
1841.20820680425565e-1062.41641360851130e-1061
1851.60437161084332e-1073.20874322168665e-1071
1862.23267853558793e-1084.46535707117586e-1081
1873.01437197879636e-1096.02874395759272e-1091
1884.0042549461801e-1108.0085098923602e-1101
1895.6126216547143e-1031.12252433094286e-1021
1908.5723576214126e-1041.71447152428252e-1031
1911.20010226219475e-982.40020452438951e-981
1922.59517675918107e-975.19035351836215e-971
1931.59712849196091e-973.19425698392182e-971
1942.83791581314473e-985.67583162628947e-981
1954.56532405269223e-999.13064810538446e-991
1961.53465720044492e-993.06931440088984e-991
1973.47231958865668e-996.94463917731337e-991
1988.94658216367703e-1001.78931643273541e-991
1991.61923375197565e-1003.2384675039513e-1001
2001.47991705905365e-862.95983411810729e-861
2012.85917677318880e-855.71835354637761e-851
2023.10029023731162e-856.20058047462324e-851
2037.28090936186494e-861.45618187237299e-851
2042.45308246638862e-844.90616493277724e-841
2056.45572340853737e-851.29114468170747e-841
2061.89928288150252e-853.79856576300504e-851
2071.72221677557471e-853.44443355114942e-851
2086.43382387794173e-861.28676477558835e-851
2091.36101749952513e-862.72203499905026e-861
21012.87205023184026e-181.43602511592013e-18
21112.48359627594267e-241.24179813797134e-24
21212.88633580676684e-261.44316790338342e-26
21318.42209436640951e-274.21104718320476e-27
21411.71798183538212e-268.5899091769106e-27
21513.60651598354739e-261.80325799177369e-26
21613.55573656393091e-261.77786828196545e-26
21715.05188335317963e-262.52594167658981e-26
21817.23239999330526e-263.61619999665263e-26
21911.20816711562765e-256.04083557813826e-26
22012.11008669252113e-251.05504334626056e-25
22113.83892944042328e-251.91946472021164e-25
22215.47554449540178e-252.73777224770089e-25
22311.07221213729811e-245.36106068649056e-25
22411.70237752664851e-248.51188763324253e-25
22512.89177179446864e-241.44588589723432e-24
22611.46223039167597e-247.31115195837987e-25
22715.25275067499466e-252.62637533749733e-25
22813.2568831893633e-251.62844159468165e-25
22916.8028582181691e-253.40142910908455e-25
23011.24413241794967e-246.22066208974835e-25
23112.35905281697421e-241.17952640848711e-24
23214.76548537802909e-242.38274268901454e-24
23315.97407275531286e-242.98703637765643e-24
23415.20321178488178e-242.60160589244089e-24
23511.09945878326863e-235.49729391634314e-24
23612.18643204749881e-231.09321602374941e-23
23714.48838760572811e-232.24419380286406e-23
23818.50604152051511e-234.25302076025756e-23
23911.02564818198774e-225.12824090993871e-23
24012.03432069031763e-221.01716034515882e-22
24113.40553050492374e-221.70276525246187e-22
24213.86071050644833e-221.93035525322416e-22
24317.75077982721375e-223.87538991360687e-22
24411.56191742111319e-217.80958710556593e-22
24512.43495930700041e-211.21747965350020e-21
24614.90695033702607e-212.45347516851304e-21
24719.1847085498921e-214.59235427494605e-21
24811.69275607644872e-208.46378038224362e-21
24913.21263496412422e-201.60631748206211e-20
25016.1957872956267e-203.09789364781335e-20
25111.10299029398001e-195.51495146990005e-20
25212.15231325697818e-191.07615662848909e-19
25314.15914846564308e-192.07957423282154e-19
25418.09488182737711e-194.04744091368855e-19
25511.51011564391910e-187.55057821959548e-19
25612.30647873755178e-181.15323936877589e-18
25713.55463299149204e-181.77731649574602e-18
25816.19718681646665e-183.09859340823332e-18
25918.86899733956722e-184.43449866978361e-18
26011.68940286161549e-178.44701430807746e-18
26113.11215594909104e-171.55607797454552e-17
26213.77151507837287e-171.88575753918643e-17
26316.86992178918235e-173.43496089459117e-17
26411.15931582639157e-165.79657913195787e-17
26511.97409113436132e-169.87045567180658e-17
26613.50626772641398e-161.75313386320699e-16
26715.42655453863401e-162.71327726931700e-16
26818.61082137179879e-164.30541068589940e-16
26911.58660442769745e-157.93302213848726e-16
2700.9999999999999992.83225402225122e-151.41612701112561e-15
2710.9999999999999975.08967036268838e-152.54483518134419e-15
2720.9999999999999976.16904040963063e-153.08452020481531e-15
2730.9999999999999941.10560061199355e-145.52800305996777e-15
2740.999999999999991.89092343008074e-149.45461715040368e-15
2750.9999999999999843.11565580920532e-141.55782790460266e-14
2760.9999999999999735.40933582160433e-142.70466791080217e-14
2770.9999999999999568.76075125458876e-144.38037562729438e-14
2780.999999999999921.58063306576808e-137.9031653288404e-14
2790.9999999999998672.65040984095432e-131.32520492047716e-13
2800.9999999999997664.68180070578942e-132.34090035289471e-13
2810.9999999999995948.11477255833444e-134.05738627916722e-13
2820.9999999999993541.29203948515724e-126.46019742578618e-13
2830.9999999999988822.23578472705283e-121.11789236352642e-12
2840.9999999999980833.83388252127461e-121.91694126063730e-12
2850.9999999999967426.51508467703466e-123.25754233851733e-12
2860.9999999999942961.14082794209444e-115.70413971047218e-12
2870.9999999999900861.98273831433311e-119.91369157166553e-12
2880.9999999999831673.36666095506018e-111.68333047753009e-11
2890.999999999971665.66818435325538e-112.83409217662769e-11
2900.9999999999678286.4344327651622e-113.2172163825811e-11
2910.999999999993031.39386822355290e-116.96934111776448e-12
2920.9999999999885582.28829370397055e-111.14414685198527e-11
2930.9999999999816433.67145234685414e-111.83572617342707e-11
2940.9999999999697436.05139172712364e-113.02569586356182e-11
2950.9999999999492951.01410891933376e-105.0705445966688e-11
2960.999999999969986.00409596819801e-113.00204798409901e-11
2970.9999999999480641.03871896131082e-105.1935948065541e-11
2980.9999999999130351.73929609599006e-108.69648047995031e-11
2990.9999999998567562.86487778424826e-101.43243889212413e-10
3000.999999999763774.72461135674046e-102.36230567837023e-10
3010.9999999998268463.4630821513398e-101.7315410756699e-10
3020.9999999997107615.78477409447327e-102.89238704723664e-10
3030.9999999995233729.5325535355398e-104.7662767677699e-10
3040.9999999992062141.58757147432023e-097.93785737160114e-10
3050.999999998722732.55453928920038e-091.27726964460019e-09
3060.9999999979165554.16688911674683e-092.08344455837341e-09
3070.9999999965441186.91176445905013e-093.45588222952507e-09
3080.9999999943668171.12663656014637e-085.63318280073186e-09
3090.9999999911550751.76898497174158e-088.84492485870789e-09
3100.9999999862569782.74860430553073e-081.37430215276536e-08
3110.9999999780881974.38236059998177e-082.19118029999089e-08
3120.9999999646428487.07143046169262e-083.53571523084631e-08
3130.9999999438632781.12273443892702e-075.61367219463509e-08
3140.9999999152380311.69523937949002e-078.47619689745009e-08
3150.9999998674286062.65142788842968e-071.32571394421484e-07
3160.999999799865934.00268139042665e-072.00134069521332e-07
3170.9999997337300935.32539814651143e-072.66269907325572e-07
3180.9999995959410628.08117876562181e-074.04058938281091e-07
3190.9999993886179321.22276413592380e-066.11382067961898e-07
3200.9999991409806731.71803865303101e-068.59019326515506e-07
3210.9999986794888042.64102239138295e-061.32051119569147e-06
3220.9999981661009673.66779806546661e-061.83389903273331e-06
3230.9999972016715525.59665689652003e-062.79832844826002e-06
3240.9999958008125368.39837492704436e-064.19918746352218e-06
3250.9999940215988071.19568023867114e-055.97840119335571e-06
3260.9999912545256631.74909486741038e-058.7454743370519e-06
3270.999987069766562.58604668782764e-051.29302334391382e-05
3280.999981239805243.75203895219092e-051.87601947609546e-05
3290.9999723953017675.52093964655085e-052.76046982327542e-05
3300.99995995432968.00913408026725e-054.00456704013362e-05
3310.9999426324542780.0001147350914437855.73675457218924e-05
3320.9999176765130940.0001646469738110278.23234869055136e-05
3330.9998849051559740.0002301896880523620.000115094844026181
3340.9998697521907920.0002604956184167490.000130247809208374
3350.999815184360770.0003696312784588740.000184815639229437
3360.9997855735474140.0004288529051727370.000214426452586368
3370.9997031801658830.0005936396682337350.000296819834116867
3380.9995858598413680.0008282803172639550.000414140158631977
3390.9994234356803660.001153128639268320.000576564319634162
3400.99922531232890.001549375342200520.00077468767110026
3410.9989377930878820.00212441382423680.0010622069121184
3420.9986651690711150.002669661857770680.00133483092888534
3430.998214673174160.003570653651677550.00178532682583877
3440.9976334979078060.004733004184387970.00236650209219399
3450.9968420226658680.006315954668263770.00315797733413188
3460.9957990865837520.008401826832495150.00420091341624757
3470.9944618636614450.01107627267711020.00553813633855509
3480.9930724122210160.01385517555796870.00692758777898436
3490.9916410933618670.01671781327626570.00835890663813285
3500.9893241523116090.02135169537678240.0106758476883912
3510.9863720500839130.02725589983217450.0136279499160872
3520.9827537664423220.03449246711535550.0172462335576778
3530.9787515189859050.04249696202818950.0212484810140947
3540.9733271191165020.05334576176699580.0266728808834979
3550.9665943605470070.06681127890598660.0334056394529933
3560.958672629544440.08265474091111790.0413273704555589
3570.9585873682452180.08282526350956310.0414126317547816
3580.951891591231270.09621681753745890.0481084087687294
3590.9418464314105070.1163071371789850.0581535685894926
3600.9337117685687560.1325764628624870.0662882314312436
3610.9491276461428080.1017447077143850.0508723538571923
3620.9376856166930980.1246287666138040.0623143833069021
3630.9239843962769350.1520312074461310.0760156037230653
3640.9096982726641020.1806034546717960.0903017273358979
3650.8917917047789880.2164165904420240.108208295221012
3660.8714156202126780.2571687595746430.128584379787322
3670.8479915195687660.3040169608624690.152008480431234
3680.8270035591882680.3459928816234640.172996440811732
3690.7984923529155550.4030152941688890.201507647084445
3700.7672686045571160.4654627908857690.232731395442885
3710.7343526802366660.5312946395266690.265647319763334
3720.6976265972869080.6047468054261830.302373402713092
3730.6609060726329070.6781878547341850.339093927367092
3740.6676885249438820.6646229501122350.332311475056118
3750.6443026118198210.7113947763603580.355697388180179
3760.6183035694171520.7633928611656960.381696430582848
3770.5755672101268570.8488655797462860.424432789873143
3780.5307761674560010.9384476650879980.469223832543999
3790.515245803905280.969508392189440.48475419609472
3800.4697218836601260.9394437673202530.530278116339874
3810.4234797678572680.8469595357145360.576520232142732
3820.378380230405070.756760460810140.62161976959493
3830.3524361067516610.7048722135033220.647563893248339
3840.3600813545557860.7201627091115720.639918645444214
3850.3252119128030370.6504238256060740.674788087196963
3860.3455598649160820.6911197298321650.654440135083918
3870.3010644254437160.6021288508874320.698935574556284
3880.2669734977010950.533946995402190.733026502298905
3890.2329100795720870.4658201591441730.767089920427913
3900.1973429268572260.3946858537144530.802657073142774
3910.1970760120952690.3941520241905370.802923987904731
3920.1680193074643160.3360386149286330.831980692535684
3930.1409200369866510.2818400739733020.859079963013349
3940.1309024039383190.2618048078766380.869097596061681
3950.1166710843006170.2333421686012340.883328915699383
3960.1026449209087940.2052898418175890.897355079091206
3970.08602611469580760.1720522293916150.913973885304192
3980.06587761903201670.1317552380640330.934122380967983
3990.06963542117706540.1392708423541310.930364578822935
4000.05493476171712270.1098695234342450.945065238282877
4010.04243365368631040.08486730737262070.95756634631369
4020.03060330979258570.06120661958517140.969396690207414
4030.02540188210543740.05080376421087490.974598117894563
4040.02809359944115980.05618719888231970.97190640055884
4050.01947636338427940.03895272676855880.98052363661572
4060.01391881868480040.02783763736960070.9860811813152
4070.03699660725027520.07399321450055030.963003392749725
4080.04086750778516520.08173501557033040.959132492214835
4090.03302705467245480.06605410934490960.966972945327545
4100.02565637525830490.05131275051660980.974343624741695
4110.06563556322230280.1312711264446060.934364436777697
4120.06732974787412250.1346594957482450.932670252125878
4130.05616854843694490.1123370968738900.943831451563055
4140.04745379362775360.09490758725550720.952546206372246
4150.4261368814304060.8522737628608120.573863118569594
4160.3127712179175080.6255424358350160.687228782082492
4170.2419994271603990.4839988543207980.758000572839601
4180.8007104215976330.3985791568047350.199289578402367


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3340.822660098522167NOK
5% type I error level3430.844827586206897NOK
10% type I error level3570.879310344827586NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/108swj1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/108swj1291404105.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/1j9zp1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/1j9zp1291404105.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/2cizs1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/2cizs1291404105.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/3cizs1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/3cizs1291404105.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/4cizs1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/4cizs1291404105.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/55ayv1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/55ayv1291404105.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/65ayv1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/65ayv1291404105.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/7fjxg1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/7fjxg1291404105.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/8fjxg1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/8fjxg1291404105.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/98swj1291404105.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291404030rt8l4s6sg09awr8/98swj1291404105.ps (open in new window)


 
Parameters (Session):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
 




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Software written by Ed van Stee & Patrick Wessa


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