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*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 19:13:39 +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/t1291403508iywep5akdkpp1tw.htm/, Retrieved Fri, 03 Dec 2010 20:12:00 +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/t1291403508iywep5akdkpp1tw.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 «
162556 1081 213118 6282154 1 5626 37 56289 29790 309 81767 4321023 2 13337 138 28328 87550 458 153198 4111912 3 8541 45 17936 84738 588 -26007 223193 415 39 0 4145 54660 302 126942 1491348 6 4276 24 3040 42634 156 157214 1629616 5 9164 34 2964 40949 481 129352 1398893 9 2493 29 2865 45187 353 234817 1926517 4 4891 38 2854 37704 452 60448 983660 14 1734 21 2167 16275 109 47818 1443586 7 11409 76 1974 25830 115 245546 1073089 10 7592 34 1910 12679 110 48020 984885 13 7135 62 1871 18014 239 -1710 1405225 8 5043 67 943 43556 247 32648 227132 414 110 1 929 24811 505 95350 929118 15 1444 29 822 6575 159 151352 1071292 11 5480 133 819 7123 109 288170 638830 28 4026 62 769 21950 519 114337 856956 17 1266 30 745 37597 248 37884 992426 12 3195 21 652 17821 373 122844 444477 46 655 14 643 12988 119 82340 857217 16 5523 51 601 22330 84 79801 711969 20 6095 23 446 13326 102 165548 702380 21 4925 38 436 16189 295 116384 358589 89 538 10 379 7146 105 134028 297978 367 933 14 305 15824 64 63838 58571 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 time56 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework
error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.


Multiple Linear Regression - Estimated Regression Equation
CCScore [t] = + 34267.2709217981 + 0.00327740153970557Costs[t] + 0.21113518371683Trades[t] -0.200510302061976Dividends[t] -0.00777428564764741Wealth[t] -0.201968909600285WRank[t] -0.110949694939462`Profit/Trades`[t] -0.125756912714287`Profit/Cost`[t] + 126.294849270578t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)34267.27092179819895.2914353.4630.0005890.000294
Costs0.003277401539705570.0080930.4050.6856950.342848
Trades0.211135183716830.0473954.45481.1e-055e-06
Dividends-0.2005103020619760.037307-5.374600
Wealth-0.007774285647647410.008408-0.92460.3556780.177839
WRank-0.2019689096002850.0414-4.87852e-061e-06
`Profit/Trades`-0.1109496949394620.058385-1.90030.0580750.029037
`Profit/Cost`-0.1257569127142870.03806-3.30420.0010340.000517
t126.29484927057835.4075063.56690.0004030.000201


Multiple Linear Regression - Regression Statistics
Multiple R0.445426980990321
R-squared0.198405195394151
Adjusted R-squared0.18320908535423
F-TEST (value)13.0563147327129
F-TEST (DF numerator)8
F-TEST (DF denominator)422
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation70400.402000287
Sum Squared Residuals2091523405960.45


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
156289-57046.1080024099113335.10800241
228328-16802.752249961645130.7522499616
317936-28609.050125004446545.0501250044
4414538565.6794121608-34420.6794121608
53040-2384.344229528915424.34422952891
62964-10016.449649028512980.4496490285
72865-1706.767419245194571.76741924519
82854-27108.506279416629962.5062794166
9216715657.3730257928-13490.3730257928
10197413518.9249373456-11544.9249373456
111910-22660.178457894624570.1784578946
12187117760.2575344703-15889.2575344703
1394324867.2956738625-23924.2956738625
1492927822.3049620562-26893.3049620562
158229840.85866064976-9018.85866064976
16819-2960.008993926723779.00899392672
17769-26746.994149779327515.9941497793
187456986.46070185053-6241.46070185053
1965221171.3707603073-20519.3707603073
206438759.62597845933-8116.62597845933
2160113190.4663724850-12589.4663724850
2244614917.5341666065-14471.5341666065
23436-1972.766699633652408.76669963365
2437911210.8019877704-10831.8019877704
253058100.27155379844-7795.27155379844
2628419584.8478308177-19300.8478308177
2724717487.6776317258-17240.6776317258
2823829930.2533057207-29692.2533057207
2922323628.3408825596-23405.3408825596
3022024678.1228400488-24458.1228400488
3121713408.3035558648-13191.3035558648
3219912124.6553566657-11925.6553566657
3319515199.8687901868-15004.8687901868
3416312543.5084888903-12380.5084888903
35154-29067.025357686429221.0253576864
3614315311.2995891611-15168.2995891611
3713515707.3378200853-15572.3378200853
3813210428.5496087983-10296.5496087983
39120-4034.317577546164154.31757754616
4011921658.4217817492-21539.4217817492
4110811497.9173644005-11389.9173644005
4210417315.7098164116-17211.7098164116
4310116885.6678322113-16784.6678322113
449013143.6875308869-13053.6875308869
458522745.7701019312-22660.7701019312
467415942.7439207079-15868.7439207079
4773-4946.745553711175019.74555371117
48709509.62113388596-9439.62113388596
496712025.6484136487-11958.6484136487
506623698.1809487166-23632.1809487166
516614582.3292518237-14516.3292518237
52669577.20175572624-9511.20175572624
5359-975.4004346795331034.40043467953
54587442.9947650438-7384.9947650438
55587935.5449080978-7877.5449080978
565425652.9521215900-25598.9521215900
575320704.7328755277-20651.7328755277
584920137.4587742466-20088.4587742466
594918961.4193377552-18912.4193377552
604421132.9782757292-21088.9782757292
613918537.3756561685-18498.3756561685
623828950.2918061806-28912.2918061806
633819317.3465716214-19279.3465716214
643722804.3055110552-22767.3055110552
653627663.1289180748-27627.1289180748
663521814.4178520807-21779.4178520807
673424336.1943497163-24302.1943497163
683319212.9587157048-19179.9587157048
693213563.6094112005-13531.6094112005
703219312.3564291058-19280.3564291058
713136116.4110303769-36085.4110303769
723132194.6124694184-32163.6124694184
733019213.3135097976-19183.3135097976
743023865.3294822784-23835.3294822784
753016208.156830569-16178.156830569
762826044.7050863750-26016.7050863750
772617697.3113327604-17671.3113327604
782627265.4586874384-27239.4586874384
792627733.0777898730-27707.0777898730
802517994.236353821-17969.236353821
812528916.7538181518-28891.7538181518
822422917.1216419964-22893.1216419964
832413828.0561344947-13804.0561344947
842333874.3768968-33851.3768968
852320103.6124099570-20080.6124099570
862229239.7323665955-29217.7323665955
872223272.5621206237-23250.5621206237
882220509.8492942627-20487.8492942627
892021488.5877340646-21468.5877340646
902019981.0701153576-19961.0701153576
911923123.4871449150-23104.4871449150
921825834.2920292008-25816.2920292008
931729466.6122216536-29449.6122216536
941617628.9294105718-17612.9294105718
951435548.6383494864-35534.6383494864
961430924.1859214649-30910.1859214649
971417233.8234293146-17219.8234293146
981329939.5113136855-29926.5113136855
991316503.2393158190-16490.2393158190
1001328520.4284182277-28507.4284182277
1011328940.2605350493-28927.2605350493
1021326919.5406933951-26906.5406933951
1031223011.5570603436-22999.5570603436
1041221364.4734191922-21352.4734191922
1051130410.0837154696-30399.0837154696
1061030788.9449203103-30778.9449203103
1071034886.9619658619-34876.9619658619
1081022567.950639239-22557.950639239
1091024962.2889705097-24952.2889705097
1101031364.3907090593-31354.3907090593
111930682.2770161641-30673.2770161641
112937586.0548933462-37577.0548933462
113928810.1420195571-28801.1420195571
114630791.3428579623-30785.3428579623
115632097.8580304772-32091.8580304772
116634517.7325831995-34511.7325831995
117528791.1302669881-28786.1302669881
118526669.2177837657-26664.2177837657
119531978.5930689852-31973.5930689852
120434465.3115721252-34461.3115721252
121424339.3440137943-24335.3440137943
122332542.1078575949-32539.1078575949
123228202.8675061389-28200.8675061389
124223532.4753826150-23530.4753826150
125134224.2525810527-34223.2525810527
126133134.8179283829-33133.8179283829
127735629.4798188744-35622.4798188744
12828116554.421669838-116526.421669838
1290117782.590499858-117782.590499858
130314353109800.522701218204552.477298782
13136944845842.7760558577323605.223944142
13231538043848.5914988996271531.408501100
133376154008.4142349707933606.5857650292
134224154140.4242945755118274.5757054245
135127794046.331971719678732.66802828033
13691463711.983133094275434.01686690573
137574434333.6469543853753109.3530456146
138229834356.2311326135918626.7688673864
13904544.16519727825-4544.16519727825
140036533.2934862453-36533.2934862453
1411537277.5633335395-37262.5633335395
1423115276.950804625-115273.950804625
14315118170.844234582-118155.844234582
14411122102.805426755-122091.805426755
1450118606.803284449-118606.803284449
146314987111799.528182759203187.471817241
14732524946691.1302239767278557.869776023
14831587758412.7137084497257464.286291550
14929165068457.3632088023223192.636791198
15030595947147.2449900655258811.755009934
15131538045997.3037682306269382.696231769
15288888449.20869211508438.791307884919
153384156265.7796203371332149.2203796629
15406453.1174548074-6453.1174548074
155239159.5774413309-39157.5774413309
1567119931.461848315-119924.461848315
1570125084.296716845-125084.296716845
158315380113330.006734303202049.993265697
15907067.94810307758-7067.94810307758
1602739742.8372451984-39715.8372451984
16151119262.152464696-119211.152464696
1623118598.160538441-118595.160538441
1630120684.951568648-120684.951568648
164382712115827.039682205266884.960317795
16534157048468.161549155293101.838450845
16631538047666.5936449402267713.40635506
16708077.73832156053-8077.73832156053
168037133.4813426461-37133.4813426461
169940920.559922572-40911.559922572
1700121036.096235044-121036.096235044
171315547114917.836430841200629.163569159
17231326750511.9308341672262755.069165833
17331617648564.3222279788267611.677772021
17431538072805.9372080043242574.062791996
17509090.79372671833-9090.79372671833
176038788.8909311345-38788.8909311345
177038196.3400347307-38196.3400347307
178042846.7619297522-42846.7619297522
179039848.0356813982-39848.0356813982
180038776.3278178923-38776.3278178923
181041331.4148605922-41331.4148605922
182040911.679345996-40911.679345996
183039830.2673169597-39830.2673169597
184039891.4212808832-39891.4212808832
185442933.2328577338-42929.2328577338
1860124904.291863684-124904.291863684
187315366116855.792608427198510.207391573
18831538054271.1990532278261108.800946772
1895784410787.727344473747056.2726555263
190010988.9458958138-10988.9458958138
191032206.2997947106-32206.2997947106
192043836.7322854043-43836.7322854043
193269587115212.901306841154374.098693159
19431538051838.6768481355263541.323151864
195011609.2567290194-11609.2567290194
1962744318.8066561208-44291.8066561208
19717122593.120278941-122576.120278941
1982128144.728634612-128142.728634612
1994123511.233743742-123507.233743742
2000126361.654863447-126361.654863447
201343613120058.673348152223554.326651848
20230694853008.5575116759253939.442488324
20321014155.5889178921-13945.5889178921
204267-4160.444798424614427.44479842461
2054745271.64726166555-4797.64726166555
20653414502.7119636853-13968.7119636853
20709540.17068446114-9540.17068446114
2086083110115.555928917050715.444071083
2096064658725.24829240631920.75170759374
2106072061017.1754891563-297.175489156258
21113515197.2649715649-15062.2649715649
21237317046.382360119-16673.382360119
21319814919.9186252257-14721.9186252257
21436505-10155.108622004446660.1086220044
215239-2711.666520398372950.66652039837
2165924-9944.4669778896315868.4669778896
217413-2944.442020384833357.44202038483
2181874-3254.96155961725128.9615596172
219192-2267.02060662042459.0206066204
22007467.16834873017-7467.16834873017
2216391511723.655338936752191.3446610633
2226072061075.776641923-355.776641922991
2236072061078.5792676235-358.579267623458
22435518652.9433227122-18297.9433227122
22520416364.7607678956-16160.7607678956
22652257-15923.042125310668180.0421253106
22753-1255.219466421571308.21946642157
22808341.74192824984-8341.74192824984
2296072012770.464710038947949.5352899611
23020017991.5927687083-17791.5927687083
2311200-4977.571826380746177.57182638074
23224-627.628459391555651.628459391555
23323329584.99261618025-7252.99261618025
23407835.7256007023-7835.7256007023
2356072013527.915059950847192.0849400492
23633819437.3552081342-19099.3552081342
23726018009.6288280442-17749.6288280442
238206130.47348531672875.5265146832717
239013948.2679840457-13948.2679840457
2406424514140.578096280850104.4219037192
2417300754941.62345516118065.376544839
2428273265344.35854944417387.6414505560
2436072065758.0133889408-5038.0133889408
24433521151.0656731818-20816.0656731818
24536522480.8169032097-22115.8169032097
24623919004.1601991226-18765.1601991226
24701267.01617905699-1267.01617905699
2486072015169.545969041545550.4540309585
24910215471.4156654568-15369.4156654568
25011617733.4669255877-17617.4669255877
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26114019911.3425657780-19771.3425657780
26223221046.1943139046-20814.1943139046
263698130.80217687311-8061.8021768731
2646293831.55109262659-3202.55109262659
26503537.99352233366-3537.99352233366
2665672617406.226704156039319.773295844
2676072068762.819880345-8042.81988034503
26811819671.1812658253-19553.1812658253
26924121958.800339766-21717.800339766
2701981035.50405254468-837.50405254468
271391240.94097695289613871.05902304710
2722494422.27936661759-4173.27936661759
273021168.8031818095-21168.8031818095
2746284118431.918281648944409.0817183511
2758112568986.87084282912138.129157171
2766072068754.300960236-8034.300960236
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286213781.9895016212-13779.9895016212
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3006072021736.784839683738983.2151603163
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30432427715.2228175431-27391.2228175431
30525227661.6671916548-27409.6671916548
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31818728118.1324315839-27931.1324315839
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3206072024188.460967007236531.5390329928
3216072075436.689867472-14716.6898674720
32235431255.3117515518-30901.3117515518
32317828903.1700064458-28725.1700064458
324159510998.2656048929-9403.2656048929
32531222668.8477134353-22356.8477134353
3266022155.2019295822-22095.2019295822
32758722982.6103693497-22395.6103693497
32813520828.2700909153-20693.2700909153
329014290.2192759383-14290.2192759383
3306072025525.645866372435194.3541336276
33113327864.9109519121-27731.9109519121
33227929857.0421706214-29578.0421706214
333012126.5980212076-12126.5980212076
3346072226030.226834680234691.7731653198
3356072070522.7319123362-9802.73191233617
33628931394.4707949846-31105.4707949846
3376113837.4829009458-13776.4829009458
33864711278.8097923762-10631.8097923762
339012880.3729278133-12880.3729278133
3406072026788.780941933733931.2190580663
34129232030.801150563-31738.801150563
34253112587.0744316124-12056.0744316124
34344813385.2194758108-12937.2194758108
34422724843.0813106303-24616.0813106303
34517423387.6310386910-23213.6310386910
34608241.846143607-8241.846143607
3476072027672.704949686133047.2950503139
34837735927.0840038703-35550.0840038703
34913431274.792578639-31140.792578639
35014231567.3587085981-31425.3587085981
35127132238.1448191185-31967.1448191185
35253014524.7578113144-13994.7578113144
35357130313.1509376103-29742.1509376103
354031288.8214606173-31288.8214606173
3556270028672.389782756334027.6102172437
3566780478537.496853164-10733.4968531639
3575966180014.61462433-20353.6146243300
3585862076221.1859933611-17601.1859933611
3596039877948.8775522906-17550.8775522906
3605858073117.811750243-14537.8117502429
3616271079541.9155306233-16831.9155306233
3625932578565.1472946029-19240.1472946029
3636095078769.4196358963-17819.4196358963
3646806079294.6822239304-11234.6822239304
3658362077002.29233543296617.70766456708
3665845680037.8477160964-21581.8477160964
3675281179788.3855165639-26977.3855165639
36812117381245.412181621539927.5878183785
3696387081851.8011882858-17981.8011882858
3702100179647.5952586668-58646.5952586668
3717041581803.3338213235-11388.3338213235
3726423078052.8189657288-13822.8189657288
3735919079448.442334793-20258.442334793
3746935179950.934787365-10599.9347873650
3756427081924.9840511362-17654.9840511362
3767069480408.8193421633-9714.81934216334
3776800580948.6235785586-12943.6235785586
3785893081500.4509282217-22570.4509282217
3795832081249.4211923199-22929.4211923199
3806998081443.760192748-11463.7601927481
3816986380951.5075318768-11088.5075318768
3826325582410.8034895362-19155.8034895362
3835732082928.703331377-25608.7033313770
3847523082538.6384553975-7308.63845539752
3857942082646.4301090636-3226.43010906357
3867349084004.9841632099-10514.9841632099
3873525083244.8172070556-47994.8172070556
3886228583859.7812653094-21574.7812653094
3896920683218.9538457046-14012.9538457046
3906592083854.5106018981-17934.5106018981
3916977081320.5996349311-11550.5996349311
3927268379008.4510598953-6325.4510598953
393-1454583369.3732458533-97914.3732458533
3945583083934.9293428046-28104.9293428046
3955517485725.4919254796-30551.4919254796
3966703884076.4341332591-17038.4341332591
3975125284727.293752392-33475.2937523919
39815727881845.685488794775432.3145112053
3997951084621.894023423-5111.89402342296
4007744083801.5090754555-6361.50907545546
4012728483768.652167062-56484.6521670620
40221311867238.190015851145879.809984149
4038176789912.4106465567-8145.4106465567
40415319881284.643251632271913.3567483678
405-2600784082.6819920546-110089.681992055
40612694279414.623963642347527.3760363577
40715721484336.886125372472877.1138746276
40812935284097.386372686345254.6136273137
40923481784371.8839434544150445.116056546
4106044886565.4734447555-26117.4734447555
4114781886796.3301236208-38978.3301236208
41224554685466.2455719257160079.754428074
4134802086617.2349643858-38597.2349643858
414-171085946.2363450758-87656.2363450758
4153264885221.0711467767-52573.0711467767
4169535084611.767804338710738.2321956613
41715135288775.212491651662576.7875083483
41828817088502.6493014017199667.350698598
41911433785820.762762010128516.2372379899
4203788485516.1763237996-47632.1763237996
42112284487895.895341255134948.1046587449
4228234087312.8503293872-4972.85032938716
4237980186785.5461716614-6984.54617166141
42416554887350.207939840578197.7920601595
42511638487339.866988388829044.1330116112
42613402888272.358385159745755.6416148403
4276383887236.7498443705-23398.7498443705
4287499685876.6265540452-10880.6265540452
4293108088888.1526200722-57808.1526200722
4303216888335.226819338-56167.226819338
4314985786412.7422924982-36555.7422924982


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.0005904270218074130.001180854043614830.999409572978193
134.35953989456791e-058.71907978913583e-050.999956404601054
144.92360868480845e-069.8472173696169e-060.999995076391315
153.63568496395594e-077.27136992791189e-070.999999636431504
163.34235306584317e-086.68470613168633e-080.99999996657647
172.94917071569894e-095.89834143139787e-090.99999999705083
182.25002595426245e-104.5000519085249e-100.999999999774997
191.35027981066510e-112.70055962133020e-110.999999999986497
208.97200269576404e-131.79440053915281e-120.999999999999103
215.01561800533409e-141.00312360106682e-130.99999999999995
222.73986592148090e-155.47973184296179e-150.999999999999997
231.41234701088144e-162.82469402176288e-161
247.05269849067389e-181.41053969813478e-171
253.81081691238533e-197.62163382477065e-191
261.75791980517713e-203.51583961035426e-201
279.9460254460511e-221.98920508921022e-211
285.97929101074546e-231.19585820214909e-221
298.08546511584913e-241.61709302316983e-231
303.72508689525513e-257.45017379051026e-251
312.91985729585183e-265.83971459170366e-261
321.3063707516593e-272.6127415033186e-271
336.7648051971161e-291.35296103942322e-281
343.09184378711723e-306.18368757423447e-301
352.16558871934833e-314.33117743869665e-311
369.17453413060338e-331.83490682612068e-321
374.42131241737616e-348.84262483475231e-341
382.53141321814096e-355.06282643628192e-351
391.15692811861942e-362.31385623723885e-361
409.67521059955417e-381.93504211991083e-371
412.17894814664441e-384.35789629328883e-381
429.11528656971441e-401.82305731394288e-391
434.14377898026361e-418.28755796052721e-411
441.67465594334475e-423.3493118866895e-421
456.67894278463218e-441.33578855692644e-431
462.62958256238303e-455.25916512476606e-451
471.04064133501621e-462.08128267003242e-461
484.41058914524802e-488.82117829049604e-481
491.8390640652769e-493.6781281305538e-491
501.0274812539964e-502.0549625079928e-501
515.64976005184057e-521.12995201036811e-511
522.72823183073896e-535.45646366147792e-531
531.17348668157563e-542.34697336315127e-541
547.71715069681013e-561.54343013936203e-551
555.04594369954858e-571.00918873990972e-561
561.90747711281616e-583.81495422563231e-581
571.03971334784228e-592.07942669568456e-591
584.05264410446015e-618.10528820892031e-611
591.54182516811887e-623.08365033623775e-621
605.93079568752911e-641.18615913750582e-631
612.45176649516245e-654.90353299032491e-651
621.33884336738644e-662.67768673477288e-661
634.84514230155928e-689.69028460311856e-681
641.79519972300436e-693.59039944600872e-691
656.5093135744991e-711.30186271489982e-701
664.20435992006078e-728.40871984012155e-721
673.03023964255300e-736.06047928510601e-731
681.09446644598916e-742.18893289197833e-741
694.05873552401917e-768.11747104803834e-761
701.48949711559638e-772.97899423119276e-771
719.1795637037396e-791.83591274074792e-781
723.16301612229408e-806.32603224458816e-801
731.72539528538717e-813.45079057077435e-811
746.30958902502635e-831.26191780500527e-821
752.19230530415765e-844.38461060831531e-841
761.17739646936013e-852.35479293872026e-851
774.20434805314980e-878.40869610629959e-871
782.24050929285259e-884.48101858570517e-881
791.16490198917995e-892.32980397835991e-891
804.32096124791289e-918.64192249582578e-911
811.43475517159197e-922.86951034318394e-921
825.20380071924882e-941.04076014384976e-931
831.78918429561048e-953.57836859122095e-951
848.83376059127889e-971.76675211825578e-961
852.88714658322663e-985.77429316645325e-981
869.94777741069432e-1001.98955548213886e-991
873.82127465619314e-1017.64254931238628e-1011
881.21990203670313e-1022.43980407340625e-1021
896.30263233012329e-1041.26052646602466e-1031
901.9923800969608e-1053.9847601939216e-1051
916.53600109100418e-1071.30720021820084e-1061
922.09413122720912e-1084.18826245441823e-1081
937.61172680227645e-1101.52234536045529e-1091
942.59713660087091e-1115.19427320174181e-1111
958.0443130134701e-1131.60886260269402e-1121
962.49874169784171e-1144.99748339568342e-1141
978.70146011326316e-1161.74029202265263e-1151
982.93246194565128e-1175.86492389130255e-1171
999.63478922537244e-1191.92695784507449e-1181
1002.96122650644844e-1205.92245301289688e-1201
1011.52556984835285e-1213.05113969670569e-1211
1027.87304254556257e-1231.57460850911251e-1221
1034.27926360778848e-1248.55852721557696e-1241
1041.78246674714529e-1253.56493349429059e-1251
1055.34842070743737e-1271.06968414148747e-1261
1062.48421487018548e-1284.96842974037096e-1281
1071.36862897491390e-1292.73725794982780e-1291
1085.78574622620401e-1311.15714924524080e-1301
1091.81886402047943e-1323.63772804095886e-1321
1108.25403114317437e-1341.65080622863487e-1331
1112.55169244183383e-1355.10338488366766e-1351
1121.35027667321730e-1362.70055334643461e-1361
1136.551344468434e-1381.3102688936868e-1371
1142.61600839352121e-1395.23201678704242e-1391
1157.20587660731879e-1411.44117532146376e-1401
1162.08508407522933e-1424.17016815045865e-1421
1176.40334945553998e-1441.28066989110800e-1431
1182.03602102098389e-1454.07204204196779e-1451
1198.81536070938127e-1471.76307214187625e-1461
1202.74440184832762e-1485.48880369665524e-1481
1218.77801281555649e-1501.75560256311130e-1491
1222.41071173664677e-1514.82142347329355e-1511
1237.0140482255406e-1531.40280964510812e-1521
1242.75067685186366e-1545.50135370372732e-1541
1257.50197378478653e-1561.50039475695731e-1551
1263.26076175803138e-1576.52152351606275e-1571
1279.6343619611316e-1591.92687239222632e-1581
1281.41612810916305e-1592.8322562183261e-1591
1297.66822922546051e-1601.53364584509210e-1591
1308.59037034771612e-1601.71807406954322e-1591
1314.31546005249637e-1238.63092010499274e-1231
1324.90594246285584e-1169.8118849257117e-1161
1338.11514297482561e-1011.62302859496512e-1001
1344.64488426396966e-1019.28976852793933e-1011
1354.79433420718007e-1019.58866841436014e-1011
1362.64619498403036e-1015.29238996806072e-1011
1374.32677100059522e-978.65354200119045e-971
1386.0564494628944e-981.21128989257888e-971
1392.51814588607711e-965.03629177215422e-961
1402.74059900223474e-975.48119800446949e-971
1415.31067604738192e-981.06213520947638e-971
1422.65161936978098e-985.30323873956197e-981
1436.00509283692494e-991.20101856738499e-981
1442.39239915266779e-994.78479830533557e-991
1451.17149875340616e-992.34299750681231e-991
1461.95905261914866e-983.91810523829731e-981
1472.72753015231268e-985.45506030462536e-981
1481.85184671374283e-973.70369342748565e-971
1491.02380003171597e-952.04760006343193e-951
1501.93111064497299e-943.86222128994597e-941
1512.70453519480642e-945.40907038961283e-941
1521.77791011843439e-943.55582023686877e-941
1531.40203296505640e-942.80406593011279e-941
1547.1901148055724e-941.43802296111448e-931
1559.49588870757922e-951.89917774151584e-941
1563.01048008234867e-956.02096016469733e-951
1571.64760402329709e-953.29520804659418e-951
1582.32144044330093e-954.64288088660187e-951
1595.5114017925603e-951.10228035851206e-941
1608.17009301394328e-961.63401860278866e-951
1614.8615896536952e-969.7231793073904e-961
1624.13467169187532e-968.26934338375064e-961
1633.35797054516508e-966.71594109033016e-961
1641.87602073224274e-623.75204146448549e-621
1651.73093757931460e-613.46187515862919e-611
1661.66735700180513e-603.33471400361026e-601
1679.64591183714112e-611.92918236742822e-601
1683.15164117792464e-616.30328235584929e-611
1697.461043007955e-621.492208601591e-611
1701.48023395571958e-612.96046791143917e-611
1711.49114605801746e-602.98229211603493e-601
1723.37035199374146e-596.74070398748292e-591
1731.45875527664886e-572.91751055329773e-571
1748.23215834149789e-561.64643166829958e-551
1754.72116743623111e-569.44233487246223e-561
1761.25162948388048e-562.50325896776097e-561
1773.32704856152393e-576.65409712304785e-571
1788.6824572186618e-581.73649144373236e-571
1792.25955682848971e-584.51911365697942e-581
1801.52981634962177e-583.05963269924355e-581
1814.0324638622116e-598.0649277244232e-591
1821.05056302032721e-592.10112604065441e-591
1832.76267730032394e-605.52535460064787e-601
1847.33345619696967e-611.46669123939393e-601
1851.86343306876491e-613.72686613752981e-611
1864.02693436229006e-618.05386872458011e-611
1874.18363357586279e-598.36726715172558e-591
1883.60020897467321e-567.20041794934642e-561
1896.21841831460849e-551.24368366292170e-541
1905.05677552779248e-551.01135510555850e-541
1911.57426515248299e-553.14853030496599e-551
1924.41112319969756e-568.82224639939511e-561
1931.26482279217646e-362.52964558435292e-361
1946.8946848632067e-331.37893697264134e-321
1951.96155628490828e-323.92311256981656e-321
1968.06092268846844e-331.61218453769369e-321
1971.03968944910294e-322.07937889820589e-321
1983.20509276549106e-326.41018553098212e-321
1996.33596835280762e-311.26719367056152e-301
2006.21011513350888e-261.24202302670178e-251
2011.20510286744452e-222.41020573488904e-221
2022.86957607532073e-175.73915215064147e-171
20311.47436068357265e-237.37180341786323e-24
20414.50305335381139e-282.25152667690570e-28
20516.88927254508512e-283.44463627254256e-28
20611.18600722587012e-275.93003612935059e-28
20712.06967845302452e-271.03483922651226e-27
20812.13272633235107e-271.06636316617553e-27
20914.54267783807618e-272.27133891903809e-27
21019.8461102556662e-274.9230551278331e-27
21114.27158740588036e-312.13579370294018e-31
21211.15113522277748e-335.75567611388741e-34
21316.20704623634317e-353.10352311817158e-35
21416.7292518718768e-353.3646259359384e-35
21511.63360571261834e-348.1680285630917e-35
21613.29621630749118e-341.64810815374559e-34
21718.13510187553598e-344.06755093776799e-34
21811.99288075767263e-339.96440378836316e-34
21914.88263436422094e-332.44131718211047e-33
22011.00312760021816e-325.01563800109078e-33
22111.22418255368066e-326.12091276840331e-33
22212.49534658025378e-321.24767329012689e-32
22315.29168775857342e-322.64584387928671e-32
22415.35008975315932e-322.67504487657966e-32
22511.94975428981137e-329.74877144905684e-33
22614.79039086243057e-332.39519543121528e-33
22711.17188735860067e-325.85943679300336e-33
22812.41390840161779e-321.20695420080890e-32
22913.48257178849467e-321.74128589424733e-32
23015.95831451033414e-322.97915725516707e-32
23111.33374592970966e-316.66872964854829e-32
23213.20989493102855e-311.60494746551427e-31
23316.61298357407698e-313.30649178703849e-31
23411.41725803874472e-307.08629019372362e-31
23512.15058002652761e-301.07529001326381e-30
23614.08658600994265e-302.04329300497133e-30
23713.19103255836306e-301.59551627918153e-30
23817.55232405024093e-303.77616202512046e-30
23911.45753979161365e-297.28769895806823e-30
24012.05953367661254e-291.02976683830627e-29
24113.84173442156574e-291.92086721078287e-29
24217.58572563124333e-293.79286281562167e-29
24311.77544889820446e-288.87724449102232e-29
24413.50753324014107e-281.75376662007053e-28
24513.89121988936841e-281.94560994468420e-28
24614.91682562857398e-282.45841281428699e-28
24711.12936389422072e-275.64681947110358e-28
24811.70018172426411e-278.50090862132054e-28
24913.63677863458434e-271.81838931729217e-27
25015.12354577692619e-272.56177288846309e-27
25117.97928238998567e-273.98964119499284e-27
25211.80429376149304e-269.02146880746521e-27
25311.62928389598125e-268.14641947990623e-27
25413.65049489386456e-261.82524744693228e-26
25517.98897000829904e-263.99448500414952e-26
25611.33609228459909e-256.68046142299545e-26
25712.97307483494005e-251.48653741747003e-25
25814.20590315332476e-252.10295157666238e-25
25919.482376134996e-254.741188067498e-25
26012.04892650693745e-241.02446325346872e-24
26113.46372057574127e-241.73186028787064e-24
26216.01312391530362e-243.00656195765181e-24
26311.29926851385013e-236.49634256925063e-24
26412.81491254911552e-231.40745627455776e-23
26516.08851459914466e-233.04425729957233e-23
26619.18394155897377e-234.59197077948688e-23
26712.00566762776284e-221.00283381388142e-22
26814.26063432074966e-222.13031716037483e-22
26917.45246216754034e-223.72623108377017e-22
27011.53835010346152e-217.6917505173076e-22
27113.01107486966503e-211.50553743483252e-21
27216.29262007568128e-213.14631003784064e-21
27311.16108593559373e-205.80542967796863e-21
27411.51085120789414e-207.55425603947071e-21
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27615.56029321113902e-202.78014660556951e-20
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28415.60283939361117e-182.80141969680558e-18
28511.1089894960685e-175.5449474803425e-18
28612.13877666120469e-171.06938833060234e-17
28712.70629518741823e-171.35314759370911e-17
28813.36006540317772e-171.68003270158886e-17
28916.71835726475216e-173.35917863237608e-17
29011.30580869798526e-166.5290434899263e-17
29112.53950411091509e-161.26975205545754e-16
29214.82862947268511e-162.41431473634255e-16
29319.26384538164242e-164.63192269082121e-16
29411.72614416362174e-158.63072081810871e-16
2950.9999999999999992.68596262035754e-151.34298131017877e-15
2960.9999999999999984.93064189199773e-152.46532094599886e-15
2970.9999999999999959.59799367951528e-154.79899683975764e-15
2980.9999999999999921.65381271350529e-148.26906356752643e-15
2990.9999999999999951.03878209371147e-145.19391046855734e-15
3000.9999999999999921.57568906346758e-147.8784453173379e-15
3010.9999999999999852.99685089428848e-141.49842544714424e-14
3020.9999999999999725.66463928846297e-142.83231964423149e-14
3030.9999999999999588.32296076513552e-144.16148038256776e-14
3040.9999999999999221.56408859652453e-137.82044298262265e-14
3050.9999999999998662.68667787547242e-131.34333893773621e-13
3060.999999999999754.98841637740843e-132.49420818870422e-13
3070.9999999999996467.08461954040014e-133.54230977020007e-13
3080.9999999999993471.30591469272968e-126.52957346364842e-13
3090.9999999999988042.39165587796596e-121.19582793898298e-12
3100.9999999999983513.29729613173704e-121.64864806586852e-12
3110.999999999997015.97941783304465e-122.98970891652232e-12
3120.9999999999945111.09783773156728e-115.48918865783641e-12
3130.9999999999901821.96361112235161e-119.81805561175807e-12
3140.9999999999827133.45730261249484e-111.72865130624742e-11
3150.9999999999692246.15530566562642e-113.07765283281321e-11
3160.9999999999485661.02867686898292e-105.14338434491462e-11
3170.9999999999161111.67777086549756e-108.38885432748778e-11
3180.9999999998630342.73932516773903e-101.36966258386952e-10
3190.9999999997611754.77650769615793e-102.38825384807896e-10
3200.9999999996837486.32504455723993e-103.16252227861996e-10
3210.9999999994658561.06828778635115e-095.34143893175574e-10
3220.9999999990773871.84522663113783e-099.22613315568917e-10
3230.9999999985139532.97209477829354e-091.48604738914677e-09
3240.9999999974738385.05232435960141e-092.52616217980070e-09
3250.999999995934418.1311777944378e-094.0655888972189e-09
3260.999999993607721.27845615027222e-086.3922807513611e-09
3270.9999999899963642.00072718888090e-081.00036359444045e-08
3280.9999999847856963.04286090000188e-081.52143045000094e-08
3290.9999999755434324.89131355618966e-082.44565677809483e-08
3300.9999999652322616.95354779542631e-083.47677389771315e-08
3310.999999942410321.15179360380088e-075.75896801900442e-08
3320.999999910080511.79838981597276e-078.99194907986378e-08
3330.9999998532525142.93494971691246e-071.46747485845623e-07
3340.999999798931784.02136440844745e-072.01068220422372e-07
3350.999999678283736.43432541402227e-073.21716270701114e-07
3360.9999994826730351.03465393112873e-065.17326965564365e-07
3370.9999991752794331.64944113285843e-068.24720566429213e-07
3380.9999986951957842.60960843278873e-061.30480421639436e-06
3390.999997943772654.11245470023167e-062.05622735011584e-06
3400.9999972645723545.47085529166661e-062.73542764583331e-06
3410.9999957302436728.53951265509086e-064.26975632754543e-06
3420.9999933881019791.32237960426587e-056.61189802132936e-06
3430.9999898281321862.03437356291097e-051.01718678145549e-05
3440.9999855426755212.89146489579106e-051.44573244789553e-05
3450.9999786480137324.27039725360955e-052.13519862680477e-05
3460.9999681150076986.37699846035084e-053.18849923017542e-05
3470.9999579297985138.41404029734255e-054.20702014867128e-05
3480.9999372452805190.0001255094389626736.27547194813365e-05
3490.9999100683373060.0001798633253887998.99316626943994e-05
3500.9998713905833530.0002572188332946710.000128609416647336
3510.9998179210996490.000364157800702290.000182078900351145
3520.9997380774264190.0005238451471628220.000261922573581411
3530.9996571734490980.0006856531018036860.000342826550901843
3540.9995919822166450.000816035566709670.000408017783354835
3550.9996209807356830.000758038528633980.00037901926431699
3560.999452904487930.001094191024139020.000547095512069508
3570.999222915441970.001554169116059550.000777084558029776
3580.9989165155545260.002166968890947690.00108348444547385
3590.998473827681710.003052344636580800.00152617231829040
3600.99790745823140.004185083537201380.00209254176860069
3610.9970999832594890.00580003348102260.0029000167405113
3620.9961853416032640.00762931679347250.00381465839673625
3630.994799947858590.01040010428282090.00520005214141045
3640.9930280710634640.01394385787307220.0069719289365361
3650.9906758734989050.01864825300219060.00932412650109528
3660.9876600999155860.02467980016882880.0123399000844144
3670.9839275312351270.03214493752974530.0160724687648727
3680.9854980383471220.02900392330575580.0145019616528779
3690.9814152676763670.03716946464726590.0185847323236330
3700.9791657209088310.04166855818233750.0208342790911688
3710.9739029703899140.05219405922017190.0260970296100859
3720.9664426341377760.06711473172444710.0335573658622236
3730.9594524671502160.08109506569956850.0405475328497842
3740.94856556104150.1028688779170010.0514344389585003
3750.9356917631822830.1286164736354330.0643082368177167
3760.9198175072207850.1603649855584290.0801824927792147
3770.9021974074228010.1956051851543980.097802592577199
3780.8940702520389020.2118594959221950.105929747961098
3790.872642903647310.2547141927053820.127357096352691
3800.8713775599942140.2572448800115720.128622440005786
3810.8455135058745760.3089729882508470.154486494125424
3820.8264222007407730.3471555985184530.173577799259227
3830.7938341073254420.4123317853491150.206165892674558
3840.757176296935630.4856474061287410.242823703064371
3850.725431368448450.54913726310310.27456863155155
3860.6921840880039510.6156318239920990.307815911996049
3870.6497312947474940.7005374105050120.350268705252506
3880.6392751313774490.7214497372451010.360724868622551
3890.6068759986075320.7862480027849360.393124001392468
3900.5722109166747550.8555781666504890.427789083325245
3910.5251708728345050.949658254330990.474829127165495
3920.4863943598665250.972788719733050.513605640133475
3930.543496821703780.913006356592440.45650317829622
3940.4967866234015680.9935732468031350.503213376598432
3950.4534749832596880.9069499665193770.546525016740312
3960.4156656819782910.8313313639565810.584334318021709
3970.3829232369864940.7658464739729870.617076763013506
3980.373057717370460.746115434740920.62694228262954
3990.3194651458969010.6389302917938010.6805348541031
4000.272579634940710.545159269881420.72742036505929
4010.3823550910716920.7647101821433850.617644908928308
4020.4055103586740950.811020717348190.594489641325905
4030.3673069721420270.7346139442840540.632693027857973
4040.3398847157166180.6797694314332370.660115284283382
4050.2962868270804150.592573654160830.703713172919585
4060.240300761130940.480601522261880.75969923886906
4070.2093849645335340.4187699290670680.790615035466466
4080.1726435248763030.3452870497526050.827356475123697
4090.6159476299357720.7681047401284550.384052370064228
4100.6614321746879140.6771356506241710.338567825312086
4110.578766573943360.842466852113280.42123342605664
4120.9868874565886720.02622508682265540.0131125434113277
4130.9804284263255770.03914314734884520.0195715736744226
4140.9776531431199030.04469371376019420.0223468568800971
4150.9838741692709980.03225166145800380.0161258307290019
4160.9701987751105370.05960244977892660.0298012248894633
4170.9725344233972370.05493115320552630.0274655766027631
4180.977578097098110.04484380580378050.0224219029018903
4190.9284907964533310.1430184070933370.0715092035466687


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3510.860294117647059NOK
5% type I error level3640.892156862745098NOK
10% type I error level3690.904411764705882NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/10qi0m1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/10qi0m1291403561.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/1jh3s1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/1jh3s1291403561.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/2uqkd1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/2uqkd1291403561.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/3uqkd1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/3uqkd1291403561.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/4uqkd1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/4uqkd1291403561.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/55zky1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/55zky1291403561.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/65zky1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/65zky1291403561.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/7yrjj1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/7yrjj1291403561.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/8yrjj1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/8yrjj1291403561.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/9qi0m1291403561.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403508iywep5akdkpp1tw/9qi0m1291403561.ps (open in new window)


 
Parameters (Session):
par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 8 ; 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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