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deterministic trend

*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: Wed, 01 Dec 2010 13:39:14 +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/01/t1291210642sjlnb7qlf0s372r.htm/, Retrieved Wed, 01 Dec 2010 14:37:34 +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/01/t1291210642sjlnb7qlf0s372r.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 time35 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] = + 31076.1227627801 + 0.00561681402600102Group[t] + 0.41047874700291Costs[t] -0.163174697165881Trades[t] -0.125546264864473Orders[t] -0.212756110249819Dividends[t] -0.0123762259145777Wealth[t] -0.184886561099009WRank[t] -0.120441035357399`Profit/Trades`[t] -0.113097097012691`Profit/Cost`[t] + 129.552423577030t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)31076.12276278018641.5731533.59610.0003610.000181
Group0.005616814026001020.0069540.80770.4196940.209847
Costs0.410478747002910.0563737.281500
Trades-0.1631746971658810.041812-3.90250.0001115.5e-05
Orders-0.1255462648644730.050313-2.49530.0129690.006484
Dividends-0.2127561102498190.034433-6.178900
Wealth-0.01237622591457770.007195-1.720.0861610.043081
WRank-0.1848865610990090.035893-5.151100
`Profit/Trades`-0.1204410353573990.047896-2.51460.0122880.006144
`Profit/Cost`-0.1130970970126910.038917-2.90610.0038530.001927
t129.55242357703031.3264524.13564.3e-052.1e-05


Multiple Linear Regression - Regression Statistics
Multiple R0.549689148515856
R-squared0.302158159996087
Adjusted R-squared0.285542878091232
F-TEST (value)18.1855572313699
F-TEST (DF numerator)10
F-TEST (DF denominator)420
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation60783.2393873717
Sum Squared Residuals1551732919977.46


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
156289-26119.733881680382408.7338816803
228328-29039.458997828257367.4589978282
317936-17242.358081762035178.358081762
4414568917.2416596191-64772.2416596191
530408088.05193039824-5048.05193039824
62964-5417.985090236008381.985090236
728653541.53639129386-676.536391293858
82854-23826.151729272126680.1517292721
9216722367.9177987462-20200.9177987462
1019749594.48759512352-7620.48759512352
111910-23370.237072886425280.2370728864
12187114527.4816281073-12656.4816281073
1394322422.3201314962-21479.3201314962
1492940857.5135944598-39928.5135944598
1582211121.0017811870-10299.0017811870
16819-10322.696424008911141.6964240089
17769-33541.549209476534310.5492094765
187457209.3266881643-6464.3266881643
1965228168.5994649944-27516.5994649944
206439160.94173137284-8517.94173137284
2160110298.0676146828-9697.0676146828
2244616540.1216105634-16094.1216105634
23436-5019.215684088365455.21568408836
2437911464.3132664706-11085.3132664706
253054835.7392085097-4530.7392085097
2628419357.4085387747-19073.4085387747
2724721553.0773141081-21306.0773141081
2823830269.0372975903-30031.0372975903
2922319838.9011942695-19615.9011942695
3022024662.6148606420-24442.6148606420
312177275.72768004138-7058.72768004138
321999826.47928756862-9627.47928756862
3319510426.8060013815-10231.8060013815
341637202.5455371225-7039.5455371225
35154-38097.278100211338251.2781002113
3614316709.7182055388-16566.7182055388
3713512722.4326372182-12587.4326372182
381324849.88788206052-4717.88788206052
39120-11097.423987206611217.4239872066
4011918258.8041663064-18139.8041663064
411088657.51924591057-8549.51924591057
4210417024.3167283795-16920.3167283795
4310112112.4182989402-12011.4182989402
44909640.02984209853-9550.02984209853
458518900.5085693090-18815.5085693090
467411342.8123387845-11268.8123387845
4773-10371.365097888410444.3650978884
48705719.12818972179-5649.12818972179
49677270.27217761211-7203.27217761211
506619760.5892882673-19694.5892882673
51668999.3694852062-8933.3694852062
52664117.82670174892-4051.82670174892
5359-5679.377527634655738.37752763465
5458590.185887995799-532.185887995799
55583758.33137411998-3700.33137411998
565420931.4412971936-20877.4412971936
575315124.3495439068-15071.3495439068
584916130.0385364740-16081.0385364740
594913640.7891382515-13591.7891382515
604419015.7853220024-18971.7853220024
613914304.0022500128-14265.0022500128
623825172.3818029122-25134.3818029122
633814462.127572565-14424.127572565
643720156.2119276464-20119.2119276464
653623363.1054592027-23327.1054592027
663521090.3706893627-21055.3706893627
673419500.465532449-19466.465532449
683313963.4862482659-13930.4862482659
69327786.86664373049-7754.86664373049
703214742.9502189527-14710.9502189527
713133110.9832395433-33079.9832395433
723129046.3982995457-29015.3982995457
733014062.2185748041-14032.2185748041
743018562.167497632-18532.167497632
753010726.4195284117-10696.4195284117
762821707.2440549773-21679.2440549773
772612018.117732655-11992.117732655
782622644.2715007958-22618.2715007958
792622979.5499383557-22953.5499383557
802512258.6845449517-12233.6845449517
812524141.8431199910-24116.8431199910
822418768.6345187610-18744.6345187610
83247891.41714614569-7867.41714614569
842329359.2102115124-29336.2102115124
852314289.2173904442-14266.2173904442
862226161.2216357785-26139.2216357785
872218561.6166395354-18539.6166395354
882214815.8491037245-14793.8491037245
892016676.9596438975-16656.9596438975
902014109.8557870934-14089.8557870934
911918006.3680730135-17987.3680730135
921821326.8615907176-21308.8615907176
931724567.2214286114-24550.2214286114
941614120.3492701349-14104.3492701349
951431272.9967601605-31258.9967601605
961426761.8757224741-26747.8757224741
971410846.5706633598-10832.5706633598
981325182.8427247955-25169.8427247955
991311131.3968027361-11118.3968027361
1001323755.3163325993-23742.3163325993
1011324204.0737293031-24191.0737293031
1021322248.9242648131-22235.9242648131
1031218778.9953191433-18766.9953191433
1041215848.1908555198-15836.1908555198
1051125656.1708001919-25645.1708001919
1061026096.5340980544-26086.5340980544
1071032024.7606222176-32014.7606222176
1081016857.3074448667-16847.3074448667
1091021678.9470195514-21668.9470195514
1101027252.9646266507-27242.9646266507
111925443.9520134073-25434.9520134073
112934835.6205061967-34826.6205061967
113925639.7637216512-25630.7637216512
114626648.3485873373-26642.3485873373
115627213.6173736785-27207.6173736785
116629994.7279350677-29988.7279350677
117523519.7272094546-23514.7272094546
118521533.6186587558-21528.6186587558
119528145.0211350868-28140.0211350868
120429763.1102635034-29759.1102635034
121422710.1583602712-22706.1583602712
122327651.9566524188-27648.9566524188
123223214.9543846495-23212.9543846495
124217789.8237734542-17787.8237734542
125130070.0127588554-30069.0127588554
126129755.885367108-29754.885367108
1279430672.0946003014-30578.0946003014
128422-2237.569905134642659.56990513464
1290-1722.258131047271722.25813104727
1307-1631.908409334191638.90840933419
13125174672.721405982-174647.721405982
1320199765.948748807-199765.948748807
133312846171182.601675290141663.398324710
13431207556574.8869884699255500.11301153
13531500950890.8273528136264118.172647186
13631890346985.0975796551271917.902420345
13731488745542.6251079867269344.374892013
13831491361606.2022981750253306.797701825
13931538051631.289729047263748.710270953
1408965-962.6614680759219927.66146807592
1410108.772844269257-108.772844269257
142033241.1890600366-33241.1890600366
143027707.5993578149-27707.5993578149
144031260.9315572553-31260.9315572553
145032747.9475704388-32747.9475704388
146933131.3720095318-33122.3720095318
147271-1953.385030231572224.38503023157
14814-1379.273330523001393.27333052300
149520-2189.164935244662709.16493524466
15017664098.58484961834-2332.58484961834
15102280.45269667482-2280.45269667482
15271166.55069560016-1159.55069560016
1532172228.979852946-172226.979852946
1540174692.160299746-174692.160299746
155315380174051.711198650141328.288801350
156576181904.8861509292355713.1138490708
15719489-411.43700570889619900.4370057089
15802312.03997696825-2312.03997696825
159034813.8895369833-34813.8895369833
16002257.53650885245-2257.53650885245
161306268174804.60914323131463.39085677
16230218746472.5291321072255714.470867893
16331488246112.3328405175268769.667159483
16431538060778.9971451922254601.002854808
1659616-2195.0861594202811811.0861594203
166360-462.922167978506822.922167978506
16703478.66246629278-3478.66246629278
1683635980.0462357377-35944.0462357377
16902705.16846461234-2705.16846461234
17093531.42295055659-3522.42295055659
1710178961.496420393-178961.496420393
172315547176225.563268057139321.436731943
17331326751168.3189709645262098.681029036
17431617647008.51769944269167.48230056
17531538094300.4148357446221079.585164255
17604645.86588239074-4645.86588239074
177034109.4552401364-34109.4552401364
178033407.8772656909-33407.8772656909
179039199.9586360771-39199.9586360771
180035244.9675073053-35244.9675073053
181033943.4733235727-33943.4733235727
182036754.2044142689-36754.2044142689
183036903.6908353279-36903.6908353279
184035021.1197210536-35021.1197210536
185035208.2988602631-35208.2988602631
18627538316.9817972739-38041.9817972739
18705100.92087504632-5100.92087504632
18875873.30077152881-5866.30077152881
1890182722.163337247-182722.163337247
190315688178570.800302413137117.199697587
19131538072021.1963243028243358.803675697
1922304-15026.451626237717330.4516262377
19306839.67855026119-6839.67855026119
19447239339.2724009685-38867.2724009685
19509284.91297534438-9284.91297534438
19606913.94942421763-6913.94942421763
197315380179487.740979989135892.259020011
198373911015.5313773119-7276.53137731191
19973811421.075162084935959.92483791507
200580777490.7887960747750586.2112039252
201296436641.7996148546723001.2003851453
20208019.54186159592-8019.54186159592
203042683.2657988884-42683.2657988884
204140365.56663033-40364.56663033
2050-17090.541105565017090.5411055650
2065764031145.817745689426494.1822543106
2076197758981.55099408532995.44900591474
2086262058954.92936637853665.07063362151
2096072057546.5017810533173.49821894702
21016417101.5985489634-16937.5985489634
21115218322.7423043783-18170.7423043783
21219915704.5300097989-15505.5300097989
21366-2426.709056869762492.70905686976
214335378.221262955778-43.2212629557785
2151-154.551091350901155.551091350901
2161-30717.84223090830718.842230908
2171-15860.669358941415861.6693589414
2181-26855.050530057726856.0505300577
2191-16124.553363184916125.5533631849
2201-16705.787576723616706.7875767236
2214-15039.478852088615043.4788520886
222029641.6790873785-29641.6790873785
2236391533341.182780526330573.8172194737
2246072059955.3490440286764.65095597137
2256072060086.3674000022633.632599997805
22635520410.0725578753-20055.0725578753
22720417652.1050684111-17448.1050684111
22852257-11549.001971527663806.0019715276
22911658.70107458608-1657.70107458608
2301-17723.27218364217724.272183642
2310-13721.992449173613721.9924491736
2326072034570.150206882626149.8497931174
23332120317.7742816197-19996.7742816197
23423719113.4518021645-18876.4518021645
2354772762.5297278818144009.47027211819
23653-3137.122211276693190.12221127669
23702690.61498925316-2690.61498925316
23811-12830.411500427812841.4115004278
239034830.1761917760-34830.1761917760
2406072035598.295918243725121.7040817563
24137224167.2527434129-23795.2527434129
24224019698.9343447114-19458.9343447114
243642-1195.215696269361837.21569626936
2441685185.87318356908-5017.87318356908
2452314391.5129379352-14368.5129379352
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26112-9850.423214803279862.42321480327
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2646417566358.5661602679-2183.56616026789
2656072066624.2532350027-5904.25323500271
26638125986.3171786366-25605.3171786366
26733122317.7103278731-21986.7103278731
26825623094.8189395560-22838.8189395560
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2701-8086.98069109438087.9806910943
27113-8578.138522015588591.13852201558
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3256072046497.966168323914222.0338316761
3266072074791.7863575974-14071.7863575974
32735433794.2000349916-33440.2000349916
32817831042.4460407732-30864.4460407732
329114617.0748992083-14616.0748992083
3301-3037.223933641413038.22393364141
3311-2810.195817601732811.19581760173
3321-2742.188985652312743.18898565231
3331-4516.731638641394517.73163864139
3341-10957.887674976910958.8876749769
3350-249.493366557887249.493366557887
3367878047931.195206330230848.8047936698
3376072076186.7780039697-15466.7780039697
33825133768.9286407217-33517.9286407217
339115901.2776776550-15900.2776776550
3401-25681.425556699725682.4255566997
3410527.870679807954-527.870679807954
3426160048476.989970637213123.0100293628
3435963576604.169205554-16969.1692055541
3446072075676.3011574298-14956.3011574298
34524834679.0794357369-34431.0794357369
346116805.6153809518-16804.6153809518
347211280.23302469941-1259.23302469941
348048395.5778317884-48395.5778317884
3495978149627.292823502310153.7071764977
3507664477708.2995461166-1064.29954611664
3516482075899.3497371176-11079.3497371176
3526072060940.0453497332-220.045349733197
35328035706.8787706661-35426.8787706661
35478821112.9243167674-20324.9243167674
35520115347.4175627667-15146.4175627667
3565415956.8890074582-15902.8890074582
357118233.1008610062-18232.1008610062
358602670.36992410438-2610.36992410438
3598055451.5350238423-55371.5350238423
360048721.9937164407-48721.9937164407
3616270051255.542597488711444.4574025113
3626780477965.1444750176-10161.1444750176
3635966179425.9485563555-19764.9485563555
3645862076370.9547860834-17750.9547860834
3656039877983.9872744562-17585.9872744562
3665858073864.8341451269-15284.8341451269
3676271079291.7917727155-16581.7917727155
3685932578498.2655597947-19173.2655597947
3696095078656.2556721655-17706.2556721655
3706806079035.3803021261-10975.3803021261
3718362077154.00850241016465.99149758989
3725845679773.5895780231-21317.5895780231
3735281179167.3283557202-26356.3283557202
37412117380586.041461436240586.9585385638
3756387081691.782751084-17821.7827510840
3762100179423.5416337653-58422.5416337653
3777041581147.806765155-10732.8067651550
3786423078341.6467344709-14111.6467344709
3795919079540.2143918305-20350.2143918305
3806935179842.5748959803-10491.5748959803
3816427081530.7188691214-17260.7188691214
3827069480252.038676872-9558.03867687195
3836800580857.7533452102-12852.7533452102
3845893081267.3674918619-22337.3674918619
3855832081161.547161741-22841.5471617409
3866998081290.7521127269-11310.7521127269
3876986380692.2595057096-10829.2595057096
3886325581538.5910609448-18283.5910609448
3895732082533.933010075-25213.9330100750
3907523081983.5292966328-6753.52929663283
3917942082673.1310786079-3253.13107860790
3927349083164.5610969687-9674.56109696873
3933525082464.040896684-47214.0408966839
3946228583697.4173498598-21412.4173498598
3956920682921.2948449897-13715.2948449897
3966592083485.877753639-17565.8777536389
3976977081334.0699033455-11564.0699033455
3987268379318.4262092092-6635.4262092092
399-1454582042.08164572-96587.08164572
4005583082063.1745896659-26233.1745896659
4015517484072.9413451325-28898.9413451325
4026703883392.320314028-16354.3203140280
4035125283901.642598403-32649.6425984031
40415727878062.8607083879215.13929162
4057951083624.2789945599-4114.27899455991
4067744082270.32129895-4830.32129895001
4072728482277.2906345355-54993.2906345356
40821311854060.5122872879159057.487712712
40981767100855.468391185-19088.4683911847
41015319883954.702145738569243.2978542615
411-2600786417.7723436634-112424.772343663
41212694274809.567431344152132.4325686559
41315721483692.591486317573521.4085136825
41412935284077.309073396645274.6909266034
41523481783254.2314148993151562.768585101
4166044887331.2080664082-26883.2080664082
4174781886846.8951140616-39028.8951140616
41824554686247.8359974492159298.164002551
4194802087369.343886782-39349.343886782
420-171086051.5116776501-87761.5116776501
4213264884377.7649832339-51729.7649832339
4229535082297.506350077813052.4936499222
42315135289445.570747079361906.4292529207
42428817089601.3479815263198568.652018474
42511433784756.511199988429580.4888000116
4263788483709.4328339565-45825.4328339565
42712284487940.380855181734903.6191448183
4288234086370.65224386-4030.65224386008
4297980186293.8140101783-6492.8140101783
43016554887210.800696601578337.1993033985
43111638486904.33823065229479.661769348


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.001302575989266950.002605151978533890.998697424010733
150.0001061229985273780.0002122459970547560.999893877001473
161.03652278530706e-052.07304557061412e-050.999989634772147
179.20827792557977e-071.84165558511595e-060.999999079172207
187.16137088441701e-081.43227417688340e-070.999999928386291
194.88269731371686e-099.76539462743372e-090.999999995117303
203.14442612137638e-106.28885224275275e-100.999999999685557
212.36182921830235e-114.72365843660469e-110.999999999976382
221.53913781883304e-123.07827563766608e-120.99999999999846
231.00939946447580e-132.01879892895159e-130.999999999999899
245.82184824063406e-151.16436964812681e-140.999999999999994
253.11320784103197e-166.22641568206394e-161
261.78646334318238e-173.57292668636476e-171
279.75565374919107e-191.95113074983821e-181
285.6175187693148e-201.12350375386296e-191
291.44718443233152e-202.89436886466304e-201
308.12017085599149e-221.6240341711983e-211
314.55021371441149e-239.10042742882299e-231
322.54594701302657e-245.09189402605314e-241
331.33523672443629e-252.67047344887259e-251
346.48455379467102e-271.29691075893420e-261
356.81802127616078e-281.36360425523216e-271
364.0892166565651e-298.1784333131302e-291
372.85075918638101e-305.70151837276201e-301
381.56534082421783e-313.13068164843566e-311
397.62527467821066e-331.52505493564213e-321
406.42167073708903e-341.28433414741781e-331
411.00163634048992e-342.00327268097985e-341
424.88423029236511e-369.76846058473022e-361
433.09493172987387e-376.18986345974774e-371
441.44262519606704e-382.88525039213408e-381
456.63114707519782e-401.32622941503956e-391
463.39896176094904e-416.79792352189808e-411
471.60511972743297e-423.21023945486594e-421
487.00785363109704e-441.40157072621941e-431
494.05467892919543e-458.10935785839086e-451
502.47317839200696e-464.94635678401392e-461
511.31389154074240e-472.62778308148481e-471
526.60898916321815e-491.32179783264363e-481
532.96271293721023e-505.92542587442047e-501
542.01145610917887e-514.02291221835774e-511
559.60615342185768e-531.92123068437154e-521
565.53434411621792e-541.10686882324358e-531
574.85608926652432e-559.71217853304864e-551
582.65901174425537e-565.31802348851075e-561
591.38142013542186e-572.76284027084371e-571
605.555279862641e-591.1110559725282e-581
612.36013806483316e-604.72027612966633e-601
621.55504146726112e-613.11008293452224e-611
636.10209943856278e-631.22041988771256e-621
642.39373679570920e-644.78747359141839e-641
651.23108676532081e-652.46217353064162e-651
661.11973610633696e-662.23947221267391e-661
671.02693039239773e-672.05386078479546e-671
684.02645403167636e-698.05290806335271e-691
692.30155100045122e-704.60310200090243e-701
709.31348335463205e-721.86269667092641e-711
714.9879149523808e-739.9758299047616e-731
722.28097721550083e-744.56195443100165e-741
731.83632705733935e-753.6726541146787e-751
748.9149772733998e-771.78299545467996e-761
754.14221618921268e-788.28443237842536e-781
761.96211427908610e-793.92422855817221e-791
779.3963105027006e-811.87926210054012e-801
786.3430644656315e-821.2686128931263e-811
794.40867887054261e-838.81735774108523e-831
801.6477815637552e-843.2955631275104e-841
816.94512483036829e-861.38902496607366e-851
822.73224938780047e-875.46449877560094e-871
831.25204222856894e-882.50408445713789e-881
847.88159957020816e-901.57631991404163e-891
853.16884889417234e-916.33769778834468e-911
861.19420074869364e-922.38840149738727e-921
877.13570765698365e-941.42714153139673e-931
882.75236857617456e-955.50473715234912e-951
891.91409722943487e-963.82819445886973e-961
907.23389101752176e-981.44677820350435e-971
913.03503028107751e-996.07006056215503e-991
921.07134044916608e-1002.14268089833216e-1001
935.23830856260792e-1021.04766171252158e-1011
941.9132246368688e-1033.8264492737376e-1031
957.40059343916646e-1051.48011868783329e-1041
962.53675878653339e-1065.07351757306678e-1061
979.90360410637083e-1081.98072082127417e-1071
983.45296312034789e-1096.90592624069578e-1091
991.49565914632450e-1102.99131829264900e-1101
1005.04199734418211e-1121.00839946883642e-1111
1012.37429199824223e-1134.74858399648446e-1131
1021.64521882588118e-1143.29043765176236e-1141
1031.24854912288761e-1152.49709824577521e-1151
1046.44256163274623e-1171.28851232654925e-1161
1052.43878368978497e-1184.87756737956994e-1181
1061.43542720872508e-1192.87085441745017e-1191
1078.84180438924922e-1211.76836087784984e-1201
1084.11887229607370e-1228.23774459214741e-1221
1091.69558763382996e-1233.39117526765993e-1231
1107.53182501218477e-1251.50636500243695e-1241
1112.94992271923912e-1265.89984543847825e-1261
1121.72466930726414e-1273.44933861452827e-1271
1131.14991606160160e-1282.29983212320320e-1281
1144.57959123004722e-1309.15918246009444e-1301
1151.47272511148011e-1312.94545022296022e-1311
1165.40745320244826e-1331.08149064048965e-1321
1172.02087963345874e-1344.04175926691748e-1341
1188.42948834549625e-1361.68589766909925e-1351
1193.84479530528560e-1377.68959061057119e-1371
1201.51230488280672e-1383.02460976561345e-1381
1216.61381966805584e-1401.32276393361117e-1391
1222.19656052300265e-1414.39312104600531e-1411
1238.10720922910456e-1431.62144184582091e-1421
1243.56033866700183e-1447.12067733400365e-1441
1251.09509557274135e-1452.19019114548271e-1451
1264.98812341896097e-1479.97624683792194e-1471
1271.74567268016871e-1483.49134536033742e-1481
1285.34311135775036e-1501.06862227155007e-1491
1291.64914570579286e-1513.29829141158571e-1511
1303.31497313834413e-1516.62994627668826e-1511
1311.58564593200475e-1513.17129186400951e-1511
1323.92471639925540e-1517.84943279851079e-1511
1335.85096777818321e-1521.17019355563664e-1511
1346.37813682580921e-1531.27562736516184e-1521
1351.83295944139377e-1533.66591888278754e-1531
1361.32097508284538e-1532.64195016569076e-1531
1372.93058506438474e-1545.86117012876949e-1541
1383.04125645881649e-1546.08251291763298e-1541
1391.18441521268659e-1542.36883042537319e-1541
1403.24795037166283e-1546.49590074332566e-1541
1415.13888401490017e-1521.02777680298003e-1511
1422.31060419993654e-1534.62120839987307e-1531
1431.25976294313709e-1472.51952588627417e-1471
1446.04723748914579e-1491.20944749782916e-1481
1453.06070615986853e-1506.12141231973707e-1501
1461.43955687213136e-1512.87911374426272e-1511
1472.93044304438779e-1505.86088608877558e-1501
1481.41868446431618e-1512.83736892863237e-1511
1491.75286218353560e-1523.50572436707119e-1521
1502.3251217439725e-1534.650243487945e-1531
1511.22105470158209e-1542.44210940316418e-1541
1525.91837939021945e-1561.18367587804389e-1551
1532.01835419307616e-1554.03670838615232e-1551
1541.36058144496248e-1542.72116288992496e-1541
1551.26937717271590e-1552.53875434543181e-1551
1561.12817789147605e-1212.25635578295209e-1211
1579.10437828379475e-1231.82087565675895e-1221
1582.96349720000943e-1195.92699440001885e-1191
1592.62889310735588e-1205.25778621471176e-1201
1602.36333784540658e-1214.72667569081316e-1211
1611.94921066820245e-1213.89842133640491e-1211
1628.65982461401186e-1161.73196492280237e-1151
1631.62287516136541e-1143.24575032273082e-1141
1643.23106942632097e-1136.46213885264193e-1131
1656.81544627512867e-1141.36308925502573e-1131
1666.6069391064162e-1131.32138782128324e-1121
1674.82136083643013e-1129.64272167286026e-1121
1685.10549982358861e-1131.02109996471772e-1121
1695.54275355210997e-1141.10855071042199e-1131
1705.80402792672482e-1151.16080558534496e-1141
1711.94399263505091e-1123.88798527010182e-1121
1724.58947456953856e-1139.17894913907712e-1131
1731.42619914518544e-1102.85239829037088e-1101
1741.20356289863188e-1062.40712579726376e-1061
1754.14961100034029e-1048.29922200068058e-1041
1761.27877393206888e-1032.55754786413777e-1031
1771.74480989979214e-1043.48961979958428e-1041
1782.32999607788404e-1054.65999215576808e-1051
1792.99606782365638e-1065.99213564731275e-1061
1803.95656264137415e-1077.9131252827483e-1071
1812.46201143178253e-1034.92402286356505e-1031
1823.33971791874115e-1046.67943583748229e-1041
1834.60578052311408e-1059.21156104622815e-1051
1846.71724368204328e-1061.34344873640866e-1051
1858.96392857339814e-1071.79278571467963e-1061
1861.23259069705505e-1072.4651813941101e-1071
1871.65870298974421e-1083.31740597948841e-1081
1882.20046495628253e-1094.40092991256505e-1091
1892.73746130162234e-1025.47492260324467e-1021
1904.17934529484056e-1038.35869058968112e-1031
1912.11424786945062e-974.22849573890123e-971
1924.76364394784956e-969.52728789569913e-961
1932.90351047550654e-965.80702095101309e-961
1945.18194042532953e-971.03638808506591e-961
1958.40491864866082e-981.68098372973216e-971
1962.79711343707633e-985.59422687415266e-981
1971.28252053482393e-972.56504106964787e-971
1983.20056756781452e-986.40113513562904e-981
1995.74269133860812e-991.14853826772162e-981
2004.07879900843884e-858.15759801687769e-851
2018.43991338330452e-841.68798267666090e-831
2027.75175773572053e-841.55035154714411e-831
2031.77605063285355e-843.55210126570709e-841
2043.79927145232053e-837.59854290464105e-831
2059.36386213028163e-841.87277242605633e-831
2063.00628496666013e-846.01256993332026e-841
2073.00500514412895e-846.01001028825791e-841
2081.09709262191374e-842.19418524382749e-841
2092.23639766543421e-854.47279533086841e-851
21011.00503190766049e-175.02515953830247e-18
21115.36285101488424e-242.68142550744212e-24
21215.05190023950841e-262.52595011975420e-26
21311.52426176473229e-267.62130882366143e-27
21413.09407154785101e-261.54703577392550e-26
21516.71464249353114e-263.35732124676557e-26
21611.00542827202806e-255.02714136014032e-26
21711.92979677416460e-259.64898387082298e-26
21813.55526269725599e-251.77763134862800e-25
21917.1844912219065e-253.59224561095325e-25
22011.45613785314091e-247.28068926570454e-25
22112.97181613727517e-241.48590806863758e-24
22214.42573957107476e-242.21286978553738e-24
22317.77645528534485e-243.88822764267243e-24
22411.37826496262212e-236.89132481311061e-24
22512.51408777774583e-231.25704388887292e-23
22611.75099773356632e-238.7549886678316e-24
22714.60750248286607e-242.30375124143303e-24
22812.39557942694906e-241.19778971347453e-24
22915.04036898837550e-242.52018449418775e-24
23011.02570778084664e-235.12853890423321e-24
23112.10578743010087e-231.05289371505044e-23
23213.78775755695272e-231.89387877847636e-23
23315.40418200717981e-232.70209100358990e-23
23413.74084924289727e-231.87042462144864e-23
23517.70570106006075e-233.85285053003038e-23
23611.4268975229953e-227.1344876149765e-23
23712.94995234717187e-221.47497617358594e-22
23815.96332495475363e-222.98166247737681e-22
23918.14673962789368e-224.07336981394684e-22
24011.45507433320592e-217.2753716660296e-22
24112.55748610295819e-211.27874305147909e-21
24212.28252711282529e-211.14126355641264e-21
24314.31227107221211e-212.15613553610606e-21
24418.06971690149243e-214.03485845074622e-21
24511.08767254677137e-205.43836273385687e-21
24612.20263671953781e-201.10131835976890e-20
24714.23761409051129e-202.11880704525565e-20
24817.28223447518357e-203.64111723759179e-20
24911.42285784638157e-197.11428923190787e-20
25012.47252479691493e-191.23626239845747e-19
25114.44617848361495e-192.22308924180748e-19
25218.0740104856429e-194.03700524282145e-19
25311.59353765354552e-187.96768826772762e-19
25413.11215934792689e-181.55607967396344e-18
25515.93228088770028e-182.96614044385014e-18
25617.44643171178428e-183.72321585589214e-18
25711.28021618277866e-176.40108091389329e-18
25812.21816856435990e-171.10908428217995e-17
25912.53041412237093e-171.26520706118546e-17
26014.84000051117538e-172.42000025558769e-17
26119.04740149152309e-174.52370074576154e-17
26211.24460653109655e-166.22303265548274e-17
26312.01032015474606e-161.00516007737303e-16
26413.54096687005844e-161.77048343502922e-16
26516.36356108318609e-163.18178054159305e-16
26611.12940321618282e-155.64701608091408e-16
26711.39613153079146e-156.98065765395732e-16
26811.83103216877654e-159.15516084388272e-16
2690.9999999999999983.39039830264752e-151.69519915132376e-15
2700.9999999999999975.86806660023566e-152.93403330011783e-15
2710.9999999999999951.05813774009961e-145.29068870049805e-15
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2730.9999999999999882.39542674895846e-141.19771337447923e-14
2740.999999999999984.09029450126918e-142.04514725063459e-14
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2760.9999999999999371.25898971876911e-136.29494859384555e-14
2770.9999999999999151.70654065046252e-138.53270325231262e-14
2780.9999999999998483.03978987961472e-131.51989493980736e-13
2790.9999999999997634.74283169616483e-132.37141584808241e-13
2800.9999999999995818.37349462694324e-134.18674731347162e-13
2810.9999999999992581.48333555731829e-127.41667778659146e-13
2820.9999999999988962.20791758470634e-121.10395879235317e-12
2830.999999999998113.78057029657669e-121.89028514828834e-12
2840.9999999999970925.81514394192477e-122.90757197096239e-12
2850.9999999999951579.68560704563788e-124.84280352281894e-12
2860.9999999999918871.62256726066772e-118.11283630333862e-12
2870.9999999999865032.69937478391765e-111.34968739195882e-11
2880.9999999999770614.58785777814048e-112.29392888907024e-11
2890.9999999999617397.65226234074021e-113.82613117037011e-11
2900.9999999999499131.00173659579419e-105.00868297897095e-11
2910.9999999999811663.76679492424311e-111.88339746212155e-11
2920.9999999999720855.58296445625355e-112.79148222812678e-11
2930.999999999952689.46397876367542e-114.73198938183771e-11
2940.9999999999236981.52603127106736e-107.63015635533679e-11
2950.9999999998748472.50306701475706e-101.25153350737853e-10
2960.9999999999217361.56527559171645e-107.82637795858227e-11
2970.9999999998696522.60695753368856e-101.30347876684428e-10
2980.9999999997783974.43205303952831e-102.21602651976416e-10
2990.9999999996256267.48748381634657e-103.74374190817329e-10
3000.9999999993873981.22520330432697e-096.12601652163486e-10
3010.9999999993422911.31541814953534e-096.57709074767669e-10
3020.9999999988988992.20220294938258e-091.10110147469129e-09
3030.9999999981944853.61102949778412e-091.80551474889206e-09
3040.9999999970846845.83063249626701e-092.91531624813351e-09
3050.9999999956591248.68175281767927e-094.34087640883964e-09
3060.9999999930758971.38482065814175e-086.92410329070875e-09
3070.999999988903612.21927805994804e-081.10963902997402e-08
3080.9999999824702443.50595128562957e-081.75297564281479e-08
3090.9999999734531575.30936865272984e-082.65468432636492e-08
3100.999999961993237.6013541533221e-083.80067707666105e-08
3110.9999999406455431.18708913975326e-075.93544569876628e-08
3120.9999999071526241.85694751893861e-079.28473759469304e-08
3130.9999998565556982.86888604553734e-071.43444302276867e-07
3140.999999802080383.95839239561066e-071.97919619780533e-07
3150.999999696298126.07403759509766e-073.03701879754883e-07
3160.999999588967258.22065499350161e-074.11032749675081e-07
3170.9999995162983479.67403306933123e-074.83701653466561e-07
3180.999999322043991.35591202186677e-066.77956010933387e-07
3190.9999990474229971.90515400667099e-069.52577003335497e-07
3200.9999986822713352.63545733065512e-061.31772866532756e-06
3210.9999979990889344.00182213150998e-062.00091106575499e-06
3220.999997362117835.27576433884839e-062.63788216942420e-06
3230.999996096403537.80719293935314e-063.90359646967657e-06
3240.999994196983521.16060329590713e-055.80301647953563e-06
3250.9999922866186621.54267626758872e-057.71338133794361e-06
3260.9999890134851552.19730296894014e-051.09865148447007e-05
3270.9999838712750363.22574499287788e-051.61287249643894e-05
3280.9999778495737684.43008524631316e-052.21504262315658e-05
3290.9999682351219876.35297560263324e-053.17648780131662e-05
3300.9999538545962489.22908075043447e-054.61454037521723e-05
3310.9999338130666840.0001323738666309576.61869333154785e-05
3320.9999046304937540.0001907390124909909.53695062454948e-05
3330.999866401271680.0002671974566394910.000133598728319746
3340.999829903308210.0003401933835793810.000170096691789691
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3360.9997399378125890.0005201243748227620.000260062187411381
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3390.9993263673320540.001347265335891260.000673632667945629
3400.9990790697663180.001841860467364530.000920930233682267
3410.9987416955896450.002516608820709310.00125830441035465
3420.9984841160580920.003031767883816420.00151588394190821
3430.9979926435927150.004014712814569180.00200735640728459
3440.997352713974630.005294572050740090.00264728602537005
3450.9964809289240660.007038142151868630.00351907107593432
3460.9953724000656120.009255199868775890.00462759993438795
3470.993910771471360.01217845705727730.00608922852863863
3480.9925328226435260.01493435471294840.0074671773564742
3490.9912137022398580.01757259552028430.00878629776014215
3500.9894703674512670.02105926509746610.0105296325487330
3510.9868096831035810.02638063379283710.0131903168964186
3520.983154582539780.03369083492043820.0168454174602191
3530.9788249726606730.04235005467865410.0211750273393270
3540.9734182373224830.0531635253550330.0265817626775165
3550.9667530570080320.06649388598393490.0332469429919675
3560.9588619354506360.08227612909872710.0411380645493636
3570.9593446708461140.0813106583077720.040655329153886
3580.9506638546460750.09867229070784920.0493361453539246
3590.9427225606876440.1145548786247120.0572774393123558
3600.9382932874897880.1234134250204240.0617067125102119
3610.9367392706541560.1265214586916880.0632607293458441
3620.9230628310893510.1538743378212970.0769371689106485
3630.9082584512169120.1834830975661750.0917415487830876
3640.8903542813912570.2192914372174850.109645718608743
3650.8694300296655510.2611399406688980.130569970334449
3660.8574456767523280.2851086464953440.142554323247672
3670.8320822202646760.3358355594706470.167917779735324
3680.8093245097825730.3813509804348540.190675490217427
3690.7787740805355010.4424518389289970.221225919464499
3700.747636789523280.5047264209534380.252363210476719
3710.7136322389132860.5727355221734280.286367761086714
3720.6790872525924510.6418254948150980.320912747407549
3730.6398964408149940.7202071183700110.360103559185006
3740.7333161164800480.5333677670399030.266683883519952
3750.6948030101481010.6103939797037980.305196989851899
3760.6662293943144150.667541211371170.333770605685585
3770.6463776301904880.7072447396190230.353622369809512
3780.6040856979814840.7918286040370320.395914302018516
3790.5654917209917770.8690165580164460.434508279008223
3800.5211549651217930.9576900697564130.478845034878207
3810.4814148691215260.9628297382430530.518585130878474
3820.4338691059045370.8677382118090730.566130894095463
3830.3867168371952150.7734336743904290.613283162804785
3840.363773707766320.727547415532640.63622629223368
3850.3208024141910220.6416048283820440.679197585808978
3860.3427375441141770.6854750882283550.657262455885823
3870.3007973296456960.6015946592913920.699202670354304
3880.2595479006837660.5190958013675330.740452099316234
3890.2397693779892020.4795387559784040.760230622010798
3900.2100354209055400.4200708418110790.78996457909446
3910.1766925588105930.3533851176211860.823307441189407
3920.1576682674711790.3153365349423580.842331732528821
3930.1291084248525100.2582168497050210.87089157514749
3940.1024454603419360.2048909206838710.897554539658064
3950.0846371390559510.1692742781119020.915362860944049
3960.06936131645498150.1387226329099630.930638683545019
3970.05547617360183310.1109523472036660.944523826398167
3980.04137738975136680.08275477950273350.958622610248633
3990.04217927517118890.08435855034237780.957820724828811
4000.03247624149941880.06495248299883770.967523758500581
4010.02443211068598610.04886422137197220.975567889314014
4020.01683056968571390.03366113937142780.983169430314286
4030.01347317511624570.02694635023249150.986526824883754
4040.01474768625755510.02949537251511010.985252313742445
4050.009653082145000980.01930616429000200.990346917855
4060.006452357948034160.01290471589606830.993547642051966
4070.04957970471989480.09915940943978970.950420295280105
4080.04449806004933790.08899612009867580.955501939950662
4090.0369467269419680.0738934538839360.963053273058032
4100.02898341580449290.05796683160898570.971016584195507
4110.05145154930253230.1029030986050650.948548450697468
4120.1700580861396580.3401161722793170.829941913860342
4130.1433989000349220.2867978000698430.856601099965078
4140.1518064490060190.3036128980120370.848193550993982
4150.3560285202542390.7120570405084770.643971479745761
4160.2934776430828340.5869552861656690.706522356917166
4170.1769228332576040.3538456665152070.823077166742396


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3330.824257425742574NOK
5% type I error level3460.856435643564356NOK
10% type I error level3580.886138613861386NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/10r68z1291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/10r68z1291210717.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/1kntn1291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/1kntn1291210717.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/2dwa91291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/2dwa91291210717.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/3dwa91291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/3dwa91291210717.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/4dwa91291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/4dwa91291210717.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/5dwa91291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/5dwa91291210717.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/655st1291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/655st1291210717.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/7ywrw1291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/7ywrw1291210717.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/8ywrw1291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/8ywrw1291210717.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/9ywrw1291210717.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291210642sjlnb7qlf0s372r/9ywrw1291210717.ps (open in new window)


 
Parameters (Session):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 10 ; 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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