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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 12:34:34 +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/t1291379678xassivgkxarc48g.htm/, Retrieved Fri, 03 Dec 2010 13:34:51 +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/t1291379678xassivgkxarc48g.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
350 1 1595 17 10 60720 319369 143 7022 75 0 61 0 5565 47 43 94355 493408 38 6243 53 39 287 0 601 6 5 60720 319210 144 19868 198 0 268 1 188 11 9 77655 381180 67 16471 964 0 25 1 7146 105 83 134028 297978 367 933 14 305 418 0 1135 17 20 62285 290476 384 5322 80 -9 392 1 450 8 6 59325 292136 380 11517 205 -1 131 1 34 8 7 60630 314353 309 14294 3340 0 316 1 133 14 4 65990 339445 109 9960 1052 0 347 1 119 6 3 59118 303677 354 17280 872 0 68 0 2053 53 44 100423 397144 60 3720 96 33 70 0 4036 63 85 100269 424898 53 3570 56 32 147 1 0 0 0 60720 315380 203 0 169 1 4655 393 55 60720 341570 105 360 30 0 248 1 131 11 15 61808 308989 338 9908 834 0 152 1 1766 73 46 60438 305959 349 1451 60 0 351 1 312 14 3 58598 318690 147 8478 381 0 372 1 448 40 7 59781 323361 132 3084 275 0 137 1 115 13 10 60945 318903 145 9146 1037 0 352 1 60 10 7 61124 314049 314 11405 1916 0 338 1 0 0 0 60720 315380 180 0 258 1 364 35 26 47705 298466 365 2813 271 0 343 1 0 0 0 60720 315380 183 0 398 0 1442 118 239 etc...
 
Output produced by software:

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


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time34 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] = + 112525.671973323 -0.198059963761599Rank[t] + 0.00550742537579447Group[t] -0.273496863022098Costs[t] -0.137453070224780Trades[t] -0.0756323147982971Orders[t] -0.340817921286784Dividends[t] -0.121532963538801Wealth[t] -0.210333786633402WRank[t] -0.0703752629021607`Profit/Trades`[t] -0.00501056997828915`Profit/Cost`[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)112525.67197332310810.28895710.409100
Rank-0.1980599637615990.071096-2.78580.0055810.002791
Group0.005507425375794470.0592080.0930.9259330.462967
Costs-0.2734968630220980.063185-4.32851.9e-059e-06
Trades-0.1374530702247800.040373-3.40460.0007260.000363
Orders-0.07563231479829710.028988-2.60910.0094030.004702
Dividends-0.3408179212867840.076378-4.46231e-055e-06
Wealth-0.1215329635388010.041276-2.94440.0034160.001708
WRank-0.2103337866334020.053868-3.90460.000115.5e-05
`Profit/Trades`-0.07037526290216070.026679-2.63790.0086520.004326
`Profit/Cost`-0.005010569978289150.019697-0.25440.7993270.399664


Multiple Linear Regression - Regression Statistics
Multiple R0.363573085746835
R-squared0.132185388679476
Adjusted R-squared0.111523136028987
F-TEST (value)6.39743356716478
F-TEST (DF numerator)10
F-TEST (DF denominator)420
p-value3.46148265606416e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation128774.834980933
Sum Squared Residuals6964842412233.94


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1051984.0821382791-51984.0821382791
23918411.0453418126-18372.0453418126
3051384.7569158271-51384.7569158271
4038448.7630979363-38448.7630979363
530528509.3908124449-28204.3908124449
6-955142.6551073764-55151.6551073764
7-155708.7680428237-55709.7680428237
8052533.0889747368-52533.0889747368
9047951.0220221669-47951.0220221669
10054073.201371615-54073.201371615
113329173.1596004818-29140.1596004818
123225317.6328841357-25285.6328841357
13153429.4878996622-53428.4878996622
14177323.8543594538-77322.8543594538
15179134.9065203137-79133.9065203137
16180406.274680172-80405.274680172
17179142.9386618445-79141.9386618445
18179283.3001273575-79282.3001273575
19178622.9433139361-78621.9433139361
20178460.201292914-78459.201292914
213580209.0945426696-80174.0945426696
22030726.2388301576-30726.2388301576
2312117326131.039610136295041.9603898638
245753024770.832996273932759.1670037261
256204132125.138556264429915.8614437356
266072043569.631864998917150.3681350011
2713699645210.142774198791785.8572258013
286072026858.129548332933861.8704516671
2911542256.6665796992-42141.6665796992
30387103032.292028438-102645.292028438
31219106058.048427910-105839.048427910
3243035453.5739217394-35023.5739217394
33288-16296.521833215816584.5218332158
342224784.1197506741-24762.1197506741
351839844.6969428017-39826.6969428017
361245838757.3479990894-26299.3479990894
373248098.7862822128-48066.7862822128
3829-203191.121554988203220.121554988
394211264.0719439271-11222.0719439271
408448348.41712489533-7504.41712489533
4124226309.1170442768-26067.1170442768
4212317485.2155959522-17362.2155959522
4318438791.7671509101-38607.7671509101
444015254.77324521126-4853.77324521126
45371-925.8835066314811296.88350663148
46203398.428877639647-195.428877639647
47368948784.7819532842-45095.7819532842
4856730368.9202622046-29801.9202622046
492766425352.63892616572311.36107383431
501686905.434311472613780.565688527387
51116441638.6696386413-40474.6696386413
521582458331.0538866652-42507.0538866652
53657512425.3256008071-5850.32560080706
5437597-78210.422861995115807.422861995
55314-35548.475756091935862.4757560919
56193244630.649477785-42698.649477785
57569257702.5455458939-52010.5455458939
58045048.6944462182-45048.6944462182
59052510.211710181-52510.211710181
60364839100733.202203026264105.797796974
61230621112086.135091829118534.864908171
62315877112122.356861639203754.643138361
63125390110220.96864438015169.0313556197
64314882112115.619519034202766.380480966
65370837110308.891698613260528.108301387
66330068112054.614321119218013.385678881
67324385111839.097210381212545.902789619
68315380112326.948392930203053.051607070
6911409-466.18878943245411875.1887894325
70111577115.6433306854-76000.6433306854
7113337-211180.636253466224517.636253466
72077622.5047337501-77622.5047337501
73046561.4052306707-46561.4052306707
74052333.5475925108-52333.5475925108
75050190.7120526257-50190.7120526257
76052755.5158291808-52755.5158291808
7723873089.044725435-72851.044725435
783040-127304.349417872130344.349417872
79049302.1727311366-49302.1727311366
80153422.9134049436-53421.9134049436
813780218.6776460638-80181.6776460638
82015917.2363201625-15917.2363201625
836072026509.817682617234210.1823173828
8430844155.9955119562-43847.9955119562
85296103407.745332790-103111.745332790
8635795148.3444731203-94791.3444731203
87110104918.126631041-104808.126631041
88245103624.092263071-103379.092263071
8946943553.628428173-43084.628428173
9034-146656.646355435146690.646355435
91-3573756.4766363198-73791.4766363198
9246336367.4094531073-35904.4094531073
9395434186.730996354-33232.730996354
9422638796.939891209-38570.939891209
95276-453.76124920782729.76124920782
96383-2161.995672625332544.99567262533
979313534.4433239475-13441.4433239475
98302-47067.314959643447369.3149596434
99385-5269.471667337425654.47166733742
100225-2041.44974991362266.4497499136
101275-12220.090604479412495.0906044794
10218073381.07163285502617691.9283671450
1031866106436.517919465-104570.517919465
1044263451381.1119585872-8747.11195858718
105249-158269.167337541158518.167337541
10642256823.489801442-56401.489801442
107267551470.5966667093-48795.5966667093
10896546828.7494450594-45863.7494450594
109063797.554448346-63797.554448346
1101152458.5940672866-52447.5940672866
111099711.4355116967-99711.4355116967
112309560101829.770943752207730.229056248
113414462111585.244878915302876.755121085
114857217110502.827218467746714.172781533
115697458111218.748030431586239.251969569
116530670111552.992953134419117.007046866
117238125111815.989299189126309.010700811
118315380112116.846502234203263.153497766
1191203142790.620312275-30759.620312275
120371864533.3022583158-60815.3022583158
121191868682.7204458598-66764.7204458598
122077484.1719883014-77484.1719883014
123048535.5660114009-48535.5660114009
124153365.5011430724-53364.5011430724
125060449.406719651-60449.406719651
126176768.6582663855-76767.6582663855
127076620.2602023118-76620.2602023118
128080177.5438022373-80177.5438022373
1296072026465.098000560034254.9019994400
13031244158.7675617654-43846.7675617654
13134699059.5406524592-98713.5406524592
132076824.0443492215-76824.0443492215
133051274.0348928144-51274.0348928144
134153392.9593488838-53391.9593488838
13522680203.1345301216-79977.1345301216
13658-24373.431224330224431.4312243302
137057312.2708773604-57312.2708773604
1386072026505.326448191734214.6735518083
13911443897.5052499632-43783.5052499632
140283104752.850242464-104469.850242464
14120137231.711776345-37030.711776345
14210927810.4535385162-27701.4535385162
1436942715.5360209428-42646.5360209428
14426738793.2130202708-38526.2130202708
145611383.483266107156227.516733892844
146051326.5332313509-51326.5332313509
147652439.6198238678-52433.6198238678
1481100545.034958793-100544.034958793
1491398400.3333027612-98387.3333027612
1501498407.429377959-98393.429377959
1510101706.634047475-101706.634047475
152314210101831.906180554212378.093819446
153480382111789.042266213368592.957733787
154315009112170.129216435202838.870783565
155315380112122.863746969203257.136253031
1561026278506.6674491575-68244.6674491575
1571525476211.7972878361-60957.7972878361
158076997.0518144014-76997.0518144014
1591633236.29850283738-3073.29850283738
160153369.3318438351-53368.3318438351
161179767.9541182146-79766.9541182146
162177030.7274608945-77029.7274608945
163178835.9155325496-78834.9155325496
164062439.7262720458-62439.7262720458
165058091.1290936418-58091.1290936418
166185014.82421183-85013.82421183
167081764.6925839668-81764.6925839668
168179027.4823750553-79026.4823750553
169172534.520299611-72533.520299611
1701480206.90855512-80192.90855512
1711725478.7416941864-25461.7416941864
172029274.6169247696-29274.6169247696
17312192025518.745982330396401.2540176697
17410396018580.926978465685379.0730215344
1756072039201.737266836821518.2627331632
17621344187.4263340876-43974.4263340876
17730638792.4859858919-38486.4859858919
178172-6719.781586689066891.78158668906
179402-4779.579615277745181.57961527774
1804-9142.066957860529146.06695786052
18115638591.9033504311-38435.9033504311
1823397.377871908776-394.377871908776
18352938015.4398733956-37486.4398733956
184050611.7183509841-50611.7183509841
185552500.955101569-52495.955101569
1861999502.0970176999-99483.0970176999
187099600.9712354923-99600.9712354923
188308636101359.971606979207276.028393021
189158492111771.89957010446720.1004298960
190315380112119.234412486203260.765587514
191077415.9931659797-77415.9931659797
192446-7761.056320747358207.05632074735
193053388.8923421925-53388.8923421925
1942780214.296126013-80187.296126013
195028600.0927220894-28600.0927220894
1968859026053.454319481362536.5456805187
1978290320975.534746661361927.4652533387
1986072036255.615304633024464.3846953670
1999942005.6927156985-41906.6927156985
200310105800.800194537-105490.800194537
2011792186.239057746-92169.239057746
202416104498.774202499-104082.774202499
203295106383.025360203-106088.025360203
204342102804.140478887-102462.140478887
2052191442.0282794671-91421.0282794671
206423104281.778612289-103858.778612289
207160106584.493971940-106424.493971940
20820795211.2818290265-95004.2818290265
2099234243.0418956813-34151.0418956813
2109814778.6529681931-14680.6529681931
211261339094.1757714071-36481.1757714071
21233738798.4673385583-38461.4673385583
2133416655.72399240629-6314.72399240629
2141545611.90276113413-5457.90276113413
215364677.2721330092-4641.2721330092
216192-24240.174980524424432.1749805244
2177123422.9679528680946700.0320471319
218622-55679.503383035456301.5033830354
21917450146.0865977219-49972.0865977219
220222015825.4337049197-13605.4337049197
221121-65496.625984250965617.6259842509
2221181955076.0152071693-43257.0152071693
22302975.63608608226-2975.63608608226
2241952508.8521686539-52489.8521686539
22511100639.813904034-100628.813904034
2263598821.4912799071-98786.4912799071
2270102671.369161323-102671.369161323
228315380101829.124961735213550.875038265
2292952576956.5149674224-47431.5149674224
230752364003.3044830156-56480.3044830156
2311143172842.8271326502-61411.8271326502
232077444.9129567166-77444.9129567166
233053270.5394570308-53270.5394570308
234-363325.5218125579-63328.5218125579
235-849811.4799645645-49819.4799645645
236153397.5585467333-53396.5585467333
237178907.1370475124-78906.1370475124
238280191.3931912918-80189.3931912918
239021281.1782898015-21281.1782898015
2407878026334.989708420252445.0102915798
2416072047893.301504594312826.6984954057
24227044159.7870195575-43889.7870195575
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2440387.392416543903-387.392416543903
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258153403.3328155355-53402.3328155355
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260328030.5394958896-28027.5394958896
2619721738.5346711874-21641.5346711874
262271567.88437143016-1540.88437143016
263129950.9993310212-29949.9993310212
2641416955.1908217616-16941.1908217616
2651328796.6809836748-28783.6809836748
266734609.8242297449-34602.8242297449
267540050.040529712-40045.040529712
2682425622.5392268472-25598.5392268472
269028083.8066115605-28083.8066115605
2706391526429.719756055637485.2802439444
2716071950215.471903473910503.5280965261
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298178468.8442600207-78467.8442600207
299172313.1891541467-72312.1891541467
300178899.0976793003-78898.0976793003
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323052517.511643903-52517.511643903
324375195100290.200374455274904.799625545
325288985112126.323013129176858.676986871
326315380112091.403280007203288.596719993
327300460908.2012741324-57904.2012741324
328077497.718991571-77497.718991571
329051979.2262176166-51979.2262176166
330049075.2270407685-49075.2270407685
331-5888957.1393050346-89015.1393050346
332153408.8385432363-53407.8385432363
333180310.3675919587-80309.3675919587
334080199.4864207749-80199.4864207749
3356079826469.465800721834328.5341992782
3366072042115.720286835018604.2797131650
33739643398.0529985053-43002.0529985053
338228106041.515803240-105813.515803240
33924938794.454703858-38545.454703858
3400397.628159158063-397.628159158063
34113652255.1918534147-52119.1918534147
34211488025.316437781-87911.316437781
3431582565.4707305681-82550.4707305681
34411105358.763163616-105347.763163616
34512101860.035492401-101848.035492401
346895896.2867517512-95888.2867517512
347122100448.952383483-100326.952383483
348113106761.737125082-106648.737125082
3496101980.818485746-101974.818485746
350099408.2688707023-99408.2688707023
351215362101145.167644927114216.832355073
352314073111794.825669010202278.174330990
353298096112063.108580695186032.891419305
354325176112078.481079256213097.518920744
355207393111383.60196555996009.3980344414
356314806112094.236771516202711.763228484
357341340110545.583789004230794.416210996
358426280111428.494066366314851.505933634
359929118108895.391708080820222.60829192
360307322112047.569053789195274.430946211
361315380112246.841374192203133.158625808
362302470284.8430793366-67260.8430793366
3631835778081.8909518386-59724.8909518386
364235378566.0595704073-76213.0595704073
365865275905.9068973812-67253.9068973812
3663861177390.2886142145-38779.2886142145
367950577867.315325648-68362.315325648
368410873050.4716691736-68942.4716691736
369077473.9239480622-77473.9239480622
370053424.4362422015-53424.4362422015
371080196.249596069-80196.249596069
3726072026498.367007187234221.6329928128
3739037184.109978968-37094.109978968
374168106647.622493980-106479.622493980
375208103813.419486878-103605.419486878
37622238793.8488328374-38571.8488328374
3770396.308985596709-396.308985596709
378352490.6426106174-52487.6426106174
3791599445.7005195782-99430.7005195782
3805592328.4986873729-92273.4986873729
3818591748.3272569415-91663.3272569415
3821796992.6662173235-96975.6662173235
383277101343.881650793-101066.881650793
3841090244.5814936573-90234.5814936573
3854100058.655549078-100054.655549078
386798921.5718627423-98914.5718627423
38726101153.294023462-101127.294023462
3884796150.3281979054-96103.3281979054
38920393264.5587373232-93061.5587373232
390588560.6314300246-88555.6314300246
391099597.080321643-99597.080321643
392315380101833.503324737213546.496675263
3931062081273.6435062477-70653.6435062477
394566975103.5040668951-69434.5040668951
3951058177576.4018597897-66995.4018597897
396253337478.9211866526-34945.9211866526
39782777027.7919647552-76200.7919647552
3985769477450.0271981998-19756.0271981998
399054133.655596195-54133.655596195
400052052.4458082697-52052.4458082697
4011455336.1652217946-55322.1652217946
4022854-214413.529305946217267.529305946
403053945.306278227-53945.306278227
404-1054757.1196607063-54767.1196607063
405-2897310.818925129-97338.818925129
406153383.0542496367-53382.0542496367
407174825.704039787-74824.704039787
408172597.729860615-72596.729860615
4097280183.6225203528-80111.6225203528
4101726694.6111752033-26677.6111752033
4114724894.6611654333-24847.6611654333
4126-22472.307716474122478.3077164741
4131123908.8390588624-23897.8390588624
4141057075.40236258142-6970.40236258142
4151731494.8782040044-31477.8782040044
416832854.221208905-32846.221208905
417831932.1019103272-31924.1019103272
4181424554.5459513553-24540.5459513553
419618850.6989151894-18844.6989151894
4205328118.5914832437-28065.5914832437
421634091.11681638275-4028.11681638275
4220-3478.465869829633478.46586982963
4236072025511.982610867635208.0173891324
4246180844659.278578210517148.7214217895
4256043848075.997356372712362.0026436273
4265859851337.90142963057260.09857036949
4275978146811.690268612712969.3097313873
4286094547528.300133926413416.6998660736
4296112446587.164488441114536.8355115589
4306072046828.957949848813891.0420501512
43136545040.4566629128-44675.4566629128


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
143.68839408296165e-127.37678816592329e-120.999999999996312
151.73319227036517e-153.46638454073034e-150.999999999999998
165.27652292070855e-191.05530458414171e-181
171.11609320394281e-222.23218640788563e-221
182.06588940286025e-264.1317788057205e-261
193.60251441067542e-307.20502882135083e-301
205.82707328206062e-341.16541465641212e-331
211.15146767145075e-372.30293534290149e-371
224.32649170723946e-418.65298341447892e-411
235.49152758598826e-211.09830551719765e-201
242.78343515583306e-225.56687031166612e-221
254.71750528641264e-159.43501057282528e-150.999999999999995
262.51867585941375e-145.0373517188275e-140.999999999999975
275.68878654127134e-151.13775730825427e-140.999999999999994
286.19819292816547e-161.23963858563309e-151
295.17666716850703e-171.03533343370141e-161
301.37579955770503e-172.75159911541005e-171
311.27365788985947e-182.54731577971894e-181
322.33326417380376e-144.66652834760753e-140.999999999999977
335.60729057019191e-151.12145811403838e-140.999999999999994
348.8376870463654e-161.76753740927308e-151
351.48734737542715e-162.97469475085431e-161
362.10350391408771e-174.20700782817541e-171
374.38537323785287e-188.77074647570573e-181
381.84178446908476e-183.68356893816952e-181
392.71248985878573e-195.42497971757146e-191
403.77859139182515e-207.5571827836503e-201
415.2505825907346e-211.05011651814692e-201
426.99254208260732e-221.39850841652146e-211
439.51371403009468e-231.90274280601894e-221
441.2269872461375e-232.453974492275e-231
451.59161685155202e-243.18323370310403e-241
461.99294854739888e-253.98589709479776e-251
477.4378044167204e-261.48756088334408e-251
481.60743154548681e-263.21486309097362e-261
498.12118992850647e-271.62423798570129e-261
501.83795151879222e-263.67590303758443e-261
512.67535360256123e-275.35070720512245e-271
524.02561265450512e-288.05122530901025e-281
536.25335344341475e-291.25067068868295e-281
541.81059145374061e-293.62118290748122e-291
558.6502880469504e-301.73005760939008e-291
561.26101249314456e-302.52202498628911e-301
571.71450042955601e-313.42900085911201e-311
582.56318592682222e-325.12637185364445e-321
593.74421661868736e-337.48843323737472e-331
606.84224242273993e-341.36844848454799e-331
613.20572872810498e-216.41145745620997e-211
626.97544501780135e-151.39508900356027e-140.999999999999993
633.99055525410566e-157.98111050821132e-150.999999999999996
648.54578506070364e-131.70915701214073e-120.999999999999145
654.47806413675636e-138.95612827351271e-130.999999999999552
666.53439394027038e-121.30687878805408e-110.999999999993466
672.67690213383586e-115.35380426767171e-110.99999999997323
687.87173446694572e-111.57434689338914e-100.999999999921283
691.84996591027837e-063.69993182055673e-060.99999815003409
701.90763532413682e-063.81527064827364e-060.999998092364676
714.82042636043367e-069.64085272086733e-060.99999517957364
726.95149562039196e-061.39029912407839e-050.99999304850438
734.23626982810384e-068.47253965620767e-060.999995763730172
742.58235799515406e-065.16471599030813e-060.999997417642005
751.55477146740532e-063.10954293481063e-060.999998445228533
769.3582337576359e-071.87164675152718e-060.999999064176624
776.33998368145309e-071.26799673629062e-060.999999366001632
784.39150423824525e-068.7830084764905e-060.999995608495762
792.88214550820863e-065.76429101641727e-060.999997117854492
801.87980687305867e-063.75961374611735e-060.999998120193127
811.17479232003698e-062.34958464007396e-060.99999882520768
827.71354861620487e-071.54270972324097e-060.999999228645138
834.62791087801011e-079.25582175602023e-070.999999537208912
843.88845371608508e-077.77690743217015e-070.999999611154628
853.74727152426632e-077.49454304853264e-070.999999625272848
863.96555620936524e-077.93111241873048e-070.999999603444379
872.96778072733145e-075.9355614546629e-070.999999703221927
882.13765427920900e-074.27530855841799e-070.999999786234572
891.41877113686256e-072.83754227372512e-070.999999858122886
901.35107913571914e-072.70215827143827e-070.999999864892086
911.00743548358202e-072.01487096716405e-070.999999899256452
926.33777094737441e-081.26755418947488e-070.99999993662229
933.98446647682357e-087.96893295364714e-080.999999960155335
942.54022877202610e-085.08045754405221e-080.999999974597712
951.49406083021900e-082.98812166043801e-080.999999985059392
968.67817901718458e-091.73563580343692e-080.999999991321821
974.94377315542391e-099.88754631084781e-090.999999995056227
983.0958473835937e-096.1916947671874e-090.999999996904153
991.74107573770241e-093.48215147540482e-090.999999998258924
1009.71737031780202e-101.94347406356040e-090.999999999028263
1015.41476266471096e-101.08295253294219e-090.999999999458524
1023.04425543818587e-106.08851087637175e-100.999999999695574
1032.4782901242229e-104.9565802484458e-100.999999999752171
1041.35818936845109e-102.71637873690218e-100.999999999864181
1051.37584399935374e-102.75168799870749e-100.999999999862416
1068.59874876211991e-111.71974975242398e-100.999999999914013
1075.1643161259386e-111.03286322518772e-100.999999999948357
1083.06590123676197e-116.13180247352395e-110.99999999996934
1091.88241363400628e-113.76482726801256e-110.999999999981176
1101.12019002511986e-112.24038005023971e-110.999999999988798
1117.36056272457522e-101.47211254491504e-090.999999999263944
1127.24075040485156e-101.44815008097031e-090.999999999275925
1137.45026257188177e-081.49005251437635e-070.999999925497374
1140.05541553471334510.1108310694266900.944584465286655
1150.5364233019423360.9271533961153280.463576698057664
1160.7875478509325050.424904298134990.212452149067495
1170.7796290737681910.4407418524636180.220370926231809
1180.8014341824289010.3971316351421980.198565817571099
1190.7937186174649240.4125627650701520.206281382535076
1200.7808217414818450.4383565170363090.219178258518155
1210.766699623768570.4666007524628590.233300376231429
1220.7540077370188890.4919845259622220.245992262981111
1230.7330802417862140.5338395164275730.266919758213786
1240.7122219240989110.5755561518021770.287778075901089
1250.6877259638412010.6245480723175970.312274036158799
1260.6645081152829970.6709837694340060.335491884717003
1270.6409021785602140.7181956428795720.359097821439786
1280.6174116818643570.7651766362712860.382588318135643
1290.5889579221807750.8220841556384490.411042077819225
1300.566245850756870.867508298486260.43375414924313
1310.5638005331733140.8723989336533730.436199466826686
1320.547311267313520.905377465372960.45268873268648
1330.522630294855940.9547394102881210.477369705144060
1340.4984627097744420.9969254195488840.501537290225558
1350.4744810404352720.9489620808705440.525518959564728
1360.4452757204753830.8905514409507670.554724279524617
1370.4235338476286930.8470676952573860.576466152371307
1380.3952909219880660.7905818439761310.604709078011934
1390.3708983062430150.7417966124860290.629101693756985
1400.3677157530080210.7354315060160420.632284246991979
1410.3438152007851420.6876304015702840.656184799214858
1420.3193867810524530.6387735621049060.680613218947547
1430.2972579959630790.5945159919261590.702742004036921
1440.2752237336031970.5504474672063940.724776266396803
1450.2507305270412090.5014610540824180.749269472958791
1460.2327051969077880.4654103938155760.767294803092212
1470.2155123773624390.4310247547248770.784487622637561
1480.2438836371026530.4877672742053060.756116362897347
1490.2508665699322430.5017331398644870.749133430067757
1500.2528364344556690.5056728689113390.74716356554433
1510.2508619466335230.5017238932670450.749138053366477
1520.2834217753628640.5668435507257270.716578224637136
1530.4655944596849430.9311889193698870.534405540315057
1540.4978454177993440.9956908355986880.502154582200656
1550.5288026340945940.9423947318108120.471197365905406
1560.5099241199624390.9801517600751220.490075880037561
1570.4892659601531520.9785319203063040.510734039846848
1580.4714300604782860.942860120956570.528569939521714
1590.4431016700282480.8862033400564960.556898329971752
1600.4205812206800370.8411624413600740.579418779319963
1610.3990023604844960.7980047209689910.600997639515504
1620.3781101327839120.7562202655678240.621889867216088
1630.3574678788207660.7149357576415320.642532121179234
1640.3363932850210370.6727865700420750.663606714978963
1650.3186686382355720.6373372764711440.681331361764428
1660.3022864651891030.6045729303782060.697713534810897
1670.2853743041296040.5707486082592080.714625695870396
1680.2683662194280940.5367324388561890.731633780571906
1690.2512385672865090.5024771345730190.748761432713491
1700.2359147492896280.4718294985792560.764085250710372
1710.2155391599434810.4310783198869630.784460840056519
1720.1964694298857880.3929388597715760.803530570114212
1730.1872941821246130.3745883642492250.812705817875387
1740.1794983432873620.3589966865747240.820501656712638
1750.1618013663753050.3236027327506100.838198633624695
1760.1480624927286750.2961249854573510.851937507271325
1770.1340210882415170.2680421764830350.865978911758483
1780.1193389186791000.2386778373581990.8806610813209
1790.1056876238279180.2113752476558370.894312376172082
1800.09325825945329230.1865165189065850.906741740546708
1810.08437846545921040.1687569309184210.91562153454079
1820.07386283197780230.1477256639556050.926137168022198
1830.06561575508528460.1312315101705690.934384244914715
1840.05896315295852260.1179263059170450.941036847041477
1850.05296952942539430.1059390588507890.947030470574606
1860.05292990560367960.1058598112073590.94707009439632
1870.05159874026386680.1031974805277340.948401259736133
1880.06198249630766130.1239649926153230.93801750369234
1890.05419346215935270.1083869243187050.945806537840647
1900.06350326063962240.1270065212792450.936496739360378
1910.05811809599992480.1162361919998500.941881904000075
1920.05071795486212360.1014359097242470.949282045137876
1930.04545221066042340.09090442132084680.954547789339577
1940.04125983648673680.08251967297347350.958740163513263
1950.03579667003225510.07159334006451020.964203329967745
1960.03161142714447510.06322285428895030.968388572855525
1970.02828140771820740.05656281543641470.971718592281793
1980.02417455776146370.04834911552292730.975825442238536
1990.02115347944706880.04230695889413760.978846520552931
2000.02141254159890730.04282508319781470.978587458401093
2010.02023788936660390.04047577873320790.979762110633396
2020.01973622330116520.03947244660233030.980263776698835
2030.01927808468328220.03855616936656440.980721915316718
2040.01856732291545670.03713464583091340.981432677084543
2050.01649810190946750.03299620381893490.983501898090533
2060.01594242413443880.03188484826887760.984057575865561
2070.01539827401172860.03079654802345710.984601725988271
2080.01433758290774750.02867516581549510.985662417092253
2090.01216813911355870.02433627822711750.987831860886441
2100.01020224014337450.02040448028674900.989797759856625
2110.0085819036950880.0171638073901760.991418096304912
2120.00721951423914690.01443902847829380.992780485760853
2130.005935394991385830.01187078998277170.994064605008614
2140.004857280430470480.009714560860940950.99514271956953
2150.003956889900846600.007913779801693190.996043110099153
2160.003250732891042610.006501465782085220.996749267108957
2170.002623990622066980.005247981244133960.997376009377933
2180.002268645584701620.004537291169403250.997731354415298
2190.001918567548041170.003837135096082340.99808143245196
2200.001541219343485320.003082438686970640.998458780656515
2210.001377353909815170.002754707819630340.998622646090185
2220.001140832961536550.00228166592307310.998859167038463
2230.0009062064333287850.001812412866657570.999093793566671
2240.0007564473850504570.001512894770100910.99924355261495
2250.0007458736957978290.001491747391595660.999254126304202
2260.0007141789481549860.001428357896309970.999285821051845
2270.0006825969320038560.001365193864007710.999317403067996
2280.001025696627619780.002051393255239570.99897430337238
2290.0008371815549991790.001674363109998360.999162818445
2300.0007027261384391070.001405452276878210.99929727386156
2310.0005779794442855510.001155958888571100.999422020555714
2320.0004838227042870170.0009676454085740350.999516177295713
2330.000406337394908770.000812674789817540.999593662605091
2340.0003514075076786890.0007028150153573790.999648592492321
2350.0002940221124612280.0005880442249224550.999705977887539
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3450.6820578592073580.6358842815852850.317942140792642
3460.6658529829380460.6682940341239080.334147017061954
3470.6511513568599230.6976972862801540.348848643140077
3480.6827561715767850.634487656846430.317243828423215
3490.6650358736405330.6699282527189350.334964126359467
3500.643472410818170.713055178363660.35652758918183
3510.6473735521756570.7052528956486870.352626447824343
3520.6413151314604730.7173697370790530.358684868539527
3530.6303541221849770.7392917556300450.369645877815023
3540.6354571190042160.7290857619915680.364542880995784
3550.6019631255404560.7960737489190880.398036874459544
3560.6023991929449910.7952016141100190.397600807055009
3570.6300596832192290.7398806335615430.369940316780771
3580.7423376864354170.5153246271291660.257662313564583
3590.999999999930261.39480228000936e-106.9740114000468e-11
3600.9999999999997684.64857230576134e-132.32428615288067e-13
36119.07019384983164e-194.53509692491582e-19
36212.79765674272994e-181.39882837136497e-18
36318.56370640203937e-184.28185320101968e-18
36412.55706735593457e-171.27853367796729e-17
36517.74920046048732e-173.87460023024366e-17
36611.97753785918482e-169.88768929592411e-17
36715.90919388702874e-162.95459694351437e-16
36811.74514102060455e-158.72570510302274e-16
3690.9999999999999984.99134502863829e-152.49567251431915e-15
3700.9999999999999931.41169478634429e-147.05847393172145e-15
3710.999999999999984.03855943718911e-142.01927971859456e-14
3720.9999999999999666.81810787529591e-143.40905393764796e-14
3730.9999999999999181.63583021985131e-138.17915109925655e-14
3740.9999999999997774.45674723475491e-132.22837361737745e-13
3750.9999999999994351.13036810018707e-125.65184050093535e-13
3760.9999999999984533.09472957624582e-121.54736478812291e-12
3770.999999999996886.24184914385749e-123.12092457192874e-12
3780.9999999999962577.48561876030413e-123.74280938015207e-12
3790.9999999999901281.97438145160336e-119.87190725801681e-12
3800.9999999999742355.15291130290041e-112.57645565145021e-11
3810.9999999999486591.02682159877618e-105.1341079938809e-11
3820.9999999999056651.88669022818661e-109.43345114093307e-11
3830.999999999800023.99959432608701e-101.99979716304351e-10
3840.9999999997767234.46554039409323e-102.23277019704662e-10
3850.9999999994404971.1190069083512e-095.595034541756e-10
3860.9999999988798282.24034384047313e-091.12017192023657e-09
3870.99999999810513.78980206540988e-091.89490103270494e-09
3880.9999999966723136.65537348422925e-093.32768674211463e-09
3890.9999999990783281.84334308458940e-099.21671542294701e-10
3900.9999999999999271.46979887273474e-137.34899436367369e-14
39113.11591977005588e-181.55795988502794e-18
39219.63258044683543e-224.81629022341772e-22
39318.52375039383287e-214.26187519691643e-21
39416.61532393641525e-203.30766196820763e-20
39515.43551191378426e-192.71775595689213e-19
39614.60566666279695e-182.30283333139847e-18
39711.14410550680062e-185.72052753400309e-19
39812.34702658097632e-211.17351329048816e-21
39915.56366354014403e-282.78183177007201e-28
40011.72177410102724e-268.60887050513621e-27
40115.24089033306712e-252.62044516653356e-25
40211.41887773703844e-237.09438868519222e-24
40313.90628458048749e-221.95314229024374e-22
40419.814741286439e-214.9073706432195e-21
40512.69113671351351e-191.34556835675676e-19
40617.05474174124867e-183.52737087062434e-18
40711.41434361398490e-167.07171806992452e-17
4080.9999999999999983.37365058632937e-151.68682529316468e-15
4090.999999999999967.94600345563314e-143.97300172781657e-14
4100.999999999999221.56002596110064e-127.80012980550319e-13
4110.9999999999829873.40250535672922e-111.70125267836461e-11
4120.9999999998151533.6969421884029e-101.84847109420145e-10
4130.9999999965033636.9932735878007e-093.49663679390035e-09
4140.9999999287180131.42563974015312e-077.12819870076562e-08
4150.9999992095604011.58087919789973e-067.90439598949866e-07
4160.999982843589243.43128215193214e-051.71564107596607e-05
4170.999719806825720.0005603863485600840.000280193174280042


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1990.492574257425743NOK
5% type I error level2270.561881188118812NOK
10% type I error level2410.596534653465347NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/10iw5v1291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/10iw5v1291379638.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/1bvpj1291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/1bvpj1291379638.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/2l4741291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/2l4741291379638.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/3l4741291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/3l4741291379638.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/4l4741291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/4l4741291379638.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/5l4741291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/5l4741291379638.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/6edo71291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/6edo71291379638.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/7p4na1291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/7p4na1291379638.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/8p4na1291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/8p4na1291379638.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/9iw5v1291379638.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379678xassivgkxarc48g/9iw5v1291379638.ps (open in new window)


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




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Software written by Ed van Stee & Patrick Wessa


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