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Multiple Regression

*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, 29 Dec 2010 16:09:21 +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/29/t1293638861q6weu7z7u60z0yd.htm/, Retrieved Wed, 29 Dec 2010 17:07:51 +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/29/t1293638861q6weu7z7u60z0yd.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 «
1595 17 60720 319369 0 5565 47 94355 493408 39 601 6 60720 319210 0 188 11 77655 381180 0 7146 105 134028 297978 305 1135 17 62285 290476 -9 450 8 59325 292136 -1 34 8 60630 314353 0 133 14 65990 339445 0 119 6 59118 303677 0 2053 53 100423 397144 33 4036 63 100269 424898 32 0 0 60720 315380 0 4655 393 60720 341570 0 131 11 61808 308989 0 1766 73 60438 305959 0 312 14 58598 318690 0 448 40 59781 323361 0 115 13 60945 318903 0 60 10 61124 314049 0 0 0 60720 315380 0 364 35 47705 298466 0 0 0 60720 315380 0 1442 118 121173 438493 -3 1389 9 57530 378049 6 149 5 62041 313332 0 2212 14 60720 319864 0 7489 322 136996 430866 70 0 0 60720 315380 0 402 26 84990 332743 5 7419 29 63255 286963 -5 0 0 60720 315380 0 307 15 58856 331955 0 1134 162 146216 527021 58 7561 87 82425 364304 37 5131 71 86111 292154 9 9 6 60835 314987 0 2264 24 55174 272713 -31 40949 481 129352 1398893 2865 4980 91 113521 409642 0 272 8 112995 429112 14 757 19 45689 382712 0 1424 94 131116 374943 32 0 0 6 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 time17 seconds
R Server'Herman Ole Andreas Wold' @ www.yougetit.org
R Framework
error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.


Multiple Linear Regression - Estimated Regression Equation
CCScore [t] = -1244.96536407372 -0.0664508926911222Costs[t] + 0.0454692964889564Trades[t] + 0.00287777874906581Dividends[t] + 0.00507952195665718Wealth[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-1244.96536407372985.044893-1.26390.2069690.103485
Costs-0.06645089269112220.126319-0.52610.5991230.299561
Trades0.04546929648895640.0158662.86590.0043640.002182
Dividends0.002877778749065810.0015421.86640.0626790.031339
Wealth0.005079521956657180.0025461.99520.0466630.023331


Multiple Linear Regression - Regression Statistics
Multiple R0.222740924544764
R-squared0.0496135194670562
Adjusted R-squared0.0406897027484371
F-TEST (value)5.55967486014584
F-TEST (DF numerator)4
F-TEST (DF denominator)426
p-value0.000226986196598222
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10593.7375800105
Sum Squared Residuals47808819539.4184


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
10446.799013543048-446.799013543048
2391165.18205849359-1126.18205849359
30511.543394625761-511.543394625761
40902.728118558956-902.728118558956
5305184.239556667321120.760443332679
6-9335.10752002968-344.10752002968
7-1380.130939205515-381.130939205515
80524.381751143607-524.381751143607
90660.956187577554-660.956187577554
100460.062308794023-460.062308794023
1133927.317669217414-894.317669217414
1232936.534116433514-904.534116433514
130531.752996260096-531.752996260096
140373.326204347929-373.326204347929
150494.215890032924-494.215890032924
160369.054268450362-369.054268450362
170522.363459062329-522.363459062329
180541.63919868474-541.63919868474
190543.244900526817-543.244900526817
200522.62241455383-522.62241455383
210531.752996260096-531.752996260096
220385.786971903649-385.786971903649
230531.752996260096-531.752996260096
24-31240.62173135241-1243.62173135241
256749.010377272757-743.010377272757
260515.477844491846-515.477844491846
270408.176768231828-408.176768231828
2870854.862498915975-784.862498915975
290531.752996260096-531.752996260096
305664.261369080244-659.261369080244
31-5-96.977173330613891.9771733306138
320531.752996260096-531.752996260096
330590.86350849459-590.86350849459
34581784.83738833859-1726.83738833859
3537344.246345373023-307.246345373023
369149.114489164868-140.114489164868
370529.762446431986-529.762446431986
38-31149.711314056115-180.711314056115
3928653533.76190799188-668.761907991885
400835.720751046642-835.720751046642
41141242.18218310197-1228.18218310197
420781.072067134641-781.072067134641
4332946.536718061507-914.536718061507
440531.752996260096-531.752996260096
450449.831057633348-449.831057633348
46-4432.85109023577-436.85109023577
470531.752996260096-531.752996260096
480492.797868231255-492.797868231255
4974720.497911482395-646.497911482395
5020977.03352077848-957.03352077848
51247487.473166664298-240.473166664298
5211712.71796278821-701.71796278821
53-6213.629339144719-219.629339144719
54284865.289587583569-581.289587583569
558194202.5584398437-3383.5584398437
566521418.02823643447-766.028236434467
570511.069046739918-511.069046739918
5810107.596254704692-97.5962547046922
5922379.90972251394-357.90972251394
600531.752996260096-531.752996260096
610531.752996260096-531.752996260096
6216542.747763400634-526.747763400634
63-3-148.084534344209145.084534344209
640534.244497981092-534.244497981092
6510-645.246817273854655.246817273854
660526.236610258665-526.236610258665
6773929.532956586266-856.532956586266
680602.958505423928-602.958505423928
690457.130720748878-457.130720748878
700531.752996260096-531.752996260096
7119745148.83891824132-3174.83891824132
72-129-340.927550357312211.927550357312
732832818973.55109396859354.44890603154
740531.752996260096-531.752996260096
750578.746650237668-578.746650237668
760581.164369653861-581.164369653861
770633.399028100315-633.399028100315
780518.399530265865-518.399530265865
79238-875.3967194724581113.39671947246
8030403077.20646994987-37.2064699498656
810529.007476064986-529.007476064986
820531.752996260096-531.752996260096
830531.752996260096-531.752996260096
841555.257188807659-554.257188807659
850531.752996260096-531.752996260096
860531.752996260096-531.752996260096
870521.732620173946-521.732620173946
880531.449203471648-531.449203471648
890444.20152772646-444.20152772646
900670.889173364311-670.889173364311
910531.752996260096-531.752996260096
920401.698128647291-401.698128647291
9319103201.24324447623-1291.24324447623
9438-427.050124504501465.050124504501
950563.96554512403-563.96554512403
960630.510274312688-630.510274312688
970531.752996260096-531.752996260096
980514.103829408888-514.103829408888
990542.910900000312-542.910900000312
1000264.378146696525-264.378146696525
101171091.15503924165-1074.15503924165
1020434.808578851112-434.808578851112
1030542.900405112643-542.900405112643
1040531.755114496427-531.755114496427
1050531.752996260096-531.752996260096
106-366-2395.650731352112029.65073135211
1070431.446381740313-431.446381740313
10829644659.15784836072-1695.15784836072
1090384.687405913222-384.687405913222
1100510.562046785999-510.562046785999
11136554.842890355353-518.842890355353
112-8-59.592783568367351.5927835683673
1130531.752996260096-531.752996260096
114-5520.641602409467-525.641602409467
1150531.752996260096-531.752996260096
1160461.320462392514-461.320462392514
11738926.829048399327-888.829048399327
1186012488.59016325405-1887.59016325405
1191322270.57283074694-2138.57283074694
120661556.58103188393-1490.58103188393
1210-3.856787891721883.85678789172188
1220531.752996260096-531.752996260096
1231952290.85364788896-2095.85364788896
12419897.399845318161-878.399845318161
125-158-137.676395872889-20.3236041271107
1260531.752996260096-531.752996260096
1270545.554348619183-545.554348619183
1280531.752996260096-531.752996260096
129-70-1593.730438814511523.73043881451
13013489.086445226845-476.086445226845
1313588.320450831163-585.320450831163
1320531.752996260096-531.752996260096
1330531.752996260096-531.752996260096
1340531.752996260096-531.752996260096
1350520.235254429314-520.235254429314
1360472.722528749572-472.722528749572
1373352423.52418061608-2088.52418061608
13802592.86841428943-2592.86841428943
13902423.52418061608-2423.52418061608
14045252423.524180616082101.47581938392
14130454449.97254838621-1404.97254838621
1420575.483407516511-575.483407516511
14302423.52418061608-2423.52418061608
1446382423.52418061608-1785.52418061608
14502457.99040944311-2457.99040944311
1466072423.52418061608-1816.52418061608
14715582429.90928068711-871.909280687109
14813242343.81308172766-1019.81308172766
14902653.56306460274-2653.56306460274
15002423.52418061608-2423.52418061608
1516112423.52418061608-1812.52418061608
15202300.27755827211-2300.27755827211
1539232423.52418061608-1500.52418061608
1546612309.25617802116-1648.25617802116
15523972461.60080411016-64.6008041101566
1563662952.92648565118-2586.92648565118
15702553.05796165005-2553.05796165005
1581352423.52418061608-2288.52418061608
15916592481.18048147193-822.180481471934
1603165027.00133850833-4711.00133850833
16102412.08256792083-2412.08256792083
1623092423.52418061608-2114.52418061608
163492225.30401256003-2176.30401256003
16402596.13718714268-2596.13718714268
16545192423.524180616082095.47581938392
16605116.65627712426-5116.65627712426
1678372423.52418061608-1586.52418061608
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16912801616.32307398756-336.323073987563
17025642804.87139973332-240.871399733317
17120455832.36938711829-3787.36938711829
17222344674.81307609624-2440.81307609624
1739752880.68988293156-1905.68988293156
17411363546.75033811508-2410.75033811508
1754531948.01160090004-1495.01160090004
17603495.48487807572-3495.48487807572
177612423.52418061608-2362.52418061608
1783682420.20818953543-2052.20818953543
17902431.35284404751-2431.35284404751
18049012423.524180616082477.47581938392
1815405643.39587248289-5103.39587248289
18204500.59394662763-4500.59394662763
18302423.52418061608-2423.52418061608
18402423.52418061608-2423.52418061608
18522423.52418061608-2421.52418061608
186362423.64845093168-2387.64845093168
1877762453.09321595476-1677.09321595476
188847382634.6627749970882103.337225003
1890-1803.203791913811803.20379191381
19032423.52418061608-2420.52418061608
1915292425.9168851361-1896.9168851361
19202511.11891605281-2511.11891605281
1934052423.52418061608-2018.52418061608
1949722489.30554020245-1517.30554020245
19503044.37327143215-3044.37327143215
19620992423.52418061608-324.524180616077
19734374486.59665921398-1049.59665921398
19801391.588625157-1391.588625157
19902423.52418061608-2423.52418061608
200223302423.5241806160819906.4758193839
20104429.10281504173-4429.10281504173
20202423.52418061608-2423.52418061608
2034832423.52418061608-1940.52418061608
20402400.50930916571-2400.50930916571
20522392423.52418061608-184.524180616077
20629494056.30594868995-1107.30594868995
20703611.20782407526-3611.20782407526
2083652423.52418061608-2058.52418061608
20924613711.54788129044-1250.54788129044
210219502192.5241300198819757.4758699801
21132946389.28358481953-3095.28358481953
2121412826.57243611272-2685.57243611272
2135722464.46224616239-1892.46224616239
214133262303.7121406441611022.2878593558
21522848299.11664936747-6015.11664936747
216102786.8754868044-2776.8754868044
21702468.18081887488-2468.18081887488
21814142423.52418061608-1009.52418061608
21919752884.39875212849-909.398752128487
220435293.60917053836-5250.60917053836
22102423.62590910153-2423.62590910153
2228442423.52418061608-1579.52418061608
2233042372.29087585563-2068.29087585563
2244582467.80784579787-2009.80784579787
225185622276.8429719873116285.1570280127
22604485.17168462298-4485.17168462298
22771232423.524180616084699.47581938392
22862213692.9962084959-13070.9962084959
2291742945.68743405471-2771.68743405471
23022202586.98623760054-366.98623760054
23112114324.9139268526-14203.9139268526
232118192157.823461769859661.17653823015
23305228.10116220923-5228.10116220923
2341252423.52418061608-2298.52418061608
23511822411.36935888851-1229.36935888851
23615032909.60561809755-1406.60561809755
23702303.72411137323-2303.72411137323
23802423.52418061608-2423.52418061608
239302423.52418061608-2393.52418061608
24033102491.32699753314818.67300246686
2415543463.41063401742-2909.41063401742
24203296.89112805547-3296.89112805547
2434682423.52418061608-1955.52418061608
24449172412.865142076322504.13485792368
24532561854.678268955531401.32173104447
24601525.1144597596-1525.1144597596
2471252423.52418061608-2298.52418061608
24802384.49828381332-2384.49828381332
249222423.52418061608-2401.52418061608
25002415.19997018386-2415.19997018386
2515142423.52418061608-1909.52418061608
25203261.38292447265-3261.38292447265
25302423.52418061608-2423.52418061608
25402423.52418061608-2423.52418061608
25502423.52418061608-2423.52418061608
25602423.52418061608-2423.52418061608
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258103272423.524180616087903.47581938392
259137184619.771248258429098.22875174158
26037484132.01907871069-384.019078710694
261144162923.0585605084911492.9414394915
26215262264.76741182678-738.76741182678
2636662709.56571952865-2043.56571952865
26428445081.19184814934-2237.19184814934
26502748.59937730714-2748.59937730714
2663682423.52418061608-2055.52418061608
26702390.23148436668-2390.23148436668
2683332423.52418061608-2090.52418061608
269262927.37141647751-2901.37141647751
27002427.14365778184-2427.14365778184
27102423.52418061608-2423.52418061608
27213032423.52418061608-1120.52418061608
273202818.02525056373-2798.02525056373
27423842432.84863444147-48.848634441472
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2806452283.56301957446-1638.56301957446
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2941262473.71202699366-2347.71202699366
29522433.84366822921-2431.84366822921
29602424.81144586878-2424.81144586878
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29802710.11700743809-2710.11700743809
29902423.52418061608-2423.52418061608
300377042423.5241806160835280.4758193839
301634315.29003498046-4252.29003498046
30214312658.34146732696-1227.34146732696
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3041922445.25087526952-2253.25087526952
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31519202745.30952047473-825.309520474726
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33102799.0650691491-2799.0650691491
33202423.52418061608-2423.52418061608
33323652423.52418061608-58.5241806160768
33412619054.93330156318-7793.93330156318
33503098.75536635604-3098.75536635604
33615852423.52418061608-838.524180616077
337161894005.1784400351412183.8215599649
33805061.19516782342-5061.19516782342
33902423.52418061608-2423.52418061608
340105792423.524180616088155.47581938392
3414744844.34229604255-4370.34229604255
34202403.39109836728-2403.39109836728
34336422423.524180616081218.47581938392
34404172.59099350713-4172.59099350713
3454722423.52418061608-1951.52418061608
346983169.10719515537-3071.10719515537
34739992457.988236987331541.01176301267
34801206.17543316161-1206.17543316161
3496212423.52418061608-1802.52418061608
35002399.83324195856-2399.83324195856
35102423.52418061608-2423.52418061608
352302423.52418061608-2393.52418061608
35302420.42782446352-2420.42782446352
3547462423.52418061608-1677.52418061608
35502783.41008191198-2783.41008191198
35602423.52418061608-2423.52418061608
35702423.52418061608-2423.52418061608
35802423.52418061608-2423.52418061608
35941502423.524180616081726.47581938392
36046586364.53895728652-1706.53895728651
3618146538.43256320241-5724.43256320241
36210021424.42294878446-422.422948784464
3634962193.63309383526-1697.63309383526
3643893664.12334038761-3275.12334038761
365126792373.0723728055510305.9276271945
3664003774.94556398451-3374.94556398451
367532407.34289843025-2354.34289843025
36802407.44488888589-2407.44488888589
36951092423.524180616082685.47581938392
3705762416.36631913755-1840.36631913755
3714384830.36471705683-4392.36471705683
3721652263.90198371434-2098.90198371434
37340692546.560457670411522.43954232959
374232971.46662470858-2948.46662470858
37512852504.97624020789-1219.97624020789
37646774254.1691235801422.830876419897
377248114075.105414499320735.8945855007
3781675734.94575816214-5567.94575816214
37902560.48140779429-2560.48140779429
38046282423.524180616082204.47581938392
3812263011.02483661975-2785.02483661975
38217652583.77970698292-818.779706982918
3834602994.2950637937-2534.2950637937
384362583.38131490725-2547.38131490725
3859892432.54299707916-1443.54299707916
38630552310.22582751282744.774172487178
38702575.52653224499-2575.52653224499
38802423.52418061608-2423.52418061608
38902423.52418061608-2423.52418061608
39002423.52418061608-2423.52418061608
39102423.52418061608-2423.52418061608
392132532423.5241806160810829.4758193839
393243851.76827987746-3827.76827987746
39402382.71885718002-2382.71885718002
39502423.52418061608-2423.52418061608
39602423.52418061608-2423.52418061608
39702423.52418061608-2423.52418061608
3981312423.52418061608-2292.52418061608
3995142408.61760163345-1894.61760163345
40033664511.39570560128-1145.39570560128
40193274543.398478806664783.60152119334
4023843839.1389677327-3455.1389677327
403178212131.6097126882215689.3902873118
4043975598.2513085085-5201.2513085085
4058972533.33042114999-1636.33042114999
4062182421.84800347681-2203.84800347681
40733692422.62656759315946.373432406849
40857024699.579187472241002.42081252776
40986364291.647071896754344.35292810325
4105345400.16384997034-4866.16384997034
41102478.09728822518-2478.09728822518
41202423.52418061608-2423.52418061608
4137262423.52418061608-1697.52418061608
41413802603.99591247273-1223.99591247273
4151802525.93970999898-2345.93970999898
41672852376.354079527524908.64592047248
4178808749.47624357318-7869.47624357318
41813175.52175996891-3174.52175996891
41902423.73258613611-2423.73258613611
420962423.52418061608-2327.52418061608
42118892402.93568333518-513.93568333518
422451871490.5542189773343696.4457810227
42328814967.1279024319-14679.1279024319
42412702359.82629548512-1089.82629548512
42565262692.288395495513833.71160450449
42601170.43727925807-1170.43727925807
4272262423.52418061608-2197.52418061608
4286943055.74500237457-2361.74500237457
42902907.16293476699-2907.16293476699
4302492423.52418061608-2174.52418061608
43115952231.69922210844-636.699222108439


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
86.35500903842742e-091.27100180768548e-080.999999993644991
99.13522085249507e-121.82704417049901e-110.999999999990865
101.44907203374393e-142.89814406748787e-140.999999999999986
111.08092301776106e-162.16184603552211e-161
121.9740321530969e-193.9480643061938e-191
133.06681250295087e-226.13362500590174e-221
149.77678488268413e-241.95535697653683e-231
151.85512934188279e-263.71025868376557e-261
164.13311216037453e-298.26622432074905e-291
171.03999163890566e-312.07998327781133e-311
182.58706130673981e-345.17412261347962e-341
195.25649143406214e-371.05129828681243e-361
209.42202914642255e-401.88440582928451e-391
211.69578197999035e-423.39156395998069e-421
227.2039736575718e-451.44079473151436e-441
231.24582428285304e-472.49164856570608e-471
241.03924271709737e-492.07848543419473e-491
259.8609318036535e-521.9721863607307e-511
261.78033646065243e-543.56067292130485e-541
271.70399327897214e-563.40798655794428e-561
286.96839586617382e-581.39367917323476e-571
291.47169673067431e-602.94339346134861e-601
304.12621665689049e-638.25243331378098e-631
311.46346772664779e-612.92693545329558e-611
324.2460664145492e-648.4921328290984e-641
331.37797503108572e-662.75595006217144e-661
344.18115819614139e-698.36231639228278e-691
351.42960671489771e-712.85921342979542e-711
367.94287611703941e-731.58857522340788e-721
372.35492233012431e-754.70984466024861e-751
381.81897696292939e-773.63795392585879e-771
391.02696445869948e-632.05392891739895e-631
402.20827445957569e-654.41654891915138e-651
411.95086795640402e-673.90173591280803e-671
423.23917214225005e-696.4783442845001e-691
435.6398389518673e-711.12796779037346e-701
445.49175680602922e-731.09835136120584e-721
457.64723854249548e-751.5294477084991e-741
467.0586722742643e-771.41173445485286e-761
476.59883627925054e-791.31976725585011e-781
486.26262720136225e-811.25252544027245e-801
495.40120293709955e-831.08024058741991e-821
504.61798871141252e-859.23597742282505e-851
512.73852313238451e-775.47704626476902e-771
524.01898048759636e-798.03796097519271e-791
537.05837243974737e-811.41167448794947e-801
541.37404130737605e-812.74808261475209e-811
551.25538111581292e-812.51076223162585e-811
561.03658318727069e-792.07316637454138e-791
571.83809475699405e-813.6761895139881e-811
584.10390517027132e-838.20781034054264e-831
597.08003852001502e-851.416007704003e-841
601.22531218315056e-862.45062436630112e-861
612.09774347253789e-884.19548694507577e-881
623.52162383323127e-907.04324766646253e-901
636.5505129586075e-921.3101025917215e-911
641.08681189595188e-932.17362379190377e-931
655.9694029487869e-951.19388058975738e-941
669.88346329353033e-971.97669265870607e-961
671.58511810201512e-983.17023620403024e-981
682.53017596522047e-1005.06035193044094e-1001
694.00317205856166e-1028.00634411712333e-1021
706.39327380200399e-1041.2786547604008e-1031
711.69958080546129e-1053.39916161092257e-1051
722.65537456397822e-1075.31074912795644e-1071
731.6210164924928e-613.2420329849856e-611
741.23938411791787e-622.47876823583574e-621
759.40497189507295e-641.88099437901459e-631
767.2115890670009e-651.44231781340018e-641
775.54125882069988e-661.10825176413998e-651
784.09892871694833e-678.19785743389666e-671
792.24264551292747e-674.48529102585495e-671
801.89896749754429e-683.79793499508857e-681
811.41117979498882e-692.82235958997765e-691
821.040438826315e-702.08087765262999e-701
837.6107841371558e-721.52215682743116e-711
845.53971504970619e-731.10794300994124e-721
853.98991666501519e-747.97983333003038e-741
862.85178797891352e-755.70357595782703e-751
872.02231904893933e-764.04463809787867e-761
881.42404206611329e-772.84808413222659e-771
899.9522821052728e-791.99045642105456e-781
906.90878693518714e-801.38175738703743e-791
914.75798756022345e-819.5159751204469e-811
923.25246486748167e-826.50492973496333e-821
932.45889146510005e-834.91778293020011e-831
945.77414205674743e-841.15482841134949e-831
953.94851427579167e-857.89702855158334e-851
962.67853390611291e-865.35706781222582e-861
971.79691434767453e-873.59382869534906e-871
981.19705013411514e-882.39410026823027e-881
997.94640426715974e-901.58928085343195e-891
1005.21250398874138e-911.04250079774828e-901
1016.26580311426187e-921.25316062285237e-911
1024.09715159170406e-938.19430318340813e-931
1032.6572813746418e-945.3145627492836e-941
1041.71086833142419e-953.42173666284837e-951
1051.09423671731029e-962.18847343462059e-961
1066.4663604411919e-951.29327208823838e-941
1074.36569276796071e-968.73138553592142e-961
1081.43175709878003e-962.86351419756005e-961
1099.74340774449414e-981.94868154889883e-971
1106.62187798521253e-991.32437559704251e-981
1114.60302788751421e-1009.20605577502842e-1001
1125.7359587987668e-1011.14719175975336e-1001
1133.81737933537456e-1027.63475867074912e-1021
1142.52101733730706e-1035.04203467461412e-1031
1151.65727196552525e-1043.3145439310505e-1041
1161.08312865739911e-1052.16625731479823e-1051
1177.09887183417794e-1071.41977436683559e-1061
1187.72639722881052e-1071.5452794457621e-1061
1193.58361992144263e-1077.16723984288526e-1071
1204.06013530381102e-1088.12027060762203e-1081
1213.90745682467291e-1097.81491364934582e-1091
1222.58705230641427e-1105.17410461282855e-1101
1232.42266439303586e-1104.84532878607171e-1101
1241.62629707220391e-1113.25259414440781e-1111
1251.83091090796074e-1123.66182181592148e-1121
1261.21607261082421e-1132.43214522164843e-1131
1278.02945480619391e-1151.60589096123878e-1141
1285.27231954281032e-1161.05446390856206e-1151
1291.43519320852971e-1132.87038641705941e-1131
1301.02598014750098e-1142.05196029500197e-1141
1317.10198305767077e-1161.42039661153415e-1151
1324.87958411907899e-1179.75916823815797e-1171
1333.33518429785248e-1186.67036859570496e-1181
1342.26918842663875e-1194.5383768532775e-1191
1351.53960159926975e-1203.07920319853949e-1201
1363.16480116146974e-1216.32960232293947e-1211
1375.54952053623582e-1221.10990410724716e-1211
1384.19106616229001e-1238.38213232458003e-1231
1392.8586827652932e-1245.7173655305864e-1241
1402.75775283138885e-1215.5155056627777e-1211
1412.27287392562467e-1224.54574785124934e-1221
1422.25622421571164e-1234.51244843142327e-1231
1434.02631623263999e-1248.05263246527998e-1241
1443.31627845821876e-1256.63255691643753e-1251
1453.7227894436957e-1267.4455788873914e-1261
1462.8825574893436e-1275.76511497868721e-1271
1472.60344704843777e-1285.20689409687554e-1281
1483.84559331892273e-1297.69118663784546e-1291
1491.55215348988155e-1293.1043069797631e-1291
1501.80733249433384e-1303.61466498866768e-1301
1511.33557303460602e-1312.67114606921204e-1311
1521.44328142162908e-1322.88656284325816e-1321
1531.02270305866341e-1332.04540611732682e-1331
1547.17710751304688e-1351.43542150260938e-1341
1556.72409982797285e-1361.34481996559457e-1351
1562.13375230728303e-1364.26750461456607e-1361
1571.91765238831779e-1373.83530477663558e-1371
1581.64886088978666e-1383.29772177957331e-1381
1591.71831709627895e-1393.43663419255791e-1391
1605.82778920953655e-1391.16555784190731e-1381
1614.92769511670594e-1409.85539023341189e-1401
1623.60147768766487e-1417.20295537532973e-1411
1632.61436669291812e-1425.22873338583625e-1421
1642.46215903087312e-1434.92431806174624e-1431
1659.30464414319314e-1411.86092882863863e-1401
1662.82651564056332e-1415.65303128112665e-1411
1672.15985489243538e-1424.31970978487077e-1421
1682.86787663846494e-1395.73575327692989e-1391
1693.45915891543945e-1396.9183178308789e-1391
1705.38006188799617e-1401.07601237759923e-1391
1716.23601052570616e-1401.24720210514123e-1391
1724.92611862042585e-1419.8522372408517e-1411
1735.09870194186275e-1421.01974038837255e-1411
1746.89476216846517e-1431.37895243369303e-1421
1755.92156473430267e-1441.18431294686053e-1431
1761.25124643397294e-1442.50249286794589e-1441
1771.1048259860057e-1452.20965197201139e-1451
1788.7760017520786e-1471.75520035041572e-1461
1798.11603240123672e-1481.62320648024734e-1471
1801.81226568846297e-1453.62453137692593e-1451
1812.6035898164364e-1455.20717963287281e-1451
1821.66837718095084e-1453.33675436190168e-1451
1831.52363395838145e-1463.0472679167629e-1461
1841.38221629796816e-1472.76443259593632e-1471
1851.2447876067178e-1482.48957521343561e-1481
1861.09904402121389e-1492.19808804242777e-1491
1879.14101159712157e-1511.82820231942431e-1501
1881.32013101061958e-222.64026202123916e-221
1896.53956287702357e-231.30791257540471e-221
1903.26842582784206e-236.53685165568413e-231
1911.59543184990255e-233.19086369980509e-231
1927.90588552252102e-241.5811771045042e-231
1933.83845811800059e-247.67691623600118e-241
1941.82937790715175e-243.65875581430349e-241
1959.14538520026395e-251.82907704005279e-241
1964.26951937315274e-258.5390387463055e-251
1971.98930719491798e-253.97861438983595e-251
1989.35810300602482e-261.87162060120496e-251
1994.49008840676824e-268.98017681353648e-261
2001.44629574835868e-242.89259149671736e-241
2018.39158149482942e-251.67831629896588e-241
2024.11322479596186e-258.22644959192373e-251
2031.97173623583177e-253.94347247166353e-251
2049.56266300943826e-261.91253260188765e-251
2054.43429360512119e-268.86858721024239e-261
2062.04630190100476e-264.09260380200952e-261
2071.03461358409699e-262.06922716819398e-261
2084.86716095453169e-279.73432190906338e-271
2092.22240028615798e-274.44480057231596e-271
2106.58507377264224e-261.31701475452845e-251
2113.33528850718396e-266.67057701436791e-261
2121.64554255101182e-263.29108510202364e-261
2137.77452131001539e-271.55490426200308e-261
2141.30997640222173e-262.61995280444346e-261
2157.86309905958616e-271.57261981191723e-261
2163.86143071633563e-277.72286143267127e-271
2171.84948629857919e-273.69897259715838e-271
2188.47162706403411e-281.69432541280682e-271
2193.84044446825864e-287.68088893651727e-281
2202.10966698397484e-284.21933396794968e-281
2219.91156662883129e-291.98231332576626e-281
2224.50971968750037e-299.01943937500073e-291
2232.06592264299311e-294.13184528598622e-291
2249.42932290797334e-301.88586458159467e-291
2257.72024180935485e-291.54404836187097e-281
2263.97007307257489e-297.94014614514979e-291
2272.31357384692146e-294.62714769384292e-291
2282.55587084682964e-295.11174169365927e-291
2291.1887132641291e-292.37742652825821e-291
2305.30208512175701e-301.0604170243514e-291
2317.12014566533484e-301.42402913306697e-291
2329.24459609874332e-301.84891921974866e-291
2335.25092552550806e-301.05018510510161e-291
2342.41100104035381e-304.82200208070762e-301
2351.07039914760017e-302.14079829520034e-301
2364.7496616109523e-319.4993232219046e-311
2372.16415879851996e-314.32831759703992e-311
2389.81116491839545e-321.96223298367909e-311
2394.42317684669543e-328.84635369339087e-321
2401.94986847789745e-323.8997369557949e-321
2419.0074380159468e-331.80148760318936e-321
2424.14804325599948e-338.29608651199896e-331
2431.80945563712064e-333.61891127424128e-331
2448.52669700909096e-341.70533940181819e-331
2453.79314730187625e-347.58629460375251e-341
2461.69777867615201e-343.39555735230401e-341
2477.37115320266502e-351.474230640533e-341
2483.19533734138084e-356.39067468276167e-351
2491.38107375272282e-352.76214750544565e-351
2505.94396944527583e-361.18879388905517e-351
2512.50081452028543e-365.00162904057086e-361
2521.09625187660474e-362.19250375320948e-361
2534.66205493051933e-379.32410986103866e-371
2541.97412900355916e-373.94825800711833e-371
2558.32352136249792e-381.66470427249958e-371
2563.49441482844504e-386.98882965689009e-381
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3243.92773170978519e-267.85546341957038e-261
3252.4560249320689e-264.91204986413779e-261
3261.13552509406083e-262.27105018812166e-261
3270.9999999999991571.68651867369646e-128.43259336848231e-13
3280.9999999999996277.47012443063417e-133.73506221531708e-13
3290.999999999999529.61823682200873e-134.80911841100437e-13
3300.9999999999990721.85642976269455e-129.28214881347277e-13
3310.9999999999982263.54747131577044e-121.77373565788522e-12
3320.9999999999968356.3296397516324e-123.1648198758162e-12
3330.9999999999938981.22035828463484e-116.10179142317421e-12
3340.9999999999892532.14930213203011e-111.07465106601505e-11
3350.9999999999806843.86312776063512e-111.93156388031756e-11
3360.999999999963967.20794001710278e-113.60397000855139e-11
3370.999999999988112.37816710313186e-111.18908355156593e-11
3380.999999999977784.44387218334157e-112.22193609167078e-11
3390.9999999999607947.84118033731377e-113.92059016865688e-11
3400.99999999995858.30018269646638e-114.15009134823319e-11
3410.9999999999247121.50577000341567e-107.52885001707833e-11
3420.999999999865162.69678516953101e-101.34839258476551e-10
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3470.9999999976670134.6659736994881e-092.33298684974405e-09
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3490.9999999926406441.47187117444836e-087.35935587224182e-09
3500.999999987635382.47292380607238e-081.23646190303619e-08
3510.9999999794796764.10406478719542e-082.05203239359771e-08
3520.9999999661413576.77172857382949e-083.38586428691474e-08
3530.999999944529961.10940079829661e-075.54700399148305e-08
3540.9999999069213671.86157267010233e-079.30786335051167e-08
3550.9999998436948523.12610295449354e-071.56305147724677e-07
3560.9999997499241135.00151773633909e-072.50075886816954e-07
3570.9999996029429757.94114050682174e-073.97057025341087e-07
3580.9999993744183981.25116320354652e-066.2558160177326e-07
3590.999998943400442.11319911834213e-061.05659955917106e-06
3600.9999982367782783.52644344388899e-061.7632217219445e-06
3610.9999972192019045.56159619147998e-062.78079809573999e-06
3620.9999956078690518.78426189762893e-064.39213094881446e-06
3630.999993135057741.37298845200836e-056.86494226004182e-06
3640.999989194427062.16111458814844e-051.08055729407422e-05
3650.9999897608396852.04783206290735e-051.02391603145368e-05
3660.999983590990063.28180198802008e-051.64090099401004e-05
3670.9999751495726444.9700854712212e-052.4850427356106e-05
3680.9999628569822037.42860355943024e-053.71430177971512e-05
3690.999941987911650.000116024176701445.80120883507199e-05
3700.9999094057757280.0001811884485443979.05942242721983e-05
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3760.9989851259863820.002029748027235230.00101487401361761
3770.9998717593881250.0002564812237498140.000128240611874907
3780.9998282547148440.0003434905703119470.000171745285155974
3790.9997414491202160.0005171017595680120.000258550879784006
3800.9995979252712870.0008041494574255230.000402074728712761
3810.9994111155793250.001177768841350070.000588884420675037
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3830.9986318799986030.002736240002794180.00136812000139709
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3890.9898955083977310.02020898320453760.0101044916022688
3900.9865382753429080.0269234493141840.013461724657092
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3920.982281953544940.03543609291011950.0177180464550597
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3940.9721992499842550.05560150003148920.0278007500157446
3950.9638944464520930.07221110709581430.0361055535479071
3960.953678273087760.09264345382447770.0463217269122388
3970.941299569395810.1174008612083790.0587004306041894
3980.926266144314880.1474677113702410.0737338556851207
3990.9085606013867260.1828787972265490.0914393986132745
4000.881787290225240.2364254195495210.118212709774761
4010.8809948480216370.2380103039567260.119005151978363
4020.8511728488509170.2976543022981650.148827151149083
4030.8754872829281120.2490254341437770.124512717071888
4040.8413464256398430.3173071487203140.158653574360157
4050.8029967339444090.3940065321111820.197003266055591
4060.7619042725629580.4761914548740840.238095727437042
4070.7064265755190050.5871468489619890.293573424480995
4080.65424593767070.69150812465860.3457540623293
4090.6338069688941440.7323860622117110.366193031105856
4100.8200568566700680.3598862866598640.179943143329932
4110.7666385015067750.466722996986450.233361498493225
4120.7027284958028240.5945430083943520.297271504197176
4130.6285611527687820.7428776944624370.371438847231218
4140.5651762430034440.869647513993110.434823756996556
4150.5252115243327130.9495769513345740.474788475667287
4160.4402294510796210.8804589021592420.559770548920379
4170.7995580199002890.4008839601994220.200441980099711
4180.7717689780992740.4564620438014530.228231021900726
4190.696708109961130.606583780077740.30329189003887
4200.6112945985982340.7774108028035330.388705401401766
4210.5061653324235180.9876693351529640.493834667576482
4220.9951746894520640.00965062109587260.0048253105479363
4230.9811266065058380.03774678698832470.0188733934941624


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3800.913461538461538NOK
5% type I error level3880.932692307692308NOK
10% type I error level3910.939903846153846NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/10bf031293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/10bf031293638943.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/14w391293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/14w391293638943.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/2x5kc1293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/2x5kc1293638943.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/3x5kc1293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/3x5kc1293638943.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/4x5kc1293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/4x5kc1293638943.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/57w1x1293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/57w1x1293638943.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/67w1x1293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/67w1x1293638943.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/7i60i1293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/7i60i1293638943.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/8i60i1293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/8i60i1293638943.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/9bf031293638943.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293638861q6weu7z7u60z0yd/9bf031293638943.ps (open in new window)


 
Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 5 ; 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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