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w4 - beurs variant 01

*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: Thu, 02 Dec 2010 22:59:30 +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/02/t1291330659j6893h9x8vxcdyl.htm/, Retrieved Thu, 02 Dec 2010 23:57:39 +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/02/t1291330659j6893h9x8vxcdyl.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 «
1 162556 162556 213118 213118 230380558 6282929 1 29790 29790 81767 81767 25266003 4324047 1 87550 87550 153198 153198 70164684 4108272 0 84738 0 -26007 0 -15292116 -1212617 1 54660 54660 126942 126942 37955658 1485329 1 42634 42634 157214 157214 24525384 1779876 0 40949 0 129352 0 62218312 1367203 1 42312 42312 234817 234817 75845891 2519076 1 37704 37704 60448 60448 27322496 912684 1 16275 16275 47818 47818 5212162 1443586 0 25830 0 245546 0 28237790 1220017 0 12679 0 48020 0 5282200 984885 1 18014 18014 -1710 -1710 -408690 1457425 0 43556 0 32648 0 8064056 -572920 1 24524 24524 95350 95350 47388950 929144 0 6532 0 151352 0 15589256 1151176 0 7123 0 288170 0 31410530 790090 1 20813 20813 114337 114337 57397174 774497 1 37597 37597 37884 37884 9395232 990576 0 17821 0 122844 0 45820812 454195 1 12988 12988 82340 82340 9798460 876607 1 22330 22330 79801 79801 6703284 711969 0 13326 0 165548 0 16885896 702380 0 16189 0 116384 0 34333280 264449 0 7146 0 134028 0 14072940 450033 0 15824 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'Sir Ronald Aylmer Fisher' @ 193.190.124.24
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
Wealth [t] = + 137309.298470465 + 52168.5352089629Group[t] -8.64314404706032Costs[t] + 41.8552589169229GrCosts[t] + 3.06277623571778Dividends[t] -1.62307602433212GrDiv[t] + 0.00237005636299135TrDiv[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)137309.29847046532600.3240764.21193.1e-051.5e-05
Group52168.535208962955523.3575770.93960.3479690.173984
Costs-8.643144047060321.789579-4.82972e-061e-06
GrCosts41.85525891692292.18503919.155400
Dividends3.062776235717780.4550496.730700
GrDiv-1.623076024332120.697778-2.32610.0204860.010243
TrDiv0.002370056362991350.0012181.94660.0522450.026123


Multiple Linear Regression - Regression Statistics
Multiple R0.885491408653423
R-squared0.784095034799023
Adjusted R-squared0.7810397758575
F-TEST (value)256.637833259429
F-TEST (DF numerator)6
F-TEST (DF denominator)424
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation209360.604875516
Sum Squared Residuals18584709858508.9


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
162829296441147.31551229-158218.315512290
243240471356468.554014512967578.44598549
341082723484051.93929123624220.060708769
4-1212617-710990.240181046-501626.759818954
514853292277567.50545426-792238.505454257
617798761889910.71047794-110034.710477939
71367203327018.3307801401040184.66921986
825190762112573.95916130406502.040838703
99126841593490.26660817-680806.266608171
101443586811201.705607522632384.294392478
111220017733034.495176769486982.504823231
12984885187316.501657548797568.498342452
131457425784330.365248672673094.634751328
14-572920-120045.697865262-452874.302134738
159291441253561.63638654-324417.636386539
161151176581357.005760526569818.994239474
177900901032789.13776148-242699.137761478
187744971181367.12099150-406870.120991501
199905761514962.54863316-524386.548633164
20454195468121.419346349-13926.4193463487
21876607762604.599485215114002.400514785
227119691061881.03618938-349912.036189384
23702380569187.766429557133192.233570444
24264449435215.397636747-170766.397636747
25450033519396.82542395-69363.8254239502
26541063205744.874523975335318.125476025
275888641211345.09090619-622481.090906187
28-37216144110.448776837-181326.448776837
29783310164599.105229956618710.894770044
30467359208067.736604592259291.263395408
31688779432639.300314912256139.699685088
32608419756159.700079019-147740.700079019
33696348533740.719560574162607.280439426
34597793498459.04649344599333.9535065554
358217301057798.67358149-236068.673581493
36377934327822.65805726850111.3419427316
37651939338446.936431933313492.063568067
38697458550223.562493794147234.437506206
39700368580034.761351131120333.238648869
40225986373987.436867062-148001.436867062
41348695711722.705256235-363027.705256235
42373683328085.10977736545597.8902226347
43501709435769.41589433765939.5841056627
44413743467122.552729529-53379.5527295294
45379825319289.83464317960535.1653568206
46336260463141.719871644-126881.719871644
47636765547621.46183516289143.538164838
48481231739986.145863224-258755.145863224
49469107492635.33898371-23528.3389837103
50211928357549.270942479-145621.270942479
51563925477719.46954784486205.5304521561
52511939569228.634988531-57289.634988531
53521016754827.148907857-233811.148907857
54543856493787.08415954450068.9158404564
55329304628293.261777399-298989.261777399
56423262318702.866693497104559.133306503
57509665260199.569091363249465.430908637
58455881373275.19190553482605.8080944664
59367772456145.418797567-88373.418797567
60406339646030.36552659-239691.36552659
61493408520656.61977762-27248.6197776198
62232942314300.702328313-81358.7023283129
63416002437091.165335089-21089.1653350891
64337430567567.727346119-230137.727346119
65361517305994.76894153355522.2310584667
66360962504811.160505037-143849.160505037
67235561391317.549116987-155756.549116987
68408247414855.752852695-6608.75285269454
69450296555791.147418281-105495.147418281
70418799482850.750221652-64051.7502216516
71247405333712.601378562-86307.601378562
72378519217166.472447136161352.527552864
73326638460918.795422-134280.795422
74328233377420.219623415-49187.2196234155
75386225494206.404122994-107981.404122994
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85416776451142.371842709-34366.3718427094
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88388386454497.692983225-66111.6929832252
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90407560451625.514099203-44065.5140992027
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92373177414831.703144262-41654.7031442625
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95358649239921.552894378118727.447105622
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97467427483179.200194485-15752.200194485
98364885370419.010825782-5534.01082578185
99436230520523.637421868-84293.6374218677
100329118342735.394588818-13617.3945888182
101317365346892.669930153-29527.6699301531
102286849411102.742452697-124253.742452697
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104407198485608.494525234-78410.4945252335
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107153661390137.778226693-236476.778226693
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110264512364900.112138036-100388.112138036
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113191521371190.293365514-179669.293365514
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311325560276896.43051476648663.5694852342
312369376418356.082653354-48980.0826533541
313325560276896.43051476648663.5694852342
314332013284615.58638545047397.4136145495
315325871323956.7438754591914.25612454102
316342165340461.9643787361703.03562126428
317324967320904.7136303014062.28636969936
318314832323933.932509733-9101.93250973305
319325557324132.8102967931424.18970320706
320325560323281.0715032492278.92849675115
321325560323281.0715032492278.92849675115
322325560323281.0715032492278.92849675115
323325560323281.0715032492278.92849675115
324325560276896.43051476648663.5694852342
325325560323281.0715032492278.92849675115
326322649323384.877011632-735.877011632105
327325560276896.43051476648663.5694852342
328325560276896.43051476648663.5694852342
329325560323281.0715032492278.92849675115
330325560323281.0715032492278.92849675115
331325560323281.0715032492278.92849675115
332325560323281.0715032492278.92849675115
333325560323281.0715032492278.92849675115
334324598322122.4311440572475.56885594344
335325567323581.7206183631985.27938163742
336325560323281.0715032492278.92849675115
337324005324219.615751166-214.615751165847
338325560323281.0715032492278.92849675115
339325748323739.7253277922008.27467220767
340323385321021.2294371792363.77056282141
341315409329780.34065389-14371.3406538898
342325560323281.0715032492278.92849675115
343325560323281.0715032492278.92849675115
344325560323281.0715032492278.92849675115
345325560323281.0715032492278.92849675115
346325560323281.0715032492278.92849675115
347312275318186.647784433-5911.64778443273
348325560323281.0715032492278.92849675115
349325560323281.0715032492278.92849675115
350325560323281.0715032492278.92849675115
351320576316029.5272669934546.47273300714
352325246325448.51771097-202.517710970069
353332961316456.84478987216504.1552101284
354323010326527.876190274-3517.87619027425
355325560323281.0715032492278.92849675115
356325560323281.0715032492278.92849675115
357345253383861.312374499-38608.3123744988
358325560323281.0715032492278.92849675115
359325560323281.0715032492278.92849675115
360325560323281.0715032492278.92849675115
361325559323854.2121615671704.78783843251
362325560323281.0715032492278.92849675115
363325560323281.0715032492278.92849675115
364319634280527.65577911839106.3442208821
365319951319956.914782301-5.9147823010952
366325560323281.0715032492278.92849675115
367325560323281.0715032492278.92849675115
368325560323281.0715032492278.92849675115
369325560323281.0715032492278.92849675115
370325560323281.0715032492278.92849675115
371325560323281.0715032492278.92849675115
372318519322200.369662266-3681.36966226625
373343222371725.581981102-28503.5819811020
374317234334488.174058952-17254.1740589523
375325560323281.0715032492278.92849675115
376325560323281.0715032492278.92849675115
377314025311519.6020435672505.39795643252
378320249318740.4586056311508.54139436943
379325560323281.0715032492278.92849675115
380325560323281.0715032492278.92849675115
381325560323281.0715032492278.92849675115
382349365368079.700377200-18714.7003771996
383289197267273.05936988421923.9406301164
384325560323281.0715032492278.92849675115
385329245331048.843757652-1803.84375765228
386240869329892.964889391-89023.964889391
387327182319753.7391954937428.26080450662
388322876309983.08108479312892.9189152067
389323117322101.9911765951015.00882340476
390306351314004.258391799-7653.25839179885
391335137327430.9303708767706.06962912386
392308271316243.912583121-7972.9125831211
393301731319620.658987463-17889.6589874627
394382409341053.95539327941355.044606721
395279230425995.268141657-146765.268141657
396298731313290.571342951-14559.5713429512
397243650261816.166932171-18166.1669321707
398532682445710.54411738986971.4558826105
399319771329718.972944003-9947.97294400323
400171493297190.684439793-125697.684439793
401347262344638.7488686022623.25113139813
402343945328694.39479185415250.6052081462
403311874314436.331938850-2562.33193885043
404302211344113.314801391-41902.3148013909
405316708330103.450521333-13395.4505213334
406333463345791.139353502-12328.1393535017
407344282342804.8148546721477.18514532799
408319635310786.9742149538848.02578504675
409301186313665.1500029-12479.1500029000
410300381345865.026500676-45484.0265006761
411318765350122.352042044-31357.3520420444
412286146494742.120906211-208596.120906211
413306844312969.829226928-6125.82922692774
414307705413602.459027415-105897.459027415
415312448370066.061477328-57618.0614773283
416299715354574.616354300-54859.6163543004
417373399222644.019890788150754.980109212
418299446319354.83405255-19908.8340525499
419325586342559.57283687-16973.5728368700
420291221333963.092060220-42742.0920602196
421261173342574.409181714-81401.4091817145
422255027346881.569985598-91854.5699855982
423-78375548993.581438619-627368.58143862
424-58143255339.264074864-313482.264074864
425227033289865.208131934-62832.208131934
426235098449086.781700205-213988.781700205
42721267263362.87692076-242095.87692076
428238675394238.228338067-155563.228338067
429197687309909.540957213-112222.540957213
430418341259322.092290943159018.907709057
431-29770676306.1741286821-374012.174128682


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
1013.45277667412963e-881.72638833706481e-88
1111.11676959663635e-905.58384798318173e-91
1211.01390300325992e-975.0695150162996e-98
1312.54006636256014e-1121.27003318128007e-112
1413.36870536101724e-1211.68435268050862e-121
1513.12564121841006e-1301.56282060920503e-130
1611.72390093494148e-1408.61950467470738e-141
1711.20810920716778e-1436.0405460358389e-144
1818.01966969877486e-1494.00983484938743e-149
1911.61547665131007e-1538.07738325655036e-154
2015.5506932013362e-1542.7753466006681e-154
2113.38830686431781e-1581.69415343215891e-158
2212.82341439896929e-1601.41170719948465e-160
2313.53219735231347e-1611.76609867615674e-161
2419.43720399314443e-1624.71860199657221e-162
2513.16263041018159e-1611.58131520509080e-161
2614.85383516028729e-1632.42691758014365e-163
2712.23787382902504e-1661.11893691451252e-166
2811.97820191448766e-1689.89100957243832e-169
2913.63681203860464e-1811.81840601930232e-181
3013.02136172505333e-1831.51068086252666e-183
3118.19157697508362e-1874.09578848754181e-187
3212.58305904539838e-1881.29152952269919e-188
3311.36691422436280e-1946.83457112181398e-195
3415.61698103429136e-1962.80849051714568e-196
3511.84349551284026e-1959.21747756420131e-196
3613.60582478392293e-1951.80291239196147e-195
3711.79998714218343e-2028.99993571091714e-203
3811.95383108319305e-2079.76915541596526e-208
3911.05053880460226e-2065.2526940230113e-207
4013.13216494816923e-2071.56608247408461e-207
4114.35018496996429e-2102.17509248498214e-210
4213.91748200203807e-2101.95874100101903e-210
4312.62267779890532e-2101.31133889945266e-210
4411.70696298484852e-2098.53481492424259e-210
4515.4235563808611e-2092.71177819043055e-209
4612.49863995908849e-2081.24931997954424e-208
4712.86217959910963e-2071.43108979955482e-207
4819.77396570289593e-2074.88698285144796e-207
4913.80439865475718e-2061.90219932737859e-206
5011.37884689440655e-2066.89423447203276e-207
5112.32178371126629e-2071.16089185563314e-207
5211.68773127333488e-2068.43865636667438e-207
5318.142704919587e-2064.0713524597935e-206
5419.6291261408195e-2054.81456307040975e-205
5513.80254488006277e-2081.90127244003139e-208
5613.46123275892145e-2081.73061637946073e-208
5719.47392684778139e-2134.73696342389069e-213
5812.05487182215494e-2131.02743591107747e-213
5911.19356877224322e-2125.9678438612161e-213
6013.91680643999874e-2131.95840321999937e-213
6112.39482218978063e-2141.19741109489031e-214
6215.18797417118221e-2142.59398708559110e-214
6313.54116620639588e-2131.77058310319794e-213
6418.0710327083224e-2134.0355163541612e-213
6513.52606262193312e-2121.76303131096656e-212
6618.15695541312826e-2124.07847770656413e-212
6716.22495627428319e-2133.11247813714159e-213
6817.09890434310308e-2123.54945217155154e-212
6914.35169419486908e-2112.17584709743454e-211
7012.33329244164356e-2101.16664622082178e-210
7111.27403037488617e-2096.37015187443084e-210
7212.40800952356535e-2111.20400476178268e-211
7313.46585461226455e-2111.73292730613227e-211
7412.85909849551207e-2101.42954924775604e-210
7511.54168118362382e-2097.70840591811911e-210
7617.02227075062832e-2093.51113537531416e-209
7711.47328416905501e-2087.36642084527504e-209
7812.26057114450078e-2081.13028557225039e-208
7912.47903739362521e-2071.23951869681260e-207
8014.31465041208794e-2082.15732520604397e-208
8113.48309873610519e-2071.74154936805259e-207
8213.73352657619503e-2071.86676328809751e-207
8311.51698171581418e-2077.58490857907088e-208
8411.31493666561352e-2066.57468332806759e-207
8511.37278885935989e-2056.86394429679947e-206
8612.62010904735844e-2061.31005452367922e-206
8714.35470948955653e-2062.17735474477827e-206
8813.43447057277142e-2051.71723528638571e-205
8917.19769706935405e-2053.59884853467703e-205
9017.19395968411012e-2043.59697984205506e-204
9117.39257190723737e-2033.69628595361868e-203
9214.18567606231072e-2022.09283803115536e-202
9312.78788776204345e-2021.39394388102172e-202
9411.77337341403807e-2018.86686707019033e-202
9514.27194481187138e-2022.13597240593569e-202
9611.46877344842384e-2027.34386724211919e-203
9711.44406880965959e-2017.22034404829793e-202
9811.30054919410396e-2006.50274597051978e-201
9919.33233669031698e-2004.66616834515849e-200
10019.65233109423221e-1994.82616554711611e-199
10118.3371351265711e-1984.16856756328555e-198
10212.55145168601330e-1981.27572584300665e-198
10316.91417132359893e-1983.45708566179946e-198
10411.49931747149343e-1977.49658735746713e-198
10518.7077250219844e-1974.3538625109922e-197
10611.30589767377524e-1966.52948836887622e-197
10711.53462497322668e-1977.67312486613339e-198
10812.00152202628319e-1991.00076101314160e-199
10916.76704064411882e-1993.38352032205941e-199
11012.62681801619814e-1981.31340900809907e-198
11113.19821867698021e-1981.59910933849011e-198
11215.19239296991033e-1992.59619648495516e-199
11311.43254689425637e-2017.16273447128184e-202
11419.31868475443436e-2014.65934237721718e-201
11512.5440888605146e-2001.2720444302573e-200
11617.44857472121722e-2003.72428736060861e-200
11714.01333104745497e-1992.00666552372748e-199
11816.25475829012006e-1993.12737914506003e-199
11911.08681723108176e-1995.43408615540881e-200
12011.15728299910938e-1985.78641499554688e-199
12111.73492278503241e-1988.67461392516206e-199
12211.57760651259459e-1977.88803256297297e-198
12311.22106907523559e-1976.10534537617796e-198
12415.78432789922724e-1982.89216394961362e-198
12515.78674830030057e-1972.89337415015029e-197
12617.31824065320553e-1973.65912032660276e-197
12717.76079767706241e-1963.88039883853121e-196
12818.217113022139e-1954.1085565110695e-195
12918.57538525600622e-1944.28769262800311e-194
13018.93780993821369e-1934.46890496910684e-193
13119.31357575450482e-1924.65678787725241e-192
13211.03175335676144e-1915.15876678380722e-192
13311.06454025579835e-1905.32270127899176e-191
13411.09833566715842e-1895.49167833579208e-190
13511.12056728918607e-1885.60283644593037e-189
13611.07208407031931e-1875.36042035159653e-188
13711.08930538149305e-1865.44652690746525e-187
13811.09297290504055e-1855.46486452520273e-186
13911.09058530736135e-1845.45292653680675e-185
14011.08220323739864e-1835.41101618699321e-184
14111.04780490165380e-1825.23902450826899e-183
14211.02838751465086e-1815.14193757325431e-182
14317.13963156976764e-1813.56981578488382e-181
14416.95146748128757e-1803.47573374064378e-180
14515.92115600629081e-1792.96057800314541e-179
14614.95004867947324e-1782.47502433973662e-178
14714.73398742842177e-1772.36699371421089e-177
14814.53482713442832e-1762.26741356721416e-176
14914.29069983088711e-1752.14534991544356e-175
15014.04737587133721e-1742.02368793566860e-174
15113.68769559874076e-1731.84384779937038e-173
15213.31991978225648e-1721.65995989112824e-172
15313.07552014815871e-1711.53776007407935e-171
15412.81099805579964e-1701.40549902789982e-170
15512.58918762523655e-1691.29459381261828e-169
15612.36164950803314e-1681.18082475401657e-168
15712.14285644697689e-1671.07142822348844e-167
15811.93576577549684e-1669.6788288774842e-167
15911.42272400042113e-1657.11362000210565e-166
16011.27138998164624e-1646.35694990823122e-165
16111.13028126117837e-1635.65140630589186e-164
16219.99653523300762e-1634.99826761650381e-163
16318.79575642775263e-1624.39787821387632e-162
16417.54017343922798e-1613.77008671961399e-161
16515.94588232674165e-1602.97294116337082e-160
16615.15564018200847e-1592.57782009100423e-159
16714.44765237046528e-1582.22382618523264e-158
16811.55044440570315e-1597.75222202851573e-160
16911.35011272293214e-1586.7505636146607e-159
17011.16971308166998e-1575.84856540834992e-158
17111.00829705496825e-1565.04148527484123e-157
17218.74887762849358e-1564.37443881424679e-156
17317.46629793697583e-1553.73314896848791e-155
17416.35534820687582e-1543.17767410343791e-154
17515.36941056805024e-1532.68470528402512e-153
17614.55231557285529e-1522.27615778642764e-152
17713.8078471795827e-1511.90392358979135e-151
17813.12186762551612e-1501.56093381275806e-150
17912.58589384367684e-1491.29294692183842e-149
18012.13128643201439e-1481.06564321600719e-148
18111.19389077181494e-1475.96945385907468e-148
18219.28243922668372e-1474.64121961334186e-147
18315.85034477814982e-1462.92517238907491e-146
18412.93764243416946e-1451.46882121708473e-145
18512.34973306989906e-1441.17486653494953e-144
18611.81914669539672e-1439.09573347698361e-144
18711.02835049273892e-1425.1417524636946e-143
18818.22312689313718e-1424.11156344656859e-142
18915.17623890775896e-1412.58811945387948e-141
19014.09460299798186e-1402.04730149899093e-140
19112.86133648286275e-1391.43066824143137e-139
19212.24169942065022e-1381.12084971032511e-138
19311.71477266090002e-1378.5738633045001e-138
19411.33033019242680e-1366.65165096213399e-137
19511.02979425099125e-1355.14897125495626e-136
19617.86625418013291e-1353.93312709006645e-135
19714.82394781009247e-1342.41197390504624e-134
19813.65606874198137e-1331.82803437099069e-133
19912.760554579139e-1321.3802772895695e-132
20011.08473964925735e-1315.42369824628673e-132
20118.152679153685e-1314.0763395768425e-131
20216.10355084770988e-1303.05177542385494e-130
20314.55138916753169e-1292.27569458376584e-129
20413.37781895033894e-1281.68890947516947e-128
20511.36185904061636e-1276.8092952030818e-128
20611.00651567910875e-1265.03257839554377e-127
20717.2527881687471e-1263.62639408437355e-126
20815.31440011675515e-1252.65720005837758e-125
20917.06946776071252e-1253.53473388035626e-125
21015.17014438287797e-1242.58507219143899e-124
21113.7616639600893e-1231.88083198004465e-123
21212.72371038139153e-1221.36185519069577e-122
21311.92968774206204e-1219.6484387103102e-122
21411.35620312944486e-1206.78101564722432e-121
21519.69837140532942e-1204.84918570266471e-120
21616.8920184078478e-1193.4460092039239e-119
21714.90819954073315e-1182.45409977036657e-118
21813.45336267725205e-1171.72668133862602e-117
21912.4189522308754e-1161.2094761154377e-116
22019.56828101158086e-1164.78414050579043e-116
22116.24001973230026e-1153.12000986615013e-115
22214.32052519218502e-1142.16026259609251e-114
22312.98302177549752e-1131.49151088774876e-113
22412.04553939212164e-1121.02276969606082e-112
22511.39790387519517e-1116.98951937597587e-112
22619.58905026466825e-1114.79452513233413e-111
22716.48101979399692e-1103.24050989699846e-110
22814.35917831466066e-1092.17958915733033e-109
22912.7963397157576e-1081.3981698578788e-108
23011.86421904739282e-1079.32109523696412e-108
23111.23520065311839e-1066.17600326559197e-107
23218.15834499509773e-1064.07917249754886e-106
23315.36231922266724e-1052.68115961133362e-105
23413.42683112840561e-1041.71341556420280e-104
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3770.9999999999997265.48410162908328e-132.74205081454164e-13
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3890.9999999852973732.94052548940487e-081.47026274470244e-08
3900.9999999645643597.08712821871938e-083.54356410935969e-08
3910.9999999177001411.64599717659873e-078.22998588299365e-08
3920.9999998076436153.84712769202265e-071.92356384601132e-07
3930.9999995517385568.9652288760724e-074.4826144380362e-07
3940.9999991270976551.74580469007006e-068.72902345035029e-07
3950.9999981502138583.69957228409926e-061.84978614204963e-06
3960.9999959001651488.19966970401097e-064.09983485200549e-06
3970.999992408339351.51833212993339e-057.59166064966695e-06
3980.9999835207309543.29585380928391e-051.64792690464195e-05
3990.9999650934797186.9813040563984e-053.4906520281992e-05
4000.9999276564706370.0001446870587268757.23435293634375e-05
4010.9998548680150540.0002902639698921960.000145131984946098
4020.9997280408947660.0005439182104675680.000271959105233784
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4060.996544329584510.00691134083097850.00345567041548925
4070.9938935542992080.01221289140158490.00610644570079244
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4180.8403354515015680.3193290969968650.159664548498433
4190.7553978073281710.4892043853436570.244602192671829
4200.6242974071511650.751405185697670.375702592848835
4210.4614339246511770.9228678493023550.538566075348823


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3970.963592233009709NOK
5% type I error level4000.970873786407767NOK
10% type I error level4020.975728155339806NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/10zem71291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/10zem71291330748.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/1avpv1291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/1avpv1291330748.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/2kmog1291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/2kmog1291330748.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/3kmog1291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/3kmog1291330748.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/4kmog1291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/4kmog1291330748.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/5vdnj1291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/5vdnj1291330748.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/6vdnj1291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/6vdnj1291330748.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/76n4m1291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/76n4m1291330748.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/86n4m1291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/86n4m1291330748.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/9zem71291330748.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291330659j6893h9x8vxcdyl/9zem71291330748.ps (open in new window)


 
Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 7 ; 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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