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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: Sat, 25 Dec 2010 11:28:24 +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/25/t1293276471q6whi38qfgmipdb.htm/, Retrieved Sat, 25 Dec 2010 12:27:54 +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/25/t1293276471q6whi38qfgmipdb.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 1595 17 60720 319369 0 5565 47 94355 493408 0 601 6 60720 319210 1 188 11 77655 381180 1 7146 105 134028 297978 0 1135 17 62285 290476 1 450 8 59325 292136 1 34 8 60630 314353 1 133 14 65990 339445 1 119 6 59118 303677 0 2053 53 100423 397144 0 4036 63 100269 424898 1 0 0 60720 315380 1 4655 393 60720 341570 1 131 11 61808 308989 1 1766 73 60438 305959 1 312 14 58598 318690 1 448 40 59781 323361 1 115 13 60945 318903 1 60 10 61124 314049 1 0 0 60720 315380 1 364 35 47705 298466 1 0 0 60720 315380 0 1442 118 121173 438493 0 1389 9 57530 378049 1 149 5 62041 313332 1 2212 14 60720 319864 0 7489 322 136996 430866 1 0 0 60720 315380 1 402 26 84990 332743 0 7419 29 63255 286963 1 0 0 60720 315380 1 307 15 58856 331955 0 1134 162 146216 527021 0 7561 87 82425 364304 1 5131 71 86111 292154 1 9 6 60835 314987 1 2264 24 55174 272713 1 40949 481 129352 1398893 0 4980 91 113521 409642 1 272 8 112995 429112 1 757 19 45689 382712 1 1424 94 131116 374943 1 0 0 60720 315380 0 37 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 time15 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Multiple Linear Regression - Estimated Regression Equation
wealth[t] = + 205978.970830014 -55485.8185572918group[t] + 29.8111343947846costs[t] -407.150166147615trades[t] + 1.92615430632799dividends[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)205978.97083001438537.0686855.34500
group-55485.818557291827840.298166-1.9930.0468990.023449
costs29.81113439478461.57819718.889400
trades-407.150166147615166.31643-2.4480.0147660.007383
dividends1.926154306327990.4105394.69184e-062e-06


Multiple Linear Regression - Regression Statistics
Multiple R0.810480761025606
R-squared0.656879063992645
Adjusted R-squared0.653657271166284
F-TEST (value)203.886189893458
F-TEST (DF numerator)4
F-TEST (DF denominator)426
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation253359.467439276
Sum Squared Residuals27345374409714.4


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1319369308076.44828812811292.5517118717
2493408534484.16550163-41076.1655016293
3319210338408.651084628-19198.6510846283
4381180301194.50636921779985.4936307828
5297978578931.36058088-280953.36058088
6290476352863.576513223-62387.5765132232
7292136274920.06564410217215.9343558984
8314353265032.26510562949320.7348943708
9339445275864.85349574563580.1465042548
10303677265468.16655031338208.8334496868
11397144439032.464841058-41888.4648410581
12424898493779.814921265-68881.8149212653
13315380267449.24175295747930.758247043
14341570246210.05706466795359.9429353334
15308989268971.50441633540017.4955836651
16305959289830.56745098616128.4325490138
17318690266962.91392003551727.0860799648
18323361262709.96442227460651.0355777261
19318903266017.95476736252885.045232638
20314049265944.57449492448104.4255050756
21315380267449.24175295747930.758247043
22298466238981.34056063359484.6594393667
23315380267449.24175295747930.758247043
24438493434320.8027825554172.19721744464
25378049354533.9422520923515.0577479102
26313332272399.79978570140932.2002142989
27319864327691.368708154-7827.36870815397
28430866562007.638162732-131141.638162732
29315380267449.24175295747930.758247043
30332743315595.17847440317147.8215255974
31286963537179.312733417-250216.312733417
32315380267449.24175295747930.758247043
33331955266903.65589294665051.3441070537
34527021455461.04837183871559.9516281616
35364304554722.162233222-190418.162233222
36292154440409.49452809-148255.49452809
37314987265496.14871085249490.8512891479
38272713314487.594252312-41774.5942523116
3913988931424541.97651989-25648.9765198922
40409642536046.718005267-126404.718005267
41429112372990.38534245356121.614657547
42382712253328.391954588129383.608045412
43374943407221.740061519-32278.7400615194
44315380267449.24175295747930.758247043
45297413334514.259450277-37101.2594502774
46304252315717.170514935-11465.1705149352
47315380267449.24175295747930.758247043
48309596272182.60648487737413.3935151228
49377305501840.590608422-124535.590608422
50386212361975.83043440724236.1695655935
516579541060311.71423108-402357.714231081
52368186316787.81444393151398.1855560687
53269753344164.743279753-74411.7432797535
54585715719128.770909712-133413.770909712
551071292573291.791072313498000.208927687
569924261298785.37920705-306359.379207052
57315371274774.18712218140596.8128778187
58249898389437.337763275-139539.337763275
59357312470083.362326703-112771.362326703
60315380267449.24175295747930.758247043
61315380267449.24175295747930.758247043
62364839614647.092386155-249808.092386155
63230621253621.886212149-23000.8862121487
64315877268029.58914300947847.4108569906
65125390391950.840151219-266560.840151219
66314882267628.91457106947253.085428931
67370837669020.103075744-298183.103075744
68330068355728.802191811-25660.8021918112
69324385358377.012000687-33992.012000687
70315380267449.24175295747930.758247043
711443586738880.661615035704705.338384965
72253537559911.088614322-306374.088614322
7343210231125739.122276563195283.87772344
74315380267449.24175295747930.758247043
75352108387692.633434153-35584.6334341527
76330059256447.45405615173611.5459438494
77340968323162.82107488217805.1789251178
78317736267741.52203688549994.477963115
79209458491605.419860362-282147.419860362
8014913481957006.10662625-465658.106626249
81314887266783.99709263648103.0029073643
82315380267449.24175295747930.758247043
83315380267449.24175295747930.758247043
84353058391018.116379704-37960.1163797035
85315380267449.24175295747930.758247043
86315380322935.060310249-7555.06031024882
87314533267542.04648739546990.9535126046
88315354266681.99942910748672.0005708931
89302187265113.1963870437073.8036129598
90336639301224.09893921135414.9010607886
91315380322935.060310249-7555.06031024882
92296702255267.94967179841434.0503282019
9310730891346651.96988464-273562.969884645
94146494296770.043997885-150276.043997885
95325249274713.75884164850535.2411583516
96331420286642.50302721744777.4969727827
97315380267449.24175295747930.758247043
98314922266468.32234039748453.6776596025
99320016266977.57592641853038.4240735821
100280398214834.398883965563.6011160997
101452469299839.702417873152629.297582127
102301164271562.69258393629601.3074160637
103317330267073.89310391850256.1068960822
104315576267394.88722459548181.1127754047
105315380267449.24175295747930.758247043
106-7170668535.948376946-675705.948376946
107322331302611.400108419719.5998916
10816296161716249.87181328-86633.8718132815
109292754237307.29481852655446.7051814739
110318056263264.99607229954791.0039277006
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112204325319592.52643729-115267.52643729
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114317046293479.58767535623566.4123246442
115315380322935.060310249-7555.06031024882
116309560263708.79407053745851.2059294627
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118857217703314.660160956153902.339839044
119697458548720.07841548148737.92158452
120530670464415.66116180866254.3388381922
121238125339008.025721699-100883.025721699
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123741409551048.428427393190360.571572607
124393343373019.39368095520323.6063190449
125372631717213.470440414-344582.470440414
126315380322935.060310249-7555.06031024882
127317291283307.33270968433983.6672903164
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1293062751424632.21389226-1118357.21389226
130317892383764.062334018-65872.0623340183
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132315380322935.060310249-7555.06031024882
133315380322935.060310249-7555.06031024882
134315380322935.060310249-7555.06031024882
135314210267753.97124430146456.028755699
136306948318213.355774291-11265.3557742906
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138320398270654.87942070249743.1205792983
139315380322935.060310249-7555.06031024882
140315380267449.24175295747930.758247043
141501749374067.059490696127681.940509304
142202055325780.962617515-123725.962617515
143315380267449.24175295747930.758247043
144315380267449.24175295747930.758247043
145333210335580.30772084-2370.30772084
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147322340280518.9206799741821.0793200297
148369448278453.88633366790994.1136663335
149291841327712.189748197-35871.1897481967
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152296919279459.62745030817459.3725496924
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154309038287662.79342687421375.2065731265
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156289513414310.099402643-124797.099402643
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164322031327662.489201945-5631.48920194521
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175343613320276.47343741223336.5265625883
176365959311706.71076531754252.2892346829
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178314551263567.61862497250983.3813750278
179303230273358.85144564129871.1485543589
180315380322935.060310249-7555.06031024882
181469107497233.641012669-28126.6410126695
182354228361134.064205696-6906.0642056962
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185315380322935.060310249-7555.06031024882
186315394263873.40484101351520.595158987
187312412268519.5161611243892.4838388804
188333505293679.13216319339825.8678368074
1892231932387131.26587851-2163938.26587851
190315380267449.24175295747930.758247043
191315656265989.0468181149666.9531818897
192296261285622.36641740310638.6335825973
193315380267449.24175295747930.758247043
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196315380267449.24175295747930.758247043
197308636392358.34948586-83722.3494858601
198158492381801.564034433-223309.564034433
199315380322935.060310249-7555.06031024882
200315380267449.24175295747930.758247043
201711969991170.027708434-279201.027708434
202315380322935.060310249-7555.06031024882
203315380322935.060310249-7555.06031024882
204306268271066.84215334835201.1578466516
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206442882379328.13869114163553.8613088592
207378509436882.81980569-58373.8198056902
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211856956869253.139487547-12297.1394875466
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214307930273864.8377528734065.1622471299
215702380825098.00537455-122718.00537455
216194493352309.795213916-157816.795213916
217316155324221.751752624-8066.7517526244
218315380267449.24175295747930.758247043
219330546302701.39580233427844.6041976655
220394510424103.036977832-29593.0369778321
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222315380267449.24175295747930.758247043
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224295580274736.49515168920843.5048483111
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226377934837809.778699169-459875.778699169
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230307424280126.46162744727297.5383725534
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255315380322935.060310249-7555.06031024882
256315380267449.24175295747930.758247043
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258315380267449.24175295747930.758247043
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327352850420265.43630455-67415.4363045503
32862821545022326.557359061259827.44264094
3292271321411265.9167265-1184133.9167265
330283910464261.327233526-180351.327233526
331236761299697.024971016-62936.0249710157
332315380267449.24175295747930.758247043
333315380322935.060310249-7555.06031024882
334550608613711.461106986-63103.4611069861
335307528376742.164690814-69214.1646908139
336315380267449.24175295747930.758247043
337355864333501.7722754922362.2277245104
338358589737169.85076402-378580.85076402
339315380267449.24175295747930.758247043
340315380322935.060310249-7555.06031024882
341375195640879.483126301-265684.483126301
342288985275857.89209618713127.1079038131
343315380267449.24175295747930.758247043
344458343397908.92944760960434.070552391
345315380267449.24175295747930.758247043
346269587366085.844391423-96498.8443914232
347315236271039.57140722344196.4285927768
34842754356212.634240959-313458.634240959
349315380267449.24175295747930.758247043
350308256266418.74823703341837.2517629673
351315380267449.24175295747930.758247043
352315380267449.24175295747930.758247043
353313164322351.033713395-9187.03371339546
354315380267449.24175295747930.758247043
355269661301205.333024973-31544.3330249729
356315380322935.060310249-7555.06031024882
357315380267449.24175295747930.758247043
358315380322935.060310249-7555.06031024882
359315380267449.24175295747930.758247043
360518365528189.28181268-9824.28181268044
361233773582160.68219501-348387.68219501
362301881243754.57706766658126.4229323343
363298568283565.91332819515002.0866718052
364325479382170.36549532-56691.3654953199
365325506270013.42375651455492.5762434858
366984885576175.936777828408709.063222172
367313267217486.87025643395780.1297435667
368315793265871.72763134749921.2723686528
369315380267449.24175295747930.758247043
370215362447916.022724265-232554.022724265
371314073379456.902735639-65383.9027356385
372298096271405.09645506226690.9035449383
373325176266681.84822427558494.1517757246
374207393366472.702295895-159079.702295895
375314806325119.458722703-10313.4587227026
376341340336866.557974494473.44202550992
377426280488475.597716998-62195.5977169978
378929118923671.0055028435446.99449715738
379307322278403.09479439728918.9052056031
380315380267449.24175295747930.758247043
381387475396426.67762197-8951.67762196996
382291787281829.7326847389957.26731526164
383247060399966.495433953-152906.495433953
384329784280113.19229076149670.807709239
385315834267636.14294202748197.8570579725
386304555289005.98563344815549.0143665519
387376641395589.547755075-18948.5477550752
388315380322935.060310249-7555.06031024882
389315380267449.24175295747930.758247043
390315380322935.060310249-7555.06031024882
391315380267449.24175295747930.758247043
392315380267449.24175295747930.758247043
393357760334959.57855447522800.4214455249
394315637263170.11646494352466.8835350569
395315380267449.24175295747930.758247043
396315380322935.060310249-7555.06031024882
397315380267449.24175295747930.758247043
398315380267449.24175295747930.758247043
399327071266871.80195138760199.1980486132
400377516357573.02397696919942.9760230309
401299243427212.710349911-127969.710349911
402387699611287.737854147-223588.737854147
403309836258311.22015800551524.7798419953
404444477766506.865955673-322029.865955673
405322327279045.80751580343281.192484197
406314913292154.07847434122758.9215256593
407315553265397.9155619250155.0844380799
408688779462898.013447763225880.986552237
409321896491108.145617249-169212.145617249
410301607306942.757425913-5335.75742591317
411304485284178.02953876220306.9704612382
412315380267449.24175295747930.758247043
413315380322935.060310249-7555.06031024882
414231861305706.863701725-73845.863701725
415347385298507.3553261848877.6446738203
416316386266246.71669402950139.2833059714
417491303740129.35808813-248826.358088129
418261216302213.770807753-40997.7708077529
419315388266678.23563520148709.7643647992
420315380267449.24175295747930.758247043
421313729267618.5412056646110.4587943396
422358649260767.42032139797881.5796786025
42319265171861624.4678260664892.5321739428
424296656274001.75538973622654.2446102644
425275311311068.823557995-35757.8235579948
426-42143441991.90593554-484134.90593554
427315380322935.060310249-7555.06031024882
428343929292636.11555357151292.8844464295
429367655299018.94030220468636.059697796
430315380267449.24175295747930.758247043
431313491237864.34195515675626.6580448439


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.0008484368368794740.001696873673758950.99915156316312
90.0005665971533834210.001133194306766840.999433402846617
107.19059485972101e-050.000143811897194420.999928094051403
119.71094973882406e-050.0001942189947764810.999902890502612
124.83229066660806e-059.66458133321613e-050.999951677093334
139.86161906896343e-061.97232381379269e-050.999990138380931
141.61192363004452e-053.22384726008905e-050.9999838807637
153.24091581203812e-066.48183162407623e-060.999996759084188
166.40156281713652e-071.2803125634273e-060.999999359843718
171.19736422262545e-072.3947284452509e-070.999999880263578
182.18109475806584e-084.36218951613169e-080.999999978189052
193.78040484926776e-097.56080969853552e-090.999999996219595
206.27373730670644e-101.25474746134129e-090.999999999372626
211.0104338168202e-102.0208676336404e-100.999999999898957
221.61554575206847e-113.23109150413693e-110.999999999983845
232.45936485662104e-124.91872971324208e-120.99999999999754
244.223567600603e-138.447135201206e-130.999999999999578
256.4067090529523e-141.28134181059046e-130.999999999999936
269.07788918111864e-151.81557783622373e-140.99999999999999
271.27927229478868e-152.55854458957736e-150.999999999999999
281.85580022045519e-163.71160044091037e-161
292.46967091559757e-174.93934183119515e-171
303.20976464619557e-186.41952929239113e-181
311.58347036932239e-183.16694073864479e-181
322.08664626230309e-194.17329252460617e-191
333.12925452517384e-206.25850905034768e-201
347.75947403333951e-211.5518948066679e-201
351.14631876584488e-212.29263753168976e-211
361.80603791228416e-223.61207582456831e-221
372.26887740536606e-234.53775481073213e-231
383.25816283594591e-246.51632567189182e-241
391.98163447738569e-133.96326895477137e-130.999999999999802
406.03416967619285e-141.20683393523857e-130.99999999999994
412.678090532856e-145.356181065712e-140.999999999999973
421.36286635336872e-142.72573270673744e-140.999999999999986
433.85428001321749e-157.70856002643498e-150.999999999999996
441.05744941735673e-152.11489883471346e-150.999999999999999
452.79964388684396e-165.59928777368792e-161
467.88350530093697e-171.57670106018739e-161
472.08975471548037e-174.17950943096075e-171
485.34367833352559e-181.06873566670512e-171
491.46554702492736e-182.93109404985473e-181
504.08340792589432e-198.16681585178864e-191
515.76524653105003e-181.15304930621001e-171
521.62212663736957e-183.24425327473915e-181
534.38399377843208e-198.76798755686417e-191
541.2506045698015e-192.501209139603e-191
555.1630397558375e-141.0326079511675e-130.999999999999948
562.80259399625188e-145.60518799250376e-140.999999999999972
579.90798375666088e-151.98159675133218e-140.99999999999999
584.01906394671579e-158.03812789343158e-150.999999999999996
591.41400917531583e-152.82801835063166e-150.999999999999999
604.89397143081615e-169.7879428616323e-161
611.67664135148066e-163.35328270296132e-161
621.89740118570137e-163.79480237140274e-161
637.15746545578129e-171.43149309115626e-161
642.43919154330042e-174.87838308660085e-171
653.5259399739216e-177.0518799478432e-171
661.21174369282236e-172.42348738564471e-171
677.19868305259141e-171.43973661051828e-161
683.29906257866378e-176.59812515732756e-171
691.32071096549959e-172.64142193099918e-171
704.6370366210557e-189.2740732421114e-181
714.36688249379747e-098.73376498759494e-090.999999995633117
721.00179950635031e-082.00359901270062e-080.999999989982005
7319.92101793266569e-164.96050896633284e-16
7411.89511035711238e-159.4755517855619e-16
750.9999999999999983.64215791862264e-151.82107895931132e-15
760.9999999999999976.75727237715731e-153.37863618857866e-15
770.9999999999999941.27137494074341e-146.35687470371707e-15
780.9999999999999882.36285096334896e-141.18142548167448e-14
790.9999999999999959.67114946618694e-154.83557473309347e-15
8018.34599291236696e-164.17299645618348e-16
8111.57199575844603e-157.85997879223014e-16
820.9999999999999992.93733616644301e-151.46866808322151e-15
830.9999999999999975.44840866774528e-152.72420433387264e-15
840.9999999999999951.00823724433511e-145.04118622167557e-15
850.999999999999991.84253843620741e-149.21269218103704e-15
860.9999999999999833.39670088936005e-141.69835044468002e-14
870.999999999999976.13303539066389e-143.06651769533195e-14
880.9999999999999451.09681986248576e-135.48409931242882e-14
890.9999999999999021.96314988719178e-139.8157494359589e-14
900.9999999999998273.4629709444824e-131.7314854722412e-13
910.9999999999996926.15351311198352e-133.07675655599176e-13
920.9999999999994611.07747491439326e-125.3873745719663e-13
930.9999999999993431.31309936625187e-126.56549683125933e-13
940.9999999999990821.83623022739671e-129.18115113698356e-13
950.9999999999984323.13670148327097e-121.56835074163549e-12
960.9999999999973385.32400397078137e-122.66200198539068e-12
970.9999999999955058.9899086865524e-124.4949543432762e-12
980.999999999992431.51401924744469e-117.57009623722347e-12
990.9999999999873632.527376094833e-111.2636880474165e-11
1000.999999999979564.0882542952108e-112.0441271476054e-11
1010.999999999969286.14394613813459e-113.07197306906729e-11
1020.9999999999492461.01507905163508e-105.0753952581754e-11
1030.9999999999174061.65187775166968e-108.25938875834838e-11
1040.9999999998665412.66917601189186e-101.33458800594593e-10
1050.9999999997857284.2854349448664e-102.1427174724332e-10
1060.9999999999941171.1766139712741e-115.88306985637049e-12
1070.9999999999901531.96945101226182e-119.8472550613091e-12
1080.9999999999844033.11934909786639e-111.55967454893319e-11
1090.9999999999745655.08709622616945e-112.54354811308473e-11
1100.9999999999587638.24745055969353e-114.12372527984676e-11
1110.9999999999332751.33449526734031e-106.67247633670154e-11
1120.9999999998996652.00670400501745e-101.00335200250872e-10
1130.9999999998402163.19567710915386e-101.59783855457693e-10
1140.999999999743815.12379721500535e-102.56189860750268e-10
1150.9999999995901768.19648711042383e-104.09824355521192e-10
1160.9999999993562751.2874504062012e-096.437252031006e-10
1170.999999998994612.01078139592713e-091.00539069796357e-09
1180.9999999986187292.76254258848752e-091.38127129424376e-09
1190.9999999981593643.68127192555793e-091.84063596277897e-09
1200.9999999971981015.60379796165993e-092.80189898082997e-09
1210.9999999959626948.07461247418552e-094.03730623709276e-09
1220.9999999938708621.22582753314546e-086.12913766572731e-09
1230.99999999256761.4864799213625e-087.43239960681249e-09
1240.9999999886822782.26354448414351e-081.13177224207176e-08
1250.9999999917378561.65242871957177e-088.26214359785884e-09
1260.999999987417952.51641011087643e-081.25820505543821e-08
1270.9999999810261313.79477372560387e-081.89738686280194e-08
1280.9999999718354555.63290899328928e-082.81645449664464e-08
1290.9999999999800883.98238285817907e-111.99119142908954e-11
1300.9999999999687856.24295273042292e-113.12147636521146e-11
1310.999999999950129.97600364121995e-114.98800182060998e-11
1320.999999999920411.59181256982507e-107.95906284912535e-11
1330.9999999998736452.52710111331979e-101.2635505566599e-10
1340.9999999998004233.99153620330902e-101.99576810165451e-10
1350.9999999996897666.20467640705276e-103.10233820352638e-10
1360.999999999520299.59421640595444e-104.79710820297722e-10
1370.9999999992631151.47377044547975e-097.36885222739874e-10
1380.9999999988698952.26020939048921e-091.13010469524461e-09
1390.9999999982602813.47943726051218e-091.73971863025609e-09
1400.9999999973679175.2641655130767e-092.63208275653835e-09
1410.9999999962591237.48175453408837e-093.74087726704419e-09
1420.9999999953092169.3815673789713e-094.69078368948565e-09
1430.9999999929907781.40184433966103e-087.00922169830515e-09
1440.999999989576822.08463585766502e-081.04231792883251e-08
1450.9999999843956953.1208610474323e-081.56043052371615e-08
1460.9999999767250684.6549864637567e-082.32749323187835e-08
1470.999999965743616.85127800110653e-083.42563900055326e-08
1480.9999999513176069.73647877738372e-084.86823938869186e-08
1490.9999999322456941.35508612548503e-076.77543062742514e-08
1500.9999999020805881.95838823251241e-079.79194116256206e-08
1510.999999857379812.85240378786831e-071.42620189393416e-07
1520.999999793324444.13351120345347e-072.06675560172673e-07
1530.9999997056230645.88753872527156e-072.94376936263578e-07
1540.9999995780150768.43969847936938e-074.21984923968469e-07
1550.999999403108611.19378278010412e-065.96891390052061e-07
1560.999999211784141.576431722078e-067.88215861039001e-07
1570.9999989184152842.16316943201905e-061.08158471600953e-06
1580.9999984939284713.01214305761464e-061.50607152880732e-06
1590.9999979054963664.18900726830591e-062.09450363415295e-06
1600.9999971149356465.77012870712307e-062.88506435356153e-06
1610.9999960236023717.95279525797471e-063.97639762898735e-06
1620.9999945637913761.08724172490588e-055.43620862452938e-06
1630.9999925950053671.48099892660674e-057.40499463303371e-06
1640.9999898600519292.02798961427059e-051.01399480713529e-05
1650.9999863273928022.73452143962038e-051.36726071981019e-05
1660.999982201523933.55969521405484e-051.77984760702742e-05
1670.999976204161224.75916775598633e-052.37958387799317e-05
1680.9999683430546426.33138907158033e-053.16569453579016e-05
1690.999957834732228.43305355590716e-054.21652677795358e-05
1700.9999445148511110.000110970297777565.54851488887801e-05
1710.9999285106328080.0001429787343833637.14893671916814e-05
1720.9999079646438970.0001840707122068019.20353561034005e-05
1730.9998970416949660.0002059166100688410.00010295830503442
1740.999870788563230.0002584228735408940.000129211436770447
1750.9998323106293740.0003353787412525370.000167689370626268
1760.9997846575063250.000430684987349660.00021534249367483
1770.9997235446474340.0005529107051314840.000276455352565742
1780.9996466667742180.0007066664515643870.000353333225782193
1790.999547457177510.0009050856449790620.000452542822489531
1800.999421567572370.001156864855261680.00057843242763084
1810.999268510351930.001462979296139920.000731489648069958
1820.9990777648312890.0018444703374220.000922235168711
1830.9988463014488130.002307397102374530.00115369855118727
1840.9985628253137180.00287434937256350.00143717468628175
1850.9982032765331120.003593446933776390.0017967234668882
1860.9977822523044210.004435495391158110.00221774769557906
1870.997268170954960.005463658090081720.00273182904504086
1880.996645645633450.006708708733100930.00335435436655046
18916.48687629450577e-173.24343814725288e-17
19011.14545217500337e-165.72726087501684e-17
19112.01037475663961e-161.00518737831981e-16
19213.5903927541747e-161.79519637708735e-16
19316.25420435837984e-163.12710217918992e-16
19411.07145391693455e-155.35726958467277e-16
19511.82706681930281e-159.13533409651407e-16
1960.9999999999999983.14034079219459e-151.57017039609729e-15
1970.9999999999999975.08691927878809e-152.54345963939404e-15
1980.9999999999999975.01742905441279e-152.50871452720639e-15
1990.9999999999999968.60837370992085e-154.30418685496043e-15
2000.9999999999999931.46125838384435e-147.30629191922173e-15
2010.9999999999999967.33542769216415e-153.66771384608207e-15
2020.9999999999999941.25869596053887e-146.29347980269437e-15
2030.999999999999992.15034554079029e-141.07517277039514e-14
2040.9999999999999823.66385122038731e-141.83192561019365e-14
2050.999999999999976.1473058343971e-143.07365291719855e-14
2060.999999999999959.94536390810581e-144.97268195405291e-14
2070.9999999999999181.639912674957e-138.19956337478498e-14
2080.9999999999998642.71620016854432e-131.35810008427216e-13
2090.9999999999997844.31236792667872e-132.15618396333936e-13
2100.999999999999647.21503870761202e-133.60751935380601e-13
2110.9999999999994071.18636795558576e-125.9318397779288e-13
2120.999999999999391.22094479511006e-126.10472397555029e-13
2130.9999999999990031.99333594050066e-129.9666797025033e-13
2140.9999999999983663.2686397171601e-121.63431985858005e-12
2150.99999999999764.79855536651767e-122.39927768325883e-12
2160.9999999999969926.01628599706465e-123.00814299853232e-12
2170.999999999995129.75787201108686e-124.87893600554343e-12
2180.99999999999221.56017500192268e-117.80087500961342e-12
2190.9999999999885962.28085646953464e-111.14042823476732e-11
2200.999999999981763.64813688204488e-111.82406844102244e-11
2210.999999999971125.77592297252015e-112.88796148626007e-11
2220.9999999999545849.08311374912188e-114.54155687456094e-11
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2250.999999999822183.55639968714474e-101.77819984357237e-10
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2310.999999999813143.73721491474082e-101.86860745737041e-10
2320.999999999709165.81680884952297e-102.90840442476148e-10
2330.9999999996321997.35602928337201e-103.678014641686e-10
2340.9999999994339931.13201476824371e-095.66007384121855e-10
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2360.9999999986527462.69450813936102e-091.34725406968051e-09
2370.9999999979592444.08151272511315e-092.04075636255657e-09
2380.9999999969150726.16985497911149e-093.08492748955574e-09
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2400.9999999930526081.38947849026709e-086.94739245133546e-09
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2420.9999999842556663.14886672897413e-081.57443336448707e-08
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2490.999999825870223.48259561998634e-071.74129780999317e-07
2500.9999997490936945.01812611845422e-072.50906305922711e-07
2510.999999638376557.23246900217512e-073.61623450108756e-07
2520.9999994824158931.03516821392601e-065.17584106963007e-07
2530.9999992607078691.47858426209201e-067.39292131046004e-07
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2550.999998521506662.95698668010862e-061.47849334005431e-06
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2570.9999971142218095.77155638273082e-062.88577819136541e-06
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2590.999994593132271.08137354602647e-055.40686773013234e-06
2600.999997692014734.61597053892634e-062.30798526946317e-06
2610.999998184134873.63173026072072e-061.81586513036036e-06
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2680.9999862227294422.75545411165744e-051.37772705582872e-05
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2940.9990933853013240.001813229397351640.000906614698675818
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2960.9985248809359980.002950238128004660.00147511906400233
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3190.9999799465577094.01068845819756e-052.00534422909878e-05
3200.9999717933891155.64132217709077e-052.82066108854538e-05
3210.9999607077755137.85844489734656e-053.92922244867328e-05
3220.9999453104973710.0001093790052577265.4689502628863e-05
3230.9999227322509230.000154535498154817.72677490774049e-05
3240.9998938420124380.0002123159751233860.000106157987561693
3250.9998521835628250.0002956328743498090.000147816437174904
3260.9997992175461210.0004015649077574650.000200782453878733
3270.9997250109904040.0005499780191927650.000274989009596383
3280.9999999999995189.63125838970796e-134.81562919485398e-13
32911.29959557097821e-206.49797785489106e-21
33011.72686998990148e-208.6343499495074e-21
33113.82988432163314e-201.91494216081657e-20
33219.28578743188293e-204.64289371594146e-20
33312.31286264099169e-191.15643132049585e-19
33412.987355737399e-191.4936778686995e-19
33517.11720229154113e-193.55860114577056e-19
33611.69786426413921e-188.48932132069604e-19
33713.74949699729829e-181.87474849864915e-18
33812.04111461625039e-191.0205573081252e-19
33915.03486532327627e-192.51743266163814e-19
34011.26914041215269e-186.34570206076345e-19
34114.37877929185276e-192.18938964592638e-19
34211.12328122570389e-185.61640612851947e-19
34312.78390144822993e-181.39195072411496e-18
34415.79560313690629e-182.89780156845314e-18
34511.41701506680602e-177.08507533403008e-18
34613.29725101721116e-171.64862550860558e-17
34717.99933572915597e-173.99966786457798e-17
34811.73658776149433e-188.68293880747164e-19
34914.47335707492907e-182.23667853746454e-18
35011.15745585744217e-175.78727928721085e-18
35112.9355811209094e-171.4677905604547e-17
35217.38648764101111e-173.69324382050556e-17
35311.87956774710529e-169.39783873552643e-17
35414.6557148939258e-162.3278574469629e-16
35511.11830535451134e-155.59152677255668e-16
3560.9999999999999992.77430958866178e-151.38715479433089e-15
3570.9999999999999976.72243461322915e-153.36121730661457e-15
3580.9999999999999921.63904614908012e-148.1952307454006e-15
3590.999999999999983.9064787957679e-141.95323939788395e-14
3600.9999999999999755.03668038837024e-142.51834019418512e-14
3610.9999999999999784.45842708495486e-142.22921354247743e-14
3620.9999999999999461.08888917158077e-135.44444585790385e-14
3630.9999999999998692.6224988800042e-131.3112494400021e-13
3640.9999999999996866.27548350639065e-133.13774175319532e-13
3650.9999999999992731.45391348478826e-127.26956742394128e-13
3660.9999999999999725.67909564531091e-142.83954782265546e-14
3670.9999999999999441.118570095603e-135.59285047801501e-14
3680.9999999999998662.68811662589556e-131.34405831294778e-13
3690.9999999999996796.42591834163463e-133.21295917081732e-13
3700.999999999999853.01330610650558e-131.50665305325279e-13
3710.9999999999996247.51467170450299e-133.75733585225149e-13
3720.9999999999990551.89068038776252e-129.4534019388126e-13
3730.9999999999978354.33018656064433e-122.16509328032217e-12
3740.9999999999976654.67057091203799e-122.33528545601899e-12
3750.9999999999941421.17169441001135e-115.85847205005674e-12
3760.9999999999858352.83292077379554e-111.41646038689777e-11
3770.9999999999664686.7063258011436e-113.3531629005718e-11
3780.999999999922441.55119549549975e-107.75597747749875e-11
3790.9999999998157013.68598020444056e-101.84299010222028e-10
3800.9999999995797798.40442523026456e-104.20221261513228e-10
3810.999999999013791.97241781771759e-099.86208908858794e-10
3820.9999999977049484.59010340276255e-092.29505170138127e-09
3830.9999999970581945.88361147973471e-092.94180573986735e-09
3840.999999993542731.29145401564448e-086.45727007822238e-09
3850.9999999858841792.8231642196856e-081.4115821098428e-08
3860.999999968162746.36745186129167e-083.18372593064583e-08
3870.9999999306697351.38660529101785e-076.93302645508925e-08
3880.9999998495335893.0093282292094e-071.5046641146047e-07
3890.9999996862516166.27496768237167e-073.13748384118584e-07
3900.9999993392983811.32140323790699e-066.60701618953494e-07
3910.9999986576811532.68463769306429e-061.34231884653215e-06
3920.9999973123826045.37523479185063e-062.68761739592531e-06
3930.9999948071450421.03857099155823e-055.19285495779113e-06
3940.9999899435841872.01128316262474e-051.00564158131237e-05
3950.9999806677459933.86645080149272e-051.93322540074636e-05
3960.9999624589344847.50821310324266e-053.75410655162133e-05
3970.9999299373754570.0001401252490850737.00626245425364e-05
3980.9998714165302410.0002571669395174980.000128583469758749
3990.9997734658129170.0004530683741660290.000226534187083014
4000.9996045210736880.0007909578526240550.000395478926312027
4010.9993498897317320.001300220536535360.000650110268267681
4020.999576328891770.0008473422164606740.000423671108230337
4030.9992352726335070.001529454732985310.000764727366492654
4040.999684055330.0006318893400006260.000315944670000313
4050.9994031674637930.001193665072413990.000596832536206994
4060.9988576191136660.002284761772668460.00114238088633423
4070.9979458593061390.004108281387721870.00205414069386094
4080.998377329636820.003245340726361480.00162267036318074
4090.99783339693880.004333206122401760.00216660306120088
4100.9969623970545630.006075205890873380.00303760294543669
4110.9943040304279120.01139193914417530.00569596957208765
4120.990248486610650.01950302677869930.00975151338934963
4130.9824527075107530.03509458497849330.0175472924892466
4140.9695199746947060.06096005061058770.0304800253052939
4150.9491104723551680.1017790552896640.0508895276448321
4160.919537028321670.160925943356660.08046297167833
4170.9640071825718140.07198563485637260.0359928174281863
4180.9844260079523260.03114798409534730.0155739920476736
4190.9682974894604460.06340502107910770.0317025105395538
4200.9400734527983260.1198530944033490.0599265472016743
4210.88914040760980.2217191847804010.110859592390201
4220.9810401289603890.0379197420792230.0189598710396115
4230.99676019894420.006479602111599710.00323980105579986


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level4040.971153846153846NOK
5% type I error level4090.983173076923077NOK
10% type I error level4120.990384615384615NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/10r0ks1293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/10r0ks1293276484.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/1kz5g1293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/1kz5g1293276484.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/2c84j1293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/2c84j1293276484.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/3c84j1293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/3c84j1293276484.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/4c84j1293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/4c84j1293276484.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/5n0m41293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/5n0m41293276484.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/6n0m41293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/6n0m41293276484.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/7grlp1293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/7grlp1293276484.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/8grlp1293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/8grlp1293276484.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/9r0ks1293276484.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/25/t1293276471q6whi38qfgmipdb/9r0ks1293276484.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')
}
 




Copyright

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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


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