Home » date » 2010 » Dec » 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: Wed, 01 Dec 2010 21:18:09 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv.htm/, Retrieved Wed, 01 Dec 2010 22:16:30 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv.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 1 1 1 1 162556 162556 1081 1081 213118 213118 6282154 1 2 2 1 1 29790 29790 309 309 81767 81767 4321023 1 3 3 1 1 87550 87550 458 458 153198 153198 4111912 1 4 4 0 0 84738 84738 588 588 -26007 -26007 223193 1 5 5 1 1 54660 54660 302 302 126942 126942 1491348 0 6 0 1 0 42634 0 156 0 157214 0 1629616 1 7 7 0 0 40949 40949 481 481 129352 129352 1398893 0 8 0 1 0 45187 0 353 0 234817 0 1926517 1 9 9 1 1 37704 37704 452 452 60448 60448 983660 1 10 10 1 1 16275 16275 109 109 47818 47818 1443586 1 11 11 0 0 25830 25830 115 115 245546 245546 1073089 1 12 12 0 0 12679 12679 110 110 48020 48020 984885 1 13 13 1 1 18014 18014 239 239 -1710 -1710 1405225 1 14 14 0 0 43556 43556 247 247 32648 32648 227132 1 15 15 1 1 24811 24811 505 505 95350 95350 929118 0 16 0 0 0 6575 0 159 0 151352 0 1071292 0 17 0 0 0 7123 0 109 0 288170 0 638830 1 18 18 1 1 21950 21950 519 519 114337 114337 856956 1 19 19 1 1 37597 37597 248 248 37884 37884 992426 1 20 20 0 0 17821 17821 373 373 122844 122844 444477 1 21 21 1 1 etc...
 
Output produced by software:

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


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time36 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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] = + 232984.239121913 -98204.63837466pop[t] -54.3096185956608t -46.1343127864423pop_t[t] -5413.89258141255Group[t] + 93504.9300411826Group_p[t] + 28.1917359018469Costs[t] + 2.15756164857987Costs_p[t] -294.107993092294Trades[t] -189.635390898209Trades_p[t] + 1.33970058272866Dividends[t] + 0.753795286055985Dividends_p[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)232984.23912191387656.0460122.65790.0081630.004081
pop-98204.63837466106025.964745-0.92620.3548590.177429
t-54.3096185956608190.618419-0.28490.7758520.387926
pop_t-46.1343127864423239.219585-0.19290.8471670.423584
Group-5413.8925814125558831.053182-0.0920.9267230.463361
Group_p93504.930041182667761.3609041.37990.1683490.084174
Costs28.19173590184695.2341525.386100
Costs_p2.157561648579875.5341010.38990.6968330.348417
Trades-294.107993092294302.709836-0.97160.3318180.165909
Trades_p-189.635390898209367.389971-0.51620.6060090.303004
Dividends1.339700582728660.9489891.41170.1587770.079388
Dividends_p0.7537952860559851.0764770.70020.4841640.242082


Multiple Linear Regression - Regression Statistics
Multiple R0.813910561405532
R-squared0.662450401967468
Adjusted R-squared0.653588718009573
F-TEST (value)74.7544603390316
F-TEST (DF numerator)11
F-TEST (DF denominator)419
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation253384.540481749
Sum Squared Residuals26901360923806.6


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
162821545079465.661352741202688.33864726
243210231148477.495421323172545.50457868
341119122978815.217191161133096.78280884
42231932367229.94400389-2144036.94400389
514913482000923.07326657-509575.073266569
616296161593909.7977589735706.2022410272
713988931414967.18853960-16074.1885395976
819265171711800.18996151214716.810038493
99836601274152.18637846-290492.186378462
101443586763179.773124978680406.226875022
1110730891376016.12066726-302927.120667261
12984885565690.916592613419194.083407387
131405225649082.566463093756142.433536907
142271321404131.22709272-1176999.22709272
15929118929684.822933343-566.822933342897
161071292573479.140474501497812.859525499
17638830786874.456112502-148044.456112502
18856956875530.949532173-18574.9495321726
199924261321346.38177755-328920.381777552
20444477750362.67804229-305885.678042290
21857217729750.979373801127466.020626199
22711969938101.372402215-226132.372402215
23702380799203.927296376-96823.9272963761
24358589724638.849353376-366049.849353376
25297978578938.591740517-280960.591740517
26585715715100.355665355-129385.355665355
276579541080329.80138476-422375.801384765
28209458470755.353185198-261297.353185198
29786690441344.578048646345345.421951354
30439798499025.818202168-59227.8182021676
31688779490930.038448971197848.961551029
32574339759383.719636444-185044.719636444
33741409517374.028926639224034.971073361
34597793546707.23393798451085.766062016
35644190819542.047999301-175352.047999301
36377934833718.898652955-455784.898652955
37640273585895.30671557654377.6932844245
38697458577284.541273642120173.458726358
39550608654000.136978414-103392.136978414
40207393351179.688858524-143786.688858524
41301607394634.588474521-93027.588474521
42345783719297.762555081-373514.762555081
43501749417426.77540565884322.224594342
44379983550803.923021073-170820.923021073
45387475435429.317068066-47954.3170680663
46377305531216.322902536-153911.322902536
47370837702439.111523509-331602.111523509
48430866576370.409250909-145504.409250909
49469107493204.327471195-24097.3274711946
50194493343061.032974391-148568.032974391
51530670487701.83493531942968.1650646809
52518365553153.040792401-34788.0407924008
53491303778416.098564686-287113.098564686
54527021404847.221172333122173.778827667
55233773610139.996319023-376366.996319023
56405972350811.47488984655160.5251101543
57652925343857.290414866309067.709585134
58446211479359.502345052-33148.5023450523
59341340324371.52455054016968.4754494596
60387699634065.134147173-246366.134147173
61493408493728.84290797-320.842907969512
62146494342336.688843188-195842.688843187
63414462493037.15632455-78575.1563245496
64364304522089.671236624-157785.671236624
65355178360697.585287243-5519.5852872429
66357760262414.69333867595345.3066613253
67261216288095.853508712-26879.8535087122
68397144462944.295023531-65800.2950235315
69374943400085.295430157-25142.2954301570
70424898517766.231999565-92868.2319995655
71202055337310.85594238-135255.855942380
72378525411276.722052203-32751.7220522027
73310768402556.236059789-91788.2360597888
74325738304821.9034006120916.0965993897
75394510419132.032493009-24622.0324930093
76247060421631.971741349-174571.971741349
77368078375289.301972987-7211.30197298702
78236761285677.0363318-48916.0363318001
79312378254518.34563085157859.6543691492
80339836399085.901760442-59249.9017604417
81347385283613.91001136563771.0899886349
82426280453660.160368590-27380.1603685904
83352850415423.093822145-62573.0938221451
84301881299161.9958989442719.00410105603
85377516349639.6178817427876.3821182598
86357312486635.219232733-129323.219232733
87458343383934.31348072274408.6865192782
88354228378586.290749673-24358.2907496728
89308636411329.043992255-102693.043992255
90386212379297.256502736914.74349726985
91393343397169.842108812-3826.84210881175
92378509456853.484262322-78344.4842623222
93452469279056.370278392173412.629721608
94364839641105.676462216-276266.676462216
95358649239361.417527019119287.582472981
96376641411271.004905973-34630.0049059733
97429112365976.16695830463135.8330416965
98330546297903.28154929332642.7184507075
99403560467769.455721557-64209.4557215566
100317892402766.007365439-84874.0073654394
101307528394377.078233508-86849.078233508
102235133332531.342010713-97398.3420107133
103299243414990.479000284-115747.479000283
104314073371265.429889542-57192.4298895425
105368186299866.96046456368319.039535437
106269661285459.680027686-15798.6800276864
107125390405229.443615926-279839.443615926
108510834469344.45002389941489.5499761011
109321896505827.336064575-183931.336064575
110249898405770.784410075-155872.784410075
111408881279754.981180685129126.018819315
112158492393729.373136268-235237.373136268
113292154465980.338502776-173826.338502776
114289513432355.45308941-142842.45308941
115378049369559.8872708918489.1127291086
116343466333097.17433961210368.8256603878
117332743344177.436284474-11434.4362844735
118442882402204.80758085540677.1924191448
119214215432596.128026534-218381.128026534
120315688243002.14144924972685.8585507506
121375195630118.148298623-254923.148298623
122334280336668.661706888-2388.66170688773
123355864313670.4636056842193.5363943202
124480382471948.5222923238433.47770767716
125353058404473.992256522-51415.9922565223
126217193457813.169873881-240620.169873881
127315380307433.5369435507946.4630564504
128314533308068.2926906216464.70730937934
129318056307255.74032745410800.2596725460
130315380248838.95882018566541.0411798154
131314353305701.3804926468651.6195073542
132369448254805.203848551114642.796151449
133315380248537.62702603866842.3729739618
134312846307607.0202751985238.97972480191
135312075265493.31916334746581.6808366533
136315009312918.3313580552090.66864194511
137318903245808.39309740973094.6069025912
138314887306388.8738323228498.12616767814
139314913330599.268658897-15686.2686588967
140315380306727.5119018068652.48809819407
141325506311204.59363761614301.4063623840
142315380247633.63164359967746.3683564007
143298568325082.471209852-26514.4712098523
144315834306875.9598420088958.0401579924
145329784259534.3461142270249.6538857798
146312878306271.0657211686606.93427883203
147315380247131.41198668968248.5880133113
148314987304936.17818461710050.8218153828
149325249254187.69699210971061.3030078908
150315877247390.90175141868486.098248582
151291650315075.548629043-23425.5486290428
152305959334014.723021253-28055.7230212525
153315380246528.74839839668851.2516016038
154297765250621.2316761147143.7683238899
155315245305886.4330886669358.56691133412
156315380305858.5580042759521.44199572472
157315380305804.2483856809575.75161432036
158315236308956.0823479826279.91765201843
159336425315221.98880993121203.0111900687
160315380305641.3195298939738.6804701074
161315380305587.0099112979792.99008870306
162315380245624.75301595769755.2469840428
163315380305478.3906741069901.6093258944
164306268247251.78904787259016.2109521283
165302187239750.66272877162436.3372712293
166314882305758.1494264299123.8505735714
167315380245122.53335904770257.4666409534
168382712227338.046973816155373.953026184
169341570196086.475685251145483.524314749
170315380305098.22334393610281.7766560640
171315380305043.91372534010336.0862746597
172312412245544.82539124466867.1746087562
173315380244519.86977075470860.130229246
174309596248776.52075437460819.4792456264
175315380244318.9819079971061.01809201
176315547305692.9152596829854.08474031787
177313267182728.818698840130538.181301160
178316176333832.081712423-17656.0817124231
179315380304609.43677657510770.5632234250
180315380304555.12715797910824.8728420206
181359335279633.72236190179701.2776380991
182330068325692.2172363134375.78276368706
183314289350405.86027869-36116.8602786899
184297413309228.537231456-11815.5372314558
185314806333526.461896866-18720.4618968662
186333210343162.857029788-9952.85702978753
187352108360636.704541041-8528.70454104055
188313332308620.4233629124711.5766370877
189291787257872.80793572733914.1920642728
190315380304012.03097202311367.9690279774
191318745246568.89733636472176.1026636358
192315380303903.41173483111476.5882651685
193315366307278.6164501258087.38354987457
194315380242410.5472117372969.4527882702
195315688304169.38459717511518.6154028246
196315380330300.696808736-14920.6968087358
197409642547857.782103041-138215.782103041
198315380330099.808945971-14719.8089459714
199315380329999.365014590-14619.3650145896
200269587375005.729511462-105418.729511462
201315380329798.477151825-14418.4771518252
202315380329698.033220443-14318.0332204434
203315380329597.589289061-14217.5892890611
204300962301837.876357803-875.876357803361
205325479392059.850303957-66580.8503039566
206316155330591.775835620-14436.7758356205
207318574339573.33324937-20999.3332493697
208315380329095.369632151-13715.3696321506
209343613303838.94413471339774.0558652869
210306948295865.40824682511082.5917531747
211315380302871.52898151412508.4710184862
212315380240602.55644685274777.443553148
213330059226677.619397881103381.380602119
214288985247743.89225024241241.1077497578
215304485257099.18800739147385.8119926093
216315380240200.78072132475179.2192786764
217315688236918.00722270978769.992777291
218317736305349.31915467112386.6808453288
219315380302437.05203274812942.9479672515
220322331340437.435687957-18106.4356879572
221296656246064.55109052950591.4489094709
222315380239598.11713303175781.8828669691
223315354301686.69936706713667.3006329332
224312161243442.08946006668718.9105399342
225315576302200.68760255913375.3123974413
226314922303163.73466147911758.265338521
227314551299604.75907070414946.2409292963
228315380238995.45354473876384.5464552617
229312339252325.06838525960013.9316147411
230315380301839.64622819613540.3537718039
231298700250649.87791893048050.1220810703
232321376256154.0255715265221.9744284803
233315380238493.23388782876886.7661121722
234303230308291.28144604-5061.28144604003
235315380301568.09813521813811.9018647822
236315487301306.87710873614180.1228912637
237315380301459.47889802713920.5211019734
238315793300637.65440748315155.3455925174
239315380237890.57029953577489.4297004649
240315380237790.12636815377589.873631847
241315380301242.24042364414137.7595763561
242312887240330.67967918072556.3203208205
243315380237488.79457400777891.2054259933
244315637232358.45343826783278.546561733
245324385331165.70565354-6780.7056535401
246315380325278.500239631-9898.50023963059
247315380325178.056308249-9798.05630824855
248308989302777.5968066106211.40319339032
249315380324977.168445484-9597.16844548423
250315380295339.56127487020040.4387251296
251296702222652.00440097274049.9955990279
252315380324675.836651338-9295.83665133804
253307322247964.77412059259357.2258794082
254304376313062.492677099-8686.49267709854
255253588367955.604819152-114367.604819152
256315380300427.59614470914952.4038552911
257309560300406.172747459153.82725255015
258298466282850.78593334615615.214066654
259315380323972.729131663-8592.72913166317
260315380235781.24774051179598.752259489
261315380323771.841268899-8391.8412688989
262315380323671.397337517-8291.39733751692
263343929319119.93777100724809.0622289931
264331955233538.27930369198416.7206963087
265315380323370.065543371-7990.06554337056
266315380235178.58415221880201.4158477817
267315380294416.29775874420963.7022412559
268381180270815.539542907110364.460457093
269315380322968.289817842-7588.28981784211
270331420255107.08310304076312.9168969603
271315380322767.401955078-7387.40195507798
272315380322666.958023696-7286.95802369575
273315380322566.514092314-7186.51409231369
274310201299549.19072606310651.8092739369
275315380299395.71339139115984.2866086087
276320016231846.28007263788169.7199273625
277320398303654.42742260116743.5725773995
278315380233973.25697563381406.7430243669
279291841319616.63677213-27775.63677213
280310670241094.39609291769575.6039070833
281315380321762.962641257-6382.96264125687
282315380299015.54606122216364.4539387784
283313491197837.066261537115653.933738463
284315380298906.9268240316473.0731759697
285331323317935.97993086513387.0200691353
286315380298798.30758683916581.6924131610
287319210336497.766576828-17287.7665768277
288318098300134.11172644917963.8882735507
289315380293221.48614963922158.5138503606
290292754278796.05115257913957.9488474213
291315380320758.523327436-5378.52332743587
292325176298906.99940082226269.0005991778
293365959278774.00193399587184.9980660051
294315380298363.83063807417016.1693619263
295302409224560.31155173877848.6884482618
296340968318854.43736878522113.5626312154
297315380298200.90178228717179.0982177133
298315380292732.69958227922647.3004177215
299315380231863.93441660983516.0655833912
300315380298037.972926517342.0270735003
301313164318892.882081931-5728.88208193077
302301164235355.96511735165808.0348826492
303315380231462.15869108083917.8413089196
304315380319452.752219469-4072.75221946857
305344425293954.47077371450470.5292262856
306315394295141.62225764020252.3777423604
307315380319151.420425322-3771.42042532218
308316647326005.478073617-9358.47807361681
309309836293816.66467948516019.3353205155
310315380318850.088631176-3470.0886311759
311315380318749.644699794-3369.64469979387
312346611256903.97868180389707.0213181974
313315380318548.756837030-3168.75683702957
314322031323156.151921268-1125.15192126789
315315656296186.39960679319469.6003932071
316339445303861.23007160335583.7699283973
317314964296742.07613958318221.9238604172
318297141303033.968881568-5892.96888156805
319315372295268.29424994720103.7057500525
320315380296951.78055458618428.2194454135
321315380229654.16792620385725.8320737974
322315380229553.72399482085826.2760051795
323315380229453.28006343885926.7199365617
324315380291320.64949879124059.3505012087
325315380229252.39220067486127.6077993259
326312502228856.33303915883645.6669608421
327315380291157.72064300424222.2793569957
328315380291103.41102440924276.5889755913
329315380228850.61647514686529.3835248544
330315380228750.17254376486629.8274562364
331315380228649.72861238186730.2713876185
332315380296300.06513143819079.9348685616
333315380228448.84074961786931.1592503827
334313729228129.33037186885599.669628132
335315388295586.48992944819801.5100705522
336315371303464.49176477411906.5082352258
337296139216359.05485849179779.9451415094
338315380295974.20741986419405.7925801355
339313880296594.56224418517285.4377558152
340317698223186.54450524894511.4554947523
341295580302982.932992411-7402.93299241119
342315380295756.96894548219623.0310545183
343315380295702.65932688619677.3406731138
344315380227343.95750441488036.042495586
345308256298983.9244163129272.07558368834
346315380295539.73047109919840.2695289008
347303677294929.3891327388747.61086726186
348315380295431.11123390819948.8887660922
349315380226841.73784750488538.2621524964
350319369335288.474877593-15919.4748775933
351318690224895.02521104493794.9747889555
352314049294505.53601813519543.4639818647
353325699236094.89632380789604.1036761931
354314210300124.81916666914085.1808333314
355315380295050.94390373820329.0560962618
356315380294996.63428514220383.3657148575
357322378318334.2538033764043.74619662364
358315380294888.01504795120491.9849520488
359315380294833.70542935620546.2945706444
360315380225736.85460230089643.1453976996
361315398293579.52535686221818.4746431377
362315380225535.96673953689844.0332604639
363315380294616.46695497320763.5330450272
364308336357164.363066703-48828.363066703
365316386223104.8882629493281.1117370601
366315380225134.19101400890245.8089859922
367315380225033.74708262690346.2529173743
368315380294344.91886199521035.0811380054
369315380224832.85921986190547.1407801386
370315553294205.83019646821347.1698035325
371315380224631.97135709790748.0286429027
372323361216812.484747897106548.515252103
373336639319159.31496027617479.6850397241
374307424237711.00701475269712.9929852483
375315380224230.19563156991149.8043684311
376315380224129.75170018791250.2482998132
377295370206583.07332061488786.9266793857
378322340307298.54347894915041.4565210512
379319864351990.120969035-32126.1209690355
380315380223727.97597465891652.0240253418
381315380223627.53204327691752.4679567238
382317291307911.3381523269379.6618476741
383280398260319.25802218320078.7419778171
384315380223326.2002491392053.79975087
385317330293567.49001053823762.5099894617
386238125294145.303364228-56020.3033642282
387327071293822.76263615633248.2373638445
388309038242670.53776627466367.4622337263
389314210293920.51428950320289.485710497
390307930227863.35955663480066.6404433658
391322327234000.38679595188326.613204049
392292136301506.022913885-9370.02291388495
393263276238394.76830494324881.2316950566
394367655319978.47182687947676.5281731211
395283910411171.209469704-127261.209469704
396283587228148.83965708055438.1603429205
397243650334373.244242792-90723.2442427922
398438493423251.09618202515241.9038179753
399296261240115.14121333156145.8587866688
400230621224424.5919960476196.40800395316
401304252333981.99042429-29729.9904242901
402333505316430.55599712217074.4440028778
403296919305266.627010862-8347.62701086155
404278990334206.72489958-55216.7248995802
405276898311722.151744889-34824.1517448891
406327007335142.554893278-8135.55489327823
407317046247374.12686640569671.8731335954
408304555241862.46634431162692.5336556886
409298096298015.73550201780.2644979827091
410231861260682.789647779-28821.7896477794
411309422249791.83511489659630.1648851037
412286963490562.900895716-203599.900895716
413269753328489.422565809-58736.4225658095
414448243423273.18804372824969.811956272
415165404310375.311659519-144971.311659519
416204325335168.734776853-130843.734776853
417407159233580.460216392173578.539783608
418290476315309.961128796-24833.9611287963
419275311331100.524233597-55789.5242335971
420246541316239.675177053-69698.675177053
421253468331560.811832505-78092.811832505
422240897298456.417440931-57559.4174409306
423-83265465937.623102906-549202.623102906
424-42143394553.436025821-436696.436025821
425272713264698.4394125788014.56058742187
426215362428556.752427884-213194.752427884
42742754306040.431698488-263286.431698488
4283062751394339.04267295-1088064.04267295
429253537515715.029216403-262178.029216403
430372631672574.023250789-299943.023250789
431-7170610036.25311606-617206.25311606


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
1517.28888121519193e-1273.64444060759596e-127
1611.76059549208642e-1338.80297746043208e-134
1713.6058612055455e-1321.80293060277275e-132
1811.76662331070681e-1348.83311655353407e-135
1913.40590961390872e-1411.70295480695436e-141
2017.04724864416035e-1453.52362432208017e-145
2111.27318487552469e-1506.36592437762344e-151
2211.56745230819543e-1497.83726154097714e-150
2315.53899283172609e-1492.76949641586305e-149
2411.99164668765808e-1529.95823343829042e-153
2513.86469188937132e-1541.93234594468566e-154
2611.48122266435578e-1607.40611332177889e-161
2719.2898276232586e-1634.6449138116293e-163
2816.72070562276494e-1673.36035281138247e-167
2914.22335802034316e-1832.11167901017158e-183
3014.63768248306874e-1862.31884124153437e-186
3111.15224138881846e-1895.76120694409229e-190
3212.30711220804092e-1911.15355610402046e-191
3318.40436547786953e-1974.20218273893477e-197
3412.53458693311971e-1981.26729346655986e-198
3512.09250581783259e-1971.04625290891629e-197
3617.08676671959024e-1973.54338335979512e-197
3713.32736864247487e-2041.66368432123743e-204
3817.96451199544664e-2103.98225599772332e-210
3911.61053504402539e-2098.05267522012694e-210
4014.70639577000513e-2112.35319788500256e-211
4111.97413618730078e-2119.87068093650392e-212
4219.45513983890797e-2114.72756991945398e-211
4317.75152382018497e-2113.87576191009248e-211
4419.88522763388876e-2104.94261381694438e-210
4511.05659174605418e-2085.28295873027089e-209
4611.34313921660876e-2076.7156960830438e-208
4718.2973748630715e-2094.14868743153575e-209
4819.84324741646486e-2084.92162370823243e-208
4915.96819368036553e-2092.98409684018276e-209
5019.17093869313987e-2114.58546934656993e-211
5112.12881616113691e-2111.06440808056845e-211
5213.70580784496183e-2111.85290392248092e-211
5312.04623919159224e-2101.02311959579612e-210
5411.79581945925980e-2108.97909729629899e-211
5513.73916793553995e-2131.86958396776997e-213
5611.26741184616292e-2126.33705923081462e-213
5713.29508931231799e-2281.64754465615900e-228
5818.24162706313201e-2284.12081353156601e-228
5912.32295204537527e-2281.16147602268763e-228
6016.31641598356517e-2283.15820799178258e-228
6111.89772826943679e-2289.48864134718394e-229
6217.71344747725422e-2313.85672373862711e-231
6314.51307904171621e-2302.25653952085810e-230
6415.61697371044775e-2292.80848685522388e-229
6515.7260052103694e-2282.8630026051847e-228
6619.10297276033924e-2294.55148638016962e-229
6712.04465536255772e-2291.02232768127886e-229
6812.46968880503791e-2281.23484440251896e-228
6912.85042430545355e-2281.42521215272677e-228
7011.45846493323168e-2277.29232466615838e-228
7111.45223062760303e-2267.26115313801515e-227
7215.73530108210288e-2262.86765054105144e-226
7313.89475240818113e-2251.94737620409056e-225
7416.98300122003902e-2253.49150061001951e-225
7513.14374597244976e-2241.57187298622488e-224
7615.23642313778831e-2242.61821156889416e-224
7711.4528270703577e-2237.2641353517885e-224
7814.43407044813229e-2242.21703522406614e-224
7911.49603130320073e-2237.48015651600364e-224
8011.23314663644759e-2236.16573318223794e-224
8114.13471200778788e-2232.06735600389394e-223
8217.03373877344097e-2233.51686938672048e-223
8317.51852387251913e-2243.75926193625956e-224
8418.5189714592744e-2234.2594857296372e-223
8514.44333641685834e-2222.22166820842917e-222
8611.35450504738232e-2216.77252523691158e-222
8717.92689470230865e-2223.96344735115432e-222
8819.24467898497819e-2214.62233949248909e-221
8916.02335054451052e-2203.01167527225526e-220
9016.30797888409559e-2193.15398944204780e-219
9114.22717579648647e-2182.11358789824324e-218
9213.63192493747427e-2171.81596246873713e-217
9317.96316653994572e-2193.98158326997286e-219
9412.99101301949817e-2181.49550650974908e-218
9515.06164504034295e-2192.53082252017147e-219
9615.98800970352026e-2192.99400485176013e-219
9714.79738690477428e-2182.39869345238714e-218
9815.1890481059532e-2172.5945240529766e-217
9913.91760109312078e-2161.95880054656039e-216
10014.48630261516574e-2152.24315130758287e-215
10113.98458880355787e-2141.99229440177893e-214
10211.04950398241782e-2155.2475199120891e-216
10312.54844712865554e-2161.27422356432777e-216
10414.98287737352529e-2182.49143868676264e-218
10512.49208655321904e-2171.24604327660952e-217
10614.3019570316484e-2172.1509785158242e-217
10714.35311455220855e-2202.17655727610428e-220
10818.79422321722112e-2214.39711160861056e-221
10911.09289140171802e-2195.46445700859012e-220
11019.25725938374234e-2204.62862969187117e-220
11119.85661686274438e-2204.92830843137219e-220
11212.39994120534133e-2201.19997060267067e-220
11312.28768084587733e-2191.14384042293866e-219
11411.24116903269794e-2186.20584516348971e-219
11511.86188389265281e-2189.30941946326403e-219
11611.62945160394974e-2178.14725801974869e-218
11711.94990821773011e-2169.74954108865056e-217
11819.67095619792485e-2184.83547809896242e-218
11911.23538072630137e-2196.17690363150683e-220
12019.86376025285559e-2194.93188012642779e-219
12111.16222499084392e-2175.81112495421961e-218
12211.24483396419559e-2166.22416982097797e-217
12313.95274239249095e-2161.97637119624547e-216
12412.32560651933397e-2151.16280325966699e-215
12511.34764862871687e-2146.73824314358437e-215
12614.84769379106379e-2162.42384689553190e-216
12715.89756892374999e-2152.94878446187500e-215
12817.20993089137592e-2143.60496544568796e-214
12918.88784709729872e-2134.44392354864936e-213
13017.25820322294e-2123.62910161147e-212
13118.80628479747692e-2114.40314239873846e-211
13211.26552381458046e-2106.3276190729023e-211
13311.14246740642997e-2095.71233703214983e-210
13411.36859589004306e-2086.8429794502153e-209
13511.34678845450817e-2076.73394227254084e-208
13611.6312019030477e-2068.1560095152385e-207
13711.53741914890000e-2057.68709574449998e-206
13811.82585227803878e-2049.1292613901939e-205
13912.20977599127463e-2031.10488799563731e-203
14012.60515261586976e-2021.30257630793488e-202
14113.03875200441548e-2011.51937600220774e-201
14212.8713052442548e-2001.4356526221274e-200
14312.99364872982657e-1991.49682436491329e-199
14413.47599618700371e-1981.73799809350185e-198
14513.40181269344712e-1971.70090634672356e-197
14613.86934658960520e-1961.93467329480260e-196
14713.74712540934126e-1951.87356270467063e-195
14814.25213085317476e-1942.12606542658738e-194
14914.20109860130966e-1932.10054930065483e-193
15014.15547957170318e-1922.07773978585159e-192
15113.49622443441413e-1911.74811221720707e-191
15213.95514742099914e-1901.97757371049957e-190
15313.92459798315608e-1891.96229899157804e-189
15413.70780484182528e-1881.85390242091264e-188
15514.05222283096782e-1872.02611141548391e-187
15614.39651952148427e-1862.19825976074213e-186
15714.74185517596381e-1852.37092758798190e-185
15815.10541560813922e-1842.55270780406961e-184
15915.10809062757215e-1832.55404531378607e-183
16015.44045471554404e-1822.72022735777202e-182
16115.75743929864767e-1812.87871964932383e-181
16215.62395783885443e-1802.81197891942721e-180
16315.88673868681415e-1792.94336934340708e-179
16415.4582185939288e-1782.7291092969644e-178
16514.01271905190602e-1772.00635952595301e-177
16614.14182227096981e-1762.07091113548491e-176
16714.03266155211581e-1752.01633077605790e-175
16815.96584972955283e-1762.98292486477642e-176
16914.5551154858378e-1752.2775577429189e-175
17014.68899054516388e-1742.34449527258194e-174
17114.79349305122676e-1732.39674652561338e-173
17214.7475331556882e-1722.3737665778441e-172
17314.78925206138138e-1712.39462603069069e-171
17414.64523937763860e-1702.32261968881930e-170
17514.66231498063061e-1692.33115749031531e-169
17614.70283906683542e-1682.35141953341771e-168
17714.29233759948987e-1672.14616879974494e-167
17814.07045620571509e-1662.03522810285754e-166
17914.00279957870053e-1652.00139978935027e-165
18013.90815787011426e-1641.95407893505713e-164
18113.51708733934285e-1631.75854366967142e-163
18213.37292771480959e-1621.68646385740479e-162
18313.32063841300787e-1611.66031920650393e-161
18412.89366705138455e-1601.44683352569228e-160
18512.57301432268071e-1591.28650716134036e-159
18612.09222051956747e-1581.04611025978374e-158
18711.16651563166984e-1575.8325781583492e-158
18811.10905572443128e-1565.54527862215642e-157
18916.37029032151913e-1563.18514516075956e-156
19016.01490007860498e-1553.00745003930249e-155
19115.65201338654889e-1542.82600669327444e-154
19215.27825977319258e-1532.63912988659629e-153
19314.93777326438972e-1522.46888663219486e-152
19414.60640808484218e-1512.30320404242109e-151
19514.24344770469412e-1502.12172385234706e-150
19613.73842962806824e-1491.86921481403412e-149
19712.29269225494889e-1481.14634612747445e-148
19812.04692244900404e-1471.02346122450202e-147
19911.82584724339474e-1469.12923621697368e-147
20014.31341887375427e-1472.15670943687713e-147
20113.86347595043393e-1461.93173797521697e-146
20213.4489517247236e-1451.7244758623618e-145
20313.06753450121478e-1441.53376725060739e-144
20412.76190390650445e-1431.38095195325222e-143
20514.09065435532018e-1432.04532717766009e-143
20613.51664382953644e-1421.75832191476822e-142
20712.68051160517562e-1411.34025580258781e-141
20812.30431706068687e-1401.15215853034343e-140
20911.55746416971e-1397.78732084855e-140
21011.38017976121234e-1386.90089880606171e-139
21111.21778140188794e-1376.08890700943971e-138
21211.0501832591329e-1365.2509162956645e-137
21318.09974337872467e-1364.04987168936233e-136
21414.98650611280143e-1352.49325305640072e-135
21513.90522278084943e-1341.95261139042471e-134
21613.3242595352115e-1331.66212976760575e-133
21712.80624506826117e-1321.40312253413058e-132
21812.4457771927228e-1311.2228885963614e-131
21912.08720853902406e-1301.04360426951203e-130
22011.71273763919511e-1298.56368819597553e-130
22111.24088773142005e-1286.20443865710026e-129
22211.02996782075923e-1275.14983910379613e-128
22318.66522535722027e-1274.33261267861014e-127
22417.12000395845983e-1263.56000197922991e-126
22515.93717925859993e-1252.96858962929997e-125
22614.98042883776166e-1242.49021441888083e-124
22714.12492552389251e-1232.06246276194626e-123
22813.34389808967561e-1221.67194904483780e-122
22912.36125694466545e-1211.18062847233272e-121
23011.91674087517605e-1209.58370437588025e-121
23111.13948347260874e-1195.69741736304372e-120
23219.26985332393336e-1194.63492666196668e-119
23317.3091416024089e-1183.65457080120445e-118
23415.52827782717837e-1172.76413891358919e-117
23514.37401042087745e-1162.18700521043873e-116
23613.44234246207275e-1151.72117123103638e-115
23712.68641379008779e-1141.34320689504389e-114
23812.09892555452728e-1131.04946277726364e-113
23911.61846663941776e-1128.0923331970888e-113
24011.24152232744905e-1116.20761163724526e-112
24119.47078055252868e-1114.73539027626434e-111
24217.04281195919931e-1103.52140597959965e-110
24315.32052094141245e-1092.66026047070622e-109
24414.0328726783968e-1082.0164363391984e-108
24512.86776978494754e-1071.43388489247377e-107
24612.06312561354490e-1061.03156280677245e-106
24711.47829516651491e-1057.39147583257453e-106
24811.06658448812654e-1045.3329224406327e-105
24917.5830409252675e-1043.79152046263375e-104
25015.63028557927416e-1032.81514278963708e-103
25114.15579329928605e-1022.07789664964303e-102
25212.91605773209464e-1011.45802886604732e-101
25311.76135384053481e-1008.80676920267405e-101
25411.29017325716133e-996.45086628580667e-100
25513.65018139289150e-991.82509069644575e-99
25612.57690298112148e-981.28845149056074e-98
25711.77588741311237e-978.87943706556187e-98
25811.09979888458846e-965.4989944229423e-97
25917.54421360815631e-963.77210680407815e-96
26015.27353503412811e-952.63676751706406e-95
26113.58080144163198e-941.79040072081599e-94
26212.41855287001995e-931.20927643500998e-93
26311.64476068083141e-928.22380340415706e-93
26411.10125483082874e-915.50627415414368e-92
26517.35865309948823e-913.67932654974411e-91
26615.02286999599371e-902.51143499799686e-90
26713.46205937220524e-891.73102968610262e-89
26812.33175587124503e-881.16587793562252e-88
26911.53115262217106e-877.65576311085532e-88
27019.66127212605628e-874.83063606302814e-87
27116.26625552075465e-863.13312776037733e-86
27214.0371621879522e-852.0185810939761e-85
27312.58158473695441e-841.29079236847720e-84
27411.63374088656251e-838.16870443281255e-84
27511.05799934055747e-825.28999670278737e-83
27616.87008606618463e-823.43504303309231e-82
27714.49887811829713e-812.24943905914856e-81
27812.89753405018367e-801.44876702509184e-80
27918.05626719900362e-804.02813359950181e-80
28013.50320770274548e-791.75160385137274e-79
28112.12646230747406e-781.06323115373703e-78
28211.34447171086015e-776.72235855430077e-78
28318.0544154458536e-774.0272077229268e-77
28415.03529741512576e-762.51764870756288e-76
28513.2240724497442e-751.6120362248721e-75
28611.99302263956202e-749.9651131978101e-75
28711.2237376087578e-736.118688043789e-74
28817.56594798169557e-733.78297399084779e-73
28914.71778995524449e-722.35889497762224e-72
29012.26446074408971e-711.13223037204486e-71
29111.32648269741394e-706.63241348706971e-71
29218.1513646801541e-704.07568234007705e-70
29314.57986948751191e-692.28993474375596e-69
29412.70238982037321e-681.35119491018660e-68
29511.43900029843014e-677.19500149215072e-68
29618.31867987826344e-674.15933993913172e-67
29714.83284774376491e-662.41642387188245e-66
29812.87223682208708e-651.43611841104354e-65
29911.66072353881014e-648.3036176940507e-65
30019.4702108352805e-644.73510541764025e-64
30115.24801571066809e-632.62400785533404e-63
30212.86516955158317e-621.43258477579158e-62
30311.61396733615039e-618.06983668075194e-62
30418.90327323843002e-614.45163661921501e-61
30514.89673868626768e-602.44836934313384e-60
30612.72853117011076e-591.36426558505538e-59
30711.48063154580122e-587.40315772900608e-59
30817.34360454431461e-583.67180227215730e-58
30913.93492399862467e-571.96746199931234e-57
31012.05433359808857e-561.02716679904429e-56
31111.04617512224296e-555.23087561121479e-56
31211.73555645401393e-558.67778227006965e-56
31318.52754061517534e-554.26377030758767e-55
31413.59751970933942e-541.79875985466971e-54
31511.94663992587760e-539.73319962938802e-54
31611.03921175349702e-525.19605876748509e-53
31715.5634967468759e-522.78174837343795e-52
31812.57012453821030e-511.28506226910515e-51
31911.34750596246495e-506.73752981232475e-51
32017.00587210985443e-503.50293605492721e-50
32113.51116479038153e-491.75558239519076e-49
32211.74183856016235e-488.70919280081176e-49
32318.54533863393944e-484.27266931696972e-48
32414.4701030738164e-472.2350515369082e-47
32512.16616910053545e-461.08308455026772e-46
32611.00960175685309e-455.04800878426546e-46
32715.18681401301566e-452.59340700650783e-45
32812.64622153537913e-441.32311076768957e-44
32911.24713512591005e-436.23567562955025e-44
33015.79821242492595e-432.89910621246297e-43
33112.65394856982265e-421.32697428491132e-42
33211.30244174455934e-416.51220872279672e-42
33315.86560578736692e-412.93280289368346e-41
33412.56167336393015e-401.28083668196508e-40
33511.23593753291717e-396.17968766458585e-40
33615.91464136482187e-392.95732068241093e-39
33711.42628693672556e-387.13143468362778e-39
33816.79235932681436e-383.39617966340718e-38
33913.19384809202508e-371.59692404601254e-37
34011.33126451759400e-366.65632258796998e-37
34115.51482814563072e-362.75741407281536e-36
34212.54350695923619e-351.27175347961809e-35
34311.16259841374239e-345.81299206871194e-35
34414.78210316777176e-342.39105158388588e-34
34512.08911866206565e-331.04455933103282e-33
34619.34295506924272e-334.67147753462136e-33
34713.90049549677302e-321.95024774838651e-32
34811.70405601205997e-318.52028006029986e-32
34916.9762614172357e-313.48813070861785e-31
35013.02596655610135e-301.51298327805067e-30
35111.26534068422598e-296.32670342112992e-30
35215.3390910213075e-292.66954551065375e-29
35312.26865554394503e-281.13432777197252e-28
35419.40791489765568e-284.70395744882784e-28
35513.87340252615237e-271.93670126307618e-27
35611.57249546321350e-267.86247731606749e-27
35716.45975817670709e-263.22987908835354e-26
35812.56384059595058e-251.28192029797529e-25
35919.97550267471815e-254.98775133735907e-25
36013.94503229012452e-241.97251614506226e-24
36111.49673393770027e-237.48366968850135e-24
36215.84617452134206e-232.92308726067103e-23
36312.13601076270666e-221.06800538135333e-22
36417.27457831176128e-223.63728915588064e-22
36512.77857953588944e-211.38928976794472e-21
36611.04892271017638e-205.24461355088192e-21
36713.9229394184449e-201.96146970922245e-20
36811.37042857840346e-196.85214289201732e-20
36915.05099172487357e-192.52549586243678e-19
37011.76485877364974e-188.82429386824871e-19
37116.398502705516e-183.199251352758e-18
37212.29063497432768e-171.14531748716384e-17
37318.16253539006724e-174.08126769503362e-17
37412.73120689287077e-161.36560344643539e-16
37519.49035694783428e-164.74517847391714e-16
3760.9999999999999983.26091307145389e-151.63045653572695e-15
3770.9999999999999951.07252324176692e-145.36261620883459e-15
3780.9999999999999823.55716317474751e-141.77858158737375e-14
3790.9999999999999431.14845131276328e-135.74225656381638e-14
3800.9999999999998123.76151906315433e-131.88075953157716e-13
3810.9999999999993921.21516370902464e-126.07581854512321e-13
3820.9999999999980943.81311425456168e-121.90655712728084e-12
3830.999999999995069.87949317424732e-124.93974658712366e-12
3840.9999999999846793.06417372018331e-111.53208686009166e-11
3850.9999999999558768.8248732066372e-114.4124366033186e-11
3860.99999999991961.60798182718556e-108.03990913592781e-11
3870.9999999997576794.84643155980239e-102.42321577990120e-10
3880.9999999992789021.44219538464979e-097.21097692324894e-10
3890.9999999979160264.16794847942228e-092.08397423971114e-09
3900.9999999939950881.20098238305457e-086.00491191527285e-09
3910.9999999831434333.37131331426504e-081.68565665713252e-08
3920.9999999593689528.12620960799007e-084.06310480399504e-08
3930.999999895002992.09994019005120e-071.04997009502560e-07
3940.9999997346810745.30637852962882e-072.65318926481441e-07
3950.9999993035600021.39287999651220e-066.96439998256102e-07
3960.9999981911516083.61769678437117e-061.80884839218558e-06
3970.9999954925009679.01499806556901e-064.50749903278451e-06
3980.9999928361938661.43276122672928e-057.16380613364642e-06
3990.999982239744083.55205118404616e-051.77602559202308e-05
4000.9999639725271327.20549457351905e-053.60274728675953e-05
4010.9999139152891980.0001721694216041578.60847108020787e-05
4020.999805054775050.0003898904499009030.000194945224950451
4030.9995545563308370.0008908873383266730.000445443669163337
4040.999033207931310.001933584137381810.000966792068690904
4050.9979415642956390.004116871408722330.00205843570436117
4060.9959447254781520.008110549043696060.00405527452184803
4070.9917401022921550.01651979541568890.00825989770784446
4080.9842827156811630.03143456863767440.0157172843188372
4090.9704335237472310.05913295250553750.0295664762527687
4100.9488664229378650.1022671541242690.0511335770621347
4110.9131375660108030.1737248679783950.0868624339891974
4120.854777025741720.2904459485165620.145222974258281
4130.7701768757174770.4596462485650450.229823124282523
4140.7129985924458180.5740028151083650.287001407554182
4150.6378516759734740.7242966480530520.362148324026526
4160.4682705332093500.9365410664186990.53172946679065


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3920.975124378109453NOK
5% type I error level3940.980099502487562NOK
10% type I error level3950.982587064676617NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/10yjmr1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/10yjmr1291238251.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/1az7x1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/1az7x1291238251.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/2az7x1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/2az7x1291238251.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/37udo1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/37udo1291238251.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/47udo1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/47udo1291238251.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/57udo1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/57udo1291238251.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/6i3ca1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/6i3ca1291238251.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/76rmo1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/76rmo1291238251.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/86rmo1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/86rmo1291238251.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/96rmo1291238251.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912381794bottj7z4vbahgv/96rmo1291238251.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 12 ; 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

Creative Commons License

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

Software written by Ed van Stee & Patrick Wessa


Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.


Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

  • personalize online software applications according to your needs
  • enforce strict security rules with respect to the data that you upload (e.g. statistical data)
  • manage user sessions of online applications
  • alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by