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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: Wed, 29 Dec 2010 15:49:21 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15.htm/, Retrieved Wed, 29 Dec 2010 16:47:30 +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/29/t12936376504994k4j2rylnw15.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 «
315.42 316.32 316.49 317.56 318.13 318.00 316.39 314.66 313.68 313.18 314.66 315.43 316.27 316.81 317.42 318.87 319.87 319.43 318.01 315.75 314.00 313.68 314.84 316.03 316.73 317.54 318.38 319.31 320.42 319.61 318.42 316.64 314.83 315.15 315.95 316.85 317.78 318.40 319.53 320.41 320.85 320.45 319.44 317.25 316.12 315.27 316.53 317.53 318.58 318.92 319.70 321.22 322.08 321.31 319.58 317.61 316.05 315.83 316.91 318.20 319.41 320.07 320.74 321.40 322.06 321.73 320.27 318.54 316.54 316.71 317.53 318.55 319.27 320.28 320.73 321.97 322.00 321.71 321.05 318.71 317.65 317.14 318.71 319.25 320.46 321.43 322.22 323.54 323.91 323.59 322.26 320.21 318.48 317.94 319.63 320.87 322.17 322.34 322.88 324.25 324.83 323.93 322.39 320.76 319.10 319.23 320.56 321.80 322.40 322.99 323.73 324.86 325.41 325.19 323.97 321.92 320.10 319.96 320.97 322.48 323.52 323.89 325.04 326.01 326.67 325.96 325.13 322.90 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 time14 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 311.204697580645 + 0.911006384408663M1[t] + 1.56369898582600M2[t] + 2.2848290872434M3[t] + 3.3956466886608M4[t] + 3.8852142900782M5[t] + 3.2625943914956M6[t] + 1.80216199291300M7[t] -0.220770405669603M8[t] -2.01089030425220M9[t] -2.24444770283480M10[t] -1.07675510141740M11[t] + 0.103869898582600t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)311.2046975806450.3086841008.166100
M10.9110063844086630.3880422.34770.0194150.009707
M21.563698985826000.3880294.02996.8e-053.4e-05
M32.28482908724340.3880165.888500
M43.39564668866080.3880058.751600
M53.88521429007820.38799510.013600
M63.26259439149560.3879868.40900
M71.802161992913000.3879794.6455e-062e-06
M8-0.2207704056696030.387973-0.5690.5696770.284838
M9-2.010890304252200.387969-5.183100
M10-2.244447702834800.387965-5.785200
M11-1.076755101417400.387963-2.77540.0057930.002896
t0.1038698985826000.000715145.322800


Multiple Linear Regression - Regression Statistics
Multiple R0.991533766635438
R-squared0.98313921037826
Adjusted R-squared0.98259384791071
F-TEST (value)1802.72620298888
F-TEST (DF numerator)12
F-TEST (DF denominator)371
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.55185108866948
Sum Squared Residuals893.457708321129


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1315.42312.2195738636343.20042613636561
2316.32312.9761363636363.34386363636361
3316.49313.8011363636362.68886363636362
4317.56315.0158238636362.54417613636363
5318.13315.6092613636362.52073863636362
6318315.0905113636362.90948863636363
7316.39313.7339488636362.65605113636361
8314.66311.8148863636362.84511363636366
9313.68310.1286363636363.55136363636364
10313.18309.9989488636363.18105113636364
11314.66311.2705113636363.38948863636365
12315.43312.4511363636362.97886363636363
13316.27313.4660126466282.80398735337231
14316.81314.2225751466282.58742485337243
15317.42315.0475751466282.37242485337244
16318.87316.2622626466282.60773735337243
17319.87316.8557001466283.01429985337243
18319.43316.3369501466283.09304985337243
19318.01314.9803876466283.02961235337242
20315.75313.0613251466282.68867485337242
21314311.3750751466282.62492485337242
22313.68311.2453876466282.43461235337243
23314.84312.5169501466282.3230498533724
24316.03313.6975751466282.33242485337239
25316.73314.7124514296192.01754857038118
26317.54315.4690139296192.07098607038124
27318.38316.2940139296192.08598607038122
28319.31317.5087014296191.80129857038122
29320.42318.1021389296192.31786107038124
30319.61317.5833889296192.02661107038123
31318.42316.2268264296192.19317357038124
32316.64314.3077639296192.33223607038121
33314.83312.6215139296192.2084860703812
34315.15312.4918264296192.6581735703812
35315.95313.7633889296192.18661107038121
36316.85314.9440139296191.90598607038124
37317.78315.958890212611.82110978738993
38318.4316.715452712611.68454728738999
39319.53317.540452712611.98954728738999
40320.41318.755140212611.65485978739004
41320.85319.348577712611.50142228739004
42320.45318.829827712611.62017228739000
43319.44317.473265212611.96673478739002
44317.25315.554202712611.69579728739002
45316.12313.867952712612.25204728739002
46315.27313.738265212611.53173478739000
47316.53315.009827712611.52017228738999
48317.53316.190452712611.33954728738999
49318.58317.2053289956011.37467100439874
50318.92317.9618914956010.95810850439883
51319.7318.7868914956010.913108504398805
52321.22320.0015789956011.21842100439884
53322.08320.5950164956011.48498350439880
54321.31320.0762664956011.23373350439882
55319.58318.7197039956010.860296004398806
56317.61316.8006414956010.80935850439883
57316.05315.1143914956010.935608504398824
58315.83314.9847039956010.845296004398803
59316.91316.2562664956010.653733504398841
60318.2317.4368914956010.763108504398803
61319.41318.4517677785920.95823222140758
62320.07319.2083302785920.861669721407603
63320.74320.0333302785920.706669721407621
64321.4321.2480177785920.151982221407588
65322.06321.8414552785920.218544721407619
66321.73321.3227052785920.407294721407628
67320.27319.9661427785920.303857221407598
68318.54318.0470802785920.492919721407636
69316.54316.3608302785920.179169721407632
70316.71316.2311427785920.478857221407597
71317.53317.5027052785920.0272947214075874
72318.55318.683330278592-0.133330278592376
73319.27319.698206561584-0.428206561583664
74320.28320.454769061584-0.174769061583619
75320.73321.279769061584-0.549769061583568
76321.97322.494456561584-0.524456561583562
77322323.087894061584-1.08789406158358
78321.71322.569144061584-0.859144061583612
79321.05321.212581561584-0.162581561583573
80318.71319.293519061584-0.583519061583608
81317.65317.6072690615840.0427309384163862
82317.14317.477581561584-0.337581561583599
83318.71318.749144061584-0.0391440615836078
84319.25319.929769061584-0.679769061583589
85320.46320.944645344575-0.484645344574872
86321.43321.701207844575-0.271207844574779
87322.22322.526207844575-0.306207844574758
88323.54323.740895344575-0.200895344574768
89323.91324.334332844575-0.424332844574757
90323.59323.815582844575-0.225582844574815
91322.26322.459020344575-0.199020344574793
92320.21320.539957844575-0.329957844574809
93318.48318.853707844575-0.373707844574775
94317.94318.724020344575-0.784020344574789
95319.63319.995582844575-0.365582844574794
96320.87321.176207844575-0.306207844574786
97322.17322.191084127566-0.0210841275660363
98322.34322.947646627566-0.607646627566012
99322.88323.772646627566-0.89264662756599
100324.25324.987334127566-0.737334127565991
101324.83325.580771627566-0.750771627566
102323.93325.062021627566-1.13202162756599
103322.39323.705459127566-1.315459127566
104320.76321.786396627566-1.026396627566
105319.1320.100146627566-1.00014662756597
106319.23319.970459127566-0.740459127565969
107320.56321.242021627566-0.682021627565989
108321.8322.422646627566-0.622646627565979
109322.4323.437522910557-1.03752291055728
110322.99324.194085410557-1.20408541055718
111323.73325.019085410557-1.28908541055717
112324.86326.233772910557-1.37377291055718
113325.41326.827210410557-1.41721041055716
114325.19326.308460410557-1.11846041055720
115323.97324.951897910557-0.981897910557158
116321.92323.032835410557-1.11283541055717
117320.1321.346585410557-1.24658541055717
118319.96321.216897910557-1.25689791055721
119320.97322.488460410557-1.51846041055716
120322.48323.669085410557-1.18908541055717
121323.52324.683961693548-1.16396169354847
122323.89325.440524193548-1.55052419354840
123325.04326.265524193548-1.22552419354837
124326.01327.480211693548-1.4702116935484
125326.67328.073649193548-1.40364919354837
126325.96327.554899193548-1.59489919354842
127325.13326.198336693548-1.06833669354839
128322.9324.279274193548-1.37927419354841
129321.61322.593024193548-0.98302419354838
130321.01322.463336693548-1.45333669354840
131322.08323.734899193548-1.65489919354841
132323.37324.915524193548-1.54552419354839
133324.34325.930400476540-1.59040047653968
134325.3326.686962976540-1.38696297653958
135326.29327.511962976540-1.22196297653957
136327.54328.726650476540-1.18665047653957
137327.54329.320087976540-1.78008797653957
138327.21328.801337976540-1.59133797653961
139325.98327.444775476540-1.46477547653957
140324.42325.525712976540-1.10571297653958
141322.91323.839462976540-0.92946297653957
142322.9323.709775476540-0.80977547653961
143323.85324.981337976540-1.13133797653957
144324.96326.161962976540-1.20196297653961
145326.01327.176839259531-1.16683925953087
146326.51327.933401759531-1.4234017595308
147327.01328.758401759531-1.74840175953080
148327.62329.973089259531-2.35308925953079
149328.76330.566526759531-1.80652675953080
150328.4330.047776759531-1.64777675953082
151327.2328.691214259531-1.49121425953080
152325.28326.772151759531-1.49215175953082
153323.2325.085901759531-1.88590175953081
154323.4324.956214259531-1.55621425953081
155324.64326.227776759531-1.58777675953081
156325.85327.408401759531-1.55840175953077
157326.6328.423278042522-1.82327804252203
158327.47329.179840542522-1.70984054252196
159327.58330.004840542522-2.42484054252201
160329.56331.219528042522-1.65952804252199
161329.9331.812965542522-1.91296554252202
162328.92331.294215542522-2.37421554252198
163327.89329.937653042522-2.04765304252201
164326.17328.018590542522-1.84859054252198
165324.68326.332340542522-1.65234054252199
166325.04326.202653042522-1.16265304252197
167326.34327.474215542522-1.13421554252202
168327.39328.654840542522-1.26484054252201
169328.37329.669716825513-1.29971682551325
170329.4330.426279325513-1.02627932551321
171330.14331.251279325513-1.11127932551321
172331.33332.465966825513-1.13596682551322
173332.31333.059404325513-0.749404325513197
174331.9332.540654325513-0.640654325513222
175330.7331.184091825513-0.484091825513206
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177327.34327.578779325513-0.238779325513224
178327.02327.449091825513-0.42909182551321
179327.99328.720654325513-0.730654325513188
180328.48329.901279325513-1.42127932551318
181329.18330.916155608504-1.73615560850445
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183331.32332.497718108504-1.17771810850441
184332.48333.712405608504-1.23240560850438
185332.92334.305843108504-1.38584310850438
186332.08333.787093108504-1.70709310850442
187331.02332.430530608504-1.41053060850442
188329.24330.511468108504-1.27146810850439
189327.28328.825218108504-1.54521810850443
190327.21328.695530608504-1.48553060850441
191328.29329.967093108504-1.67709310850438
192329.41331.147718108504-1.73771810850438
193330.23332.162594391496-1.93259439149564
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195331.87333.744156891496-1.87415689149560
196333.14334.958844391496-1.81884439149561
197333.8335.552281891496-1.75228189149559
198333.42335.033531891496-1.61353189149559
199331.73333.676969391496-1.94696939149558
200329.9331.757906891496-1.85790689149562
201328.4330.071656891496-1.67165689149563
202328.17329.941969391496-1.77196939149558
203329.32331.213531891496-1.89353189149560
204330.59332.394156891496-1.80415689149562
205331.58333.409033174487-1.82903317448688
206332.39334.165595674487-1.77559567448681
207333.33334.990595674487-1.66059567448682
208334.41336.205283174487-1.79528317448678
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211332.88334.923408174487-2.04340817448680
212330.77333.004345674487-2.23434567448682
213329.14331.318095674487-2.17809567448682
214328.77331.188408174487-2.41840817448682
215330.14332.459970674487-2.31997067448681
216331.52333.640595674487-2.12059567448682
217332.75334.655471957478-1.90547195747806
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233337.84339.291598240469-1.45159824046923
234337.72338.772848240469-1.05284824046918
235336.37337.416285740469-1.04628574046920
236334.51335.497223240469-0.987223240469213
237332.6333.810973240469-1.21097324046918
238332.37333.681285740469-1.31128574046920
239333.75334.952848240469-1.20284824046921
240334.79336.133473240469-1.34347324046918
241336.05337.148349523460-1.09834952346046
242336.59337.904912023460-1.31491202346043
243337.79338.729912023460-0.939912023460387
244338.71339.944599523460-1.23459952346043
245339.3340.538037023460-1.23803702346040
246339.12340.019287023460-0.899287023460403
247337.56338.662724523460-1.10272452346040
248335.92336.743662023460-0.823662023460391
249333.74335.057412023460-1.31741202346040
250333.7334.927724523460-1.22772452346042
251335.13336.199287023460-1.06928702346041
252336.56337.379912023460-0.819912023460405
253337.84338.394788306452-0.554788306451694
254338.19339.151350806452-0.961350806451607
255339.9339.976350806452-0.0763508064516346
256340.6341.191038306452-0.591038306451589
257341.29341.784475806452-0.494475806451593
258341341.265725806452-0.265725806451614
259339.39339.909163306452-0.519163306451625
260337.43337.990100806452-0.560100806451603
261335.72336.303850806452-0.583850806451584
262335.84336.174163306452-0.334163306451631
263336.93337.445725806452-0.515725806451603
264338.04338.626350806452-0.586350806451594
265339.06339.641227089443-0.581227089442869
266340.3340.397789589443-0.097789589442795
267341.21341.222789589443-0.0127895894428335
268342.33342.437477089443-0.10747708944283
269342.74343.030914589443-0.290914589442805
270342.07342.512164589443-0.442164589442823
271340.32341.155602089443-0.83560208944282
272338.27339.236539589443-0.96653958944283
273336.52337.550289589443-1.03028958944283
274336.68337.420602089443-0.7406020894428
275338.19338.692164589443-0.502164589442814
276339.44339.872789589443-0.43278958944282
277340.57340.887665872434-0.317665872434083
278341.44341.644228372434-0.204228372434011
279342.53342.4692283724340.0607716275659586
280343.39343.683915872434-0.293915872434030
281343.96344.277353372434-0.317353372434033
282343.18343.758603372434-0.578603372434012
283341.88342.402040872434-0.522040872434017
284339.65340.482978372434-0.832978372434036
285337.8338.796728372434-0.996728372434006
286337.69338.667040872434-0.977040872434012
287339.09339.938603372434-0.848603372434038
288340.32341.119228372434-0.799228372434025
289341.2342.134104655425-0.93410465542529
290342.35342.890667155425-0.540667155425191
291342.93343.715667155425-0.785667155425207
292344.77344.930354655425-0.160354655425236
293345.58345.5237921554250.0562078445747706
294345.14345.0050421554250.134957844574765
295343.81343.6484796554250.161520344574787
296342.22341.7294171554250.49058284457481
297339.69340.043167155425-0.353167155425225
298339.82339.913479655425-0.0934796554252254
299340.98341.185042155425-0.2050421554252
300342.82342.3656671554250.454332844574771
301343.52343.3805434384160.139456561583502
302344.33344.1371059384160.192894061583569
303345.11344.9621059384160.147894061583599
304346.88346.1767934384160.703206561583578
305347.25346.7702309384160.479769061583589
306346.61346.2514809384160.358519061583593
307345.22344.8949184384160.325081561583614
308343.11342.9758559384160.134144061583600
309340.9341.289605938416-0.389605938416445
310341.17341.1599184384160.0100815615835993
311342.8342.4314809384160.368519061583592
312344.04343.6121059384160.427894061583601
313344.79344.6269822214080.163017778592340
314345.82345.3835447214080.436455278592377
315347.25346.2085447214081.04145527859239
316348.17347.4232322214080.746767778592403
317348.75348.0166697214080.73333027859239
318348.07347.4979197214080.572080278592378
319346.38346.1413572214080.238642778592383
320344.52344.2222947214080.29770527859237
321342.92342.5360447214080.383955278592394
322342.63342.4063572214080.223642778592381
323344.06343.6779197214080.382080278592385
324345.38344.8585447214080.521455278592375
325346.12345.8734210043990.246578995601123
326346.79346.6299835043990.160016495601203
327347.69347.4549835043990.235016495601186
328349.38348.6696710043990.71032899560118
329350.04349.2631085043990.776891495601204
330349.38348.7443585043990.635641495601181
331347.78347.3877960043990.392203995601166
332345.75345.4687335043990.281266495601186
333344.7343.7824835043990.917516495601166
334344.01343.6527960043990.357203995601179
335345.5344.9243585043990.575641495601182
336346.75346.1049835043990.645016495601178
337347.86347.119859787390.740140212609932
338348.32347.876422287390.443577712609972
339349.26348.701422287390.558577712609975
340350.84349.916109787390.923890212609957
341351.7350.509547287391.19045271260996
342351.11349.990797287391.11920271261000
343349.37348.634234787390.735765212609995
344347.97346.715172287391.25482771261001
345346.31345.028922287391.28107771260998
346346.22344.899234787391.32076521261001
347347.68346.170797287391.50920271260999
348348.82347.351422287391.46857771260998
349350.29348.3662985703811.92370142961873
350351.58349.1228610703812.45713892961877
351352.08349.9478610703812.13213892961876
352353.45351.1625485703812.28745142961877
353354.08351.7559860703812.32401392961876
354353.66351.2372360703812.42276392961881
355352.25349.8806735703812.36932642961879
356350.3347.9616110703812.33838892961880
357348.58346.2753610703812.30463892961877
358348.74346.1456735703812.5943264296188
359349.93347.4172360703812.51276392961879
360351.21348.5978610703812.61213892961876
361352.62349.6127373533723.00726264662752
362352.93350.3692998533722.56070014662759
363353.54351.1942998533722.3457001466276
364355.27352.4089873533722.86101264662756
365355.52353.0024248533722.51757514662755
366354.97352.4836748533722.48632514662761
367353.74351.1271123533722.61288764662759
368351.51349.2080498533722.30195014662758
369349.63347.5217998533722.10820014662757
370349.82347.3921123533722.42788764662758
371351.12348.6636748533722.45632514662759
372352.35349.8442998533722.50570014662760
373353.47350.8591761363642.61082386363634
374354.51351.6157386363642.89426136363637
375355.18352.4407386363642.73926136363639
376355.98353.6554261363642.3245738636364
377356.94354.2488636363642.69113636363637
378355.99353.7301136363642.25988636363639
379354.58352.3735511363642.20644886363637
380352.68350.4544886363642.22551136363639
381350.72348.7682386363641.95176136363640
382350.92348.6385511363642.28144886363640
383352.55349.9101136363642.63988636363639
384353.91351.0907386363642.8192613636364


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.006130571970674760.01226114394134950.993869428029325
170.00584168418170420.01168336836340840.994158315818296
180.001615113957923180.003230227915846360.998384886042077
190.0005755963215095380.001151192643019080.99942440367849
200.0001264446720818390.0002528893441636780.999873555327918
210.0001332428521000270.0002664857042000540.9998667571479
226.13065889407047e-050.0001226131778814090.99993869341106
235.1971736269397e-050.0001039434725387940.99994802826373
241.74927581767974e-053.49855163535948e-050.999982507241823
257.63376991553663e-061.52675398310733e-050.999992366230084
262.56218073112167e-065.12436146224335e-060.999997437819269
277.6492084020028e-071.52984168040056e-060.99999923507916
282.17625887008441e-074.35251774016882e-070.999999782374113
296.48889749752905e-081.29777949950581e-070.999999935111025
302.47881754566387e-084.95763509132774e-080.999999975211825
316.78382237708814e-091.35676447541763e-080.999999993216178
322.14677702751157e-094.29355405502314e-090.999999997853223
337.17918713838267e-101.43583742767653e-090.999999999282081
344.6202862421366e-109.2405724842732e-100.999999999537971
351.35112138503199e-102.70224277006398e-100.999999999864888
364.00953681760293e-118.01907363520586e-110.999999999959905
371.12624643385580e-112.25249286771160e-110.999999999988738
383.38185162214114e-126.76370324428227e-120.999999999996618
392.23628747226544e-124.47257494453088e-120.999999999997764
406.96529437857955e-131.39305887571591e-120.999999999999303
412.98537792090361e-135.97075584180723e-130.999999999999701
421.10637250130709e-132.21274500261419e-130.99999999999989
434.06769280642152e-148.13538561284303e-140.99999999999996
441.32059334143761e-142.64118668287523e-140.999999999999987
456.96405241997125e-151.39281048399425e-140.999999999999993
464.68710857765107e-159.37421715530214e-150.999999999999995
472.1533034819337e-154.3066069638674e-150.999999999999998
488.52219376091454e-161.70443875218291e-151
492.87514915476498e-165.75029830952996e-161
501.81553490305003e-163.63106980610005e-161
518.0031686255805e-171.6006337251161e-161
523.31108568333198e-176.62217136666395e-171
531.79332109702764e-173.58664219405528e-171
546.92992646366555e-181.38598529273311e-171
558.31918955171647e-181.66383791034329e-171
567.96801469830637e-181.59360293966127e-171
571.42647912549512e-172.85295825099024e-171
581.28673194036729e-172.57346388073458e-171
591.50704181608745e-173.01408363217491e-171
606.94722280365099e-181.38944456073020e-171
613.80088202467247e-187.60176404934495e-181
622.09057437341860e-184.18114874683719e-181
631.03422747096283e-182.06845494192567e-181
647.409917201349e-191.4819834402698e-181
658.8656139705631e-191.77312279411262e-181
665.72366200642219e-191.14473240128444e-181
674.26576924751378e-198.53153849502756e-191
682.14184086304262e-194.28368172608523e-191
695.43455135318798e-191.08691027063760e-181
703.08595787149517e-196.17191574299033e-191
713.28706281092787e-196.57412562185575e-191
723.22757421453043e-196.45514842906085e-191
736.64648625365548e-191.32929725073110e-181
743.76863093563464e-197.53726187126928e-191
753.70075149818641e-197.40150299637282e-191
762.14023231225564e-194.28046462451129e-191
774.13356259931082e-188.26712519862164e-181
781.43371601831479e-172.86743203662957e-171
797.83328506059103e-181.56665701211821e-171
806.44335366106715e-181.28867073221343e-171
814.26233731606323e-188.52467463212647e-181
822.6379398903818e-185.2758797807636e-181
832.18515370426340e-184.37030740852681e-181
841.21579805108555e-182.43159610217110e-181
856.79994975277161e-191.35998995055432e-181
866.99612793561488e-191.39922558712298e-181
879.98977855096201e-191.9979557101924e-181
883.30507383620503e-186.61014767241006e-181
893.14260610921535e-186.28521221843069e-181
904.91070392936886e-189.82140785873773e-181
917.03999511098628e-181.40799902219726e-171
928.00013633215671e-181.60002726643134e-171
936.27191667127722e-181.25438333425544e-171
944.03204166775997e-188.06408333551994e-181
954.79787447558273e-189.59574895116547e-181
961.39815712723279e-172.79631425446559e-171
972.76343028647809e-165.52686057295618e-161
983.19019814272158e-166.38039628544316e-161
992.35642485895157e-164.71284971790315e-161
1003.00520814093768e-166.01041628187537e-161
1013.36315001428266e-166.72630002856531e-161
1022.05044360941601e-164.10088721883203e-161
1031.52139857636240e-163.04279715272481e-161
1049.99810223183513e-171.99962044636703e-161
1057.15896430684082e-171.43179286136816e-161
1068.43724229322575e-171.68744845864515e-161
1071.30683554930966e-162.61367109861931e-161
1084.332745039278e-168.665490078556e-161
1093.92336435741378e-167.84672871482756e-161
1102.84243688724748e-165.68487377449495e-161
1112.08462974561629e-164.16925949123257e-161
1121.38894667545168e-162.77789335090336e-161
1138.47474498591835e-171.69494899718367e-161
1149.52535339136035e-171.90507067827207e-161
1151.56996850152400e-163.13993700304800e-161
1161.84145732060135e-163.68291464120269e-161
1171.37720267701062e-162.75440535402124e-161
1181.10628933017101e-162.21257866034201e-161
1196.72471374313879e-171.34494274862776e-161
1208.94496075877183e-171.78899215175437e-161
1211.61946894967087e-163.23893789934173e-161
1221.13735984618592e-162.27471969237185e-161
1233.24806216417560e-166.49612432835119e-161
1243.93982119261945e-167.87964238523889e-161
1255.48331743677889e-161.09666348735578e-151
1264.01959586318368e-168.03919172636737e-161
1271.59011663447225e-153.1802332689445e-150.999999999999998
1282.03373184815355e-154.06746369630709e-150.999999999999998
1298.27846004770146e-151.65569200954029e-140.999999999999992
1308.19730731192195e-151.63946146238439e-140.999999999999992
1315.76480407278265e-151.15296081455653e-140.999999999999994
1325.81630573385869e-151.16326114677174e-140.999999999999994
1335.9042774347444e-151.18085548694888e-140.999999999999994
1341.54777518657511e-143.09555037315022e-140.999999999999985
1351.12977286848466e-132.25954573696933e-130.999999999999887
1361.04154684115275e-122.08309368230549e-120.999999999998958
1379.23471981613104e-131.84694396322621e-120.999999999999077
1381.21274517708759e-122.42549035417519e-120.999999999998787
1391.92271284657865e-123.84542569315729e-120.999999999998077
1401.39145266975532e-112.78290533951064e-110.999999999986085
1411.18846263996225e-102.37692527992449e-100.999999999881154
1422.21787291813627e-094.43574583627253e-090.999999997782127
1439.8212373783599e-091.96424747567198e-080.999999990178763
1443.69161541658176e-087.38323083316352e-080.999999963083846
1451.58478943800655e-073.16957887601309e-070.999999841521056
1463.38160315185483e-076.76320630370966e-070.999999661839685
1473.82837109694509e-077.65674219389018e-070.99999961716289
1482.52675809216198e-075.05351618432397e-070.99999974732419
1492.80602799530806e-075.61205599061612e-070.9999997193972
1503.90894026639038e-077.81788053278075e-070.999999609105973
1516.70863540158715e-071.34172708031743e-060.99999932913646
1521.17129838738335e-062.3425967747667e-060.999998828701613
1539.76237724329731e-071.95247544865946e-060.999999023762276
1541.41103433567965e-062.82206867135931e-060.999998588965664
1552.05276416920303e-064.10552833840607e-060.99999794723583
1563.41296253737204e-066.82592507474407e-060.999996587037463
1573.64024852990393e-067.28049705980786e-060.99999635975147
1585.08820705015705e-061.01764141003141e-050.99999491179295
1593.61032936881673e-067.22065873763345e-060.999996389670631
1607.01671954073718e-061.40334390814744e-050.99999298328046
1618.01767809831886e-061.60353561966377e-050.999991982321902
1625.80198888144651e-061.16039777628930e-050.999994198011119
1634.91536030481278e-069.83072060962556e-060.999995084639695
1645.40664271640761e-061.08132854328152e-050.999994593357284
1657.30437966313697e-061.46087593262739e-050.999992695620337
1663.17942866289147e-056.35885732578293e-050.999968205713371
1670.0001397365001209490.0002794730002418970.999860263499879
1680.0004170476668019560.0008340953336039120.999582952333198
1690.001089069131814900.002178138263629790.998910930868185
1700.004606616899008080.009213233798016150.995393383100992
1710.01451559110431300.02903118220862610.985484408895687
1720.03654665889640310.07309331779280630.963453341103597
1730.1232746764541270.2465493529082540.876725323545873
1740.3151086623814950.630217324762990.684891337618505
1750.60923652184520.78152695630960.3907634781548
1760.9141570596179880.1716858807640240.0858429403820118
1770.990452187670850.01909562465829930.00954781232914964
1780.9992498735946660.001500252810668260.000750126405334132
1790.9999190345770230.0001619308459542548.09654229771269e-05
1800.9999589879605298.20240789422425e-054.10120394711212e-05
1810.9999659263430856.814731382899e-053.4073656914495e-05
1820.9999920408097261.59183805486606e-057.95919027433032e-06
1830.9999979273231884.14535362318809e-062.07267681159405e-06
1840.9999994045528781.19089424486999e-065.95447122434997e-07
1850.9999997574336084.85132783786643e-072.42566391893322e-07
1860.9999998080372843.83925431979994e-071.91962715989997e-07
1870.9999999277728161.44454367277099e-077.22271836385495e-08
1880.9999999836717023.26565959152125e-081.63282979576063e-08
1890.9999999933115551.33768907406216e-086.68844537031081e-09
1900.9999999977095134.58097468425972e-092.29048734212986e-09
1910.9999999986093532.78129467138479e-091.39064733569239e-09
1920.9999999989478992.10420284476032e-091.05210142238016e-09
1930.999999998846442.30712109525555e-091.15356054762778e-09
1940.9999999992142071.57158620577321e-097.85793102886607e-10
1950.9999999991669651.66606996960042e-098.33034984800211e-10
1960.9999999991780441.64391236298246e-098.2195618149123e-10
1970.9999999992723791.45524273020030e-097.27621365100151e-10
1980.9999999995156869.68627760332606e-104.84313880166303e-10
1990.999999999435731.12853912953220e-095.64269564766098e-10
2000.9999999994365021.12699694535933e-095.63498472679667e-10
2010.9999999996614856.77029787922278e-103.38514893961139e-10
2020.999999999731085.37839567278815e-102.68919783639408e-10
2030.999999999709255.81499979410041e-102.90749989705021e-10
2040.9999999997132225.73556992015558e-102.86778496007779e-10
2050.9999999996972966.05408472982156e-103.02704236491078e-10
2060.9999999997045475.90906485441754e-102.95453242720877e-10
2070.9999999997504984.99004751593361e-102.49502375796680e-10
2080.9999999997368095.26382234970354e-102.63191117485177e-10
2090.9999999996461127.0777615128421e-103.53888075642105e-10
2100.9999999995153839.69233236759372e-104.84616618379686e-10
2110.9999999993193331.36133402591278e-096.80667012956389e-10
2120.9999999989739252.05214933627406e-091.02607466813703e-09
2130.9999999984292063.14158757627068e-091.57079378813534e-09
2140.9999999976916634.61667361352445e-092.30833680676223e-09
2150.9999999967737066.4525878487576e-093.2262939243788e-09
2160.9999999957830448.43391123704366e-094.21695561852183e-09
2170.9999999949071141.01857714960733e-085.09288574803667e-09
2180.9999999936461821.27076355841656e-086.35381779208278e-09
2190.9999999937637061.24725886153909e-086.23629430769543e-09
2200.9999999947528881.04942246709565e-085.24711233547825e-09
2210.999999995861998.27602041512476e-094.13801020756238e-09
2220.9999999968557546.28849143714763e-093.14424571857381e-09
2230.9999999976479154.70417052840207e-092.35208526420103e-09
2240.9999999974380575.1238857274261e-092.56194286371305e-09
2250.9999999990257021.94859567917651e-099.74297839588256e-10
2260.9999999991912071.61758618462762e-098.08793092313811e-10
2270.999999999279981.44004159028944e-097.20020795144721e-10
2280.999999999578788.42439361147984e-104.21219680573992e-10
2290.9999999997924544.15091298461306e-102.07545649230653e-10
2300.9999999998048153.90370005228252e-101.95185002614126e-10
2310.9999999999076051.84790643010156e-109.23953215050782e-11
2320.9999999999435931.12814830585808e-105.64074152929042e-11
2330.9999999999444331.11134325530727e-105.55671627653637e-11
2340.9999999999675286.49436039806564e-113.24718019903282e-11
2350.9999999999825983.48034387602579e-111.74017193801289e-11
2360.9999999999918241.63521907334850e-118.17609536674251e-12
2370.9999999999943931.12133400117886e-115.60667000589431e-12
2380.9999999999947461.05073248104011e-115.25366240520053e-12
2390.9999999999955888.82313513590602e-124.41156756795301e-12
2400.9999999999952959.4098003478666e-124.7049001739333e-12
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2460.9999999999978384.32375845708191e-122.16187922854095e-12
2470.9999999999978964.20894902420665e-122.10447451210333e-12
2480.9999999999987932.41498688619546e-121.20749344309773e-12
2490.9999999999984783.0445338941882e-121.5222669470941e-12
2500.9999999999981823.63657324713454e-121.81828662356727e-12
2510.9999999999981243.75299795853446e-121.87649897926723e-12
2520.9999999999985842.83237987632878e-121.41618993816439e-12
2530.999999999999371.26063651067443e-126.30318255337213e-13
2540.999999999999341.32119920301005e-126.60599601505024e-13
2550.9999999999999141.71600164485496e-138.58000822427478e-14
2560.999999999999941.20044282485757e-136.00221412428784e-14
2570.9999999999999647.28025872493646e-143.64012936246823e-14
2580.999999999999992.06259811598805e-141.03129905799403e-14
2590.9999999999999959.7619089844011e-154.88095449220055e-15
2600.9999999999999984.96671561426885e-152.48335780713442e-15
26111.99581325626254e-159.97906628131268e-16
26214.16085443052258e-162.08042721526129e-16
26312.17252354454439e-161.08626177227219e-16
26411.6920058777565e-168.4600293887825e-17
26511.38143861994251e-166.90719309971256e-17
26613.02097037426215e-171.51048518713107e-17
26714.33498756189686e-182.16749378094843e-18
26811.43827589087172e-187.1913794543586e-19
26919.25078331455825e-194.62539165727913e-19
27017.2932369577011e-193.64661847885055e-19
27111.12250247214641e-185.61251236073204e-19
27212.0080969886206e-181.0040484943103e-18
27313.59695942979636e-181.79847971489818e-18
27414.59035566766036e-182.29517783383018e-18
27514.27392195226915e-182.13696097613458e-18
27614.22434745081955e-182.11217372540978e-18
27713.30843690938387e-181.65421845469194e-18
27812.24119485169202e-181.12059742584601e-18
27915.24088728859262e-192.62044364429631e-19
28016.29634964920811e-193.14817482460405e-19
28118.39436862010358e-194.19718431005179e-19
28211.47096366672757e-187.35481833363787e-19
28312.24182264583565e-181.12091132291782e-18
28414.52594858076019e-182.26297429038009e-18
28519.667807103676e-184.833903551838e-18
28611.94087559921769e-179.70437799608845e-18
28713.6752685800073e-171.83763429000365e-17
28816.12170335844879e-173.06085167922439e-17
28918.20778364296604e-174.10389182148302e-17
29011.47158325722558e-167.35791628612788e-17
29112.18578636312134e-161.09289318156067e-16
29213.55122932082692e-161.77561466041346e-16
29314.9552264503211e-162.47761322516055e-16
29415.490835700289e-162.7454178501445e-16
29514.4883611888805e-162.24418059444025e-16
29611.11904275884950e-165.59521379424752e-17
29712.11917295770915e-161.05958647885457e-16
29813.09883872438876e-161.54941936219438e-16
29915.88091627258002e-162.94045813629001e-16
30013.83060930153563e-161.91530465076781e-16
30116.00018905373531e-163.00009452686765e-16
30219.17835721951698e-164.58917860975849e-16
30311.52476585459043e-157.62382927295217e-16
30411.00570976698773e-155.02854883493865e-16
30511.26012082096438e-156.30060410482192e-16
30611.65595817298231e-158.27979086491156e-16
30711.74204664453341e-158.71023322266707e-16
3080.9999999999999992.76832319491777e-151.38416159745888e-15
3090.9999999999999976.0408715693209e-153.02043578466045e-15
3100.9999999999999941.22701534274725e-146.13507671373626e-15
3110.999999999999991.80988998357211e-149.04944991786054e-15
3120.9999999999999872.67381047847245e-141.33690523923623e-14
3130.9999999999999725.62985041694858e-142.81492520847429e-14
3140.9999999999999481.03451040275452e-135.17255201377261e-14
3150.9999999999999813.77093823286741e-141.88546911643370e-14
3160.9999999999999754.98686844875773e-142.49343422437886e-14
3170.9999999999999627.5337385510234e-143.7668692755117e-14
3180.9999999999999371.26038902043447e-136.30194510217235e-14
3190.9999999999998622.7555536628475e-131.37777683142375e-13
3200.999999999999715.78375253088266e-132.89187626544133e-13
3210.9999999999994321.13544585811575e-125.67722929057874e-13
3220.9999999999986992.6027065536508e-121.3013532768254e-12
3230.999999999997125.76119232399049e-122.88059616199524e-12
3240.9999999999939231.21539535540998e-116.07697677704988e-12
3250.9999999999901031.97935365784009e-119.89676828920043e-12
3260.9999999999866862.66279176910431e-111.33139588455215e-11
3270.9999999999757924.84156592523368e-112.42078296261684e-11
3280.9999999999481811.03637288453273e-105.18186442266366e-11
3290.9999999998909052.18190440800935e-101.09095220400467e-10
3300.9999999997675764.64848247722668e-102.32424123861334e-10
3310.999999999541159.17699005277686e-104.58849502638843e-10
3320.9999999992562421.48751615108839e-097.43758075544196e-10
3330.9999999985131482.97370417012750e-091.48685208506375e-09
3340.9999999978211424.35771533089436e-092.17885766544718e-09
3350.9999999966029826.7940356085898e-093.3970178042949e-09
3360.999999995052539.89493886092815e-094.94746943046408e-09
3370.999999996111747.7765202560659e-093.88826012803295e-09
3380.9999999994284271.14314637224088e-095.71573186120439e-10
3390.999999999821133.57741033478984e-101.78870516739492e-10
3400.9999999998961282.07743992929505e-101.03871996464753e-10
3410.9999999998954742.09052741158851e-101.04526370579426e-10
3420.9999999998942252.11550001358993e-101.05775000679497e-10
3430.9999999999876652.46707124657297e-111.23353562328649e-11
3440.9999999999851282.97436585889493e-111.48718292944746e-11
3450.9999999999716295.67426454354911e-112.83713227177455e-11
3460.9999999999848273.03459038260198e-111.51729519130099e-11
3470.999999999993111.37780463581116e-116.8890231790558e-12
3480.9999999999997744.52011323792703e-132.26005661896351e-13
3490.9999999999999852.97563175994069e-141.48781587997034e-14
3500.999999999999959.85494507448286e-144.92747253724143e-14
3510.9999999999999261.4741252667754e-137.370626333877e-14
3520.999999999999833.38708554592172e-131.69354277296086e-13
3530.9999999999996217.57616563051991e-133.78808281525996e-13
3540.9999999999976074.78661206105343e-122.39330603052672e-12
3550.9999999999865722.68557362180571e-111.34278681090286e-11
3560.9999999999150841.69831047382548e-108.49155236912741e-11
3570.9999999996060217.8795772247432e-103.9397886123716e-10
3580.9999999981459723.70805694195476e-091.85402847097738e-09
3590.9999999888729132.22541732499345e-081.11270866249672e-08
3600.9999999370310761.25937847246108e-076.2968923623054e-08
3610.999999801952693.96094621802532e-071.98047310901266e-07
3620.9999993487857331.30242853458855e-066.51214267294273e-07
3630.9999986687544652.6624910704809e-061.33124553524045e-06
3640.9999977733293624.45334127641052e-062.22667063820526e-06
3650.9999874949487742.50101024512005e-051.25050512256002e-05
3660.999912022648570.0001759547028601278.79773514300633e-05
3670.9997857946539690.0004284106920626570.000214205346031328
3680.9980153223077830.003969355384433680.00198467769221684


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3440.974504249291785NOK
5% type I error level3480.985835694050991NOK
10% type I error level3490.988668555240793NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/10oaei1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/10oaei1293637743.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/1a0yr1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/1a0yr1293637743.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/2a0yr1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/2a0yr1293637743.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/3a0yr1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/3a0yr1293637743.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/4layc1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/4layc1293637743.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/5layc1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/5layc1293637743.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/6layc1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/6layc1293637743.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/7djff1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/7djff1293637743.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/8djff1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/8djff1293637743.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/9oaei1293637743.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936376504994k4j2rylnw15/9oaei1293637743.ps (open in new window)


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




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