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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, 01 Dec 2010 15:22:48 +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/t1291216960ktjngawgm6ea123.htm/, Retrieved Wed, 01 Dec 2010 16:22:51 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123.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 «

0 162556 807 213118 0 29790 444 81767 0 87550 412 153198 1 84738 428 -26007 0 54660 315 126942 0 42634 168 157214 1 40949 263 129352 0 45187 267 234817 0 37704 228 60448 0 16275 129 47818 1 25830 104 245546 1 12679 122 48020 0 18014 393 -1710 1 43556 190 32648 0 24811 280 95350 1 6575 63 151352 1 7123 102 288170 0 21950 265 114337 0 37597 234 37884 1 17821 277 122844 0 12988 73 82340 0 22330 67 79801 1 13326 103 165548 1 16189 290 116384 1 7146 83 134028 1 15824 56 63838 0 27664 236 74996 1 11920 73 31080 1 8568 34 32168 1 14416 139 49857 0 3369 26 87161 0 11819 70 106113 0 6984 40 80570 0 4519 42 102129 1 2220 12 301670 1 18562 211 102313 1 10327 74 88577 0 5336 80 112477 0 2365 83 191778 1 4069 131 79804 1 8636 203 128294 1 13718 56 96448 1 4525 89 93811 1 6869 88 117520 1 4628 39 69159 0 3689 25 101792 0 4891 49 210568 0 7489 149 136996 1 4901 58 121920 1 2284 41 76403 0 3160 90 108094 0 4150 136 134759 0 7285 97 188873 0 1134 63 146216 0 4658 114 15660 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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	27 seconds
R Server	'George Udny Yule' @ 72.249.76.132
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

Orders[t] = + 3.68723754833751 -7.78189324911588Group[t] + 0.00526500410176195Costs[t] + 0.000237315498099678`Dividends `[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	3.68723754833751	5.30947	0.6945	0.487769	0.243884
Group	-7.78189324911588	3.836783	-2.0282	0.043157	0.021578
Costs	0.00526500410176195	0.000152	34.5976	0	0
`Dividends `	0.000237315498099678	5.6e-05	4.265	2.5e-05	1.2e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.884177037913136
R-squared	0.781769034372846
Adjusted R-squared	0.780235795738463
F-TEST (value)	509.8808605795
F-TEST (DF numerator)	3
F-TEST (DF denominator)	427
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	34.9175364630342
Sum Squared Residuals	520613.068580406

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	807	910.121448638363	-103.121448638363
2	444	179.936286072940	264.063713927060
3	412	500.994606335475	-88.994606335475
4	428	435.879397715249	-7.87939771524905
5	315	321.597665710416	-6.59766571041563
6	168	265.464741141100	-97.4647411410997
7	263	242.199231572462	20.800768427538
8	267	297.322691211927	-30.3226912119273
9	228	216.544199430300	11.4558005697003
10	129	100.723131792644	28.2768682073564
11	104	190.172271544117	-86.1722715441168
12	122	74.0562215242082	47.9437784757918
13	393	98.1252119357268	294.874788064273
14	190	232.975739337524	-42.9757393375242
15	280	156.945287060958	123.054712939042
16	63	66.4409215366891	-3.44092153668907
17	102	101.795175603456	0.204824396543675
18	265	146.388019688235	118.611980311765
19	234	210.62605709229	23.3739429077099
20	277	118.885767445279	158.114232554721
21	73	91.6096689355491	-18.6096689355491
22	67	140.192793204534	-73.1927932045343
23	103	105.353895038707	-2.35389503870713
24	290	108.760222633479	181.239777366521
25	83	65.3359851897163	17.6640148102837
26	56	94.3685159731903	-38.3685159731903
27	236	167.136024114964	68.8639758850363
28	73	66.0399588731623	6.96004112683766
29	34	48.6498643859887	-14.6498643859887
30	139	83.6374822189779	55.3625177810221
31	26	42.1096924970393	-16.1096924970393
32	70	91.096580476913	-21.096580476913
33	40	59.5785358769338	-19.5785358769338
34	42	51.7165855896215	-9.7165855896215
35	12	79.184619716863	-67.184619716863
36	211	117.914810993200	93.0851890068003
37	74	71.2977365332927	2.7022634667073
38	80	58.4738347150965	21.5261652849035
39	83	61.6508638435642	21.3491361564358
40	131	36.2673719996378	94.7326280003622
41	203	71.820074235238	131.179925764762
42	56	91.0192757279101	-35.0192757279101
43	89	41.9922920519235	47.0077079480765
44	88	59.9599748108988	28.0400251891012
45	39	36.6842858152517	2.31571418474830
46	25	47.2666568622995	-22.2666568622995
47	49	79.409422413908	-30.4094224139079
48	149	75.628127244096	73.371872755904
49	58	50.6426349302698	7.35736506973018
50	41	26.0622296689557	14.9377703310443
51	90	45.9770319614916	44.0229680385084
52	136	57.5174037790638	78.4825962209362
53	97	86.8652825022535	10.1347174977464
54	63	44.3570750698777	18.6429249301223
55	114	65.3771321807388	48.6228678192612
56	77	23.0159452552413	53.9840547447587
57	6	27.5874150019443	-21.5874150019443
58	47	45.000996823089	1.99900317691104
59	51	26.2811561604247	24.7188438395753
60	85	73.5398143338468	11.4601856661532
61	43	55.3788891978376	-12.3788891978376
62	32	18.2665653050752	13.7334346949248
63	25	42.5912385591345	-17.5912385591345
64	77	63.0566634926254	13.9433365073746
65	54	23.9948790227836	30.0051209772164
66	251	87.2375730367687	163.762426963231
67	15	19.7115043157433	-4.7115043157433
68	44	38.3282252349184	5.67177476508156
69	73	34.5185689889681	38.4814310110319
70	85	48.7321817820051	36.2678182179949
71	49	26.2050076012666	22.7949923987334
72	38	32.1214600264545	5.87853997354547
73	35	28.8050321832271	6.19496781677286
74	9	17.6377158384308	-8.6377158384308
75	34	34.5160311297894	-0.516031129789383
76	20	31.4011106969383	-11.4011106969383
77	29	28.4697535361086	0.530246463891366
78	11	17.7349042134965	-6.73490421349654
79	52	19.1296706249711	32.8703293750289
80	13	35.1658707256216	-22.1658707256216
81	29	17.6430236647678	11.3569763352322
82	66	49.7011448915	16.2988551085000
83	33	33.4974599787755	-0.497459978775508
84	15	9.59468425025567	5.40531574974433
85	15	22.9897909498168	-7.98979094981675
86	68	48.9005511779915	19.0994488220085
87	100	36.5010607273119	63.4989392726881
88	13	23.4197656966157	-10.4197656966157
89	45	32.2570304767248	12.7429695232752
90	14	23.4159717760319	-9.41597177603193
91	36	29.1163506657697	6.88364933423029
92	40	38.8879013833908	1.11209861660919
93	68	22.8590030111442	45.1409969888558
94	29	68.2012192089782	-39.2012192089782
95	43	14.7566757446367	28.2433242553633
96	30	34.0693718932313	-4.06937189323128
97	9	24.1528901226741	-15.1528901226740
98	22	27.7877042668564	-5.7877042668564
99	19	40.2890537388315	-21.2890537388315
100	9	28.3452163074305	-19.3452163074305
101	31	28.3894395430179	2.6105604569821
102	19	22.5238446033959	-3.52384460339585
103	55	39.3710960241074	15.6289039758926
104	8	25.9352350411540	-17.9352350411540
105	28	21.0847666722478	6.91523332775215
106	29	16.8155717186472	12.1844282813528
107	48	34.6919542079632	13.3080457920368
108	16	37.6849850454179	-21.6849850454179
109	47	50.0094117501218	-3.00941175012184
110	20	31.1723403213052	-11.1723403213052
111	22	16.9563657638649	5.04363423613509
112	33	33.1794214958358	-0.179421495835795
113	44	43.3555552022237	0.644444797776293
114	13	33.8735455495988	-20.8735455495988
115	6	24.653088851359	-18.653088851359
116	35	17.1946961990602	17.8053038009398
117	8	18.1913201316216	-10.1913201316216
118	17	28.7174684597172	-11.7174684597172
119	11	33.7700131649685	-22.7700131649685
120	21	14.8505747769789	6.14942522302107
121	92	77.7818579716389	14.2181420283611
122	12	16.4123612609418	-4.41236126094183
123	112	26.3442113580963	85.6557886419037
124	25	37.9515686922293	-12.9515686922293
125	17	32.5555945461328	-15.5555945461328
126	23	39.0262699114359	-16.0262699114359
127	0	10.3151413438341	-10.3151413438341
128	10	10.8273757607610	-0.827375760761038
129	23	11.8760494125701	11.1239505874299
130	0	10.3151413438341	-10.3151413438341
131	7	10.4727930884651	-3.47279308846507
132	25	17.3117430575386	7.68825694246137
133	0	10.3151413438341	-10.3151413438341
134	20	10.5811682083926	9.41883179160745
135	4	13.7110689894706	-9.71106898947061
136	4	11.9278598078995	-7.92785980789949
137	10	10.9740128026092	-0.974012802609193
138	1	10.3414663643429	-9.34146636434295
139	4	15.0378500231146	-11.0378500231146
140	0	10.3151413438341	-10.3151413438341
141	8	11.9526721277071	-3.95267212770711
142	0	10.3151413438341	-10.3151413438341
143	11	14.6485329263439	-3.6485329263439
144	4	10.5459743881669	-6.5459743881669
145	15	13.3602337286544	1.63976627134561
146	9	10.9488622517314	-1.94886225173143
147	0	10.3151413438341	-10.3151413438341
148	7	10.3898176630315	-3.38981766303145
149	2	11.7419574554116	-9.74195745541163
150	0	10.4590967886963	-10.4590967886963
151	7	12.5617003956840	-5.56170039568403
152	46	19.5462156170817	26.4537843829183
153	5	10.3151413438341	-5.31514134383414
154	7	12.2293372539223	-5.2293372539223
155	2	10.4721762044551	-8.4721762044551
156	0	10.3151413438341	-10.3151413438341
157	0	10.3151413438341	-10.3151413438341
158	2	11.0138446793436	-9.01384467934356
159	5	12.4771324423102	-7.4771324423102
160	0	10.3151413438341	-10.3151413438341
161	0	10.3151413438341	-10.3151413438341
162	0	10.3151413438341	-10.3151413438341
163	0	10.3151413438341	-10.3151413438341
164	7	12.8842430297761	-5.88424302977613
165	24	13.3084718867054	10.6915281132946
166	1	10.5625965366169	-9.56259653661695
167	0	10.3151413438341	-10.3151413438341
168	18	10.7336601969317	7.26633980306832
169	55	34.8237354375361	20.1762645624639
170	0	10.3151413438341	-10.3151413438341
171	0	10.3151413438341	-10.3151413438341
172	3	10.7049757718937	-7.70497577189369
173	0	10.3151413438341	-10.3151413438341
174	9	11.6729874531772	-2.67298745317723
175	0	10.3151413438341	-10.3151413438341
176	8	11.0333517407172	-3.03335174071717
177	113	12.4211429845389	100.578857015461
178	0	18.4767827716272	-18.4767827716272
179	0	10.3151413438341	-10.3151413438341
180	0	10.3151413438341	-10.3151413438341
181	19	18.0246852009915	0.975314799008546
182	11	23.4844743257318	-12.4844743257318
183	25	22.1028256636351	2.89717433636487
184	16	22.7339858937213	-6.73398589372135
185	5	18.6536036263031	-13.6536036263031
186	11	21.3730467855389	-10.3730467855389
187	23	31.1921152906101	-8.1921152906101
188	6	11.4131207279863	-5.41312072798634
189	5	12.6975426487832	-7.69754264878322
190	0	10.3151413438341	-10.3151413438341
191	7	11.4734925641371	-4.47349256413707
192	0	10.3151413438341	-10.3151413438341
193	7	11.2734640254533	-4.27346402545328
194	0	10.3151413438341	-10.3151413438341
195	3	10.5029050933604	-7.50290509336041
196	0	18.0970345929496	-18.0970345929496
197	89	56.8472506348853	32.1527493651147
198	0	18.0970345929496	-18.0970345929496
199	0	18.0970345929496	-18.0970345929496
200	19	25.1682385297575	-6.16823852975754
201	0	18.0970345929496	-18.0970345929496
202	0	18.0970345929496	-18.0970345929496
203	0	18.0970345929496	-18.0970345929496
204	12	16.8126099464509	-4.81260994645089
205	12	27.0376809617420	-15.0376809617420
206	5	18.3718119401885	-13.3718119401885
207	2	19.6077132459198	-17.6077132459198
208	0	18.0970345929496	-18.0970345929496
209	26	21.1833048067313	4.81669519326865
210	3	19.4922606799165	-16.4922606799165
211	0	10.3151413438341	-10.3151413438341
212	0	10.3151413438341	-10.3151413438341
213	11	10.9899657048576	0.0100342951424270
214	10	13.1090588691806	-3.1090588691806
215	5	13.5775529805644	-8.57755298056442
216	2	10.3151413438341	-8.31514134383414
217	6	10.4204584256496	-4.42045842564963
218	7	12.3877866253743	-5.38778662537427
219	2	10.3151413438341	-8.31514134383414
220	28	19.0729058337639	8.92709416623609
221	3	11.7816262705407	-8.78162627054065
222	0	10.3151413438341	-10.3151413438341
223	1	10.3257154151812	-9.32571541518123
224	20	12.7791632634587	7.22083673654126
225	1	10.4406132432078	-9.44061324320785
226	22	11.9411362848241	10.0588637151759
227	9	10.6363065940416	-1.63630659404162
228	0	10.3151413438341	-10.3151413438341
229	2	12.4072315407146	-10.4072315407146
230	0	10.3151413438341	-10.3151413438341
231	7	12.7423706605211	-5.74237066052115
232	9	14.2007143709345	-5.20071437093446
233	0	10.3151413438341	-10.3151413438341
234	13	12.4819096244468	0.518090375553166
235	0	10.3151413438341	-10.3151413438341
236	0	10.3311296084940	-10.3311296084940
237	0	10.3151413438341	-10.3151413438341
238	6	10.5061425114325	-4.50614251143254
239	0	10.3151413438341	-10.3151413438341
240	0	10.3151413438341	-10.3151413438341
241	0	10.3151413438341	-10.3151413438341
242	3	10.9619336481836	-7.96193364818356
243	0	10.3151413438341	-10.3151413438341
244	7	10.2274428629905	-3.22744286299052
245	2	25.4314242664152	-23.4314242664152
246	0	18.0970345929496	-18.0970345929496
247	0	18.0970345929496	-18.0970345929496
248	15	11.2630561430974	3.73694385690259
249	0	18.0970345929496	-18.0970345929496
250	0	18.0970345929496	-18.0970345929496
251	9	9.59376124825856	-0.593761248258556
252	0	18.0970345929496	-18.0970345929496
253	1	12.0309341596297	-11.0309341596297
254	38	16.5058694202808	21.4941305797192
255	57	27.7958596369061	29.2041403630939
256	0	10.3151413438341	-10.3151413438341
257	7	10.8367064021892	-3.83670640218916
258	26	9.14294162910819	16.8570583708918
259	0	18.0970345929496	-18.0970345929496
260	0	10.3151413438341	-10.3151413438341
261	0	18.0970345929496	-18.0970345929496
262	0	18.0970345929496	-18.0970345929496
263	13	14.3801095303099	-1.38010953030994
264	10	11.4891415146173	-1.48914151461726
265	0	18.0970345929496	-18.0970345929496
266	0	10.3151413438341	-10.3151413438341
267	0	18.0970345929496	-18.0970345929496
268	9	15.3239000752834	-6.32390007528342
269	0	18.0970345929496	-18.0970345929496
270	26	13.2005709960901	12.7994290039099
271	0	18.0970345929496	-18.0970345929496
272	0	18.0970345929496	-18.0970345929496
273	0	18.0970345929496	-18.0970345929496
274	19	10.9917774654062	8.00822253459384
275	0	10.3151413438341	-10.3151413438341
276	12	12.2109029432397	-0.210902943239656
277	23	12.8988427638588	10.1011572361412
278	0	10.3151413438341	-10.3151413438341
279	29	26.6626601709123	2.33733982908770
280	8	13.0017137996515	-5.00171379965146
281	0	18.0970345929496	-18.0970345929496
282	0	10.3151413438341	-10.3151413438341
283	26	10.6782892657628	15.3217107342372
284	0	10.3151413438341	-10.3151413438341
285	9	14.4631384010447	-5.46313840104472
286	0	10.3151413438341	-10.3151413438341
287	5	21.2613020581086	-16.2613020581086
288	3	10.7875345018691	-7.78753450186907
289	0	18.0970345929496	-18.0970345929496
290	13	10.1080201238292	2.89197987617077
291	0	18.0970345929496	-18.0970345929496
292	12	11.6872131920943	0.312786807905724
293	19	17.5426109406562	1.45738905934376
294	0	10.3151413438341	-10.3151413438341
295	10	10.3060855465345	-0.306085546534513
296	9	19.7867036598976	-10.7867036598976
297	0	10.3151413438341	-10.3151413438341
298	0	18.0970345929496	-18.0970345929496
299	0	10.3151413438341	-10.3151413438341
300	9	10.3151413438341	-1.31514134383414
301	4	18.2734953248543	-14.2734953248543
302	1	11.3839736220127	-10.3839736220127
303	1	10.3151413438341	-9.31514134383414
304	0	18.0970345929496	-18.0970345929496
305	14	12.5003321070115	1.49966789298854
306	12	10.3292310845092	1.67076891549084
307	0	18.0970345929496	-18.0970345929496
308	19	19.9796123319544	-0.979612331954424
309	17	10.9042292568830	6.09577074311701
310	0	18.0970345929496	-18.0970345929496
311	0	18.0970345929496	-18.0970345929496
312	32	18.5190837066719	13.4809162933281
313	0	18.0970345929496	-18.0970345929496
314	14	19.1588073859996	-5.15880738599956
315	8	10.3406662915615	-2.34066629156151
316	4	12.2660395643538	-8.26603956435378
317	0	10.3099065304816	-10.3099065304816
318	20	12.3587428809331	7.6412571190669
319	5	10.3201690324378	-5.3201690324378
320	0	10.3151413438341	-10.3151413438341
321	0	10.3151413438341	-10.3151413438341
322	0	10.3151413438341	-10.3151413438341
323	0	10.3151413438341	-10.3151413438341
324	0	18.0970345929496	-18.0970345929496
325	0	10.3151413438341	-10.3151413438341
326	1	10.4314460650691	-9.4314460650691
327	0	18.0970345929496	-18.0970345929496
328	0	18.0970345929496	-18.0970345929496
329	0	10.3151413438341	-10.3151413438341
330	0	10.3151413438341	-10.3151413438341
331	0	10.3151413438341	-10.3151413438341
332	0	10.3151413438341	-10.3151413438341
333	0	10.3151413438341	-10.3151413438341
334	4	10.7396571527513	-6.7396571527513
335	1	10.3220675564226	-9.3220675564226
336	4	11.9683526317874	-7.9683526317874
337	20	14.8074544828317	5.19254551716833
338	0	10.3151413438341	-10.3151413438341
339	1	10.4937989781455	-9.4937989781455
340	10	10.7392737754281	-0.739273775428094
341	12	12.4494251459960	-0.449425145995951
342	0	10.3151413438341	-10.3151413438341
343	0	10.3151413438341	-10.3151413438341
344	0	10.3151413438341	-10.3151413438341
345	13	13.5847088910283	-0.584708891028319
346	0	10.3151413438341	-10.3151413438341
347	3	10.5614974039881	-7.56149740398812
348	0	10.3151413438341	-10.3151413438341
349	0	10.3151413438341	-10.3151413438341
350	10	18.7128228861445	-8.71282288614447
351	3	11.4542391366163	-8.45423913661635
352	7	10.7269170511721	-3.72691705117213
353	10	13.1387188162063	-3.13871881620627
354	1	11.3451062425161	-10.3451062425161
355	0	10.3151413438341	-10.3151413438341
356	0	10.3151413438341	-10.3151413438341
357	15	17.3072713478200	-2.30727134781997
358	0	10.3151413438341	-10.3151413438341
359	0	10.3151413438341	-10.3151413438341
360	0	10.3151413438341	-10.3151413438341
361	4	10.3208809789321	-6.3208809789321
362	0	10.3151413438341	-10.3151413438341
363	0	10.3151413438341	-10.3151413438341
364	28	27.5880744626837	0.411925537316307
365	9	11.0053547667131	-2.00535476671314
366	0	10.3151413438341	-10.3151413438341
367	0	10.3151413438341	-10.3151413438341
368	0	10.3151413438341	-10.3151413438341
369	0	10.3151413438341	-10.3151413438341
370	7	11.4629122380182	-4.46291223801825
371	0	10.3151413438341	-10.3151413438341
372	7	12.4510239287079	-5.4510239287079
373	7	15.2893092666734	-8.28930926667337
374	3	12.2042455997494	-9.2042455997494
375	0	10.3151413438341	-10.3151413438341
376	0	10.3151413438341	-10.3151413438341
377	11	9.8743198477786	1.12568015222140
378	7	13.4436012321433	-6.44360123214334
379	10	21.9613304169316	-11.9613304169316
380	0	10.3151413438341	-10.3151413438341
381	0	10.3151413438341	-10.3151413438341
382	18	16.1225854451092	1.87741455489081
383	14	8.75479655600812	5.24520344399188
384	0	10.3151413438341	-10.3151413438341
385	12	11.1956963500089	0.804303649991073
386	29	25.1008795416578	3.89912045834221
387	3	10.7535397686774	-7.7535397686774
388	6	14.6763775837511	-8.67637758375111
389	3	10.6178060487729	-7.6178060487729
390	8	12.8188685241087	-4.81886852410867
391	10	12.877605813452	-2.877605813452
392	6	12.3533380697780	-6.35333806977797
393	8	13.644556459693	-5.644556459693
394	6	15.7109499465086	-9.71094994650857
395	9	41.564198547967	-32.564198547967
396	8	12.7367913613267	-4.7367913613267
397	26	34.3262382377274	-8.32623823772738
398	239	40.0356043143102	198.96439568569
399	7	13.8478723326802	-6.8478723326802
400	41	19.8644990442744	21.1355009557256
401	3	18.2020844498800	-15.2020844498800
402	8	15.2337619251313	-7.23376192513129
403	6	13.1689661379182	-7.16896613791819
404	21	28.1797009163367	-7.1797009163367
405	7	14.2744937903311	-7.27449379033108
406	11	18.9053609701630	-7.90536097016297
407	11	15.3135522946845	-4.31355229468447
408	12	15.0974356588783	-3.09743565887830
409	9	12.0516559449667	-3.05165594496665
410	3	16.3350758341163	-13.3350758341163
411	57	19.3452172875553	37.6547827124447
412	21	57.7596948116043	-36.7596948116043
413	15	23.4186266738616	-8.41862667386164
414	32	38.9676460472081	-6.96764604720806
415	11	24.8617490336811	-13.8617490336811
416	2	18.4263892127673	-16.4263892127673
417	23	21.4135689625723	1.58643103742769
418	20	24.4442130029754	-4.44421300297544
419	24	19.0155558699457	4.9844441300543
420	1	15.0293496452171	-14.0293496452171
421	1	17.7962957567211	-16.7962957567211
422	74	28.1278183130116	45.8721816869884
423	68	60.9304509135893	7.06954908641066
424	20	43.5140853262253	-23.5140853262253
425	20	20.9189588777624	-0.918958877762414
426	82	46.4952998658453	35.5047001341547
427	21	29.1229896107725	-8.12298961077249
428	244	220.790357330511	23.2096426694889
429	32	63.4861175026288	-31.4861175026288
430	86	94.500658966506	-8.50065896650608
431	69	97.5346794805174	-28.5346794805174

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.999999999996504	6.99210972330765e-12	3.49605486165382e-12
8	0.999999999991081	1.78376588946612e-11	8.91882944733062e-12
9	0.999999999997745	4.50920614528198e-12	2.25460307264099e-12
10	0.999999999998369	3.26257973287244e-12	1.63128986643622e-12
11	0.999999999999177	1.64510502667664e-12	8.22552513338322e-13
12	0.99999999999728	5.44134262216981e-12	2.72067131108491e-12
13	1	9.19149248572261e-21	4.59574624286130e-21
14	1	1.52124095453319e-22	7.60620477266593e-23
15	1	8.80566918895308e-23	4.40283459447654e-23
16	1	2.83491077913925e-22	1.41745538956963e-22
17	1	1.77226084968723e-23	8.86130424843613e-24
18	1	9.18335157728488e-24	4.59167578864244e-24
19	1	1.07362347002997e-24	5.36811735014987e-25
20	1	4.91057489340596e-30	2.45528744670298e-30
21	1	6.83912280002469e-33	3.41956140001234e-33
22	1	6.4943256693251e-39	3.24716283466255e-39
23	1	1.35148588549465e-38	6.75742942747326e-39
24	1	3.74950409989992e-46	1.87475204994996e-46
25	1	1.09894084361421e-45	5.49470421807106e-46
26	1	4.52910725746319e-48	2.26455362873159e-48
27	1	1.60258408063099e-47	8.01292040315497e-48
28	1	9.49572610530968e-48	4.74786305265484e-48
29	1	1.47103203348858e-48	7.35516016744289e-49
30	1	3.70872821261749e-48	1.85436410630875e-48
31	1	1.84228613642209e-49	9.21143068211047e-50
32	1	2.31675659752939e-50	1.15837829876469e-50
33	1	3.26730391099126e-51	1.63365195549563e-51
34	1	2.43967979581022e-51	1.21983989790511e-51
35	1	1.03945136678383e-53	5.19725683391913e-54
36	1	7.88734358010754e-55	3.94367179005377e-55
37	1	1.67638760959091e-54	8.38193804795453e-55
38	1	5.27318587555121e-54	2.63659293777561e-54
39	1	2.03017814721391e-53	1.01508907360695e-53
40	1	2.26211714573103e-55	1.13105857286552e-55
41	1	2.68388611806797e-60	1.34194305903399e-60
42	1	1.04781525539060e-61	5.23907627695302e-62
43	1	1.49943652252425e-61	7.49718261262123e-62
44	1	5.68046422861684e-61	2.84023211430842e-61
45	1	8.18932450773151e-61	4.09466225386575e-61
46	1	2.77657172501134e-61	1.38828586250567e-61
47	1	3.63441261015733e-62	1.81720630507867e-62
48	1	5.80780672687902e-63	2.90390336343951e-63
49	1	1.79950302743163e-62	8.99751513715815e-63
50	1	4.10930863380442e-62	2.05465431690221e-62
51	1	4.86053880901138e-62	2.43026940450569e-62
52	1	1.81100616650195e-63	9.05503083250973e-64
53	1	5.78089808485908e-63	2.89044904242954e-63
54	1	1.97769384876259e-62	9.88846924381293e-63
55	1	2.24587265854561e-62	1.12293632927281e-62
56	1	6.33304149056473e-63	3.16652074528237e-63
57	1	1.62780979898703e-63	8.13904899493514e-64
58	1	3.78384160904403e-63	1.89192080452202e-63
59	1	9.72198486831375e-63	4.86099243415687e-63
60	1	2.22601482671145e-62	1.11300741335572e-62
61	1	1.86825713194648e-62	9.34128565973242e-63
62	1	3.65650758494656e-62	1.82825379247328e-62
63	1	2.42116800314227e-62	1.21058400157114e-62
64	1	5.96172868422018e-62	2.98086434211009e-62
65	1	8.57454918178304e-62	4.28727459089152e-62
66	1	3.09940166925146e-74	1.54970083462573e-74
67	1	5.3863212772783e-74	2.69316063863915e-74
68	1	1.35481951042299e-73	6.77409755211495e-74
69	1	1.73647408406439e-73	8.68237042032195e-74
70	1	1.31483830765138e-73	6.57419153825691e-74
71	1	6.19317114343963e-74	3.09658557171981e-74
72	1	1.01298266105351e-73	5.06491330526754e-74
73	1	3.24907823227062e-73	1.62453911613531e-73
74	1	5.23936308911282e-73	2.61968154455641e-73
75	1	1.56023222123929e-72	7.80116110619646e-73
76	1	2.02640969793455e-72	1.01320484896727e-72
77	1	6.17497639281331e-72	3.08748819640665e-72
78	1	1.16156591681972e-71	5.80782958409858e-72
79	1	1.02271494291317e-71	5.11357471456583e-72
80	1	8.48962741951515e-72	4.24481370975758e-72
81	1	1.87599776973562e-71	9.37998884867812e-72
82	1	4.21561607059264e-71	2.10780803529632e-71
83	1	1.25937617526188e-70	6.2968808763094e-71
84	1	2.19586636802339e-70	1.09793318401169e-70
85	1	4.70800687773219e-70	2.35400343886609e-70
86	1	6.4575383687269e-70	3.22876918436345e-70
87	1	1.56296346108914e-71	7.81481730544568e-72
88	1	2.94685390400732e-71	1.47342695200366e-71
89	1	1.00224626298127e-70	5.01123131490637e-71
90	1	2.02536644789899e-70	1.01268322394949e-70
91	1	6.64373235253994e-70	3.32186617626997e-70
92	1	1.58411565882658e-69	7.9205782941329e-70
93	1	2.76992420398996e-70	1.38496210199498e-70
94	1	2.04632306100053e-71	1.02316153050027e-71
95	1	9.53258804291306e-72	4.76629402145653e-72
96	1	1.83300128179733e-71	9.16500640898663e-72
97	1	1.93667753120899e-71	9.68338765604497e-72
98	1	4.0981432611772e-71	2.0490716305886e-71
99	1	1.22884822808336e-71	6.14424114041682e-72
100	1	1.45096370195526e-71	7.2548185097763e-72
101	1	3.82217069659546e-71	1.91108534829773e-71
102	1	9.7111856546347e-71	4.85559282731735e-71
103	1	3.50820419676786e-70	1.75410209838393e-70
104	1	1.91036313009904e-70	9.5518156504952e-71
105	1	5.37573944134892e-70	2.68786972067446e-70
106	1	1.43017154762858e-69	7.15085773814288e-70
107	1	1.99590140081172e-69	9.97950700405859e-70
108	1	1.49953493888181e-69	7.49767469440905e-70
109	1	3.30827432299529e-69	1.65413716149765e-69
110	1	6.89580521803943e-69	3.44790260901972e-69
111	1	1.95058351257688e-68	9.75291756288438e-69
112	1	3.3806959239725e-68	1.69034796198625e-68
113	1	9.98210025070287e-68	4.99105012535143e-68
114	1	1.25756032561286e-67	6.28780162806432e-68
115	1	2.06306899978141e-67	1.03153449989070e-67
116	1	2.96266718572292e-67	1.48133359286146e-67
117	1	5.23434259261063e-67	2.61717129630531e-67
118	1	7.74455352929766e-67	3.87227676464883e-67
119	1	8.32024090496207e-67	4.16012045248103e-67
120	1	2.15007282046283e-66	1.07503641023142e-66
121	1	7.08646780213795e-66	3.54323390106898e-66
122	1	1.86766848326778e-65	9.33834241633889e-66
123	1	3.87047463592745e-69	1.93523731796372e-69
124	1	4.87447962607315e-69	2.43723981303657e-69
125	1	1.01453851337899e-68	5.07269256689494e-69
126	1	1.88372440659583e-68	9.41862203297916e-69
127	1	4.26322527437465e-68	2.13161263718732e-68
128	1	1.17101801424917e-67	5.85509007124586e-68
129	1	2.36686532858462e-67	1.18343266429231e-67
130	1	5.49643127032931e-67	2.74821563516465e-67
131	1	1.50261004709433e-66	7.51305023547165e-67
132	1	3.32868184922970e-66	1.66434092461485e-66
133	1	7.90524744450327e-66	3.95262372225164e-66
134	1	1.83492200490179e-65	9.17461002450897e-66
135	1	4.5021321234016e-65	2.2510660617008e-65
136	1	1.16652391614398e-64	5.83261958071988e-65
137	1	3.29438558374603e-64	1.64719279187301e-64
138	1	8.28944348759265e-64	4.14472174379633e-64
139	1	1.99458945451918e-63	9.97294727259588e-64
140	1	4.9314706832811e-63	2.46573534164055e-63
141	1	1.37682691969081e-62	6.88413459845406e-63
142	1	3.43193542995809e-62	1.71596771497904e-62
143	1	9.51079485029121e-62	4.75539742514561e-62
144	1	2.60424517513493e-61	1.30212258756746e-61
145	1	7.49722426688349e-61	3.74861213344174e-61
146	1	2.06280045569401e-60	1.03140022784701e-60
147	1	5.1994644666482e-60	2.5997322333241e-60
148	1	1.47456877819753e-59	7.37284389098766e-60
149	1	3.78985117815978e-59	1.89492558907989e-59
150	1	9.55660599293205e-59	4.77830299646602e-59
151	1	2.65062701601963e-58	1.32531350800981e-58
152	1	1.93133246345136e-58	9.65666231725681e-59
153	1	5.3936715467002e-58	2.6968357733501e-58
154	1	1.49778608433853e-57	7.48893042169263e-58
155	1	3.98149837779832e-57	1.99074918889916e-57
156	1	1.01485175599828e-56	5.0742587799914e-57
157	1	2.58884847224581e-56	1.29442423612291e-56
158	1	6.8010848987626e-56	3.4005424493813e-56
159	1	1.84284402027378e-55	9.21422010136889e-56
160	1	4.70261153964189e-55	2.35130576982094e-55
161	1	1.20009692592021e-54	6.00048462960107e-55
162	1	3.06229287304956e-54	1.53114643652478e-54
163	1	7.81196602909658e-54	3.90598301454829e-54
164	1	2.15329514317849e-53	1.07664757158924e-53
165	1	4.77436339405718e-53	2.38718169702859e-53
166	1	1.23488166977176e-52	6.17440834885879e-53
167	1	3.13698887410895e-52	1.56849443705448e-52
168	1	5.41270695191418e-52	2.70635347595709e-52
169	1	6.52811169748208e-52	3.26405584874104e-52
170	1	1.65790958973462e-51	8.28954794867311e-52
171	1	4.20619598219218e-51	2.10309799109609e-51
172	1	1.11785968551240e-50	5.58929842756198e-51
173	1	2.82815022178556e-50	1.41407511089278e-50
174	1	7.7915651250279e-50	3.89578256251395e-50
175	1	1.96413876333815e-49	9.82069381669076e-50
176	1	5.35758553136376e-49	2.67879276568188e-49
177	1	1.99978686409248e-56	9.99893432046242e-57
178	1	4.06412737284072e-56	2.03206368642036e-56
179	1	1.10442087591657e-55	5.52210437958283e-56
180	1	2.99694286284844e-55	1.49847143142422e-55
181	1	9.09616644488593e-55	4.54808322244297e-55
182	1	2.11807787864461e-54	1.05903893932231e-54
183	1	5.1958338963909e-54	2.59791694819545e-54
184	1	1.41688097206304e-53	7.08440486031519e-54
185	1	3.42380528716796e-53	1.71190264358398e-53
186	1	8.96510064178005e-53	4.48255032089002e-53
187	1	2.4319451583177e-52	1.21597257915885e-52
188	1	6.92803876012943e-52	3.46401938006472e-52
189	1	1.89758897711999e-51	9.48794488559997e-52
190	1	5.03029679005707e-51	2.51514839502853e-51
191	1	1.41473212366001e-50	7.07366061830006e-51
192	1	3.73405988940318e-50	1.86702994470159e-50
193	1	1.05250433311860e-49	5.26252166559301e-50
194	1	2.76491440995101e-49	1.38245720497550e-49
195	1	7.57909657743998e-49	3.78954828871999e-49
196	1	1.59515683258641e-48	7.97578416293203e-49
197	1	2.73459535664125e-48	1.36729767832063e-48
198	1	5.78323578123618e-48	2.89161789061809e-48
199	1	1.22747434206109e-47	6.13737171030544e-48
200	1	3.18813589870216e-47	1.59406794935108e-47
201	1	6.77400072148e-47	3.38700036074e-47
202	1	1.44127076708827e-46	7.20635383544133e-47
203	1	3.06829204249767e-46	1.53414602124884e-46
204	1	7.41913095640715e-46	3.70956547820358e-46
205	1	1.12403195916351e-45	5.62015979581757e-46
206	1	2.75078588745139e-45	1.37539294372569e-45
207	1	5.55067392857637e-45	2.77533696428819e-45
208	1	1.17333970221508e-44	5.86669851107538e-45
209	1	1.94552145779573e-44	9.72760728897866e-45
210	1	4.39680831502013e-44	2.19840415751007e-44
211	1	1.11596655572694e-43	5.57983277863471e-44
212	1	2.82511623612517e-43	1.41255811806259e-43
213	1	7.09195339346114e-43	3.54597669673057e-43
214	1	1.89285305004628e-42	9.4642652502314e-43
215	1	4.85695947559381e-42	2.42847973779691e-42
216	1	1.25370160125435e-41	6.26850800627173e-42
217	1	3.30332238603629e-41	1.65166119301814e-41
218	1	8.65547876771032e-41	4.32773938385516e-41
219	1	2.21185154397860e-40	1.10592577198930e-40
220	1	3.91690175481115e-40	1.95845087740558e-40
221	1	9.91667545116567e-40	4.95833772558283e-40
222	1	2.44300910778816e-39	1.22150455389408e-39
223	1	6.0955793518549e-39	3.04778967592745e-39
224	1	1.26948412947545e-38	6.34742064737724e-39
225	1	3.14429164681607e-38	1.57214582340804e-38
226	1	5.89583744836035e-38	2.94791872418017e-38
227	1	1.48053109346045e-37	7.40265546730223e-38
228	1	3.59087304967703e-37	1.79543652483851e-37
229	1	8.42304490518118e-37	4.21152245259059e-37
230	1	2.02984430601272e-36	1.01492215300636e-36
231	1	5.09419064496538e-36	2.54709532248269e-36
232	1	1.27429664994525e-35	6.37148324972623e-36
233	1	3.03928394259550e-35	1.51964197129775e-35
234	1	7.31077373227142e-35	3.65538686613571e-35
235	1	1.73223628822979e-34	8.66118144114896e-35
236	1	4.08899664323861e-34	2.04449832161930e-34
237	1	9.62242484637476e-34	4.81121242318738e-34
238	1	2.35192864346570e-33	1.17596432173285e-33
239	1	5.49574589026745e-33	2.74787294513373e-33
240	1	1.27966117382292e-32	6.39830586911462e-33
241	1	2.96903756457433e-32	1.48451878228716e-32
242	1	7.04878384200609e-32	3.52439192100304e-32
243	1	1.62348259177413e-31	8.11741295887063e-32
244	1	3.8565251745328e-31	1.9282625872664e-31
245	1	4.41121656246180e-31	2.20560828123090e-31
246	1	8.62326516220152e-31	4.31163258110076e-31
247	1	1.67793926439736e-30	8.3896963219868e-31
248	1	3.66913312689366e-30	1.83456656344683e-30
249	1	7.0840318114518e-30	3.5420159057259e-30
250	1	1.35977548874155e-29	6.79887744370774e-30
251	1	2.91042683318117e-29	1.45521341659058e-29
252	1	5.53673399182633e-29	2.76836699591316e-29
253	1	1.20154809535056e-28	6.00774047675279e-29
254	1	1.63248836910642e-28	8.1624418455321e-29
255	1	1.77306431318979e-28	8.86532156594895e-29
256	1	3.97527288513142e-28	1.98763644256571e-28
257	1	8.92252797841303e-28	4.46126398920651e-28
258	1	7.42823992453114e-28	3.71411996226557e-28
259	1	1.39461554533861e-27	6.97307772669306e-28
260	1	3.11181234248398e-27	1.55590617124199e-27
261	1	5.78058110482055e-27	2.89029055241028e-27
262	1	1.06446759377698e-26	5.3223379688849e-27
263	1	2.45472091403674e-26	1.22736045701837e-26
264	1	5.40279976085737e-26	2.70139988042868e-26
265	1	9.79972294595203e-26	4.89986147297601e-26
266	1	2.14377964823679e-25	1.07188982411840e-25
267	1	3.83844479409869e-25	1.91922239704935e-25
268	1	7.94743853682585e-25	3.97371926841292e-25
269	1	1.40313099609709e-24	7.01565498048543e-25
270	1	2.50264870218605e-24	1.25132435109303e-24
271	1	4.35751220558634e-24	2.17875610279317e-24
272	1	7.48438126953547e-24	3.74219063476773e-24
273	1	1.26630028249901e-23	6.33150141249506e-24
274	1	2.25531249564024e-23	1.12765624782012e-23
275	1	4.81623627212723e-23	2.40811813606362e-23
276	1	1.02976007312545e-22	5.14880036562725e-23
277	1	1.78749401027720e-22	8.93747005138602e-23
278	1	3.77923151853442e-22	1.88961575926721e-22
279	1	8.22702198857067e-22	4.11351099428534e-22
280	1	1.7664014319358e-21	8.832007159679e-22
281	1	2.90400594870637e-21	1.45200297435318e-21
282	1	6.04317509712536e-21	3.02158754856268e-21
283	1	7.13728767902409e-21	3.56864383951204e-21
284	1	1.48172100234029e-20	7.40860501170143e-21
285	1	3.09822561766953e-20	1.54911280883476e-20
286	1	6.37358605079461e-20	3.18679302539731e-20
287	1	1.08779516510374e-19	5.43897582551872e-20
288	1	2.25833733823964e-19	1.12916866911982e-19
289	1	3.54259518379302e-19	1.77129759189651e-19
290	1	6.28966430581621e-19	3.14483215290810e-19
291	1	9.6381863917126e-19	4.8190931958563e-19
292	1	1.93732066258978e-18	9.68660331294888e-19
293	1	4.00021453648136e-18	2.00010726824068e-18
294	1	8.00447312226344e-18	4.00223656113172e-18
295	1	1.56274884482035e-17	7.81374422410176e-18
296	1	2.91002905619017e-17	1.45501452809508e-17
297	1	5.74865274693375e-17	2.87432637346688e-17
298	1	8.39504423758077e-17	4.19752211879039e-17
299	1	1.64776099685449e-16	8.23880498427247e-17
300	1	3.21504874234093e-16	1.60752437117046e-16
301	1	5.16701756011614e-16	2.58350878005807e-16
302	1	1.00668537073383e-15	5.03342685366913e-16
303	0.999999999999999	1.95701671266924e-15	9.7850835633462e-16
304	0.999999999999999	2.69019637661736e-15	1.34509818830868e-15
305	0.999999999999998	5.02592978044839e-15	2.51296489022420e-15
306	0.999999999999995	9.25479341847063e-15	4.62739670923531e-15
307	0.999999999999994	1.22491677675524e-14	6.1245838837762e-15
308	0.999999999999988	2.31558134618058e-14	1.15779067309029e-14
309	0.999999999999982	3.57803549291415e-14	1.78901774645708e-14
310	0.999999999999978	4.46941438077287e-14	2.23470719038643e-14
311	0.999999999999974	5.28452284854138e-14	2.64226142427069e-14
312	0.999999999999951	9.81349338698571e-14	4.90674669349286e-14
313	0.999999999999946	1.08716089351011e-13	5.43580446755053e-14
314	0.999999999999915	1.70762691287587e-13	8.53813456437934e-14
315	0.99999999999984	3.20944265066163e-13	1.60472132533082e-13
316	0.9999999999997	5.98640948765608e-13	2.99320474382804e-13
317	0.999999999999444	1.11275977597271e-12	5.56379887986354e-13
318	0.999999999999115	1.77061143744566e-12	8.8530571872283e-13
319	0.99999999999835	3.30079962403584e-12	1.65039981201792e-12
320	0.999999999996979	6.04119046542976e-12	3.02059523271488e-12
321	0.999999999994502	1.09959614970537e-11	5.49798074852684e-12
322	0.999999999990048	1.99036551427271e-11	9.95182757136353e-12
323	0.999999999982087	3.58263731802985e-11	1.79131865901492e-11
324	0.999999999982341	3.53169275085328e-11	1.76584637542664e-11
325	0.999999999968308	6.3382934787278e-11	3.1691467393639e-11
326	0.99999999994315	1.13700236953027e-10	5.68501184765133e-11
327	0.999999999949537	1.00926371281103e-10	5.04631856405515e-11
328	0.999999999961393	7.72148557266316e-11	3.86074278633158e-11
329	0.999999999930774	1.38452341707401e-10	6.92261708537003e-11
330	0.999999999876602	2.46796056694969e-10	1.23398028347485e-10
331	0.999999999781343	4.37314468048708e-10	2.18657234024354e-10
332	0.999999999614862	7.70275664182444e-10	3.85137832091222e-10
333	0.999999999325714	1.34857132281857e-09	6.74285661409283e-10
334	0.999999998809759	2.38048294905388e-09	1.19024147452694e-09
335	0.999999997929568	4.14086476896821e-09	2.07043238448410e-09
336	0.99999999640006	7.19988156565724e-09	3.59994078282862e-09
337	0.999999994323199	1.13536021738869e-08	5.67680108694343e-09
338	0.999999990344081	1.93118374679928e-08	9.65591873399642e-09
339	0.999999983610638	3.27787239188286e-08	1.63893619594143e-08
340	0.999999972756876	5.44862489280461e-08	2.72431244640231e-08
341	0.999999954528523	9.09429531709901e-08	4.54714765854951e-08
342	0.999999924533486	1.50933028313338e-07	7.5466514156669e-08
343	0.999999875573921	2.48852158312359e-07	1.24426079156180e-07
344	0.999999796208906	4.07582187351361e-07	2.03791093675681e-07
345	0.99999967138445	6.57231102249111e-07	3.28615551124556e-07
346	0.999999468592673	1.06281465507513e-06	5.31407327537566e-07
347	0.999999137882276	1.72423544751233e-06	8.62117723756163e-07
348	0.99999862483226	2.75033547854629e-06	1.37516773927315e-06
349	0.999997821650248	4.35669950440315e-06	2.17834975220157e-06
350	0.999996545473515	6.90905297059915e-06	3.45452648529957e-06
351	0.999994557276231	1.08854475373955e-05	5.44272376869773e-06
352	0.99999148714242	1.70257151588674e-05	8.51285757943369e-06
353	0.99998686520611	2.62695877782646e-05	1.31347938891323e-05
354	0.999979966545676	4.00669086477113e-05	2.00334543238556e-05
355	0.999969587019004	6.0825961991714e-05	3.0412980995857e-05
356	0.999954171155619	9.16576887627189e-05	4.58288443813595e-05
357	0.999931689996687	0.000136620006625686	6.8310003312843e-05
358	0.999898608590265	0.00020278281947013	0.000101391409735065
359	0.999850650568131	0.00029869886373698	0.00014934943186849
360	0.999781696924367	0.000436606151266044	0.000218303075633022
361	0.999680355908764	0.000639288182472711	0.000319644091236356
362	0.999540040255541	0.00091991948891731	0.000459959744458655
363	0.999343357770774	0.00131328445845298	0.00065664222922649
364	0.999155909595968	0.00168818080806427	0.000844090404032134
365	0.998807350876314	0.00238529824737203	0.00119264912368602
366	0.998332324577998	0.00333535084400495	0.00166767542200248
367	0.997687253753069	0.00462549249386296	0.00231274624693148
368	0.996819258216925	0.00636148356614989	0.00318074178307495
369	0.995662102783847	0.0086757944323062	0.0043378972161531
370	0.99408373274234	0.0118325345153178	0.00591626725765891
371	0.992067301322992	0.0158653973540160	0.00793269867700799
372	0.989370379702634	0.0212592405947318	0.0106296202973659
373	0.986409183134458	0.0271816337310846	0.0135908168655423
374	0.98228186155549	0.0354362768890202	0.0177181384445101
375	0.97704067589305	0.0459186482138989	0.0229593241069495
376	0.970513365160032	0.0589732696799360	0.0294866348399680
377	0.962791856275071	0.0744162874498574	0.0372081437249287
378	0.952675787437838	0.094648425124325	0.0473242125621625
379	0.940816516441695	0.118366967116609	0.0591834835583047
380	0.926610924039395	0.146778151921211	0.0733890759606053
381	0.909833128396486	0.180333743207027	0.0901668716035136
382	0.889429539968987	0.221140920062025	0.110570460031013
383	0.876223023341777	0.247553953316446	0.123776976658223
384	0.851257493206251	0.297485013587498	0.148742506793749
385	0.822605151821853	0.354789696356294	0.177394848178147
386	0.791158885994277	0.417682228011447	0.208841114005723
387	0.755062960613859	0.489874078772281	0.244937039386141
388	0.715558016374876	0.568883967250247	0.284441983625124
389	0.672900490758478	0.654199018483043	0.327099509241522
390	0.627424840051958	0.745150319896083	0.372575159948042
391	0.579520787655288	0.840958424689423	0.420479212344712
392	0.529987905450971	0.940024189098058	0.470012094549029
393	0.479495630634958	0.958991261269915	0.520504369365042
394	0.431621851386996	0.863243702773992	0.568378148613004
395	0.567272825190021	0.865454349619959	0.432727174809979
396	0.51491401896972	0.97017196206056	0.48508598103028
397	0.463700646330357	0.927401292660713	0.536299353669643
398	0.99992541962186	0.000149160756277575	7.45803781387874e-05
399	0.999857934401287	0.000284131197425695	0.000142065598712847
400	0.99986154308446	0.000276913831079645	0.000138456915539822
401	0.999764662176587	0.000470675646826694	0.000235337823413347
402	0.999560042234813	0.000879915530374734	0.000439957765187367
403	0.999190881960058	0.00161823607988370	0.000809118039941848
404	0.998593167282007	0.00281366543598594	0.00140683271799297
405	0.997516715646843	0.00496656870631457	0.00248328435315729
406	0.995746308969374	0.00850738206125192	0.00425369103062596
407	0.992758473452906	0.0144830530941889	0.00724152654709444
408	0.98797316351633	0.0240536729673419	0.0120268364836710
409	0.980497763336344	0.0390044733273117	0.0195022366636558
410	0.970739735661899	0.0585205286762026	0.0292602643381013
411	0.983811555015834	0.0323768899683313	0.0161884449841657
412	0.99155594271808	0.0168881145638395	0.00844405728191976
413	0.987410244628698	0.0251795107426034	0.0125897553713017
414	0.985552176477506	0.0288956470449873	0.0144478235224936
415	0.976276322855044	0.0474473542899113	0.0237236771449556
416	0.963563291860055	0.0728734162798903	0.0364367081399452
417	0.94667205873346	0.106655882533082	0.0533279412665408
418	0.960024726631954	0.0799505467360913	0.0399752733680457
419	0.930506822475594	0.138986355048813	0.0694931775244063
420	0.887649910567536	0.224700178864928	0.112350089432464
421	0.84482495845532	0.31035008308936	0.15517504154468
422	0.978332519325112	0.0433349613497756	0.0216674806748878
423	0.941113654829331	0.117772690341337	0.0588863451706687
424	0.86629293960458	0.267414120790841	0.133707060395420

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	372	0.889952153110048	NOK
5% type I error level	387	0.925837320574163	NOK
10% type I error level	393	0.940191387559809	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/103hjy1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/103hjy1291216940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/1wgnn1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/1wgnn1291216940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/2wgnn1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/2wgnn1291216940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/3774q1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/3774q1291216940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/4774q1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/4774q1291216940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/5774q1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/5774q1291216940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/6zgls1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/6zgls1291216940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/7s7kv1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/7s7kv1291216940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/8s7kv1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/8s7kv1291216940.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/9s7kv1291216940.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291216960ktjngawgm6ea123/9s7kv1291216940.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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')

}

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

Software written by Ed van Stee & Patrick Wessa

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