Home » date » 2010 » Nov » 11 »

ws 6 - Multiple Regression

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 11 Nov 2010 15:39:58 +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/Nov/11/t1289489976iy2zv1fzwz2sk0v.htm/, Retrieved Thu, 11 Nov 2010 16:39:36 +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/Nov/11/t1289489976iy2zv1fzwz2sk0v.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1 87,28 255,00 2 87,28 280,20 3 87,09 299,90 4 86,92 339,20 5 87,59 374,20 6 90,72 393,50 7 90,69 389,20 8 90,30 381,70 9 89,55 375,20 10 88,94 369,00 11 88,41 357,40 12 87,82 352,10 1 87,07 346,50 2 86,82 342,90 3 86,40 340,30 4 86,02 328,30 5 85,66 322,90 6 85,32 314,30 7 85,00 308,90 8 84,67 294,00 9 83,94 285,60 10 82,83 281,20 11 81,95 280,30 12 81,19 278,80 1 80,48 274,50 2 78,86 270,40 3 69,47 263,40 4 68,77 259,90 5 70,06 258,00 6 73,95 262,70 7 75,80 284,70 8 77,79 311,30 9 81,57 322,10 10 83,07 327,00 11 84,34 331,30 12 85,10 333,30 1 85,25 321,40 2 84,26 327,00 3 83,63 320,00 4 86,44 314,70 5 85,30 316,70 6 84,10 314,40 7 83,36 321,30 8 82,48 318,20 9 81,58 307,20 10 80,47 301,30 11 79,34 287,50 12 82,13 277,70 1 81,69 274,40 2 80,70 258,80 3 79,88 253,30 4 79,16 251,00 5 78,38 248,40 6 77,42 249,50 7 76,47 246,10 8 75,46 244,50 9 74,48 243,60 10 78,27 244,00 11 80,70 240,80 12 79,91 249,80 1 78,75 248,00 2 77,78 259,40 3 81,14 260,50 4 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	12 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

USA[t] = + 133.224950912669 + 0.365739299248267Month[t] + 1.88330332389205Colombia[t] + 0.148037063510924t + 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)	133.224950912669	8.281466	16.0871	0	0
Month	0.365739299248267	0.474974	0.77	0.441799	0.220899
Colombia	1.88330332389205	0.087151	21.6098	0	0
t	0.148037063510924	0.015787	9.3771	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.775501662206986
R-squared	0.601402828085799
Adjusted R-squared	0.598043863153937
F-TEST (value)	179.044092536135
F-TEST (DF numerator)	3
F-TEST (DF denominator)	356
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	31.0769825639322
Sum Squared Residuals	343817.268919304

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	255	298.113441384726	-43.113441384726
2	280.2	298.627217747485	-18.4272177474852
3	299.9	298.783166478705	1.11683352129503
4	339.2	298.976781276402	40.2232187235976
5	374.2	300.752370866169	73.4476291338306
6	393.5	307.160886632711	86.3391133672894
7	389.2	307.618163895753	81.5818361042469
8	381.7	307.397451962194	74.3025480378056
9	375.2	306.498750832035	68.7012491679655
10	369	305.863712167220	63.1362878327805
11	357.4	305.379337768316	52.020662231684
12	352.1	304.781965169979	47.3180348300212
13	346.5	299.49439244884	47.0056075511602
14	342.9	299.537342980626	43.362657019374
15	340.3	299.260131947350	41.0398680526495
16	328.3	299.058253047031	29.2417469529693
17	322.9	298.894040213189	24.0059597868112
18	314.3	298.767493445825	15.5325065541754
19	308.9	298.678612744938	10.2213872550616
20	294	298.570899010813	-4.57089901081322
21	285.6	297.709863947131	-12.1098639471312
22	281.2	296.13317362037	-14.9331736203702
23	280.3	294.989643058104	-14.6896430581044
24	278.8	294.072108894706	-15.2721088947056
25	274.5	288.859868306522	-14.3598683065223
26	270.4	286.322693284576	-15.9226932845764
27	263.4	269.152251435989	-5.75225143598927
28	259.9	268.347715472024	-8.44771547202402
29	258	271.290953122604	-13.2909531226039
30	262.7	279.130779415303	-16.4307794153032
31	284.7	283.128666927263	1.57133307273733
32	311.3	287.390216904567	23.909783095433
33	322.1	295.022879831638	27.0771201683619
34	327	298.361611180235	28.6383888197646
35	331.3	301.267182764338	30.0328172356625
36	333.3	303.212269653255	30.0877303467454
37	321.4	299.619669923618	21.7803300763815
38	327	298.268975995725	28.7310240042755
39	320	297.596271264432	22.4037287355683
40	314.7	303.402129967328	11.2978700326724
41	316.7	301.76894054085	14.9310594591502
42	314.4	300.022752914939	14.3772470850614
43	321.3	299.142884818018	22.1571151819824
44	318.2	297.999354255752	20.2006457442482
45	307.2	296.818157627008	10.3818423729918
46	301.3	295.241467300247	6.05853269975283
47	287.5	293.627110907008	-6.12711090700837
48	277.7	299.395303543426	-21.6953035434264
49	274.4	294.691554852694	-20.2915548526939
50	258.8	293.3408609248	-34.5408609247999
51	253.3	292.310328561968	-39.0103285619676
52	251	291.468126531525	-40.4681265315245
53	248.4	290.512926301648	-42.1129263016479
54	249.5	289.218731473471	-39.7187314734708
55	246.1	287.943369678533	-41.8433696785325
56	244.5	286.555009684161	-42.0550096841607
57	243.6	285.223148789506	-41.6231487895057
58	244	292.874644749816	-48.8746447498158
59	240.8	297.964848189633	-57.1648481896326
60	249.8	296.990814926517	-47.1908149265171
61	248	290.931087842582	-42.9310878425823
62	259.4	289.618059981166	-30.2180599811662
63	260.5	296.459735512203	-35.9597355122027
64	260.8	296.860513675528	-36.0605136755283
65	261.3	295.396821548201	-34.0968215482009
66	259.5	293.801298188201	-34.301298188201
67	256.6	292.620101559457	-36.0201015594573
68	257.9	291.043411232696	-33.1434112326964
69	256.5	289.805715504236	-33.3057155042359
70	254.2	301.543979677392	-47.3439796773918
71	253.3	298.93147252249	-45.6314725224901
72	253.8	296.431963567022	-42.6319635670220
73	255.5	290.202739183937	-34.702739183937
74	257.1	288.343553358592	-31.2435533585922
75	257.3	286.107706868469	-28.807706868469
76	253.2	289.804265848606	-36.6042658486058
77	252.8	289.658886048003	-36.8588860480027
78	252	287.554870790552	-35.554870790552
79	250.7	285.507354632818	-34.807354632818
80	252.2	283.196176009739	-30.9961760097391
81	250	288.606541014618	-38.6065410146176
82	251	290.099635105801	-39.0996351058007
83	253.4	289.91658923872	-36.5165892387198
84	251.2	287.680742748597	-36.4807427485966
85	255.6	283.184157423492	-27.5841574234922
86	261.1	281.004810033086	-19.9048100330857
87	258.9	279.748281271386	-20.8482812713865
88	259.9	280.205558534429	-20.3055585344289
89	261.2	280.079011767065	-18.8790117670648
90	264.7	279.368640969294	-14.6686409692941
91	267.1	276.887965047065	-9.78796504706494
92	266.4	280.207863362423	-13.8078633624233
93	267.7	281.738623520084	-14.0386235200842
94	268.6	279.578109162917	-10.9781091629166
95	267.5	278.660574999518	-11.1605749995179
96	268.5	280.624494921674	-12.124494921674
97	268.5	274.037442907049	-5.53744290704943
98	270.5	271.274271486236	-0.774271486236447
99	270.9	274.481171602161	-3.5811716021613
100	270.1	268.987210361705	1.11278963829518
101	269.3	264.246570450805	5.0534295491948
102	269.8	267.924296397703	1.87570360229696
103	270.1	264.633800046200	5.46619995379971
104	264.9	261.983626824821	2.91637317517912
105	263.7	265.925015237064	-2.22501523706359
106	264.8	262.917014384145	1.88298561585537
107	263.7	269.60802564927	-5.90802564926975
108	255.9	270.084135945551	-14.1841359455511
109	276.2	276.736706297888	-0.536706297887616
110	360.1	295.367860636488	64.7321393635117
111	380.5	323.528529793983	56.9714702060173
112	373.7	326.69776384343	47.0022361565703
113	369.8	329.094843530081	40.7051564699191
114	366.6	327.649984435992	38.9500155640076
115	359.3	325.150475480524	34.1495245194757
116	345.8	322.745131691251	23.0548683087492
117	326.2	319.868962071004	6.33103792899566
118	324.5	318.160440511571	6.33955948842908
119	328.1	320.651685364417	7.44831463558327
120	327.5	317.794348777409	9.70565122259083
121	324.4	310.924801264201	13.4751987357992
122	316.5	308.764286907033	7.7357130929667
123	310.9	306.396609184238	4.50339081576231
124	301.5	304.179595727353	-2.67959572735338
125	291.7	301.962582270469	-10.2625822704691
126	290.4	300.046897345408	-9.64689734540759
127	287.4	298.093546353868	-10.6935463538682
128	277.7	296.422690860913	-18.7226908609126
129	281.6	294.977831766824	-13.3778317668240
130	288	298.297730082182	-10.2977300821824
131	276	301.918956929363	-25.9189569293635
132	272.9	300.568263001470	-27.6682630014696
133	283	296.919164172117	-13.9191641721165
134	283.3	301.048882916748	-17.7488829167485
135	276.8	298.361043628891	-21.5610436288912
136	284.5	300.419128717242	-15.9191287172419
137	282.7	299.689924886232	-16.9899248862323
138	281.2	297.077417731331	-15.8774177313307
139	287.4	294.803905174730	-7.40390517472968
140	283.1	292.605724751084	-9.50572475108426
141	284	290.332212194483	-6.33221219448327
142	285.5	293.426114110975	-7.92611411097454
143	289.2	302.094593866186	-12.8945938661863
144	292.5	299.91524647578	-7.41524647577987
145	296.4	296.171982480232	0.22801751976767
146	305.2	290.979349771599	14.2206502284014
147	303.9	289.816986176094	14.0830138239061
148	311.5	302.496902011196	9.00309798880434
149	316.3	299.827895756577	16.4721042434227
150	316.7	301.885980844928	14.8140191550720
151	322.5	301.401606446024	21.0983935539756
152	317.1	299.090427822945	18.0095721770545
153	309.8	296.685084033672	13.114915966328
154	303.8	296.502038166591	7.29796183340885
155	290.3	297.976299224535	-7.67629922453527
156	293.7	295.420291169350	-1.72029116935046
157	291.7	288.249415124319	3.45058487568064
158	296.5	285.373245504073	11.1267544959271
159	289.1	289.615962448138	-0.515962448138259
160	288.5	294.27300612346	-5.77300612345998
161	293.8	291.603999868842	2.19600013115839
162	297.7	289.085657880135	8.61434211986537
163	305.4	286.793312290295	18.6066877097053
164	302.7	284.482133667216	18.2178663327842
165	302.5	296.841887937256	5.65811206274406
166	303	295.811355574424	7.18864442557634
167	294.5	293.669674250495	0.830325749504923
168	294.1	291.377328660655	2.72267133934492
169	294.5	285.091605177853	9.40839482214674
170	297.1	283.439582718137	13.6604172818634
171	289.4	286.288655202522	3.11134479747804
172	292.4	293.695321730726	-1.29532173072603
173	287.9	292.024466237770	-4.12446623777045
174	286.6	290.353610744815	-3.75361074481483
175	280.5	288.776920418054	-8.27692041805388
176	272.4	287.087231891859	-14.6872318918594
177	269.2	284.776053268781	-15.5760532687805
178	270.6	288.623276514829	-18.0232765148286
179	267.3	287.988237850014	-20.6882378500136
180	262.5	286.11021899143	-23.6102189914299
181	266.8	282.23512376321	-15.4351237632099
182	268.8	275.667679628135	-6.867679628135
183	263.1	272.678511808455	-9.57851180845498
184	261.2	271.949307977445	-10.7493079774455
185	266	271.238937179675	-5.23893717967482
186	262.5	270.509733348665	-8.00973334866525
187	265.2	263.866957080635	1.33304291936531
188	261.3	258.071667308355	3.22833269164452
189	253.7	257.530793809735	-3.83079380973516
190	249.2	256.989920311115	-7.78992031111481
191	239.1	256.486712878972	-17.3867128789723
192	236.4	255.945839380352	-19.5458393803519
193	235.2	251.129092490186	-15.9290924901859
194	245.2	250.738883257477	-5.53888325747689
195	246.2	250.367507058007	-4.16750705800683
196	247.7	255.796705096124	-8.09670509612426
197	251.4	256.931971555768	-5.53197155576781
198	253.3	256.127435591803	-2.82743559180258
199	254.8	255.699560292616	-0.899560292615746
200	250	268.096980629134	-18.0969806291338
201	249.3	267.574940163752	-18.2749401637523
202	241.5	267.052899698371	-25.5528996983709
203	243.3	267.039351130440	-23.7393511304403
204	248	266.065317867325	-18.0653178673248
205	253	261.305070076875	-8.3050700768755
206	252.9	274.719474208295	-21.8194742082952
207	251.5	312.240160885533	-60.7401608855331
208	251.6	310.550472359339	-58.9504723593386
209	253.5	309.482273930028	-55.9822739300285
210	259.8	311.804021483724	-52.004021483724
211	334.1	309.907169591901	24.1928304080986
212	448	347.333691102945	100.666308897055
213	445.8	374.590374664971	71.209625335029
214	445	380.019572703088	64.9804272969117
215	448.2	380.024857168397	68.1751428316033
216	438.2	372.854755969676	65.3452440303236
217	439.8	365.269553193389	74.530446806611
218	423.4	364.088356564645	59.3116434353546
219	410.8	360.684862013709	50.1151379862909
220	408.4	359.729661783833	48.6703382161675
221	406.7	365.949847217985	40.7501527820154
222	405.9	360.098058345989	45.8019416540113
223	402.7	356.920560193919	45.7794398060806
224	405.1	350.541446391234	54.5585536087663
225	399.6	348.286766867872	51.3132331321284
226	386.5	342.679807427982	43.8201925720183
227	381.4	343.419580189608	37.9804198103921
228	375.2	343.763859253217	31.4361407467832
229	357.7	328.005120051238	29.694879948762
230	359	333.415485056116	25.5845149438835
231	355	333.081774923124	21.9182250768758
232	352.7	328.058639513641	24.6413604863592
233	344.4	329.984893369319	14.4151066306809
234	343.8	328.314037876363	15.4859621236365
235	338	333.912733213631	4.08726678636881
236	339	364.163869060646	-25.1638690606458
237	333.3	368.312420838517	-35.0124208385166
238	334.4	373.590954610723	-39.1909546107227
239	328.3	374.180063106438	-45.8800631064376
240	330.7	360.079405675794	-29.3794056757945
241	330	347.145621459654	-17.1456214596537
242	331.6	383.423327943123	-51.8233279431228
243	351.2	466.595287191504	-115.395287191504
244	389.4	448.370195481537	-58.9701954815373
245	410.9	495.043736312891	-84.1437363128906
246	442.8	495.670510875083	-52.8705108750832
247	462.8	422.283464808319	40.5165351916815
248	466.9	410.19794193424	56.70205806576
249	461.7	401.596530209362	60.1034697906383
250	439.2	391.318978526219	47.8810214737806
251	430.3	382.472737369235	47.8272626307649
252	416.1	396.866459229079	19.2335407709213
253	402.5	392.577037269602	9.92296273039758
254	397.3	406.311602966084	-9.0116029660838
255	403.3	378.462831271029	24.8371687289712
256	395.9	382.140557217927	13.7594427820734
257	387.8	361.015178389166	26.7848216108338
258	378.6	358.572168533415	20.0278314665852
259	377.1	360.950415186827	16.1495848131728
260	370.4	359.147728461199	11.2522715388008
261	362	343.804090836787	18.1959091632127
262	350.3	341.229249748364	9.07075025163648
263	348.2	351.969363159857	-3.76936315985657
264	344.6	354.517107112419	-9.91710711241913
265	343.5	348.344381829051	-4.84438182905084
266	342.8	343.377745519284	-0.577745519284172
267	347.6	346.094986770997	1.50501322900297
268	346.6	343.821474214396	2.778525785604
269	349.5	341.265466159211	8.23453384078883
270	342.1	341.892240721404	0.207759278596141
271	342	331.162696240528	10.8373037594724
272	342.8	326.874049127362	15.9259508726380
273	339.3	319.477951529775	19.8220484702254
274	348.2	319.935228792817	28.2647712071829
275	333.7	337.097406538782	-3.39740653878196
276	334.7	360.643982552741	-25.9439825527409
277	354	339.348331578519	14.6516684214806
278	367.7	330.276094022668	37.4239059773319
279	363.3	332.842671008470	30.4573289915304
280	358.4	325.503072510599	32.896927489401
281	353.1	323.851050050882	29.2489499491177
282	343.1	314.213821497863	28.8861785021367
283	344.6	314.124940796977	30.4750592030229
284	344.4	307.124336897407	37.2756631025930
285	333.9	306.056138468097	27.8438615319031
286	331.7	308.716880620093	22.9831193799069
287	324.3	311.697784337151	12.6022156628492
288	321.2	309.348939647594	11.8510603524059
289	322.4	299.353108616725	23.0468913832751
290	321.7	287.926741906009	33.7732580939914
291	320.5	288.836011966785	31.6639880332149
292	312.8	295.376358965999	17.4236410340012
293	309.7	303.253851325176	6.4461486748241
294	315.6	283.691614255246	31.9083857447542
295	309.7	283.470902321687	26.2290976783128
296	304.6	285.152326745259	19.4476732547406
297	302.5	287.361076099521	15.1389239004785
298	301.5	276.838694984273	24.6613050157267
299	298.8	282.983548285470	15.8164517145303
300	291.3	281.745852557009	9.55414744299075
301	293.6	281.882193408679	11.7178065913207
302	294.6	279.627513885317	14.9724861146828
303	285.9	289.877968432598	-3.97796843259828
304	297.6	296.173485999706	1.42651400029400
305	301.1	285.500440618546	15.5995593814536
306	293.8	281.136461372425	12.6635386275748
307	297.7	268.636611767090	29.0633882329096
308	292.9	265.685110013888	27.2148899861117
309	292.1	276.425223425381	15.6747765746188
310	287.2	276.938999788140	10.2610002118596
311	288.2	283.780675319177	4.4193246808231
312	283.8	270.866498982586	12.9335010174142
313	299.9	274.93894378119	24.9610562188098
314	292.4	272.853761556978	19.5462384430216
315	293.3	267.303301216805	25.9966987831948
316	300.8	272.713666221684	28.0863337783163
317	293.7	276.07123060352	17.6287693964801
318	293.1	270.125276565329	22.9747234346707
319	294.4	278.134600157179	16.2653998428211
320	292.1	273.130297780934	18.9697022190657
321	291.9	274.529226705923	17.3707732940772
322	282.5	272.764206046773	9.73579395322735
323	277.9	274.841124168362	3.05887583163774
324	287.5	278.161022483721	9.33897751627943
325	289.2	286.301402461932	2.89859753806818
326	285.6	287.342503755381	-1.74250375538075
327	293.2	292.376208095481	0.823791904519113
328	290.8	289.066878710739	1.73312128926081
329	283.1	292.556274325248	-9.45627432524781
330	275	303.277554703502	-28.2775547035019
331	287.8	287.915084045851	-0.115084045851151
332	287.8	290.274497666025	-2.47449766602455
333	287.4	303.594736631250	-16.1947366312497
334	284	301.942714171533	-17.942714171533
335	277.8	317.918410823446	-40.1184108234459
336	277.6	336.568398195286	-58.9683981952855
337	304.9	334.143446526462	-29.2434465264624
338	294	360.420812360065	-66.4208123600647
339	300.9	387.206670091118	-86.306670091118
340	324	373.689836690881	-49.6898366908814
341	332.9	368.779699480831	-35.8796994808315
342	341.6	356.39284807493	-14.7928480749302
343	333.4	338.431418830308	-5.03141883030843
344	348.2	338.832196993634	9.36780300636593
345	344.7	330.381449534667	14.3185504653329
346	344.7	349.295099371852	-4.59509937185162
347	329.3	356.890096232445	-27.5900962324449
348	323.5	357.535703827877	-34.0357038278765
349	323.2	382.493982488444	-59.2939824884437
350	317.4	365.285874573379	-47.8858745733788
351	330.1	354.405665826591	-24.3056658265910
352	329.2	357.122907078304	-27.9229070783040
353	334.9	351.101620907158	-16.2016209071578
354	315.8	338.451107035912	-22.6511070359115
355	315.4	341.300179520297	-25.9001795202969
356	319.6	353.358605258514	-33.7586052585143
357	317.3	346.885326289634	-29.585326289634
358	313.8	344.724811932466	-30.9248119324665
359	315.8	368.177222780231	-52.3772227802308
360	311.3	383.34309900287	-72.0430990028701

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.152518042147244	0.305036084294489	0.847481957852755
8	0.381295328798543	0.762590657597087	0.618704671201457
9	0.552196795810099	0.895606408379802	0.447803204189901
10	0.581504204164906	0.836991591670188	0.418495795835094
11	0.565953182496801	0.868093635006397	0.434046817503198
12	0.498456389053221	0.996912778106443	0.501543610946779
13	0.403064223368081	0.806128446736162	0.596935776631919
14	0.317699289150471	0.635398578300942	0.682300710849529
15	0.243304142179063	0.486608284358126	0.756695857820937
16	0.189091487192302	0.378182974384604	0.810908512807698
17	0.144978823868336	0.289957647736673	0.855021176131664
18	0.113895661760248	0.227791323520496	0.886104338239752
19	0.0877637625404346	0.175527525080869	0.912236237459565
20	0.0771374629980554	0.154274925996111	0.922862537001945
21	0.0570427204554123	0.114085440910825	0.942957279544588
22	0.0388882752635547	0.0777765505271093	0.961111724736445
23	0.0305185485915779	0.0610370971831558	0.969481451408422
24	0.0261441152346972	0.0522882304693943	0.973855884765303
25	0.0272349500958319	0.0544699001916637	0.972765049904168
26	0.0427756403987564	0.0855512807975128	0.957224359601244
27	0.619312130345518	0.761375739308963	0.380687869654482
28	0.67272728826607	0.654545423467861	0.327272711733930
29	0.640236276954481	0.719527446091037	0.359763723045519
30	0.58500725525525	0.829985489489499	0.414992744744749
31	0.538188150296245	0.92362369940751	0.461811849703755
32	0.525141338751483	0.949717322497035	0.474858661248517
33	0.484829982328738	0.969659964657476	0.515170017671262
34	0.439278619957311	0.878557239914622	0.560721380042689
35	0.394519052679384	0.789038105358769	0.605480947320616
36	0.352804156972814	0.705608313945629	0.647195843027186
37	0.309302978248231	0.618605956496463	0.690697021751769
38	0.275229777786501	0.550459555573002	0.724770222213499
39	0.238222091334368	0.476444182668736	0.761777908665632
40	0.216527979042144	0.433055958084289	0.783472020957856
41	0.186922135469119	0.373844270938239	0.81307786453088
42	0.158368105060661	0.316736210121322	0.841631894939339
43	0.133903544168019	0.267807088336037	0.866096455831981
44	0.112031258568521	0.224062517137042	0.887968741431479
45	0.0921957391722883	0.184391478344577	0.907804260827712
46	0.0751743311750231	0.150348662350046	0.924825668824977
47	0.0634934095819747	0.126986819163949	0.936506590418025
48	0.0756851468688807	0.151370293737761	0.92431485313112
49	0.0684129012773922	0.136825802554784	0.931587098722608
50	0.0701285470415138	0.140257094083028	0.929871452958486
51	0.0716340053090472	0.143268010618094	0.928365994690953
52	0.070827166087261	0.141654332174522	0.929172833912739
53	0.0686411004715805	0.137282200943161	0.93135889952842
54	0.0619309696454592	0.123861939290918	0.93806903035454
55	0.0554975029727411	0.110995005945482	0.944502497027259
56	0.0483466748952235	0.096693349790447	0.951653325104777
57	0.0409140635694638	0.0818281271389276	0.959085936430536
58	0.0437734184528858	0.0875468369057717	0.956226581547114
59	0.0620628440608851	0.124125688121770	0.937937155939115
60	0.0637672580678839	0.127534516135768	0.936232741932116
61	0.0541677517484683	0.108335503496937	0.945832248251532
62	0.0456250120269676	0.0912500240539353	0.954374987973032
63	0.0379463006014389	0.0758926012028777	0.962053699398561
64	0.0313948639495521	0.0627897278991041	0.968605136050448
65	0.025512528398941	0.051025056797882	0.974487471601059
66	0.0206733720765884	0.0413467441531768	0.979326627923412
67	0.0167655738754410	0.0335311477508821	0.98323442612456
68	0.0137185625237061	0.0274371250474122	0.986281437476294
69	0.0113166113111094	0.0226332226222188	0.98868338868889
70	0.0110112594716268	0.0220225189432535	0.988988740528373
71	0.00975582519060584	0.0195116503812117	0.990244174809394
72	0.00812589945814957	0.0162517989162991	0.99187410054185
73	0.00731402271235931	0.0146280454247186	0.99268597728764
74	0.00703763940144056	0.0140752788028811	0.99296236059856
75	0.00733232476916429	0.0146646495383286	0.992667675230836
76	0.00642229135509871	0.0128445827101974	0.993577708644901
77	0.0056496401927607	0.0112992803855214	0.99435035980724
78	0.00523002828304301	0.0104600565660860	0.994769971716957
79	0.00510787344064274	0.0102157468812855	0.994892126559357
80	0.00554622120180434	0.0110924424036087	0.994453778798196
81	0.004935589630449	0.009871179260898	0.99506441036955
82	0.0043254615296083	0.0086509230592166	0.995674538470392
83	0.00385254431401165	0.0077050886280233	0.996147455685988
84	0.00355685700692515	0.00711371401385031	0.996443142993075
85	0.00440630347655934	0.00881260695311867	0.99559369652344
86	0.00652216673736134	0.0130443334747227	0.993477833262639
87	0.00905347504143958	0.0181069500828792	0.99094652495856
88	0.0118054977888331	0.0236109955776662	0.988194502211167
89	0.0151794414112762	0.0303588828225524	0.984820558588724
90	0.0205691514916013	0.0411383029832025	0.979430848508399
91	0.0304528444103193	0.0609056888206386	0.96954715558968
92	0.0367022579260286	0.0734045158520571	0.963297742073971
93	0.0417087218539918	0.0834174437079836	0.958291278146008
94	0.0492086772204616	0.0984173544409233	0.950791322779538
95	0.0560183240313237	0.112036648062647	0.943981675968676
96	0.0595774956502887	0.119154991300577	0.940422504349711
97	0.0781421896185863	0.156284379237173	0.921857810381414
98	0.103330183295534	0.206660366591069	0.896669816704466
99	0.120349300934946	0.240698601869892	0.879650699065054
100	0.145836995501416	0.291673991002831	0.854163004498584
101	0.176901341652046	0.353802683304091	0.823098658347954
102	0.195659089623108	0.391318179246217	0.804340910376891
103	0.216129829669734	0.432259659339469	0.783870170330266
104	0.225290205463518	0.450580410927036	0.774709794536482
105	0.223672599962081	0.447345199924163	0.776327400037919
106	0.22238198281294	0.44476396562588	0.77761801718706
107	0.213015463181151	0.426030926362302	0.786984536818849
108	0.198635191657078	0.397270383314157	0.801364808342922
109	0.208946306342579	0.417892612685159	0.79105369365742
110	0.477647786015764	0.955295572031529	0.522352213984236
111	0.622362040444421	0.755275919111158	0.377637959555579
112	0.660953822815765	0.678092354368469	0.339046177184235
113	0.664813567798473	0.670372864403055	0.335186432201527
114	0.661179230867901	0.677641538264197	0.338820769132099
115	0.649206261927686	0.701587476144628	0.350793738072314
116	0.624763095657375	0.75047380868525	0.375236904342625
117	0.594392125443533	0.811215749112933	0.405607874556467
118	0.563025799768399	0.873948400463202	0.436974200231601
119	0.531423975310966	0.937152049378068	0.468576024689034
120	0.499929787902047	0.999859575804094	0.500070212097953
121	0.470610341837308	0.941220683674617	0.529389658162692
122	0.439470367064854	0.878940734129708	0.560529632935146
123	0.408383969506499	0.816767939012999	0.591616030493501
124	0.378062718284999	0.756125436569997	0.621937281715001
125	0.351045943558186	0.702091887116372	0.648954056441814
126	0.324177685685705	0.648355371371409	0.675822314314295
127	0.298605802461362	0.597211604922725	0.701394197538638
128	0.278388049924895	0.55677609984979	0.721611950075105
129	0.256056963249778	0.512113926499556	0.743943036750222
130	0.233846231099162	0.467692462198324	0.766153768900838
131	0.224834369364012	0.449668738728024	0.775165630635988
132	0.217444738206017	0.434889476412034	0.782555261793983
133	0.198473909200337	0.396947818400673	0.801526090799663
134	0.183734438602127	0.367468877204254	0.816265561397873
135	0.17177440678835	0.3435488135767	0.82822559321165
136	0.157156680166391	0.314313360332783	0.842843319833609
137	0.143944844538922	0.287889689077844	0.856055155461078
138	0.131037261710920	0.262074523421839	0.86896273828908
139	0.118062710268278	0.236125420536556	0.881937289731722
140	0.106710503386233	0.213421006772465	0.893289496613767
141	0.0971753368763445	0.194350673752689	0.902824663123656
142	0.0875748393983006	0.175149678796601	0.9124251606017
143	0.0786047123853767	0.157209424770753	0.921395287614623
144	0.0699311546050437	0.139862309210087	0.930068845394956
145	0.0616128173246898	0.123225634649380	0.93838718267531
146	0.0594714424585992	0.118942884917198	0.9405285575414
147	0.05754483795936	0.11508967591872	0.94245516204064
148	0.0505774045371794	0.101154809074359	0.94942259546282
149	0.0467637933003532	0.0935275866007064	0.953236206699647
150	0.0422580457224126	0.0845160914448251	0.957741954277587
151	0.040103026518297	0.080206053036594	0.959896973481703
152	0.0376068034956526	0.0752136069913052	0.962393196504347
153	0.034475411409859	0.068950822819718	0.965524588590141
154	0.0305610875455278	0.0611221750910556	0.969438912454472
155	0.026410255570059	0.052820511140118	0.973589744429941
156	0.0228945541856133	0.0457891083712265	0.977105445814387
157	0.0199220239095212	0.0398440478190423	0.980077976090479
158	0.0184083408262018	0.0368166816524036	0.981591659173798
159	0.0156766894735707	0.0313533789471414	0.98432331052643
160	0.0131943948841440	0.0263887897682879	0.986805605115856
161	0.0112152760736024	0.0224305521472048	0.988784723926398
162	0.00995167281754325	0.0199033456350865	0.990048327182457
163	0.00986174191624102	0.0197234838324820	0.990138258083759
164	0.0099076200437821	0.0198152400875642	0.990092379956218
165	0.00831061358003432	0.0166212271600686	0.991689386419966
166	0.00702700588954888	0.0140540117790978	0.992972994110451
167	0.00587831201501912	0.0117566240300382	0.99412168798498
168	0.00498146881813146	0.00996293763626292	0.995018531181869
169	0.0042948981773119	0.0085897963546238	0.995705101822688
170	0.00386279836562103	0.00772559673124205	0.99613720163438
171	0.00318747125856667	0.00637494251713334	0.996812528741433
172	0.00256356852518057	0.00512713705036113	0.99743643147482
173	0.00207175419492001	0.00414350838984002	0.99792824580508
174	0.00167573721834281	0.00335147443668562	0.998324262781657
175	0.00137440419531662	0.00274880839063325	0.998625595804683
176	0.00116931576435672	0.00233863152871344	0.998830684235643
177	0.00100725566582114	0.00201451133164228	0.998992744334179
178	0.00089234540293734	0.00178469080587468	0.999107654597063
179	0.000821427308747555	0.00164285461749511	0.999178572691252
180	0.000795682017775988	0.00159136403555198	0.999204317982224
181	0.000677502568522469	0.00135500513704494	0.999322497431477
182	0.000569141858433664	0.00113828371686733	0.999430858141566
183	0.000490369360356781	0.000980738720713562	0.999509630639643
184	0.000429379864168095	0.00085875972833619	0.999570620135832
185	0.000379896291068355	0.000759792582136711	0.999620103708932
186	0.000340674993266526	0.000681349986533052	0.999659325006733
187	0.000335569242286081	0.000671138484572162	0.999664430757714
188	0.000356050454084568	0.000712100908169136	0.999643949545915
189	0.000360360395523969	0.000720720791047938	0.999639639604476
190	0.000365685948297545	0.00073137189659509	0.999634314051702
191	0.000393000399302342	0.000786000798604685	0.999606999600698
192	0.000451359837632152	0.000902719675264304	0.999548640162368
193	0.00045550956344593	0.00091101912689186	0.999544490436554
194	0.000455692425003028	0.000911384850006056	0.999544307574997
195	0.000465233940942324	0.000930467881884648	0.999534766059058
196	0.000475828100612786	0.000951656201225571	0.999524171899387
197	0.000491714618094698	0.000983429236189396	0.999508285381905
198	0.000520039322117346	0.00104007864423469	0.999479960677883
199	0.00056406650722081	0.00112813301444162	0.99943593349278
200	0.000708293024811488	0.00141658604962298	0.999291706975189
201	0.000951947399110039	0.00190389479822008	0.99904805260089
202	0.00161425384016015	0.00322850768032029	0.99838574615984
203	0.00292445397809567	0.00584890795619134	0.997075546021904
204	0.00536205125466769	0.0107241025093354	0.994637948745332
205	0.00717643966377725	0.0143528793275545	0.992823560336223
206	0.0125612031444937	0.0251224062889875	0.987438796855506
207	0.0690420631738575	0.138084126347715	0.930957936826142
208	0.248405828942421	0.496811657884841	0.75159417105758
209	0.572098190252976	0.855803619494047	0.427901809747024
210	0.863181000600515	0.273637998798969	0.136818999399485
211	0.876526348116378	0.246947303767243	0.123473651883622
212	0.950285130095552	0.0994297398088966	0.0497148699044483
213	0.960570924235444	0.078858151529111	0.0394290757645555
214	0.965964397250186	0.0680712054996282	0.0340356027498141
215	0.973277034773191	0.0534459304536172	0.0267229652268086
216	0.977912415149126	0.0441751697017479	0.0220875848508739
217	0.98521854094954	0.0295629181009191	0.0147814590504596
218	0.986597687746515	0.0268046245069705	0.0134023122534853
219	0.98599388105464	0.0280122378907188	0.0140061189453594
220	0.985228715987021	0.0295425680259578	0.0147712840129789
221	0.983810752931648	0.0323784941367042	0.0161892470683521
222	0.982749585922746	0.0345008281545074	0.0172504140772537
223	0.98153437401222	0.0369312519755598	0.0184656259877799
224	0.982362387479272	0.0352752250414562	0.0176376125207281
225	0.982330247075044	0.035339505849912	0.017669752924956
226	0.98034654473062	0.0393069105387615	0.0196534552693808
227	0.97719927645945	0.0456014470810989	0.0228007235405495
228	0.97285602568589	0.0542879486282202	0.0271439743141101
229	0.967514317487773	0.0649713650244548	0.0324856825122274
230	0.96130052032616	0.0773989593476792	0.0386994796738396
231	0.954409650206853	0.0911806995862943	0.0455903497931471
232	0.946343767149332	0.107312465701335	0.0536562328506675
233	0.939548008403495	0.120903983193010	0.0604519915965048
234	0.931768289824055	0.136463420351890	0.0682317101759448
235	0.92993555246849	0.140128895063020	0.0700644475315102
236	0.95809821340416	0.0838035731916795	0.0419017865958397
237	0.982291016821597	0.0354179663568061	0.0177089831784030
238	0.994203512027956	0.0115929759440879	0.00579648797204393
239	0.998884224648417	0.00223155070316637	0.00111577535158318
240	0.999638533189902	0.000722933620196266	0.000361466810098133
241	0.999795188330048	0.000409623339903807	0.000204811669951903
242	0.999982451269756	3.50974604880052e-05	1.75487302440026e-05
243	0.999999998819392	2.36121633662557e-09	1.18060816831279e-09
244	0.999999999933886	1.32227698571989e-10	6.61138492859947e-11
245	0.999999999999666	6.67886989300014e-13	3.33943494650007e-13
246	0.999999999999934	1.31246180227729e-13	6.56230901138644e-14
247	0.999999999999971	5.7618232324002e-14	2.8809116162001e-14
248	0.999999999999998	3.42156404031782e-15	1.71078202015891e-15
249	1	6.41666054623938e-17	3.20833027311969e-17
250	1	6.12758743309155e-18	3.06379371654578e-18
251	1	4.37824577909827e-19	2.18912288954914e-19
252	1	3.69385772831007e-19	1.84692886415503e-19
253	1	7.49906971075625e-19	3.74953485537812e-19
254	1	1.62541363361778e-18	8.1270681680889e-19
255	1	1.59854368190288e-18	7.99271840951442e-19
256	1	2.65134843102411e-18	1.32567421551205e-18
257	1	2.89662705884839e-18	1.44831352942419e-18
258	1	4.81095556964706e-18	2.40547778482353e-18
259	1	8.59337344231709e-18	4.29668672115854e-18
260	1	1.8153903287893e-17	9.0769516439465e-18
261	1	3.61484487708757e-17	1.80742243854378e-17
262	1	8.3413181801553e-17	4.17065909007765e-17
263	1	1.65230342823828e-16	8.2615171411914e-17
264	1	2.54570789863448e-16	1.27285394931724e-16
265	1	3.75032064803265e-16	1.87516032401632e-16
266	1	6.08238977986988e-16	3.04119488993494e-16
267	1	1.14020808104177e-15	5.70104040520887e-16
268	0.999999999999999	2.16259761190226e-15	1.08129880595113e-15
269	0.999999999999998	4.64330435528173e-15	2.32165217764086e-15
270	0.999999999999996	7.76927962247223e-15	3.88463981123611e-15
271	0.999999999999992	1.60167361530509e-14	8.00836807652544e-15
272	0.999999999999983	3.43770654384394e-14	1.71885327192197e-14
273	0.999999999999964	7.25670717303159e-14	3.62835358651579e-14
274	0.999999999999937	1.25409966125044e-13	6.27049830625218e-14
275	0.999999999999906	1.87114069967507e-13	9.35570349837534e-14
276	0.999999999999966	6.88797793475748e-14	3.44398896737874e-14
277	0.999999999999925	1.50428997350949e-13	7.52144986754746e-14
278	0.99999999999993	1.40888783049179e-13	7.04443915245896e-14
279	0.999999999999913	1.73752043203049e-13	8.68760216015243e-14
280	0.999999999999908	1.8371082676079e-13	9.1855413380395e-14
281	0.999999999999887	2.26126671199656e-13	1.13063335599828e-13
282	0.999999999999838	3.23037671399395e-13	1.61518835699697e-13
283	0.999999999999815	3.69504937490087e-13	1.84752468745044e-13
284	0.999999999999876	2.48262739409633e-13	1.24131369704816e-13
285	0.99999999999984	3.20614961675171e-13	1.60307480837585e-13
286	0.99999999999977	4.61050668398325e-13	2.30525334199163e-13
287	0.999999999999536	9.28230294306314e-13	4.64115147153157e-13
288	0.999999999999064	1.87168069237253e-12	9.35840346186263e-13
289	0.999999999998472	3.05657617160361e-12	1.52828808580180e-12
290	0.999999999998385	3.22970352911345e-12	1.61485176455673e-12
291	0.999999999998335	3.33057915170095e-12	1.66528957585047e-12
292	0.999999999996966	6.06764062592692e-12	3.03382031296346e-12
293	0.999999999993461	1.3077470285513e-11	6.5387351427565e-12
294	0.99999999999374	1.25211239658257e-11	6.26056198291286e-12
295	0.999999999991746	1.65087791163569e-11	8.25438955817844e-12
296	0.999999999985879	2.82429098130162e-11	1.41214549065081e-11
297	0.999999999973939	5.21226684560267e-11	2.60613342280133e-11
298	0.999999999960944	7.81119403193683e-11	3.90559701596842e-11
299	0.999999999930994	1.38012169586727e-10	6.90060847933635e-11
300	0.999999999853754	2.92491249514547e-10	1.46245624757274e-10
301	0.999999999683112	6.33776514639886e-10	3.16888257319943e-10
302	0.99999999934621	1.30758130924315e-09	6.53790654621574e-10
303	0.999999998682793	2.63441335527951e-09	1.31720667763976e-09
304	0.999999997246997	5.50600676897706e-09	2.75300338448853e-09
305	0.99999999539	9.22000186513806e-09	4.61000093256903e-09
306	0.999999990946826	1.81063481889717e-08	9.05317409448587e-09
307	0.999999987263422	2.54731550537981e-08	1.27365775268990e-08
308	0.999999979379612	4.1240776242797e-08	2.06203881213985e-08
309	0.99999996307569	7.38486198113183e-08	3.69243099056591e-08
310	0.999999926985073	1.46029854733957e-07	7.30149273669785e-08
311	0.999999858447213	2.83105573228237e-07	1.41552786614118e-07
312	0.999999725122967	5.49754066259366e-07	2.74877033129683e-07
313	0.999999587176351	8.256472974128e-07	4.128236487064e-07
314	0.999999245013698	1.50997260462847e-06	7.54986302314235e-07
315	0.999998739805983	2.52038803478766e-06	1.26019401739383e-06
316	0.999998491365046	3.01726990841515e-06	1.50863495420758e-06
317	0.999997546420602	4.90715879636592e-06	2.45357939818296e-06
318	0.999996228809959	7.54238008228105e-06	3.77119004114053e-06
319	0.999994301995198	1.13960096032939e-05	5.69800480164694e-06
320	0.99999127300023	1.74539995405076e-05	8.7269997702538e-06
321	0.9999869062769	2.61874461996315e-05	1.30937230998158e-05
322	0.999975677141345	4.86457173095208e-05	2.43228586547604e-05
323	0.999953963835195	9.20723296091406e-05	4.60361648045703e-05
324	0.999923449223923	0.000153101552153107	7.65507760765536e-05
325	0.999859905911464	0.00028018817707284	0.00014009408853642
326	0.99974763110982	0.000504737780359827	0.000252368890179914
327	0.999561660456273	0.000876679087452956	0.000438339543726478
328	0.999240154783044	0.00151969043391179	0.000759845216955897
329	0.998740973457034	0.00251805308593264	0.00125902654296632
330	0.998524782774457	0.00295043445108501	0.00147521722554251
331	0.99754525849582	0.00490948300835906	0.00245474150417953
332	0.996083784154948	0.00783243169010418	0.00391621584505209
333	0.994404499920974	0.0111910001580527	0.00559550007902634
334	0.993792089483774	0.0124158210324516	0.00620791051622582
335	0.997502423502148	0.00499515299570344	0.00249757649785172
336	0.99993821880353	0.000123562392938959	6.17811964694796e-05
337	0.99997483358684	5.0332826321577e-05	2.51664131607885e-05
338	0.999999498379406	1.00324118794562e-06	5.01620593972812e-07
339	0.999999984738078	3.05238430549174e-08	1.52619215274587e-08
340	0.999999986972958	2.60540835791899e-08	1.30270417895950e-08
341	0.999999963802854	7.23942914450505e-08	3.61971457225253e-08
342	0.99999983683159	3.2633681893593e-07	1.63168409467965e-07
343	0.999999688801924	6.22396152868475e-07	3.11198076434238e-07
344	0.99999907312582	1.85374835989413e-06	9.26874179947065e-07
345	0.999996912734301	6.17453139703702e-06	3.08726569851851e-06
346	0.999997597254012	4.80549197651771e-06	2.40274598825885e-06
347	0.999989197731906	2.16045361873419e-05	1.08022680936709e-05
348	0.999947447343183	0.000105105313634973	5.25526568174866e-05
349	0.999801267827838	0.000397464344323435	0.000198732172161717
350	0.999930389876436	0.000139220247127931	6.96101235639656e-05
351	0.999595815310814	0.00080836937837131	0.000404184689185655
352	0.997869236624731	0.00426152675053744	0.00213076337526872
353	0.998622225325221	0.00275554934955734	0.00137777467477867

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	154	0.443804034582133	NOK
5% type I error level	205	0.590778097982709	NOK
10% type I error level	237	0.68299711815562	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/108tdg1289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/108tdg1289489981.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/1j9g41289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/1j9g41289489981.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/2u1xp1289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/2u1xp1289489981.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/3u1xp1289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/3u1xp1289489981.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/4u1xp1289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/4u1xp1289489981.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/5u1xp1289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/5u1xp1289489981.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/64sea1289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/64sea1289489981.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/7x1vv1289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/7x1vv1289489981.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/8x1vv1289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/8x1vv1289489981.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/9x1vv1289489981.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489976iy2zv1fzwz2sk0v/9x1vv1289489981.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal 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')

}

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

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

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

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

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

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

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

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

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

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

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

FreeStatistics.org is powered by