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PaperTimDamen

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 03 Feb 2011 09:33:21 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi.htm/, Retrieved Thu, 03 Feb 2011 10:34:03 +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/2011/Feb/03/t129672562075z1k1gd7nqpxdi.htm/},
    year = {2011},
}
@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 = {2011},
    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 «

349 336 331 327 323 322 385 405 412 411 410 415 414 411 408 410 411 416 479 498 502 498 499 506 510 509 502 495 490 490 553 570 573 572 575 580 580 574 563 556 546 545 605 628 631 626 614 606 602 589 574 558 552 546 607 636 631 623 618 605 619 596 570 546 528 506 555 568 564 553 541 542 540 521 505 491 482 478 523 531 532 540 525 533 531 508 495 482 470 466 515 518 516 511 500 498 494 476 458 443 430 424 476 481 470 460 451 450 444 429 421 400 389 384 432 446 431 423 416 416 413 399 386 374 365 365 418 428 424 421 417 423 423 419 406 398 390 391 444 460 455 456 452 459 461 451 443 439 430 436 488 506 502 501 501 515 521 520 512 509 505 511 570 592 594 586 586 592 594 594 586 586 572 572 563 563 555 555 554 554 601 601 622 622 617 617 606 606 595 595 599 599 600 600 592 592 575 575 567 567 555 555 555 555 608 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	18 seconds
R Server	'Herman Ole Andreas Wold' @ www.yougetit.org

Multiple Linear Regression - Estimated Regression Equation

Werkloosheid[t] = + 499.303271902373 + 0.102201472847776t + 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)	499.303271902373	5.704476	87.5283	0	0
t	0.102201472847776	0.020052	5.0969	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.224383826261559
R-squared	0.0503481014877776
Adjusted R-squared	0.048410036388773
F-TEST (value)	25.9785398919976
F-TEST (DF numerator)	1
F-TEST (DF denominator)	490
p-value	4.93876434526364e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	63.1692789346425
Sum Squared Residuals	1955275.32255011

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	349	499.405473375217	-150.405473375217
2	336	499.507674848069	-163.507674848069
3	331	499.609876320916	-168.609876320916
4	327	499.712077793764	-172.712077793764
5	323	499.814279266612	-176.814279266612
6	322	499.91648073946	-177.91648073946
7	385	500.018682212307	-115.018682212307
8	405	500.120883685155	-95.120883685155
9	412	500.223085158003	-88.2230851580028
10	411	500.325286630851	-89.3252866308506
11	410	500.427488103698	-90.4274881036984
12	415	500.529689576546	-85.5296895765461
13	414	500.631891049394	-86.6318910493939
14	411	500.734092522242	-89.7340925222417
15	408	500.836293995089	-92.8362939950895
16	410	500.938495467937	-90.9384954679372
17	411	501.040696940785	-90.040696940785
18	416	501.142898413633	-85.1428984136328
19	479	501.245099886481	-22.2450998864806
20	498	501.347301359328	-3.34730135932835
21	502	501.449502832176	0.550497167823872
22	498	501.551704305024	-3.5517043050239
23	499	501.653905777872	-2.65390577787168
24	506	501.75610725072	4.24389274928055
25	510	501.858308723567	8.14169127643277
26	509	501.960510196415	7.03948980358499
27	502	502.062711669263	-0.0627116692627823
28	495	502.16491314211	-7.16491314211056
29	490	502.267114614958	-12.2671146149583
30	490	502.369316087806	-12.3693160878061
31	553	502.471517560654	50.5284824393461
32	570	502.573719033502	67.4262809664983
33	573	502.67592050635	70.3240794936506
34	572	502.778121979197	69.2218780208028
35	575	502.880323452045	72.119676547955
36	580	502.982524924893	77.0174750751072
37	580	503.084726397741	76.9152736022595
38	574	503.186927870588	70.8130721294117
39	563	503.289129343436	59.7108706565639
40	556	503.391330816284	52.6086691837161
41	546	503.493532289132	42.5064677108683
42	545	503.59573376198	41.4042662380206
43	605	503.697935234827	101.302064765173
44	628	503.800136707675	124.199863292325
45	631	503.902338180523	127.097661819477
46	626	504.00453965337	121.995460346629
47	614	504.106741126218	109.893258873782
48	606	504.208942599066	101.791057400934
49	602	504.311144071914	97.6888559280862
50	589	504.413345544762	84.5866544552384
51	574	504.515547017609	69.4844529823906
52	558	504.617748490457	53.3822515095428
53	552	504.719949963305	47.280050036695
54	546	504.822151436153	41.1778485638473
55	607	504.924352909001	102.075647090999
56	636	505.026554381848	130.973445618152
57	631	505.128755854696	125.871244145304
58	623	505.230957327544	117.769042672456
59	618	505.333158800392	112.666841199608
60	605	505.435360273239	99.5646397267606
61	619	505.537561746087	113.462438253913
62	596	505.639763218935	90.360236781065
63	570	505.741964691783	64.2580353082173
64	546	505.84416616463	40.1558338353695
65	528	505.946367637478	22.0536323625217
66	506	506.048569110326	-0.0485691103260448
67	555	506.150770583174	48.8492294168262
68	568	506.252972056022	61.7470279439784
69	564	506.35517352887	57.6448264711306
70	553	506.457375001717	46.5426249982828
71	541	506.559576474565	34.4404235254351
72	542	506.661777947413	35.3382220525873
73	540	506.76397942026	33.2360205797395
74	521	506.866180893108	14.1338191068917
75	505	506.968382365956	-1.96838236595603
76	491	507.070583838804	-16.0705838388038
77	482	507.172785311652	-25.1727853116516
78	478	507.274986784499	-29.2749867844994
79	523	507.377188257347	15.6228117426529
80	531	507.479389730195	23.5206102698051
81	532	507.581591203043	24.4184087969573
82	540	507.68379267589	32.3162073241095
83	525	507.785994148738	17.2140058512618
84	533	507.888195621586	25.111804378414
85	531	507.990397094434	23.0096029055662
86	508	508.092598567282	-0.0925985672815637
87	495	508.19480004013	-13.1948000401293
88	482	508.297001512977	-26.2970015129771
89	470	508.399202985825	-38.3992029858249
90	466	508.501404458673	-42.5014044586727
91	515	508.60360593152	6.39639406847956
92	518	508.705807404368	9.29419259563178
93	516	508.808008877216	7.191991122784
94	511	508.910210350064	2.08978964993623
95	500	509.012411822912	-9.01241182291154
96	498	509.114613295759	-11.1146132957593
97	494	509.216814768607	-15.2168147686071
98	476	509.319016241455	-33.3190162414549
99	458	509.421217714303	-51.4212177143026
100	443	509.52341918715	-66.5234191871504
101	430	509.625620659998	-79.6256206599982
102	424	509.727822132846	-85.727822132846
103	476	509.830023605694	-33.8300236056938
104	481	509.932225078542	-28.9322250785415
105	470	510.034426551389	-40.0344265513893
106	460	510.136628024237	-50.1366280242371
107	451	510.238829497085	-59.2388294970849
108	450	510.341030969933	-60.3410309699326
109	444	510.44323244278	-66.4432324427804
110	429	510.545433915628	-81.5454339156282
111	421	510.647635388476	-89.647635388476
112	400	510.749836861324	-110.749836861324
113	389	510.852038334172	-121.852038334172
114	384	510.954239807019	-126.954239807019
115	432	511.056441279867	-79.056441279867
116	446	511.158642752715	-65.1586427527148
117	431	511.260844225563	-80.2608442255626
118	423	511.36304569841	-88.3630456984104
119	416	511.465247171258	-95.4652471712582
120	416	511.567448644106	-95.567448644106
121	413	511.669650116954	-98.6696501169537
122	399	511.771851589801	-112.771851589801
123	386	511.874053062649	-125.874053062649
124	374	511.976254535497	-137.976254535497
125	365	512.078456008345	-147.078456008345
126	365	512.180657481193	-147.180657481193
127	418	512.28285895404	-94.2828589540404
128	428	512.385060426888	-84.3850604268881
129	424	512.487261899736	-88.487261899736
130	421	512.589463372584	-91.5894633725837
131	417	512.691664845431	-95.6916648454315
132	423	512.793866318279	-89.7938663182792
133	423	512.896067791127	-89.896067791127
134	419	512.998269263975	-93.9982692639748
135	406	513.100470736823	-107.100470736823
136	398	513.20267220967	-115.20267220967
137	390	513.304873682518	-123.304873682518
138	391	513.407075155366	-122.407075155366
139	444	513.509276628214	-69.5092766282137
140	460	513.611478101061	-53.6114781010615
141	455	513.713679573909	-58.7136795739092
142	456	513.815881046757	-57.815881046757
143	452	513.918082519605	-61.9180825196048
144	459	514.020283992453	-55.0202839924526
145	461	514.1224854653	-53.1224854653004
146	451	514.224686938148	-63.2246869381481
147	443	514.326888410996	-71.3268884109959
148	439	514.429089883844	-75.4290898838437
149	430	514.531291356691	-84.5312913566914
150	436	514.633492829539	-78.6334928295392
151	488	514.735694302387	-26.735694302387
152	506	514.837895775235	-8.83789577523477
153	502	514.940097248083	-12.9400972480825
154	501	515.04229872093	-14.0422987209303
155	501	515.144500193778	-14.1445001937781
156	515	515.246701666626	-0.246701666625877
157	521	515.348903139474	5.65109686052635
158	520	515.451104612321	4.54889538767857
159	512	515.553306085169	-3.55330608516921
160	509	515.655507558017	-6.65550755801698
161	505	515.757709030865	-10.7577090308648
162	511	515.859910503713	-4.85991050371253
163	570	515.96211197656	54.0378880234397
164	592	516.064313449408	75.935686550592
165	594	516.166514922256	77.8334850777441
166	586	516.268716395104	69.7312836048964
167	586	516.370917867951	69.6290821320486
168	592	516.473119340799	75.5268806592008
169	594	516.575320813647	77.424679186353
170	594	516.677522286495	77.3224777135053
171	586	516.779723759342	69.2202762406575
172	586	516.88192523219	69.1180747678097
173	572	516.984126705038	55.0158732949619
174	572	517.086328177886	54.9136718221142
175	563	517.188529650734	45.8114703492664
176	563	517.290731123581	45.7092688764186
177	555	517.392932596429	37.6070674035708
178	555	517.495134069277	37.504865930723
179	554	517.597335542125	36.4026644578753
180	554	517.699537014973	36.3004629850275
181	601	517.80173848782	83.1982615121797
182	601	517.903939960668	83.096060039332
183	622	518.006141433516	103.993858566484
184	622	518.108342906364	103.891657093636
185	617	518.210544379211	98.7894556207886
186	617	518.312745852059	98.6872541479408
187	606	518.414947324907	87.585052675093
188	606	518.517148797755	87.4828512022453
189	595	518.619350270602	76.3806497293975
190	595	518.72155174345	76.2784482565498
191	599	518.823753216298	80.176246783702
192	599	518.925954689146	80.0740453108542
193	600	519.028156161994	80.9718438380064
194	600	519.130357634841	80.8696423651587
195	592	519.232559107689	72.7674408923109
196	592	519.334760580537	72.6652394194631
197	575	519.436962053385	55.5630379466153
198	575	519.539163526232	55.4608364737675
199	567	519.64136499908	47.3586350009198
200	567	519.743566471928	47.256433528072
201	555	519.845767944776	35.1542320552242
202	555	519.947969417624	35.0520305823764
203	555	520.050170890471	34.9498291095287
204	555	520.152372363319	34.8476276366809
205	608	520.254573836167	87.7454261638331
206	608	520.356775309015	87.6432246909853
207	631	520.458976781862	110.541023218138
208	631	520.56117825471	110.43882174529
209	629	520.663379727558	108.336620272442
210	629	520.765581200406	108.234418799594
211	624	520.867782673253	103.132217326746
212	624	520.969984146101	103.030015853899
213	610	521.072185618949	88.9278143810509
214	610	521.174387091797	88.8256129082031
215	616	521.276588564645	94.7234114353553
216	616	521.378790037492	94.6212099625076
217	621	521.48099151034	99.5190084896598
218	621	521.583192983188	99.416807016812
219	604	521.685394456036	82.3146055439642
220	604	521.787595928884	82.2124040711165
221	584	521.889797401731	62.1102025982687
222	584	521.991998874579	62.0080011254209
223	574	522.094200347427	51.9057996525731
224	574	522.196401820275	51.8035981797254
225	555	522.298603293122	32.7013967068776
226	555	522.40080476597	32.5991952340298
227	545	522.503006238818	22.496993761182
228	545	522.605207711666	22.3947922883343
229	599	522.707409184513	76.2925908154865
230	599	522.809610657361	76.1903893426387
231	620	522.911812130209	97.088187869791
232	620	523.014013603057	96.9859863969432
233	608	523.116215075905	84.8837849240954
234	608	523.218416548752	84.7815834512476
235	590	523.3206180216	66.6793819783998
236	590	523.422819494448	66.577180505552
237	579	523.525020967296	55.4749790327043
238	579	523.627222440144	55.3727775598565
239	580	523.729423912991	56.2705760870087
240	580	523.831625385839	56.1683746141609
241	579	523.933826858687	55.0661731413132
242	579	524.036028331535	54.9639716684654
243	572	524.138229804382	47.8617701956176
244	572	524.24043127723	47.7595687227698
245	560	524.342632750078	35.6573672499221
246	560	524.444834222926	35.5551657770743
247	551	524.547035695774	26.4529643042265
248	551	524.649237168621	26.3507628313787
249	537	524.751438641469	12.248561358531
250	537	524.853640114317	12.1463598856832
251	541	524.955841587165	16.0441584128354
252	541	525.058043060012	15.9419569399876
253	588	525.16024453286	62.8397554671399
254	588	525.262446005708	62.7375539942921
255	607	525.364647478556	81.6353525214443
256	607	525.466848951403	81.5331510485965
257	599	525.569050424251	73.4309495757488
258	599	525.671251897099	73.328748102901
259	578	525.773453369947	52.2265466300532
260	578	525.875654842795	52.1243451572054
261	563	525.977856315642	37.0221436843576
262	563	526.08005778849	36.9199422115099
263	566	526.182259261338	39.8177407386621
264	566	526.284460734186	39.7155392658143
265	561	526.386662207033	34.6133377929665
266	561	526.488863679881	34.5111363201188
267	554	526.591065152729	27.408934847271
268	554	526.693266625577	27.3067333744232
269	540	526.795468098425	13.2045319015754
270	540	526.897669571272	13.1023304287277
271	526	526.99987104412	-0.99987104412011
272	526	527.102072516968	-1.10207251696789
273	512	527.204273989816	-15.2042739898157
274	512	527.306475462663	-15.3064754626634
275	505	527.408676935511	-22.4086769355112
276	505	527.510878408359	-22.510878408359
277	554	527.613079881207	26.3869201187932
278	554	527.715281354055	26.2847186459455
279	584	527.817482826902	56.1825171730977
280	584	527.91968429975	56.0803157002499
281	569	528.021885772598	40.9781142274021
282	569	528.124087245446	40.8759127545543
283	540	528.226288718293	11.7737112817066
284	540	528.328490191141	11.6715098088588
285	522	528.430691663989	-6.43069166398897
286	522	528.532893136837	-6.53289313683675
287	526	528.635094609685	-2.63509460968452
288	526	528.737296082532	-2.7372960825323
289	527	528.83949755538	-1.83949755538008
290	527	528.941699028228	-1.94169902822785
291	516	529.043900501076	-13.0439005010756
292	516	529.146101973923	-13.1461019739234
293	503	529.248303446771	-26.2483034467712
294	503	529.350504919619	-26.350504919619
295	489	529.452706392467	-40.4527063924667
296	489	529.554907865314	-40.5549078653145
297	479	529.657109338162	-50.6571093381623
298	479	529.75931081101	-50.7593108110101
299	475	529.861512283858	-54.8615122838578
300	475	529.963713756706	-54.9637137567056
301	524	530.065915229553	-6.06591522955339
302	524	530.168116702401	-6.16811670240116
303	552	530.270318175249	21.7296818247511
304	552	530.372519648097	21.6274803519033
305	532	530.474721120945	1.52527887905551
306	532	530.576922593792	1.42307740620773
307	511	530.67912406664	-19.67912406664
308	511	530.781325539488	-19.7813255394878
309	492	530.883527012336	-38.8835270123356
310	492	530.985728485183	-38.9857284851834
311	492	531.087929958031	-39.0879299580311
312	492	531.190131430879	-39.1901314308789
313	493	531.292332903727	-38.2923329037267
314	493	531.394534376574	-38.3945343765745
315	481	531.496735849422	-50.4967358494223
316	481	531.59893732227	-50.59893732227
317	462	531.701138795118	-69.7011387951178
318	462	531.803340267966	-69.8033402679656
319	457	531.905541740813	-74.9055417408133
320	457	532.007743213661	-75.0077432136611
321	442	532.109944686509	-90.1099446865089
322	442	532.212146159357	-90.2121461593567
323	439	532.314347632204	-93.3143476322044
324	439	532.416549105052	-93.4165491050522
325	488	532.5187505779	-44.5187505779
326	488	532.620952050748	-44.6209520507478
327	521	532.723153523596	-11.7231535235956
328	521	532.825354996443	-11.8253549964433
329	501	532.927556469291	-31.9275564692911
330	501	533.029757942139	-32.0297579421389
331	485	533.131959414987	-48.1319594149867
332	485	533.234160887834	-48.2341608878344
333	464	533.336362360682	-69.3363623606822
334	464	533.43856383353	-69.43856383353
335	460	533.540765306378	-73.5407653063778
336	460	533.642966779226	-73.6429667792255
337	467	533.745168252073	-66.7451682520733
338	467	533.847369724921	-66.8473697249211
339	460	533.949571197769	-73.9495711977689
340	460	534.051772670617	-74.0517726706166
341	448	534.153974143464	-86.1539741434644
342	448	534.256175616312	-86.2561756163122
343	443	534.35837708916	-91.35837708916
344	443	534.460578562008	-91.4605785620077
345	436	534.562780034856	-98.5627800348555
346	436	534.664981507703	-98.6649815077033
347	431	534.767182980551	-103.767182980551
348	431	534.869384453399	-103.869384453399
349	484	534.971585926247	-50.9715859262466
350	484	535.073787399094	-51.0737873990944
351	510	535.175988871942	-25.1759888719422
352	510	535.27819034479	-25.27819034479
353	513	535.380391817638	-22.3803918176377
354	513	535.482593290486	-22.4825932904855
355	503	535.584794763333	-32.5847947633333
356	503	535.686996236181	-32.6869962361811
357	471	535.789197709029	-64.7891977090288
358	471	535.891399181877	-64.8913991818766
359	471	535.993600654724	-64.9936006547244
360	471	536.095802127572	-65.0958021275722
361	476	536.19800360042	-60.19800360042
362	476	536.300205073268	-60.3002050732677
363	475	536.402406546115	-61.4024065461155
364	475	536.504608018963	-61.5046080189633
365	470	536.606809491811	-66.606809491811
366	470	536.709010964659	-66.7090109646588
367	461	536.811212437507	-75.8112124375066
368	461	536.913413910354	-75.9134139103544
369	455	537.015615383202	-82.0156153832021
370	455	537.11781685605	-82.1178168560499
371	456	537.220018328898	-81.2200183288977
372	456	537.322219801746	-81.3222198017455
373	517	537.424421274593	-20.4244212745933
374	517	537.526622747441	-20.526622747441
375	525	537.628824220289	-12.6288242202888
376	525	537.731025693137	-12.7310256931366
377	523	537.833227165984	-14.8332271659844
378	523	537.935428638832	-14.9354286388321
379	519	538.03763011168	-19.0376301116799
380	519	538.139831584528	-19.1398315845277
381	509	538.242033057375	-29.2420330573755
382	509	538.344234530223	-29.3442345302232
383	512	538.446436003071	-26.446436003071
384	512	538.548637475919	-26.5486374759188
385	519	538.650838948767	-19.6508389487666
386	519	538.753040421614	-19.7530404216143
387	517	538.855241894462	-21.8552418944621
388	517	538.95744336731	-21.9574433673099
389	510	539.059644840158	-29.0596448401577
390	510	539.161846313005	-29.1618463130054
391	509	539.264047785853	-30.2640477858532
392	509	539.366249258701	-30.366249258701
393	501	539.468450731549	-38.4684507315488
394	501	539.570652204397	-38.5706522043965
395	507	539.672853677244	-32.6728536772443
396	507	539.775055150092	-32.7750551500921
397	569	539.87725662294	29.1227433770601
398	569	539.979458095788	29.0205419042123
399	580	540.081659568635	39.9183404313646
400	580	540.183861041483	39.8161389585168
401	578	540.286062514331	37.713937485669
402	578	540.388263987179	37.6117360128212
403	565	540.490465460026	24.5095345399735
404	565	540.592666932874	24.4073330671257
405	547	540.694868405722	6.30513159427792
406	547	540.79706987857	6.20293012143014
407	555	540.899271351418	14.1007286485824
408	555	541.001472824265	13.9985271757346
409	562	541.103674297113	20.8963257028868
410	561	541.205875769961	19.794124230039
411	555	541.308077242809	13.6919227571913
412	544	541.410278715657	2.58972128434348
413	537	541.512480188504	-4.51248018850429
414	543	541.614681661352	1.38531833864793
415	594	541.7168831342	52.2831168658002
416	611	541.819084607048	69.1809153929524
417	613	541.921286079895	71.0787139201046
418	611	542.023487552743	68.9765124472568
419	594	542.125689025591	51.874310974409
420	595	542.227890498439	52.7721095015613
421	591	542.330091971286	48.6699080287135
422	589	542.432293444134	46.5677065558657
423	584	542.534494916982	41.465505083018
424	573	542.63669638983	30.3633036101702
425	567	542.738897862678	24.2611021373224
426	569	542.841099335525	26.1589006644746
427	621	542.943300808373	78.0566991916268
428	629	543.045502281221	85.954497718779
429	628	543.147703754069	84.8522962459313
430	612	543.249905226916	68.7500947730835
431	595	543.352106699764	51.6478933002357
432	597	543.454308172612	53.545691827388
433	593	543.55650964546	49.4434903545402
434	590	543.658711118308	46.3412888816924
435	580	543.760912591155	36.2390874088446
436	574	543.863114064003	30.1368859359969
437	573	543.965315536851	29.0346844631491
438	573	544.067517009699	28.9324829903013
439	620	544.169718482547	75.8302815174535
440	626	544.271919955394	81.7280800446058
441	620	544.374121428242	75.625878571758
442	588	544.47632290109	43.5236770989102
443	566	544.578524373938	21.4214756260624
444	557	544.680725846785	12.3192741532147
445	561	544.782927319633	16.2170726803669
446	549	544.885128792481	4.1148712075191
447	532	544.987330265329	-12.9873302653287
448	526	545.089531738176	-19.0895317381764
449	511	545.191733211024	-34.1917332110242
450	499	545.293934683872	-46.293934683872
451	555	545.39613615672	9.60386384328023
452	565	545.498337629568	19.5016623704324
453	542	545.600539102415	-3.60053910241533
454	527	545.702740575263	-18.7027405752631
455	510	545.804942048111	-35.8049420481109
456	514	545.907143520959	-31.9071435209587
457	517	546.009344993806	-29.0093449938064
458	508	546.111546466654	-38.1115464666542
459	493	546.213747939502	-53.213747939502
460	490	546.31594941235	-56.3159494123498
461	469	546.418150885198	-77.4181508851975
462	478	546.520352358045	-68.5203523580453
463	528	546.622553830893	-18.6225538308931
464	534	546.724755303741	-12.7247553037409
465	518	546.826956776589	-28.8269567765886
466	506	546.929158249436	-40.9291582494364
467	502	547.031359722284	-45.0313597222842
468	516	547.133561195132	-31.133561195132
469	528	547.23576266798	-19.2357626679797
470	533	547.337964140827	-14.3379641408275
471	536	547.440165613675	-11.4401656136753
472	537	547.542367086523	-10.5423670865231
473	524	547.644568559371	-23.6445685593708
474	536	547.746770032219	-11.7467700322186
475	587	547.848971505066	39.1510284949336
476	597	547.951172977914	49.0488270220858
477	581	548.053374450762	32.946625549238
478	564	548.15557592361	15.8444240763903
479	558	548.257777396458	9.7422226035425
480	575	548.359978869305	26.6400211306947
481	580	548.462180342153	31.5378196578469
482	575	548.564381815001	26.4356181849992
483	563	548.666583287849	14.3334167121514
484	552	548.768784760696	3.23121523930362
485	537	548.870986233544	-11.8709862335442
486	545	548.973187706392	-3.97318770639193
487	601	549.07538917924	51.9246108207603
488	604	549.177590652087	54.8224093479125
489	586	549.279792124935	36.7202078750647
490	564	549.381993597783	14.618006402217
491	549	549.484195070631	-0.484195070630809
492	551	549.586396543479	1.41360345652141

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.000217787505324134	0.000435575010648267	0.999782212494676
6	2.98122430934567e-05	5.96244861869135e-05	0.999970187756906
7	0.0209614387602729	0.0419228775205459	0.979038561239727
8	0.0268023740951815	0.0536047481903629	0.973197625904819
9	0.0162443832428517	0.0324887664857035	0.983755616757148
10	0.0069875855378421	0.0139751710756842	0.993012414462158
11	0.00280042103636575	0.0056008420727315	0.997199578963634
12	0.00110169372885635	0.00220338745771271	0.998898306271144
13	0.000475906247369105	0.00095181249473821	0.99952409375263
14	0.000249958852184043	0.000499917704368085	0.999750041147816
15	0.000159883141343489	0.000319766282686977	0.999840116858657
16	9.84731720873877e-05	0.000196946344174775	0.999901526827913
17	6.0859639269612e-05	0.000121719278539224	0.99993914036073
18	3.27885648095231e-05	6.55771296190462e-05	0.99996721143519
19	3.39610289849798e-05	6.79220579699595e-05	0.999966038971015
20	4.23888618908132e-05	8.47777237816265e-05	0.99995761113811
21	3.1166200787469e-05	6.23324015749381e-05	0.999968833799213
22	1.43633313385501e-05	2.87266626771002e-05	0.999985636668661
23	5.80808766839637e-06	1.16161753367927e-05	0.999994191912332
24	2.27118660551288e-06	4.54237321102575e-06	0.999997728813395
25	8.59978158166157e-07	1.71995631633231e-06	0.999999140021842
26	3.40163823412046e-07	6.80327646824093e-07	0.999999659836177
27	1.84223583901102e-07	3.68447167802205e-07	0.999999815776416
28	1.61364189700219e-07	3.22728379400439e-07	0.99999983863581
29	2.07913787879433e-07	4.15827575758865e-07	0.999999792086212
30	2.76491904998908e-07	5.52983809997816e-07	0.999999723508095
31	1.35705497200991e-07	2.71410994401982e-07	0.999999864294503
32	8.34551749968102e-08	1.6691034999362e-07	0.999999916544825
33	4.20935501853848e-08	8.41871003707696e-08	0.99999995790645
34	1.76100088167015e-08	3.52200176334031e-08	0.999999982389991
35	7.01426271557033e-09	1.40285254311407e-08	0.999999992985737
36	2.7531549705997e-09	5.50630994119939e-09	0.999999997246845
37	1.10331981716198e-09	2.20663963432395e-09	0.99999999889668
38	5.5657134313317e-10	1.11314268626634e-09	0.999999999443429
39	5.36885954303008e-10	1.07377190860602e-09	0.999999999463114
40	9.13136140320678e-10	1.82627228064136e-09	0.999999999086864
41	3.1636617468704e-09	6.3273234937408e-09	0.999999996836338
42	1.03291700247593e-08	2.06583400495186e-08	0.99999998967083
43	4.79789310640565e-09	9.5957862128113e-09	0.999999995202107
44	2.64870465111806e-09	5.29740930223611e-09	0.999999997351295
45	1.37915704561972e-09	2.75831409123945e-09	0.999999998620843
46	6.67732383556631e-10	1.33546476711326e-09	0.999999999332268
47	3.98622997788193e-10	7.97245995576386e-10	0.999999999601377
48	3.50365104513371e-10	7.00730209026742e-10	0.999999999649635
49	4.2185670040864e-10	8.4371340081728e-10	0.999999999578143
50	1.11289104055587e-09	2.22578208111174e-09	0.999999998887109
51	7.00477070221146e-09	1.40095414044229e-08	0.99999999299523
52	8.87103961876303e-08	1.77420792375261e-07	0.999999911289604
53	8.99990999247117e-07	1.79998199849423e-06	0.999999100009
54	7.24034035084565e-06	1.44806807016913e-05	0.99999275965965
55	6.47629249411413e-06	1.29525849882283e-05	0.999993523707506
56	4.64787044229095e-06	9.2957408845819e-06	0.999995352129558
57	3.55481574205042e-06	7.10963148410084e-06	0.999996445184258
58	3.12546324310535e-06	6.2509264862107e-06	0.999996874536757
59	3.15694516175968e-06	6.31389032351937e-06	0.999996843054838
60	4.43117373624122e-06	8.86234747248244e-06	0.999995568826264
61	4.7965420593948e-06	9.5930841187896e-06	0.99999520345794
62	9.22862867206865e-06	1.84572573441373e-05	0.999990771371328
63	3.79094721535178e-05	7.58189443070356e-05	0.999962090527846
64	0.000267475306150661	0.000534950612301321	0.99973252469385
65	0.00198736590019698	0.00397473180039395	0.998012634099803
66	0.0141239311487427	0.0282478622974854	0.985876068851257
67	0.0236550198483068	0.0473100396966137	0.976344980151693
68	0.0315980215912049	0.0631960431824099	0.968401978408795
69	0.0424851249416809	0.0849702498833619	0.957514875058319
70	0.0611187304948333	0.122237460989667	0.938881269505167
71	0.092831763517489	0.185663527034978	0.907168236482511
72	0.129110202135789	0.258220404271579	0.87088979786421
73	0.171546905551012	0.343093811102024	0.828453094448988
74	0.245727969979962	0.491455939959925	0.754272030020038
75	0.355616990419023	0.711233980838046	0.644383009580977
76	0.49509795028346	0.990195900566921	0.50490204971654
77	0.636671453960551	0.726657092078898	0.363328546039449
78	0.753161473921532	0.493677052156935	0.246838526078468
79	0.781184714386258	0.437630571227485	0.218815285613742
80	0.797361921960488	0.405276156079023	0.202638078039512
81	0.810138943029883	0.379722113940234	0.189861056970117
82	0.815548356136553	0.368903287726893	0.184451643863447
83	0.829810878525611	0.340378242948778	0.170189121474389
84	0.836501281977555	0.326997436044891	0.163498718022445
85	0.843063589630681	0.313872820738637	0.156936410369319
86	0.863097377090175	0.27380524581965	0.136902622909825
87	0.88779833545377	0.224403329092461	0.112201664546231
88	0.91457397000566	0.170852059988681	0.0854260299943406
89	0.939877960357913	0.120244079284174	0.0601220396420869
90	0.958385760478078	0.0832284790438435	0.0416142395219218
91	0.958965783292346	0.0820684334153088	0.0410342167076544
92	0.958645487144626	0.0827090257107488	0.0413545128553744
93	0.95837034071585	0.0832593185682988	0.0416296592841494
94	0.958679735296634	0.0826405294067328	0.0413202647033664
95	0.960765866617715	0.0784682667645697	0.0392341333822848
96	0.962681585601032	0.0746368287979358	0.0373184143989679
97	0.964826625087617	0.0703467498247655	0.0351733749123827
98	0.969942528024715	0.0601149439505699	0.0300574719752849
99	0.977222753684936	0.0455544926301284	0.0227772463150642
100	0.984644730424039	0.0307105391519229	0.0153552695759615
101	0.990789267921625	0.0184214641567504	0.0092107320783752
102	0.99473713663622	0.0105257267275611	0.00526286336378056
103	0.994933187004483	0.0101336259910343	0.00506681299551715
104	0.994892901421035	0.0102141971579302	0.00510709857896508
105	0.995151310978535	0.00969737804292933	0.00484868902146466
106	0.995660266425797	0.00867946714840572	0.00433973357420286
107	0.99632899602396	0.0073420079520795	0.00367100397603975
108	0.996860762409381	0.00627847518123788	0.00313923759061894
109	0.997406692345944	0.00518661530811131	0.00259330765405566
110	0.998118453526089	0.00376309294782308	0.00188154647391154
111	0.998725600374162	0.00254879925167622	0.00127439962583811
112	0.999329772927616	0.00134045414476775	0.000670227072383874
113	0.999697561073655	0.000604877852690258	0.000302438926345129
114	0.999873373736594	0.000253252526811546	0.000126626263405773
115	0.999894570068467	0.000210859863065005	0.000105429931532502
116	0.999897693747836	0.000204612504327442	0.000102306252163721
117	0.999913484319066	0.000173031361868074	8.65156809340369e-05
118	0.99993231547631	0.000135369047380919	6.76845236904595e-05
119	0.999950811295657	9.8377408685736e-05	4.9188704342868e-05
120	0.999963860975931	7.22780481374687e-05	3.61390240687344e-05
121	0.999974225541216	5.15489175679483e-05	2.57744587839741e-05
122	0.999984844222276	3.03115554485454e-05	1.51557777242727e-05
123	0.99999280311045	1.43937790993766e-05	7.19688954968828e-06
124	0.99999730361168	5.39277664028663e-06	2.69638832014332e-06
125	0.999999188250653	1.62349869370407e-06	8.11749346852033e-07
126	0.999999766301499	4.67397002332378e-07	2.33698501166189e-07
127	0.999999823235477	3.53529045987869e-07	1.76764522993935e-07
128	0.999999847817754	3.04364492501975e-07	1.52182246250987e-07
129	0.99999987635966	2.47280679987356e-07	1.23640339993678e-07
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376	0.99999999999974	5.19225500925659e-13	2.5961275046283e-13
377	0.99999999999957	8.59283995459073e-13	4.29641997729536e-13
378	0.999999999999298	1.40363309066043e-12	7.01816545330215e-13
379	0.999999999998926	2.1485579636237e-12	1.07427898181185e-12
380	0.999999999998384	3.23238773898535e-12	1.61619386949268e-12
381	0.999999999997997	4.00655787813855e-12	2.00327893906928e-12
382	0.999999999997595	4.8095612702972e-12	2.4047806351486e-12
383	0.99999999999703	5.94191768046505e-12	2.97095884023252e-12
384	0.999999999996446	7.10795108769422e-12	3.55397554384711e-12
385	0.999999999995274	9.45271627303328e-12	4.72635813651664e-12
386	0.999999999993877	1.2245781254308e-11	6.12289062715401e-12
387	0.999999999992593	1.48148722908067e-11	7.40743614540336e-12
388	0.999999999991335	1.7330546066164e-11	8.665273033082e-12
389	0.999999999991768	1.64637781005211e-11	8.23188905026057e-12
390	0.999999999992616	1.4767596367359e-11	7.38379818367952e-12
391	0.999999999993972	1.20564784482959e-11	6.02823922414793e-12
392	0.99999999999544	9.12091214052135e-12	4.56045607026067e-12
393	0.999999999997642	4.71584289216002e-12	2.35792144608001e-12
394	0.999999999998948	2.10459223596734e-12	1.05229611798367e-12
395	0.999999999999485	1.02935891252816e-12	5.14679456264082e-13
396	0.999999999999785	4.29278415702643e-13	2.14639207851321e-13
397	0.999999999999602	7.9514395840174e-13	3.9757197920087e-13
398	0.999999999999266	1.4672223244555e-12	7.3361116222775e-13
399	0.999999999998628	2.7434937580419e-12	1.37174687902095e-12
400	0.999999999997444	5.11098989891587e-12	2.55549494945793e-12
401	0.99999999999524	9.51878271163454e-12	4.75939135581727e-12
402	0.99999999999117	1.76589437767365e-11	8.82947188836823e-12
403	0.999999999984205	3.1590625716192e-11	1.5795312858096e-11
404	0.999999999971893	5.62134969696974e-11	2.81067484848487e-11
405	0.999999999960005	7.99897276916e-11	3.99948638458e-11
406	0.999999999944217	1.1156553696635e-10	5.5782768483175e-11
407	0.999999999913157	1.73685923946163e-10	8.68429619730815e-11
408	0.999999999866485	2.67029590753018e-10	1.33514795376509e-10
409	0.99999999977888	4.4224069311155e-10	2.21120346555775e-10
410	0.999999999640588	7.18823964471726e-10	3.59411982235863e-10
411	0.999999999464635	1.07073011150206e-09	5.3536505575103e-10
412	0.999999999359219	1.28156253683769e-09	6.40781268418844e-10
413	0.999999999378063	1.24387302671059e-09	6.21936513355294e-10
414	0.999999999330772	1.33845523213115e-09	6.69227616065573e-10
415	0.999999998820048	2.35990332496627e-09	1.17995166248313e-09
416	0.999999998263883	3.47223447935695e-09	1.73611723967848e-09
417	0.99999999758946	4.82108104518964e-09	2.41054052259482e-09
418	0.999999996636954	6.72609276299155e-09	3.36304638149577e-09
419	0.999999994294228	1.14115447727853e-08	5.70577238639266e-09
420	0.99999999052941	1.89411816112503e-08	9.47059080562513e-09
421	0.999999983942288	3.21154237132943e-08	1.60577118566472e-08
422	0.999999972691017	5.46179667366678e-08	2.73089833683339e-08
423	0.999999952615893	9.4768213300949e-08	4.73841066504745e-08
424	0.999999916208912	1.67582175436246e-07	8.37910877181228e-08
425	0.999999854217985	2.91564030395851e-07	1.45782015197925e-07
426	0.999999746797926	5.06404147320792e-07	2.53202073660396e-07
427	0.999999754668786	4.9066242813806e-07	2.4533121406903e-07
428	0.999999826737297	3.46525406467222e-07	1.73262703233611e-07
429	0.999999891000045	2.17999909793981e-07	1.08999954896991e-07
430	0.999999902246334	1.95507332373532e-07	9.7753666186766e-08
431	0.999999877543073	2.44913854998129e-07	1.22456927499064e-07
432	0.999999861088546	2.77822908251602e-07	1.38911454125801e-07
433	0.999999837941408	3.24117184320224e-07	1.62058592160112e-07
434	0.9999998096993	3.80601398992698e-07	1.90300699496349e-07
435	0.99999973962557	5.20748859447506e-07	2.60374429723753e-07
436	0.999999619236789	7.61526422819788e-07	3.80763211409894e-07
437	0.999999452192185	1.09561563089274e-06	5.47807815446368e-07
438	0.99999923892099	1.52215802035696e-06	7.61079010178479e-07
439	0.999999797093685	4.05812630047318e-07	2.02906315023659e-07
440	0.999999979662344	4.0675312903075e-08	2.03376564515375e-08
441	0.999999998928193	2.14361352166768e-09	1.07180676083384e-09
442	0.9999999997587	4.82601398979024e-10	2.41300699489512e-10
443	0.999999999858468	2.83064376469754e-10	1.41532188234877e-10
444	0.999999999891252	2.17496622238975e-10	1.08748311119487e-10
445	0.999999999946207	1.07585272831501e-10	5.37926364157505e-11
446	0.999999999957373	8.52545890415845e-11	4.26272945207922e-11
447	0.999999999933863	1.32273275439835e-10	6.61366377199173e-11
448	0.99999999987853	2.42942212944744e-10	1.21471106472372e-10
449	0.999999999704714	5.90572946083075e-10	2.95286473041538e-10
450	0.99999999928493	1.43013837905817e-09	7.15069189529087e-10
451	0.99999999962438	7.51238123489247e-10	3.75619061744623e-10
452	0.999999999944933	1.10134347814488e-10	5.5067173907244e-11
453	0.999999999968285	6.34294666320703e-11	3.17147333160351e-11
454	0.999999999959824	8.03512764076923e-11	4.01756382038461e-11
455	0.999999999902696	1.94608019037552e-10	9.73040095187761e-11
456	0.999999999784428	4.31144373893498e-10	2.15572186946749e-10
457	0.999999999559336	8.8132857774989e-10	4.40664288874945e-10
458	0.999999998857314	2.28537092685617e-09	1.14268546342809e-09
459	0.999999996972353	6.05529472397234e-09	3.02764736198617e-09
460	0.999999992928068	1.41438631363477e-08	7.07193156817383e-09
461	0.999999994969385	1.00612301513523e-08	5.03061507567614e-09
462	0.999999996298904	7.40219203814918e-09	3.70109601907459e-09
463	0.999999989415034	2.11699314043843e-08	1.05849657021921e-08
464	0.999999972232913	5.55341744819079e-08	2.77670872409539e-08
465	0.99999992380969	1.52380622254974e-07	7.61903111274868e-08
466	0.9999998604553	2.79089400828317e-07	1.39544700414158e-07
467	0.999999841607515	3.16784970058889e-07	1.58392485029445e-07
468	0.999999738994388	5.22011223865763e-07	2.61005611932881e-07
469	0.99999943932258	1.12135483964662e-06	5.6067741982331e-07
470	0.999998774520273	2.45095945360989e-06	1.22547972680494e-06
471	0.9999974405962	5.11880759815718e-06	2.55940379907859e-06
472	0.999995321902992	9.35619401657118e-06	4.67809700828559e-06
473	0.999997409502015	5.18099597068032e-06	2.59049798534016e-06
474	0.999998470754424	3.05849115215274e-06	1.52924557607637e-06
475	0.999995344553833	9.31089233408665e-06	4.65544616704332e-06
476	0.999991599058616	1.68018827689219e-05	8.40094138446095e-06
477	0.999976407456046	4.71850879078561e-05	2.35925439539281e-05
478	0.99992575151938	0.000148496961239449	7.42484806197244e-05
479	0.99979941782459	0.000401164350818255	0.000200582175409128
480	0.999409154748813	0.00118169050237344	0.000590845251186722
481	0.998495621740534	0.00300875651893237	0.00150437825946619
482	0.996207628700931	0.00758474259813831	0.00379237129906916
483	0.989913850391017	0.0201722992179653	0.0100861496089827
484	0.976600009991768	0.0467999800164631	0.0233999900082315
485	0.976461623085737	0.0470767538285262	0.0235383769142631
486	0.99941183534955	0.00117632930090193	0.000588164650450966
487	0.997048930569652	0.00590213886069515	0.00295106943034758

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	437	0.904761904761905	NOK
5% type I error level	451	0.933747412008282	NOK
10% type I error level	463	0.958592132505176	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/109eam1296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/109eam1296725581.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/1tl341296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/1tl341296725581.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/2kemt1296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/2kemt1296725581.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/3nrt31296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/3nrt31296725581.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/4hba91296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/4hba91296725581.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/5mzeo1296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/5mzeo1296725581.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/6q5jy1296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/6q5jy1296725581.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/7h96i1296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/7h96i1296725581.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/8uuxf1296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/8uuxf1296725581.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/9hygv1296725581.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t129672562075z1k1gd7nqpxdi/9hygv1296725581.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; 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

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