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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 11:54:00 +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/t1296734880tbrq67v7p805o9b.htm/, Retrieved Thu, 03 Feb 2011 13:08:16 +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/t1296734880tbrq67v7p805o9b.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 0 336 349 331 336 327 331 323 327 322 323 385 322 405 385 412 405 411 412 410 411 415 410 414 415 411 414 408 411 410 408 411 410 416 411 479 416 498 479 502 498 498 502 499 498 506 499 510 506 509 510 502 509 495 502 490 495 490 490 553 490 570 553 573 570 572 573 575 572 580 575 580 580 574 580 563 574 556 563 546 556 545 546 605 545 628 605 631 628 626 631 614 626 606 614 602 606 589 602 574 589 558 574 552 558 546 552 607 546 636 607 631 636 623 631 618 623 605 618 619 605 596 619 570 596 546 570 528 546 506 528 555 506 568 555 564 568 553 564 541 553 542 541 540 542 521 540 505 521 491 505 482 491 478 482 523 478 531 523 532 531 540 532 525 540 533 525 531 533 508 531 495 508 482 495 470 482 466 470 515 466 518 515 516 518 511 516 500 511 498 500 494 498 476 494 458 476 443 458 430 443 424 430 476 424 481 476 470 481 460 470 451 460 450 451 444 450 429 444 421 429 400 421 389 400 384 389 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	16 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

werkloosheid[t] = + 54.7330874906929 + 0.898050150387643y1[t] + 19.696446900927M1[t] -5.15477965536067M2[t] -3.09064481210086M3[t] -5.914246935678M4[t] -11.7112303740971M5[t] -2.85516667143077M6[t] + 19.2518650578942M7[t] + 6.27276297254629M8[t] -9.30674658007266M9[t] -4.95944027150728M10[t] -5.28957134792985M11[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	54.7330874906929	7.295581	7.5022	0	0
y1	0.898050150387643	0.01275	70.4346	0	0
M1	19.696446900927	4.26556	4.6176	5e-06	2e-06
M2	-5.15477965536067	4.265068	-1.2086	0.227411	0.113706
M3	-3.09064481210086	4.26359	-0.7249	0.468872	0.234436
M4	-5.914246935678	4.26319	-1.3873	0.166001	0.083
M5	-11.7112303740971	4.262903	-2.7472	0.006236	0.003118
M6	-2.85516667143077	4.265108	-0.6694	0.503547	0.251774
M7	19.2518650578942	4.265169	4.5137	8e-06	4e-06
M8	6.27276297254629	4.26522	1.4707	0.142035	0.071017
M9	-9.30674658007266	4.268933	-2.1801	0.029735	0.014868
M10	-4.95944027150728	4.264204	-1.163	0.245392	0.122696
M11	-5.28957134792985	4.263213	-1.2407	0.215306	0.107653

Multiple Linear Regression - Regression Statistics
Multiple R	0.955684476054623
R-squared	0.9133328177718
Adjusted R-squared	0.91116161487673
F-TEST (value)	420.657516552579
F-TEST (DF numerator)	12
F-TEST (DF denominator)	479
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	19.3010671119499
Sum Squared Residuals	178442.440805136

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	349	74.4295343916197	274.57046560838
2	336	362.997810320619	-26.9978103206192
3	331	353.38729320884	-22.3872932088398
4	327	346.073440333324	-19.0734403333244
5	323	336.684256293355	-13.6842562933547
6	322	341.94811939447	-19.9481193944705
7	385	363.157100973408	21.8428990265921
8	405	406.755158362481	-1.75515836248143
9	412	409.136651817615	2.86334818238469
10	411	419.770309178894	-8.77030917889422
11	410	418.542127952084	-8.542127952084
12	415	422.933649149626	-7.93364914962622
13	414	447.120346802491	-33.1203468024914
14	411	421.371070095816	-10.3710700958161
15	408	420.741054487913	-12.741054487913
16	410	415.223301913173	-5.22330191317292
17	411	411.222418775529	-0.222418775529067
18	416	420.976532628583	-4.97653262858308
19	479	447.573815109846	31.4261848901537
20	498	491.17187249892	6.82812750108015
21	502	492.655315803666	9.34468419633389
22	498	500.594822713782	-2.59482271378206
23	499	496.672491035809	2.32750896419108
24	506	502.860112534126	3.13988746587359
25	510	528.842910487767	-18.8429104877669
26	509	507.58388453303	1.41611546697019
27	502	508.749969225902	-6.74996922590198
28	495	499.640016049611	-4.64001604961135
29	490	487.556681558479	2.44331844152131
30	490	491.922494509207	-1.92249450920685
31	553	514.029526238532	38.9704737614681
32	570	557.627583627605	12.3724163723946
33	573	557.314926631576	15.6850733684236
34	572	564.356383391305	7.64361660869529
35	575	563.128202164494	11.8717978355055
36	580	571.111923963587	8.88807603641273
37	580	595.298621616452	-15.2986216164525
38	574	570.447395060165	3.55260493983518
39	563	567.123229001099	-4.12322900109876
40	556	554.421075223258	1.57892477674245
41	546	542.337740732125	3.66225926787508
42	545	542.213302930915	2.78669706908515
43	605	563.422284509852	41.5777154901478
44	628	604.326191447763	23.6738085522372
45	631	609.40183535406	21.5981646459403
46	626	616.443292113788	9.556707886212
47	614	611.622910285427	2.37708971457279
48	606	606.135879828705	-0.135879828705362
49	602	618.647925526531	-16.6479255265312
50	589	590.204498368693	-1.20449836869295
51	574	580.593981256913	-6.5939812569134
52	558	564.299626877522	-6.29962687752162
53	552	544.1338410329	7.8661589670998
54	546	547.601603833241	-1.6016038332407
55	607	564.32033466024	42.6796653397601
56	636	606.122291748538	29.8777082514618
57	631	616.586236557161	14.4137634428392
58	623	616.443292113788	6.556707886212
59	618	608.928759834264	9.07124016573571
60	605	609.728080430256	-4.72808043025592
61	619	617.749875376144	1.25012462385644
62	596	605.471350925283	-9.4713509252829
63	570	586.880332309627	-16.8803323096269
64	546	560.707426275971	-14.7074262759711
65	528	533.357239228248	-5.35723922824848
66	506	526.048400223937	-20.0484002239373
67	555	528.398328644734	26.6016713552658
68	568	559.42368392838	8.5763160716193
69	564	555.518826330801	8.4811736691989
70	553	556.273932037816	-3.27393203781591
71	541	546.065249307129	-5.06524930712927
72	542	540.578218850407	1.42178114959259
73	540	561.172715901722	-21.1727159017221
74	521	534.525389044659	-13.5253890446591
75	505	519.526571030554	-14.5265710305537
76	491	502.334166500774	-11.3341665007743
77	482	483.964480956928	-1.96448095692812
78	478	484.738093306106	-6.73809330610571
79	523	503.25292443388	19.7470755661198
80	531	530.686079115976	0.313920884023864
81	532	522.290970766458	9.70902923354167
82	540	527.536327225411	12.4636727745887
83	525	534.39059735209	-9.39059735208992
84	533	526.209416444205	6.79058355579487
85	531	553.090264548233	-22.0902645482333
86	508	526.44293769117	-18.4429376911703
87	495	507.851919075514	-12.8519190755143
88	482	493.353664996898	-11.3536649968978
89	470	475.882029603439	-5.88202960343934
90	466	473.961491501454	-7.96149150145399
91	515	492.476322629228	22.5236773707715
92	518	523.501677912875	-5.50167791287499
93	516	510.616318811419	5.38368118858103
94	511	513.167524819209	-2.16752481920906
95	500	508.347142990848	-8.34714299084828
96	498	503.758162684514	-5.75816268451406
97	494	521.658509284666	-27.6585092846658
98	476	493.215082126828	-17.2150821268275
99	458	479.11431426311	-21.1143142631098
100	443	460.125809432555	-17.1258094325551
101	430	440.858073738321	-10.8580737383213
102	424	438.039485485948	-14.0394854859483
103	476	454.758216312947	21.2417836870526
104	481	488.477722047757	-7.47772204775692
105	470	477.388463247076	-7.38846324707618
106	460	471.857217901378	-11.8572179013775
107	451	462.546585321079	-11.5465853210785
108	450	459.75370531552	-9.75370531551956
109	444	478.552102066059	-34.5521020660589
110	429	448.312574607445	-19.3125746074454
111	421	436.905957194891	-15.9059571948906
112	400	426.897953868212	-26.8979538682123
113	389	402.241917271653	-13.2419172716526
114	384	401.219429320055	-17.2194293200549
115	432	418.836210297442	13.1637897025582
116	446	448.963515430701	-2.96351543070065
117	431	445.956707983509	-14.9567079835087
118	423	436.833262036259	-13.8332620362594
119	416	429.318729756736	-13.3187297567357
120	416	428.321950051952	-12.3219500519521
121	413	448.018396952879	-35.0183969528791
122	399	420.473019945428	-21.4730199454285
123	386	409.964452683261	-23.9644526832613
124	374	395.466198604645	-21.4661986046448
125	365	378.892613361574	-13.8926133615739
126	365	379.666225710752	-14.6662257107515
127	418	401.773257440077	16.2267425599235
128	428	436.390813325274	-8.39081332527365
129	424	429.791805276531	-5.79180527653112
130	421	430.546910983546	-9.54691098354593
131	417	427.52262945596	-10.5226294559604
132	423	429.22000020234	-6.2200002023397
133	423	454.304748005593	-31.3047480055926
134	419	429.453521449305	-10.4535214493049
135	406	427.925455691014	-21.9254556910141
136	398	413.427201612398	-15.4272016123976
137	390	400.445816970877	-10.4458169708774
138	391	402.117479470443	-11.1174794704426
139	444	425.122561350155	18.8774386498447
140	460	459.740117235352	0.259882764647652
141	455	458.529410088936	-3.52941008893568
142	456	458.386465645563	-2.38646564556285
143	452	458.954384719528	-6.95438471952791
144	459	460.651755465907	-1.6517554659072
145	461	486.634553419548	-25.6345534195477
146	451	463.579427164035	-12.5794271640353
147	443	456.663060503419	-13.6630605034187
148	439	446.65505717674	-7.65505717674042
149	430	437.265873136771	-7.2658731367707
150	436	438.039485485948	-2.0394854859483
151	488	465.534818117599	22.4651818824008
152	506	499.254323852409	6.74567614759137
153	502	499.839717006767	2.16028299323275
154	501	500.594822713782	0.405177286217939
155	501	499.366641486972	1.63335851302815
156	515	504.656212834902	10.3437871650983
157	521	536.925361841256	-15.9253618412557
158	520	517.462436187294	2.53756381270611
159	512	518.628520880166	-6.62852088016605
160	509	508.620517553488	0.379482446512234
161	505	500.129383663906	4.8706163360943
162	511	505.393246765022	5.6067532349785
163	570	532.888579396672	37.1114206033276
164	592	572.894436184195	19.1055638158046
165	594	577.072029940105	16.9279700598955
166	586	583.215436549445	2.78456345055479
167	586	575.700904269921	10.2990957300785
168	592	580.990475617851	11.0095243821486
169	594	606.075223421104	-12.0752234211042
170	594	583.020097165592	10.9799028344082
171	586	585.084232008852	0.91576799114837
172	586	575.076228682173	10.9237713178267
173	572	569.279245243754	2.7207547562458
174	572	565.562606840994	6.43739315900643
175	563	587.669638570319	-24.6696385703186
176	563	566.608085131482	-3.60808513148185
177	555	551.028575578863	3.97142442113711
178	555	548.191480684327	6.80851931567287
179	554	547.861349607905	6.13865039209545
180	554	552.252870805447	1.74712919455323
181	601	571.949317706374	29.0506822936262
182	601	589.306448218305	11.6935517816947
183	622	591.370583061565	30.6294169384349
184	622	607.406034096128	14.5939659038715
185	617	601.609050657709	15.3909493422906
186	617	605.974863608438	11.0251363915625
187	606	628.081895337762	-22.0818953377625
188	606	605.22424159815	0.77575840184951
189	595	589.644732045532	5.35526795446846
190	595	584.113486699833	10.8865133001671
191	599	583.78335562341	15.2166443765897
192	599	592.665127572891	6.3348724271093
193	600	612.361574473818	-12.3615744738177
194	600	588.408398067918	11.5916019320823
195	592	590.472532911177	1.5274670888225
196	592	580.464529584499	11.5354704155008
197	575	574.66754614608	0.332453853919945
198	575	568.256757292157	6.7432427078435
199	567	590.363789021482	-23.3637890214815
200	567	570.200285733032	-3.20028573303242
201	555	554.620776180413	0.379223819586538
202	555	548.191480684327	6.80851931567287
203	555	547.861349607905	7.13865039209544
204	555	553.150920955834	1.84907904416559
205	608	572.847367856761	35.1526321432386
206	608	595.592799271019	12.4072007289812
207	631	597.656934114279	33.3430658857214
208	631	615.488485449617	15.5115145503827
209	629	609.691502011198	19.3084979888019
210	629	616.751465413089	12.2485345869108
211	624	638.858497142414	-14.8584971424142
212	624	621.389144305128	2.61085569487193
213	610	605.809634752509	4.19036524749089
214	610	597.584238955647	12.4157610443525
215	616	597.254107879225	18.7458921207751
216	616	607.93198012948	8.06801987051936
217	621	627.628427030408	-6.62842703040764
218	621	607.267451226058	13.7325487739418
219	604	609.331586069318	-5.33158606931797
220	604	591.241131389151	12.7588686108491
221	584	585.444147950732	-1.44414795073178
222	584	576.339208645645	7.6607913543547
223	574	598.44624037497	-24.4462403749703
224	574	576.486636785746	-2.48663678574592
225	555	560.907127233127	-5.90712723312697
226	555	548.191480684327	6.80851931567287
227	545	547.861349607905	-2.86134960790456
228	545	544.170419451958	0.82958054804202
229	599	563.866866352885	35.133133647115
230	599	587.51034791753	11.48965208247
231	620	589.57448276079	30.4255172392102
232	620	605.609933795353	14.3900662046468
233	608	599.812950356934	8.18704964306594
234	608	597.892412254949	10.1075877450513
235	590	619.999443984274	-29.9994439842737
236	590	590.855439191948	-0.855439191948215
237	579	575.275929639329	3.72407036067075
238	579	569.744684293631	9.25531570636943
239	580	569.414553217208	10.585446782792
240	580	575.602174715526	4.3978252844745
241	579	595.298621616452	-16.2986216164525
242	579	569.549344909777	9.45065509022282
243	572	571.613479753037	0.386520246963019
244	572	562.503526576746	9.49647342325366
245	560	556.706543138327	3.2934568616728
246	560	554.786005036342	5.21399496365815
247	551	576.893036765667	-25.8930367656669
248	551	555.83148332683	-4.83148332683014
249	537	540.251973774211	-3.25197377421116
250	537	532.02657797735	4.97342202265044
251	541	531.696446900927	9.30355309907301
252	541	540.578218850407	0.421781149592593
253	588	560.274665751334	27.7253342486656
254	588	577.631796263266	10.368203736734
255	607	579.695931106526	27.3040688934742
256	607	593.935281840314	13.0647181596861
257	599	588.138298401895	10.8617015981053
258	599	589.80996090146	9.19003909854008
259	578	611.916992630785	-33.9169926307849
260	578	580.078837387297	-2.07883738729649
261	563	564.499327834678	-1.49932783467754
262	563	555.375881887428	7.62411811257172
263	566	555.045750811006	10.9542491889943
264	566	563.029472610099	2.97052738990151
265	561	582.725919511025	-21.7259195110255
266	561	553.3844422028	7.6155577972004
267	554	555.44857704606	-1.44857704605942
268	554	546.338623869769	7.66137613023123
269	540	540.54164043135	-0.541640431349623
270	540	536.825002028589	3.17499797141101
271	526	558.932033757914	-32.932033757914
272	526	533.380229567139	-7.38022956713907
273	512	517.80072001452	-5.80072001452011
274	512	509.575324217658	2.42467578234151
275	505	509.245193141236	-4.24519314123593
276	505	508.248413436452	-3.24841343645228
277	554	527.944860337379	26.0551396626207
278	554	547.098091150086	6.9019088499139
279	584	549.162225993346	34.8377740066541
280	584	573.280128381398	10.7198716186019
281	569	567.483144942979	1.51685505702109
282	569	562.86845638983	6.13154361016935
283	540	584.975488119156	-44.9754881191557
284	540	545.952931672566	-5.95293167256606
285	522	530.373422119947	-8.3734221199471
286	522	518.555825721535	3.44417427846508
287	526	518.225694645112	7.77430535488765
288	526	527.107466594593	-1.10746659459277
289	527	546.80391349552	-19.8039134955198
290	527	522.85073708962	4.14926291038025
291	516	524.91487193288	-8.91487193287955
292	516	512.212718155038	3.78728184496166
293	503	506.415734716619	-3.4157347166192
294	503	503.597146464246	-0.597146464246218
295	489	525.704178193571	-36.7041781935712
296	489	500.152374002796	-11.1523740027963
297	479	484.572864450177	-5.57286445017732
298	479	479.939669254866	-0.939669254866279
299	475	479.609538178444	-4.60953817844371
300	475	481.306908924823	-6.30690892482298
301	524	501.00335582575	22.99664417425
302	524	520.156586638457	3.84341336154318
303	552	522.220721481717	29.7792785182834
304	552	544.542523568993	7.45747643100652
305	532	538.745540130574	-6.74554013057435
306	532	529.640600825488	2.35939917451214
307	511	551.747632554813	-40.7476325548129
308	511	519.909477311324	-8.90947731132442
309	492	504.329967758705	-12.3299677587055
310	492	491.614321209906	0.385678790094368
311	492	491.284190133483	0.715809866516941
312	492	496.573761481413	-4.57376148141291
313	493	516.27020838234	-23.2702083823399
314	493	492.31703197644	0.682968023560115
315	481	494.3811668197	-13.3811668196997
316	481	480.780962891471	0.21903710852916
317	462	474.983979453052	-12.9839794530517
318	462	466.777090298353	-4.77709029835285
319	457	488.884122027678	-31.8841220276779
320	457	471.414769190392	-14.4147691903917
321	442	455.835259637773	-13.8352596377728
322	442	446.711813690524	-4.7118136905235
323	439	446.381682614101	-7.38168261410093
324	439	448.977103510868	-9.97710351086784
325	488	468.673550411795	19.3264495882052
326	488	487.826781224502	0.173218775498328
327	521	489.890916067762	31.1090839322385
328	521	516.702968906977	4.29703109302344
329	501	510.905985468557	-9.9059854685574
330	501	501.801046163471	-0.801046163470924
331	485	523.908077892796	-38.9080778927959
332	485	496.560173401246	-11.5601734012457
333	464	480.980663848627	-16.9806638486268
334	464	466.468916999052	-2.46891699905163
335	460	466.138785922629	-6.13878592262906
336	460	467.836156669008	-7.83615666900834
337	467	487.532603569935	-20.5326035699354
338	467	468.967728066361	-1.96772806636117
339	460	471.031862909621	-11.031862909621
340	460	461.92190973333	-1.92190973333034
341	448	456.124926294911	-8.1249262949112
342	448	454.204388192926	-6.20438819292586
343	443	476.311419922251	-33.3114199222509
344	443	458.842067084965	-15.8420670849647
345	436	443.262557532346	-7.26255753234575
346	436	441.323512788198	-5.32351278819765
347	431	440.993381711775	-9.99338171177508
348	431	441.792702307767	-10.7927023077667
349	484	461.489149208694	22.5108507913063
350	484	484.234580622951	-0.234580622951101
351	510	486.298715466211	23.7012845337891
352	510	506.824417252712	3.17558274728752
353	513	501.027433814293	11.9725661857067
354	513	512.577647968123	0.422352031877358
355	503	534.684679697448	-31.6846796974477
356	503	512.725076108223	-9.72507610822327
357	471	497.145566555604	-26.1455665556043
358	471	472.755268051765	-1.75526805176514
359	471	472.425136975343	-1.42513697534256
360	471	477.714708323272	-6.71470832327241
361	476	497.411155224199	-21.4111552241994
362	476	477.05017941985	-1.05017941984996
363	475	479.11431426311	-4.11431426310976
364	475	475.392661989145	-0.392661989144988
365	470	469.595678550726	0.404321449274159
366	470	473.961491501454	-3.961491501454
367	461	496.068523230779	-35.068523230779
368	461	475.006969791942	-14.0069697919423
369	455	459.427460239323	-4.42746023932332
370	455	458.386465645563	-3.38646564556285
371	456	458.05633456914	-2.05633456914028
372	456	464.243956067458	-8.24395606745777
373	517	483.940402968385	33.0595970316152
374	517	513.870235585743	3.12976441425668
375	525	515.934370429003	9.06562957099688
376	525	520.295169508527	4.70483049147287
377	523	514.498186070108	8.50181392989202
378	523	521.558149471999	1.44185052800093
379	519	543.665181201324	-24.6651812013241
380	519	527.093878514426	-8.09387851442557
381	509	511.514368961807	-2.51436896180661
382	509	506.881173766496	2.11882623350444
383	512	506.551042690073	5.44895730992701
384	512	514.534764489166	-2.53476448916577
385	519	534.231211390093	-15.2312113900928
386	519	515.666335886519	3.3336641134814
387	517	517.730470729778	-0.73047072977841
388	517	513.110768305426	3.88923169457402
389	510	507.313784867007	2.68621513299316
390	510	509.88349751696	0.116502483040292
391	509	531.990529246285	-22.9905292462847
392	509	518.113377010549	-9.11337701054913
393	501	502.53386745793	-1.53386745793018
394	501	499.696772563394	1.30322743660558
395	507	499.366641486972	7.63335851302815
396	507	510.044513737228	-3.04451373722756
397	569	529.740960638155	39.2590393618455
398	569	560.568843405901	8.43115659409925
399	580	562.632978249161	17.3670217508394
400	580	569.687927779848	10.3120722201525
401	578	563.890944341428	14.1090556585717
402	578	570.95090774332	7.04909225668058
403	565	593.057939472644	-28.0579394726445
404	565	568.404185432257	-3.40418543225713
405	547	552.824675879638	-5.82467587963818
406	547	541.007079481226	5.99292051877401
407	555	540.676948404803	14.3230515951966
408	555	553.150920955834	1.84907904416559
409	562	572.847367856761	-10.8473678567614
410	561	554.282492353187	6.71750764681276
411	555	555.44857704606	-0.44857704605941
412	544	547.236674020156	-3.23667402015641
413	537	531.561138927473	5.43886107252681
414	543	534.130851577426	8.86914842257393
415	594	561.626184209077	32.3738157909231
416	611	594.447639793499	16.5523602065012
417	613	594.13498279747	18.8650172025302
418	611	600.27838940681	10.7216105931896
419	594	598.152158029613	-4.15215802961257
420	595	588.174876820952	6.82512317904751
421	591	608.769373872267	-17.7693738722671
422	589	580.325946714429	8.6740532855711
423	584	580.593981256913	3.40601874308659
424	573	573.280128381398	-0.280128381398071
425	567	557.604593288715	9.39540671128515
426	569	561.072356089055	7.92764391094465
427	621	584.975488119156	36.0245118808443
428	629	618.694993853965	10.3050061460349
429	628	610.299885504447	17.7001144955527
430	612	613.749141662625	-1.74914166262506
431	595	599.05020818	-4.05020818000022
432	597	589.07292697134	7.92707302865986
433	593	610.565474173042	-17.5654741730424
434	590	582.122047015204	7.87795298479581
435	580	581.492031407301	-1.49203140730106
436	574	569.687927779848	4.31207222015251
437	573	558.502643439102	14.4973565608975
438	573	566.460656991381	6.53934300861878
439	620	588.567688720706	31.4323112792937
440	626	617.796943703578	8.2030562964225
441	620	607.605735053284	12.3942649467156
442	588	606.564740459524	-18.5647404595239
443	566	577.497004570697	-11.4970045706968
444	557	563.029472610099	-6.02947261009849
445	561	574.643468157537	-13.6434681575367
446	549	553.3844422028	-4.3844422027996
447	532	544.671975241408	-12.6719752414077
448	526	526.581520561241	-0.581520561240633
449	511	515.396236220496	-4.39623622049563
450	499	510.781547667347	-11.7815476673474
451	555	522.111977592021	32.8880224079793
452	565	559.42368392838	5.5763160716193
453	542	552.824675879638	-10.8246758796382
454	527	536.516828729288	-9.51682872928778
455	510	522.71594539705	-12.7159453970506
456	514	512.73866418839	1.26133581160952
457	517	536.027311690868	-19.0273116908681
458	508	513.870235585743	-5.87023558574332
459	493	507.851919075514	-14.8519190755143
460	490	491.557564696123	-1.55756469612256
461	469	483.06643080654	-14.0664308065405
462	478	473.063441351066	4.93655864893365
463	528	503.25292443388	24.7470755661198
464	534	535.176329867914	-1.17632986791435
465	518	524.985121217621	-6.98512121762125
466	506	514.963625119984	-8.96362511998435
467	502	503.85689223891	-1.85689223891006
468	516	505.554262985289	10.4457370147107
469	528	537.823411991643	-9.82341199164334
470	533	523.748787240007	9.25121275999261
471	536	530.303172835205	5.69682716479459
472	537	530.173721162791	6.8262788372088
473	524	525.27478787476	-1.2747878747597
474	536	522.456199622387	13.5438003776133
475	587	555.339833156364	31.6601668436365
476	597	588.161288740785	8.83871125921472
477	581	581.562280692043	-0.562280692042768
478	564	571.540784594406	-7.54078459440585
479	558	555.943800961393	2.05619903860666
480	575	555.845071406997	19.1549285930027
481	580	590.808370864514	-10.8083708645143
482	575	570.447395060165	4.55260493983518
483	563	568.021279151486	-5.02127915148642
484	552	554.421075223258	-2.42107522325755
485	537	538.745540130574	-1.74554013057434
486	545	534.130851577426	10.8691484225739
487	601	563.422284509852	37.5777154901478
488	604	600.733990846212	3.26600915378771
489	586	587.848631744756	-1.84863174475626
490	564	576.031035346344	-12.0310353463441
491	549	555.943800961393	-6.94380096139335
492	551	547.762620053508	3.23737994649145

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.999999228325874	1.54334825236984e-06	7.7167412618492e-07
17	0.999999984243295	3.15134099165279e-08	1.57567049582639e-08
18	0.999999999723986	5.5202793254567e-10	2.76013966272835e-10
19	0.999999999993439	1.31228679425692e-11	6.56143397128462e-12
20	0.999999999999584	8.32905450456068e-13	4.16452725228034e-13
21	0.999999999999949	1.02390074572552e-13	5.11950372862759e-14
22	0.99999999999999	2.15205127796965e-14	1.07602563898482e-14
23	0.999999999999998	3.98658890101369e-15	1.99329445050685e-15
24	1	8.0865600024935e-16	4.04328000124675e-16
25	0.999999999999999	2.47241206541417e-15	1.23620603270709e-15
26	1	3.8768576937188e-18	1.9384288468594e-18
27	1	4.99081714229074e-20	2.49540857114537e-20
28	1	2.56888285317657e-21	1.28444142658829e-21
29	1	2.23203058756671e-22	1.11601529378335e-22
30	1	2.89585639121036e-23	1.44792819560518e-23
31	1	1.22281409778128e-24	6.11407048890638e-25
32	1	2.85518365647099e-25	1.42759182823549e-25
33	1	8.59332738845484e-26	4.29666369422742e-26
34	1	2.62009392188552e-26	1.31004696094276e-26
35	1	7.44770228935534e-27	3.72385114467767e-27
36	1	3.41575659064856e-27	1.70787829532428e-27
37	1	6.28015369185906e-27	3.14007684592953e-27
38	1	5.4183417578627e-28	2.70917087893135e-28
39	1	1.25028263531092e-28	6.25141317655462e-29
40	1	3.92222463989781e-29	1.96111231994891e-29
41	1	2.27835596508245e-29	1.13917798254123e-29
42	1	1.20050846129778e-29	6.00254230648892e-30
43	1	1.55029521546772e-30	7.75147607733859e-31
44	1	4.5985633187547e-31	2.29928165937735e-31
45	1	2.5411220605067e-31	1.27056103025335e-31
46	1	2.24188800752442e-31	1.12094400376221e-31
47	1	3.89217492340735e-31	1.94608746170367e-31
48	1	7.95197864919663e-31	3.97598932459831e-31
49	1	6.3707268016837e-31	3.18536340084185e-31
50	1	6.51898873625203e-31	3.25949436812601e-31
51	1	7.7133138951409e-31	3.85665694757045e-31
52	1	1.29331107402919e-30	6.46655537014594e-31
53	1	1.62810962104852e-30	8.1405481052426e-31
54	1	2.79727227562861e-30	1.3986361378143e-30
55	1	5.6571416046041e-31	2.82857080230205e-31
56	1	2.25661059787353e-31	1.12830529893677e-31
57	1	4.16410449098361e-31	2.0820522454918e-31
58	1	7.7961215208999e-31	3.89806076044995e-31
59	1	1.38413610150521e-30	6.92068050752604e-31
60	1	3.0855411866384e-30	1.5427705933192e-30
61	1	7.08047621181246e-30	3.54023810590623e-30
62	1	1.12314933534391e-29	5.61574667671956e-30
63	1	1.55612504728466e-29	7.78062523642329e-30
64	1	2.76004601011944e-29	1.38002300505972e-29
65	1	6.62989872566343e-29	3.31494936283171e-29
66	1	9.90927834520833e-29	4.95463917260416e-29
67	1	8.1760897462012e-29	4.0880448731006e-29
68	1	1.89458626512792e-28	9.47293132563962e-29
69	1	4.42411397813435e-28	2.21205698906717e-28
70	1	1.04260797421333e-27	5.21303987106667e-28
71	1	2.29380677903136e-27	1.14690338951568e-27
72	1	5.57161488043494e-27	2.78580744021747e-27
73	1	1.66609249457798e-27	8.33046247288992e-28
74	1	3.13399403544756e-27	1.56699701772378e-27
75	1	5.77529898080289e-27	2.88764949040145e-27
76	1	1.21365270418526e-26	6.0682635209263e-27
77	1	2.86443194283674e-26	1.43221597141837e-26
78	1	6.47483650452239e-26	3.2374182522612e-26
79	1	5.32011413409669e-26	2.66005706704834e-26
80	1	9.9650617099632e-26	4.9825308549816e-26
81	1	1.90285741457445e-25	9.51428707287223e-26
82	1	3.46393648663328e-25	1.73196824331664e-25
83	1	5.98845543294734e-25	2.99422771647367e-25
84	1	1.27036731509528e-24	6.35183657547641e-25
85	1	5.01862500664773e-25	2.50931250332386e-25
86	1	6.89413863981044e-25	3.44706931990522e-25
87	1	1.26325031586763e-24	6.31625157933813e-25
88	1	2.47909915220697e-24	1.23954957610349e-24
89	1	5.27338474293358e-24	2.63669237146679e-24
90	1	1.10634273609242e-23	5.53171368046208e-24
91	1	6.74502997682115e-24	3.37251498841057e-24
92	1	1.02212004988323e-23	5.11060024941616e-24
93	1	1.8004313554825e-23	9.00215677741248e-24
94	1	3.50060932059088e-23	1.75030466029544e-23
95	1	5.86095532611776e-23	2.93047766305888e-23
96	1	1.04882271199046e-22	5.24411355995229e-23
97	1	2.28396329959122e-23	1.14198164979561e-23
98	1	3.21227081877707e-23	1.60613540938853e-23
99	1	3.76084266774601e-23	1.88042133387301e-23
100	1	5.47146305401004e-23	2.73573152700502e-23
101	1	9.08265273580355e-23	4.54132636790177e-23
102	1	1.47971720390612e-22	7.39858601953059e-23
103	1	5.84438497344031e-23	2.92219248672016e-23
104	1	7.12141072562354e-23	3.56070536281177e-23
105	1	7.5449322043933e-23	3.77246610219665e-23
106	1	8.58543228658031e-23	4.29271614329016e-23
107	1	1.09691124253384e-22	5.48455621266921e-23
108	1	1.48612531622686e-22	7.43062658113428e-23
109	1	1.29650642360905e-23	6.48253211804524e-24
110	1	1.52766332338788e-23	7.63831661693941e-24
111	1	2.36526899377966e-23	1.18263449688983e-23
112	1	1.58800165467265e-23	7.94000827336323e-24
113	1	2.22783244447173e-23	1.11391622223587e-23
114	1	2.97661969865998e-23	1.48830984932999e-23
115	1	1.04114087399785e-23	5.20570436998926e-24
116	1	1.38957178360134e-23	6.94785891800671e-24
117	1	9.88473981939183e-24	4.94236990969591e-24
118	1	1.06016789024774e-23	5.30083945123872e-24
119	1	1.30364533778558e-23	6.51822668892789e-24
120	1	1.65442893347243e-23	8.27214466736216e-24
121	1	1.92183419584445e-24	9.60917097922227e-25
122	1	2.03768159320777e-24	1.01884079660389e-24
123	1	1.90753993516366e-24	9.53769967581832e-25
124	1	2.14501704507811e-24	1.07250852253906e-24
125	1	3.14400306728295e-24	1.57200153364147e-24
126	1	5.04191585004134e-24	2.52095792502067e-24
127	1	1.45128920209484e-24	7.25644601047418e-25
128	1	1.89841542353803e-24	9.49207711769015e-25
129	1	2.65259958301384e-24	1.32629979150692e-24
130	1	4.17634281298257e-24	2.08817140649129e-24
131	1	6.76402551204008e-24	3.38201275602004e-24
132	1	1.21653298807588e-23	6.08266494037941e-24
133	1	3.45169821367375e-24	1.72584910683687e-24
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390	0.999999984910484	3.01790314771151e-08	1.50895157385575e-08
391	0.999999999708234	5.83531405257724e-10	2.91765702628862e-10
392	0.999999999643058	7.13884063873618e-10	3.56942031936809e-10
393	0.999999999357409	1.28518291110695e-09	6.42591455553477e-10
394	0.999999999004056	1.99188880598091e-09	9.95944402990456e-10
395	0.999999998751908	2.49618457209051e-09	1.24809228604525e-09
396	0.999999997983237	4.0335266203677e-09	2.01676331018385e-09
397	0.999999999999202	1.59525040825275e-12	7.97625204126373e-13
398	0.999999999998468	3.06415243723482e-12	1.53207621861741e-12
399	0.999999999999219	1.56235982863257e-12	7.81179914316286e-13
400	0.999999999998721	2.55744774726341e-12	1.27872387363171e-12
401	0.99999999999829	3.41933539802528e-12	1.70966769901264e-12
402	0.999999999996505	6.98922707174125e-12	3.49461353587062e-12
403	1	1.07433463012472e-19	5.37167315062362e-20
404	1	1.68476953178004e-19	8.4238476589002e-20
405	1	3.10192239316369e-19	1.55096119658185e-19
406	1	1.56151515963932e-19	7.80757579819662e-20
407	1	1.23602839011381e-20	6.18014195056907e-21
408	1	3.14564829539141e-20	1.57282414769571e-20
409	1	7.83025874387387e-20	3.91512937193693e-20
410	1	2.09522009712112e-19	1.04761004856056e-19
411	1	5.61538392721575e-19	2.80769196360787e-19
412	1	1.36454567039122e-18	6.82272835195611e-19
413	1	3.28789417012414e-18	1.64394708506207e-18
414	1	8.70830646724456e-18	4.35415323362228e-18
415	1	1.92083315118523e-17	9.60416575592616e-18
416	1	2.51145531017491e-17	1.25572765508745e-17
417	1	1.05540815851057e-17	5.27704079255286e-18
418	1	2.30111643282003e-18	1.15055821641002e-18
419	1	6.75480307189792e-18	3.37740153594896e-18
420	1	1.90435096510533e-17	9.52175482552664e-18
421	1	3.68475594838978e-17	1.84237797419489e-17
422	1	9.80896127058167e-17	4.90448063529083e-17
423	1	2.15998896506945e-16	1.07999448253472e-16
424	1	5.33798685328204e-16	2.66899342664102e-16
425	1	9.4856581437798e-16	4.7428290718899e-16
426	0.999999999999999	2.76416729314991e-15	1.38208364657496e-15
427	0.999999999999997	6.32899403739425e-15	3.16449701869712e-15
428	0.999999999999991	1.73869838077484e-14	8.6934919038742e-15
429	0.999999999999997	6.43425094857998e-15	3.21712547428999e-15
430	0.999999999999993	1.4405131428984e-14	7.202565714492e-15
431	0.999999999999979	4.18424651261397e-14	2.09212325630698e-14
432	0.99999999999994	1.20354747874009e-13	6.01773739370043e-14
433	0.999999999999892	2.15647720588927e-13	1.07823860294463e-13
434	0.999999999999722	5.56086712080068e-13	2.78043356040034e-13
435	0.999999999999226	1.54908556775564e-12	7.7454278387782e-13
436	0.99999999999781	4.38215360572498e-12	2.19107680286249e-12
437	0.99999999999925	1.49868270928865e-12	7.49341354644325e-13
438	0.999999999997824	4.35155178996624e-12	2.17577589498312e-12
439	0.999999999994863	1.02745348695386e-11	5.13726743476932e-12
440	0.9999999999855	2.90016744660934e-11	1.45008372330467e-11
441	0.999999999992198	1.56030787245207e-11	7.80153936226033e-12
442	0.999999999992361	1.5277289769432e-11	7.63864488471602e-12
443	0.999999999988637	2.27256402260007e-11	1.13628201130004e-11
444	0.99999999999715	5.70159852977026e-12	2.85079926488513e-12
445	0.999999999991325	1.73497207102219e-11	8.67486035511095e-12
446	0.999999999985159	2.96828419863306e-11	1.48414209931653e-11
447	0.999999999975846	4.83079768894139e-11	2.4153988444707e-11
448	0.999999999923869	1.52262975921085e-10	7.61314879605424e-11
449	0.99999999976223	4.75539644884825e-10	2.37769822442413e-10
450	0.999999999991098	1.78045773317852e-11	8.90228866589262e-12
451	0.999999999974101	5.17970143641956e-11	2.58985071820978e-11
452	0.999999999918237	1.63526252392892e-10	8.17631261964462e-11
453	0.99999999981668	3.66640807298545e-10	1.83320403649273e-10
454	0.999999999374867	1.25026578316628e-09	6.25132891583139e-10
455	0.999999999059736	1.88052830192609e-09	9.40264150963045e-10
456	0.9999999981815	3.63700105036018e-09	1.81850052518009e-09
457	0.999999995654773	8.69045310188667e-09	4.34522655094333e-09
458	0.999999994370137	1.12597254895034e-08	5.62986274475169e-09
459	0.999999996187338	7.62532382133074e-09	3.81266191066537e-09
460	0.999999985289988	2.94200232732704e-08	1.47100116366352e-08
461	0.999999983284953	3.34300936093396e-08	1.67150468046698e-08
462	0.999999953614074	9.27718512941136e-08	4.63859256470568e-08
463	0.99999993395736	1.32085280766922e-07	6.60426403834611e-08
464	0.999999873365534	2.53268932491394e-07	1.26634466245697e-07
465	0.999999767680416	4.6463916796744e-07	2.3231958398372e-07
466	0.999999077632614	1.84473477196468e-06	9.22367385982342e-07
467	0.9999965584191	6.88316180180336e-06	3.44158090090168e-06
468	0.99998980193741	2.03961251788278e-05	1.01980625894139e-05
469	0.999971011012441	5.79779751178119e-05	2.89889875589059e-05
470	0.99990041635894	0.000199167282121184	9.9583641060592e-05
471	0.999653130773506	0.000693738452988247	0.000346869226494124
472	0.999176798660735	0.00164640267853069	0.000823201339265347
473	0.997006591657635	0.00598681668473046	0.00299340834236523
474	0.990788062159216	0.0184238756815672	0.0092119378407836
475	0.973017045753653	0.0539659084926941	0.026982954246347
476	0.947850887592874	0.104298224814252	0.0521491124071262

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	458	0.99349240780911	NOK
5% type I error level	459	0.995661605206074	NOK
10% type I error level	460	0.997830802603037	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/107olx1296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/107olx1296734019.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/1ck5i1296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/1ck5i1296734019.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/28jgw1296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/28jgw1296734019.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/3bgjv1296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/3bgjv1296734019.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/475c31296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/475c31296734019.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/5l22c1296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/5l22c1296734019.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/60hyi1296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/60hyi1296734019.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/70h4j1296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/70h4j1296734019.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/8ppyt1296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/8ppyt1296734019.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/9098c1296734019.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296734880tbrq67v7p805o9b/9098c1296734019.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

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

Software written by Ed van Stee & Patrick Wessa

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