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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 09:54:47 +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/t1296727039r7sh0980vowsa9x.htm/, Retrieved Thu, 03 Feb 2011 10:57:34 +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/t1296727039r7sh0980vowsa9x.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 0 0 0 336 349 0 0 0 331 336 349 0 0 327 331 336 349 0 323 327 331 336 349 322 323 327 331 336 385 322 323 327 331 405 385 322 323 327 412 405 385 322 323 411 412 405 385 322 410 411 412 405 385 415 410 411 412 405 414 415 410 411 412 411 414 415 410 411 408 411 414 415 410 410 408 411 414 415 411 410 408 411 414 416 411 410 408 411 479 416 411 410 408 498 479 416 411 410 502 498 479 416 411 498 502 498 479 416 499 498 502 498 479 506 499 498 502 498 510 506 499 498 502 509 510 506 499 498 502 509 510 506 499 495 502 509 510 506 490 495 502 509 510 490 490 495 502 509 553 490 490 495 502 570 553 490 490 495 573 570 553 490 490 572 573 570 553 490 575 572 573 570 553 580 575 572 573 570 580 580 575 572 573 574 580 580 575 572 563 574 580 580 575 556 563 574 580 580 546 556 563 574 580 545 546 556 563 574 605 545 546 556 563 628 605 545 546 556 631 628 605 545 546 626 631 628 605 545 614 626 631 628 605 606 614 626 631 628 602 606 614 626 631 589 602 606 614 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	21 seconds
R Server	'Herman Ole Andreas Wold' @ www.yougetit.org

Multiple Linear Regression - Estimated Regression Equation

werkloosheid[t] = + 60.0822007106404 + 0.964313558320219y1[t] -0.0623268019206753y2[t] -0.0941033647446252y3[t] + 0.079028624895528y4[t] + 0.00087518965592128t + 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)	60.0822007106404	7.393689	8.1261	0	0
y1	0.964313558320219	0.044879	21.4868	0	0
y2	-0.0623268019206753	0.062551	-0.9964	0.319543	0.159771
y3	-0.0941033647446252	0.062545	-1.5046	0.133086	0.066543
y4	0.079028624895528	0.042635	1.8536	0.0644	0.0322
t	0.00087518965592128	0.007044	0.1242	0.90117	0.450585

Multiple Linear Regression - Regression Statistics
Multiple R	0.944488662377402
R-squared	0.892058833359454
Adjusted R-squared	0.890948327529819
F-TEST (value)	803.290545221592
F-TEST (DF numerator)	5
F-TEST (DF denominator)	486
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	21.3844043795233
Sum Squared Residuals	222244.276824149

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	349	60.0830759002974	288.916924099703
2	336	396.629382943712	-60.6293829437116
3	331	362.342128004886	-31.3421280048863
4	327	325.489609532036	1.51039046796442
5	323	350.749198328233	-27.7491983282335
6	322	346.585271192373	-24.5852711923725
7	385	345.852410365892	39.1475896341083
8	405	406.727665491038	-1.72766549103837
9	412	421.866212191259	-9.86621219125863
10	411	421.363205646936	-10.3632056469356
11	410	423.060215738352	-13.0602157383523
12	415	423.080953116307	-8.08095311630692
13	414	428.613026638498	-14.6130266384979
14	411	427.353029000079	-16.3530290000793
15	408	423.973744868077	-15.9737448680766
16	410	421.757906277756	-11.7579062777562
17	411	424.077670459153	-13.0776704591529
18	416	424.963429822835	-8.963429822835
19	479	429.298253397995	49.7017466020045
20	498	489.803202637268	8.19679736273186
21	502	503.807958715178	-1.80795871517805
22	498	500.948510047188	-2.94851004718826
23	499	500.033663234151	-1.03366323415099
24	506	502.373289603846	3.62671039615364
25	510	509.754560858384	0.245439141616266
26	509	512.766184803549	-3.76618480354906
27	502	510.973744298885	-8.97374429888521
28	495	504.46353829751	-9.46353829751048
29	490	498.560724056696	-8.56072405669636
30	490	494.756013996513	-4.75601399651277
31	553	495.174046374716	57.8259536252843
32	570	555.843992188	14.1560078120002
33	573	567.916466223619	5.08353377638082
34	572	563.822214476673	8.17778552332712
35	575	566.050841870006	8.9491581299938
36	580	570.068161065534	9.93183893446646
37	580	575.03481288046	4.96518711954027
38	574	574.362715341383	-0.362715341382872
39	563	568.344278232081	-5.34427823208095
40	556	558.506808216216	-2.50680821621619
41	546	553.007703507226	-7.00770350722577
42	545	544.362695989942	0.637304010058051
43	605	543.811934319846	61.188065680154
44	628	602.121783083813	25.8782169161869
45	631	619.866079115383	11.1339208846171
46	626	615.601148026251	10.3988519737491
47	614	613.170815123149	0.82918487685101
48	606	603.446909900929	2.55309009907105
49	602	597.188800945481	4.81119905451905
50	589	594.565133569679	-5.56513356967927
51	574	582.083723128066	-8.08372312806573
52	558	568.174327827701	-10.1743278277015
53	552	554.588317355142	-2.58831735514205
54	546	550.184718373135	-4.184718373135
55	607	545.093897486875	61.9061025131253
56	636	603.592022735727	32.4079772642727
57	631	627.846504638603	3.15349536139714
58	623	615.003857782163	7.99614221783718
59	618	609.693607055893	8.30639294410656
60	605	608.133875815007	-3.13387581500712
61	619	596.267992549583	22.732007450417
62	596	610.41779380525	-14.4177938052496
63	570	588.195082543854	-18.1950825438536
64	546	562.212502431293	-16.2125024312928
65	528	543.961127208865	-15.9611272088648
66	506	528.729230705616	-22.7292307056162
67	555	508.840826553387	46.1591734466133
68	568	557.261429310899	10.7385706891013
69	564	567.392126240867	-3.39212624086663
70	553	556.375804152085	-3.37580415208464
71	541	548.667596286102	-7.66759628610163
72	542	539.186089179663	2.81391082033726
73	540	541.618222063296	-1.61822206329574
74	521	539.888068837475	-18.8880688374753
75	505	520.649193159397	-15.6491931593974
76	491	506.672496006807	-15.6724960068075
77	482	495.800116891068	-13.800116891068
78	478	487.99885524563	-9.99885524563042
79	523	484.756406527388	38.2435934726121
80	531	528.1412285833	2.85877141669954
81	532	532.717061988006	-0.717061988006488
82	540	528.632870407527	11.367129592473
83	525	539.089388464166	-14.0893884641657
84	533	524.665071498073	8.33492850192742
85	531	532.641558890039	-1.64155889003888
86	508	532.258972018023	-24.2589720180226
87	495	508.267032678765	-13.2670326787649
88	482	497.985783783087	-15.985783783087
89	470	488.267151278884	-18.2671512788842
90	466	476.912197562749	-10.9121975627493
91	515	473.999711760211	41.0002882397892
92	518	521.603126768534	-3.60312676853364
93	516	520.870999299269	-4.87099929926929
94	511	513.829087594454	-2.829087594454
95	500	512.723141121997	-12.7231411219972
96	498	502.85349378391	-4.85349378390993
97	494	501.923796251985	-7.92379625198492
98	476	498.832064699915	-22.8320646999146
99	458	481.043494903128	-23.0434949031277
100	443	465.026964686779	-22.0269646867793
101	430	453.062765002025	-23.0627650020253
102	424	441.733811279612	-17.7338112796123
103	476	436.748088767366	39.2519112326344
104	481	487.305144169444	-6.30514416944401
105	470	488.423841515652	-18.4238415156518
106	460	472.138086838088	-12.1380868380883
107	451	466.820392936514	-15.8203929365138
108	450	460.195994257163	-10.195994257163
109	444	459.86521587938	-15.8652158793803
110	429	454.199180554782	-25.1991805547819
111	421	439.492158921843	-18.4921589218435
112	400	433.19901923732	-33.19901923732
113	389	414.385302839413	-25.385302839413
114	384	404.654989272405	-20.6549892724048
115	432	401.86383315206	30.13616684794
116	446	447.838929040074	-1.83892904007447
117	431	457.949709503893	-26.9497095038934
118	423	437.701201459637	-14.7012014596369
119	416	433.398397100102	-17.3983971001018
120	416	429.665643016588	-13.6656430165884
121	413	429.670203364213	-16.6702033642131
122	399	426.804632432957	-27.8046324329565
123	386	412.938897837623	-26.9388978376228
124	374	401.558582090239	-27.5585820902392
125	365	391.878304236759	-26.8783042367594
126	365	384.065222017724	-19.0652220177243
127	418	384.72890667796	33.2710933220401
128	428	435.736987242543	-7.73698724254259
129	424	441.366419889545	-17.3664198895452
130	421	431.899294495248	-10.8992944952483
131	417	432.504019689643	-15.504019689643
132	423	430.001320759714	-7.00132075971389
133	423	436.003580101626	-13.0035801016256
134	419	435.769822064049	-16.7698220640494
135	406	431.032708332375	-25.0327083323745
136	398	419.220986220924	-21.2209862209235
137	390	412.694014827965	-22.694014827965
138	391	406.386225208523	-15.3862252085226
139	444	407.575483166179	36.4245168338207
140	460	458.743248063679	1.25675193632122
141	455	470.143487320754	-15.1434873207535
142	456	459.417116181508	-3.41711618150797
143	452	463.376802222636	-11.3768022226364
144	459	461.193071199142	-2.19307119914241
145	461	467.7042020155	-6.70420201550027
146	451	469.652858792226	-18.6528587922259
147	443	458.911106742044	-15.9111067420438
148	439	452.185735129124	-13.1857351291242
149	430	449.927061398102	-19.927061398102
150	436	441.46096243956	-5.46096243956038
151	488	447.552844656238	40.447155343762
152	506	497.854879850141	8.14512014985938
153	502	510.696527577158	-8.69652757715783
154	501	501.299062881613	-0.299062881613388
155	501	503.000559649796	-2.00055964979599
156	515	504.862690348471	10.1373096515294
157	521	518.141944219772	2.85805578022794
158	520	522.977096907564	-2.97709690756431
159	512	520.322250620951	-8.3222506209512
160	509	513.212724706036	-4.21272470603571
161	505	511.387548750214	-6.38754875021418
162	511	508.391948405413	2.60805159458727
163	570	514.078093247742	55.9219067522577
164	592	570.738835151059	21.2611648489411
165	594	587.39659262239	6.60340737761013
166	586	582.876978515872	3.12302148412835
167	586	577.631106479579	8.36889352042112
168	592	579.681019102813	12.3189808971874
169	594	586.378659810138	7.62134018986215
170	594	587.301972305746	6.69802769425408
171	586	586.613573703093	-0.613573703092747
172	586	579.185905446071	6.81409455392915
173	572	579.843452300883	-7.84345230088323
174	572	567.096764592013	4.90323540798689
175	563	567.337986009394	-4.33798600939426
176	563	559.977486280593	3.022513719407
177	555	559.432901938998	-4.4329019389976
178	555	552.566198944793	2.43380105520659
179	554	552.354430925755	1.64556907424502
180	554	552.143819475048	1.85618052495231
181	601	551.57479246746	49.4252075325399
182	601	596.992508262911	4.00749173708919
183	622	593.9849951374	28.0150048626005
184	622	609.813596908783	12.1864030912174
185	617	612.219954628194	4.78004537180589
186	617	605.423091366612	11.5769086333882
187	606	607.395201688677	-1.39520168867718
188	606	597.259144560534	8.74085543946615
189	595	597.55047144684	-2.55047144683956
190	595	587.979034507164	7.02096549283602
191	599	587.796189644097	11.2038103559035
192	599	592.689456079224	6.31054392077582
193	600	591.571709187347	8.4282908126534
194	600	592.160484476344	7.83951552365577
195	592	592.415147363662	-0.415147363661585
196	592	584.607410722011	7.39258927798885
197	575	585.185928951928	-10.185928951928
198	575	569.546300568097	5.45369943190276
199	567	569.97450239124	-2.97450239124042
200	567	563.860626314993	3.13937368500677
201	555	563.016629296791	-8.01662929679059
202	555	552.198568704561	2.8014312954391
203	555	552.315136518101	2.6848634818993
204	555	553.445252084692	1.55474791530787
205	608	552.497783775602	55.5022162243983
206	608	603.607277556229	4.39272244377088
207	631	600.304832244089	30.6951677559107
208	631	617.497440943645	13.502559056355
209	629	620.253316808588	8.74668319141158
210	629	616.161187492478	12.8388125075225
211	624	618.104374658572	5.89562534142808
212	624	613.471888786116	10.528111213884
213	610	613.626340735584	-3.62634073558425
214	610	600.59734293248	9.40265706751972
215	616	601.075650224548	14.924349775452
216	616	608.17985387055	7.82014612945001
217	621	606.700367500144	14.2996324998555
218	621	610.958190292934	10.0418097070663
219	604	611.12160322236	-7.12160322235943
220	604	594.258631096849	9.74136890315145
221	584	595.714205043634	-11.7142050436336
222	584	578.028566267544	5.97143373245621
223	574	577.932490872389	-3.93249087238923
224	574	570.172297773736	3.82770222626451
225	555	569.215868484688	-14.2158684846876
226	555	551.835819713706	3.16418028629434
227	545	552.230617890899	-7.23061789089913
228	545	544.376321427501	0.623678572499232
229	599	543.498920763348	55.5010792366516
230	599	596.513761749742	2.48623825025771
231	620	592.358703386726	27.6412966132735
232	620	607.528581604897	12.4714183951028
233	608	610.488139698577	-2.48813969857743
234	608	596.941081528754	11.0589184712464
235	590	599.349479464264	-9.34947946426373
236	590	583.121950981091	6.87804901890874
237	579	583.296365106573	-4.29636510657298
238	579	574.38365172011	4.61634827989022
239	580	573.647606482774	6.35239351722637
240	580	575.647932242941	4.35206775705936
241	579	574.717165756825	4.28283424317492
242	579	573.659624023416	5.34037597658384
243	572	573.801854639888	-1.80185463988829
244	572	567.146638286047	4.85336171395268
245	560	567.504772464252	-7.50477246425244
246	560	556.592608507278	3.40739149272186
247	551	556.788204945713	-5.78820494571347
248	551	549.239498487423	1.76050151257706
249	537	548.852971395619	-11.8529713956186
250	537	536.200387051493	0.79961294850689
251	541	536.362579843979	4.63742015602127
252	541	541.53815637334	-0.538156373340282
253	588	540.183323606776	47.8166763932239
254	588	585.130522578504	2.86947742149629
255	607	582.51815257747	24.48184742253
256	607	596.418127232213	10.5818727677873
257	599	598.949138555466	0.0508614445344175
258	599	589.447541348412	9.55245865158812
259	578	591.448574826448	-13.4485748264482
260	578	571.951692209337	6.0483077906634
261	563	572.629201240163	-9.62920124016248
262	563	560.141543714652	2.85845628534771
263	566	559.417719810312	6.58228018968775
264	566	563.723086146098	2.2769138539018
265	561	562.351551556559	-1.35155155655917
266	561	557.24854886038	3.75145113961987
267	554	557.798143934326	-3.79814393432601
268	554	551.519341039464	2.48065896053645
269	540	551.561360718087	-11.5613607180866
270	540	538.720569644472	1.27943035552819
271	526	539.040819686748	-13.0408196867485
272	526	526.858752166346	-0.858752166346129
273	512	526.625801834354	-14.6258018343541
274	512	514.443734313952	-2.44373431395175
275	505	514.21078398196	-9.21078398195974
276	505	508.778911369799	-3.77891136979889
277	554	508.109673424362	45.8903265756379
278	554	556.020636524921	-2.02063652492107
279	584	552.414298046195	31.5857019538048
280	584	576.733515112971	7.266484887029
281	569	578.736988864888	-9.73698886488754
282	569	561.450059737401	7.54994026259855
283	540	564.756695702733	-24.7566957027333
284	540	538.204028172272	1.79597182772766
285	522	538.826951244195	-16.8269512441949
286	522	524.199179961681	-2.19917996168108
287	526	523.030107463939	2.96989253606115
288	526	528.582097452279	-2.58209745227888
289	527	526.911150186133	0.0888498138673981
290	527	527.49992547513	-0.499925475130242
291	516	527.754588362448	-11.7545883624476
292	516	517.053911045836	-1.05391104583651
293	503	517.819409681515	-14.8194096815154
294	503	506.319345625199	-3.31934562519937
295	489	506.261154365973	-17.2611543659733
296	489	493.984983480826	-4.98498348082627
297	479	493.83106177373	-14.8310617737298
298	479	485.506248486608	-6.50624848660829
299	475	485.023990946934	-10.0239909469336
300	475	482.108645550755	-7.10864555075488
301	524	481.568541699138	42.4314583008618
302	524	529.197194705463	-5.19719470546327
303	552	525.827942101424	26.172057898576
304	552	548.21853205156	3.78146794844066
305	532	550.346659407317	-18.3466594073172
306	532	528.426369217719	3.57363078228068
307	511	531.886581942863	-20.8865819428635
308	511	513.518939702687	-2.51893970268739
309	492	513.248105234767	-21.2481052347669
310	492	496.903193475976	-4.90319347597586
311	492	496.428676779319	-4.42867677931853
312	492	498.217515899122	-6.21751589912233
313	493	496.716847215763	-3.71684721576322
314	493	497.682035963739	-4.68203596373936
315	481	497.620584351475	-16.6205843514746
316	481	485.955593476543	-4.9555934765433
317	462	486.783418914143	-24.7834189141428
318	462	469.59157687265	-7.59157687265015
319	457	469.828317800053	-12.8283178000526
320	457	466.795589128255	-9.79558912825529
321	442	465.6065544545	-23.6065544544996
322	442	451.613243093075	-9.61324309307534
323	439	452.153877187064	-13.1538771870637
324	439	450.673362172928	-11.6733621729284
325	488	449.675788394913	38.3242116050866
326	488	497.210338036494	-9.21033803649386
327	521	493.92011405735	27.0798859426499
328	521	521.132271799087	-0.132271799086549
329	501	522.948765145241	-21.948765145241
330	501	500.55795813192	0.442041868079992
331	485	504.413313981542	-19.4133139815419
332	485	490.867239532967	-5.86723953296682
333	464	490.284771055443	-26.284771055443
334	464	471.540715356288	-7.54071535628836
335	460	471.58599538795	-11.58599538795
336	460	469.705787003962	-9.70578700396219
337	467	468.296368278495	-1.29636827849473
338	467	475.423851835371	-8.42385183537067
339	460	474.672324912	-14.6723249119997
340	460	467.264281640202	-7.26428164020177
341	448	468.254644817571	-20.2546448175711
342	448	457.342480860597	-9.34248086059681
343	443	457.538077299032	-14.5380772990321
344	443	453.846625074022	-10.8466250740225
345	436	453.210790774535	-17.2107907745354
346	436	446.931987879673	-10.931987879673
347	431	446.974007558296	-15.974007558296
348	431	442.812038509563	-11.8120385095632
349	484	442.571347334554	41.4286526654462
350	484	494.151357938904	-10.1513579389043
351	510	490.453769502287	19.5462304977132
352	510	510.539318876803	-0.53931887680325
353	513	513.108214335985	-0.108214335984599
354	513	513.555342717241	-0.555342717240915
355	503	515.423981748419	-12.4239817484185
356	503	505.499411260638	-2.49941126063841
357	471	506.360640344188	-35.3606403441877
358	471	476.444515315043	-5.44451531504291
359	471	477.649561917205	-6.64956191720515
360	471	480.661744778689	-9.66174477868909
361	476	478.133703971688	-2.13370397168811
362	476	482.956146952945	-6.95614695294511
363	475	482.645388132998	-7.64538813299766
364	475	481.21143294061	-6.21143294061024
365	470	481.669778056664	-11.6697780566645
366	470	476.943188819464	-6.94318881946393
367	461	477.176669393828	-16.1766693938277
368	461	468.969239382325	-7.96923938232481
369	455	469.135912664789	-14.1359126647892
370	455	464.197836787225	-9.19783678722541
371	456	463.861415164346	-7.86141516434563
372	456	465.39122410079	-9.39122410078952
373	517	464.855600739152	52.1443992608484
374	517	523.585499621596	-6.58549962159612
375	525	519.863468518986	5.13653148101362
376	525	521.838546925782	3.16145307421811
377	523	526.1615538187	-3.16155381869962
378	523	523.480974973758	-0.480974973758107
379	519	524.23873276642	-5.2387327664196
380	519	520.570560452284	-1.57056045228391
381	509	520.662685599831	-11.6626855998315
382	509	511.396838665264	-2.39683866526373
383	512	511.704867374544	0.295132625455711
384	512	515.539716886607	-3.53971688660711
385	519	514.563325421546	4.43667457845427
386	519	521.032085425209	-2.03208542520929
387	517	520.833758876107	-3.83375887610707
388	517	518.24728339591	-1.24728339591018
389	510	518.926012563676	-8.92601256367616
390	510	512.36489957458	-2.3648995745798
391	509	512.644005127889	-3.64400512788939
392	509	512.339290312437	-3.33929031243748
393	501	511.849291929745	-10.8492919297454
394	501	504.229762017584	-3.22976201758419
395	507	504.65022299771	2.34977700229002
396	507	511.189806455244	-4.18980645524421
397	569	510.184491834212	58.8155081657882
398	569	569.408187451253	-0.408187451253465
399	580	566.018972671201	13.9810273287993
400	580	570.792888388212	9.20711161178777
401	578	575.007943500263	2.99205649973654
402	578	572.045054561088	5.95494543891193
403	565	573.039898228436	-8.03989822843615
404	565	560.692903889419	4.30709611058149
405	547	561.345970254252	-14.3459702542522
406	547	545.212545135824	1.7874548641757
407	555	545.307930636411	9.6920693635895
408	555	554.717174858031	0.282825141968587
409	562	552.796920384202	9.20307961579757
410	561	558.795163564143	2.20483643585713
411	555	558.027666581198	-3.02766658119807
412	544	551.646263669641	-7.64626366964098
413	537	542.060954268312	-5.0609542683119
414	543	536.482820934426	6.51717906557405
415	594	543.266830350266	50.7331696497344
416	611	591.86314488209	19.1368551179099
417	613	603.960863102499	9.03913689750117
418	611	600.505709923541	10.494290076459
419	594	600.884007061728	-6.88400706172842
420	595	585.771485257517	9.22851474248327
421	591	588.142493617425	2.85750638257534
422	589	585.665487722747	3.3345122772534
423	584	582.549453015476	1.45054698452381
424	573	578.308856101246	-5.3088561012464
425	567	567.88600838889	-0.886008388890456
426	569	563.099056623685	5.90094337631542
427	621	566.042513629218	54.9574863707818
428	629	615.758345562301	13.241654437699
429	628	619.570357039781	8.42964296021886
430	612	613.372986538822	-1.37298653882199
431	595	601.363833173886	-6.36383317388556
432	597	586.694939066737	10.3050609332625
433	593	591.110622216704	1.88937778329624
434	590	587.464888771568	2.53511122843235
435	580	583.290437141232	-3.29043714123239
436	574	574.369627862218	-0.369627862217728
437	573	569.174085315811	3.82591468418914
438	573	569.28855553143	3.71144446856971
439	620	569.126091462519	50.8739085374806
440	626	614.069635508597	11.9303644914031
441	620	616.848003733007	3.15199626699311
442	588	606.26617861822	-18.2661786182201
443	566	578.932705934775	-12.9327059347752
444	557	560.751932440689	-3.75193244068885
445	561	555.982311170172	5.01768882982749
446	549	559.94273983812	-10.9427398381202
447	532	547.230845655251	-15.2308456552509
448	526	530.498640893473	-4.49864089347296
449	511	527.218545242377	-16.2185452423767
450	499	513.780091570666	-14.7800915706657
451	555	502.365239654533	52.6347603454671
452	565	558.052974454965	6.94702554503473
453	542	564.150495323768	-22.1504953237681
454	527	535.130758728407	-8.13075872840696
455	510	525.585016334138	-15.5850163341385
456	514	513.082126699243	0.917873300757492
457	517	517.593703853403	-0.593703853403015
458	508	520.652540337563	-12.6525403375626
459	493	510.067713014372	-17.0677130143721
460	490	496.198630451859	-6.19863045185904
461	469	495.325483152753	-26.3254831527526
462	478	475.963046870556	2.03695312944433
463	528	485.048487646229	42.9515123537713
464	534	534.44318431956	-0.443184319559911
465	518	534.607069357596	-16.6070693575957
466	506	514.811056189433	-8.81105618943254
467	502	507.624208566285	-5.62420856628532
468	516	506.495576730996	9.50442326900435
469	528	520.110931323424	7.88906867657564
470	533	530.239063946266	2.7609360537344
471	536	532.680023698468	3.31997630153237
472	537	535.239365925083	1.76063407491728
473	524	536.49540094232	-12.49540094232
474	536	524.010706102136	11.9892938978638
475	587	536.536574926545	50.4634250734545
476	597	586.27189233406	10.72810766594
477	581	590.580623708386	-9.58062370838631
478	564	570.678285842482	-6.67828584248246
479	558	558.372485593651	-0.37248559365117
480	575	555.942975150907	19.0570248490935
481	580	573.04644084586	6.95355915413961
482	575	576.03046175971	-1.0304617597097
483	563	568.824206198129	-5.82420619812936
484	552	558.437922497047	-6.4379224970469
485	537	549.444930116429	-12.4449301164293
486	545	536.400794004867	8.59920599513273
487	601	545.13787320334	55.8621267966604
488	604	599.183928840881	4.81607115911916
489	586	596.64918750655	-10.6491875065497
490	564	574.467878814145	-10.4678788141449
491	549	558.519031055244	-9.51903105524386
492	551	547.357338952441	3.64266104755877

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.644286925030521	0.711426149938957	0.355713074969479
10	0.673142901420173	0.653714197159654	0.326857098579827
11	0.773044959919238	0.453910080161524	0.226955040080762
12	0.859447301809673	0.281105396380653	0.140552698190327
13	0.940068413226146	0.119863173547707	0.0599315867738536
14	0.98020339668553	0.0395932066289412	0.0197966033144706
15	0.993350313731298	0.0132993725374029	0.00664968626870147
16	0.996379794385309	0.00724041122938293	0.00362020561469147
17	0.997376270197213	0.0052474596055734	0.0026237298027867
18	0.997139603450076	0.00572079309984758	0.00286039654992379
19	0.99930312001734	0.00139375996531946	0.000696879982659732
20	0.99977570436572	0.00044859126856077	0.000224295634280385
21	0.99982893508405	0.000342129831900243	0.000171064915950121
22	0.999743568312396	0.000512863375208876	0.000256431687604438
23	0.999558881357612	0.000882237284776257	0.000441118642388128
24	0.999256685678196	0.00148662864360775	0.000743314321803876
25	0.998796197562179	0.00240760487564188	0.00120380243782094
26	0.99836396930882	0.00327206138236084	0.00163603069118042
27	0.99864784153535	0.00270431692929821	0.0013521584646491
28	0.999401781494539	0.00119643701092271	0.000598218505461357
29	0.999840755196235	0.000318489607529848	0.000159244803764924
30	0.999957486070981	8.50278580371855e-05	4.25139290185927e-05
31	0.999973974508914	5.20509821723201e-05	2.602549108616e-05
32	0.99997215796957	5.56840608616783e-05	2.78420304308391e-05
33	0.999962655908877	7.46881822469e-05	3.734409112345e-05
34	0.999942718565705	0.000114562868589651	5.72814342948254e-05
35	0.999908574382827	0.000182851234346095	9.14256171730474e-05
36	0.999855359332254	0.000289281335491159	0.000144640667745579
37	0.999785458941711	0.000429082116578605	0.000214541058289302
38	0.999768290369832	0.000463419260336958	0.000231709630168479
39	0.999883048265435	0.000233903469130431	0.000116951734565216
40	0.999968863308783	6.22733824345316e-05	3.11366912172658e-05
41	0.999997053168312	5.89366337617477e-06	2.94683168808738e-06
42	0.999999693124926	6.13750147826624e-07	3.06875073913312e-07
43	0.99999981084065	3.7831869821877e-07	1.89159349109385e-07
44	0.999999743895959	5.1220808270656e-07	2.5610404135328e-07
45	0.999999589729157	8.20541686862105e-07	4.10270843431052e-07
46	0.99999931773816	1.36452367970895e-06	6.82261839854474e-07
47	0.99999916643068	1.66713863924557e-06	8.33569319622783e-07
48	0.999999372512043	1.25497591442791e-06	6.27487957213955e-07
49	0.999999676250944	6.47498112593972e-07	3.23749056296986e-07
50	0.999999950887831	9.82243369978768e-08	4.91121684989384e-08
51	0.999999998018782	3.96243590356532e-09	1.98121795178266e-09
52	0.999999999979096	4.18071955280251e-11	2.09035977640125e-11
53	0.999999999999667	6.65904436119146e-13	3.32952218059573e-13
54	0.999999999999993	1.32039706077717e-14	6.60198530388586e-15
55	0.999999999999998	4.0385802186047e-15	2.01929010930235e-15
56	0.999999999999997	6.20150799580494e-15	3.10075399790247e-15
57	0.999999999999995	1.0543070917465e-14	5.2715354587325e-15
58	0.999999999999992	1.5964551452857e-14	7.98227572642848e-15
59	0.99999999999999	1.83525674741561e-14	9.17628373707804e-15
60	0.999999999999996	8.55682935272785e-15	4.27841467636393e-15
61	0.999999999999995	9.30600849062539e-15	4.6530042453127e-15
62	1	1.48341509047992e-15	7.41707545239962e-16
63	1	4.25934410411315e-17	2.12967205205657e-17
64	1	4.61275522354532e-19	2.30637761177266e-19
65	1	2.99423153098784e-21	1.49711576549392e-21
66	1	9.09588913528921e-24	4.54794456764461e-24
67	1	2.60153929515728e-24	1.30076964757864e-24
68	1	3.19458162892744e-24	1.59729081446372e-24
69	1	2.6072183084976e-24	1.3036091542488e-24
70	1	1.94384016766591e-24	9.71920083832957e-25
71	1	8.16707137985e-25	4.083535689925e-25
72	1	6.28297228777246e-25	3.14148614388623e-25
73	1	4.76092502150083e-25	2.38046251075042e-25
74	1	9.41362195474973e-26	4.70681097737487e-26
75	1	2.27940129325844e-26	1.13970064662922e-26
76	1	5.28659351699977e-27	2.64329675849989e-27
77	1	1.67216180225319e-27	8.36080901126596e-28
78	1	9.69479741006347e-28	4.84739870503173e-28
79	1	4.12422127722522e-28	2.06211063861261e-28
80	1	8.94518402502746e-28	4.47259201251373e-28
81	1	1.88285720194703e-27	9.41428600973516e-28
82	1	3.56408823812613e-27	1.78204411906307e-27
83	1	4.04380415234267e-27	2.02190207617134e-27
84	1	7.85225292198918e-27	3.92612646099459e-27
85	1	1.48339662687071e-26	7.41698313435357e-27
86	1	6.68630105871762e-27	3.34315052935881e-27
87	1	6.55206749664394e-27	3.27603374832197e-27
88	1	4.86951374550592e-27	2.43475687275296e-27
89	1	3.01634554430553e-27	1.50817277215277e-27
90	1	3.40805796187937e-27	1.70402898093969e-27
91	1	6.458540124057e-28	3.2292700620285e-28
92	1	1.376899455258e-27	6.88449727629001e-28
93	1	2.91176101478026e-27	1.45588050739013e-27
94	1	6.20238317599089e-27	3.10119158799544e-27
95	1	9.1809489952335e-27	4.59047449761675e-27
96	1	1.70576377079644e-26	8.5288188539822e-27
97	1	2.95173264381461e-26	1.47586632190731e-26
98	1	2.2065325420838e-26	1.1032662710419e-26
99	1	1.57394756745571e-26	7.86973783727856e-27
100	1	1.13802713742535e-26	5.69013568712677e-27
101	1	7.50775138385967e-27	3.75387569192983e-27
102	1	7.87035656398294e-27	3.93517828199147e-27
103	1	1.03672223737045e-27	5.18361118685227e-28
104	1	2.17581027057392e-27	1.08790513528696e-27
105	1	3.11664777032011e-27	1.55832388516005e-27
106	1	6.12580645456342e-27	3.06290322728171e-27
107	1	9.29774838551637e-27	4.64887419275818e-27
108	1	1.67880732593115e-26	8.39403662965577e-27
109	1	2.56959011330439e-26	1.2847950566522e-26
110	1	2.32739532853863e-26	1.16369766426931e-26
111	1	3.21749432396095e-26	1.60874716198048e-26
112	1	1.51072846476043e-26	7.55364232380215e-27
113	1	1.33535790868532e-26	6.67678954342662e-27
114	1	1.69479409775717e-26	8.47397048878586e-27
115	1	4.26469888140024e-27	2.13234944070012e-27
116	1	8.60722946007908e-27	4.30361473003954e-27
117	1	8.4923029625131e-27	4.24615148125655e-27
118	1	1.7174744836787e-26	8.58737241839351e-27
119	1	2.99389188219431e-26	1.49694594109715e-26
120	1	5.58279656274592e-26	2.79139828137296e-26
121	1	9.77358184198014e-26	4.88679092099007e-26
122	1	9.72287312707666e-26	4.86143656353833e-26
123	1	1.05528339532252e-25	5.27641697661259e-26
124	1	1.08480413922433e-25	5.42402069612165e-26
125	1	1.13714965391956e-25	5.68574826959782e-26
126	1	1.84757426796188e-25	9.23787133980938e-26
127	1	2.06885526198664e-26	1.03442763099332e-26
128	1	3.86978152692463e-26	1.93489076346231e-26
129	1	6.01366435194799e-26	3.006832175974e-26
130	1	1.20532687086504e-25	6.02663435432518e-26
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385	0.999913001815317	0.000173996369366172	8.6998184683086e-05
386	0.99988305194578	0.000233896108440975	0.000116948054220487
387	0.999843017454043	0.000313965091913812	0.000156982545956906
388	0.99978616806591	0.000427663868179327	0.000213831934089664
389	0.999747254202928	0.000505491594143898	0.000252745797071949
390	0.999663217000575	0.000673565998850053	0.000336782999425026
391	0.999576152425378	0.000847695149243166	0.000423847574621583
392	0.99946473857367	0.00107052285265914	0.000535261426329572
393	0.99943504277986	0.00112991444028024	0.00056495722014012
394	0.999289068203626	0.0014218635927477	0.000710931796373848
395	0.999088122066246	0.00182375586750701	0.000911877933753503
396	0.998981410906628	0.00203717818674474	0.00101858909337237
397	0.999884233709714	0.000231532580572711	0.000115766290286355
398	0.999888204426819	0.000223591146362651	0.000111795573181325
399	0.999897842949246	0.000204314101508637	0.000102157050754319
400	0.999859457561429	0.000281084877142073	0.000140542438571037
401	0.999801634972131	0.000396730055737154	0.000198365027868577
402	0.99972459772555	0.000550804548899597	0.000275402274449799
403	0.99966701680242	0.000665966395160583	0.000332983197580291
404	0.999540973683907	0.000918052632185048	0.000459026316092524
405	0.99958943170085	0.000821136598300486	0.000410568299150243
406	0.999429226683171	0.00114154663365774	0.000570773316828869
407	0.999221913867924	0.0015561722641519	0.000778086132075949
408	0.998997134696512	0.00200573060697614	0.00100286530348807
409	0.998673724198903	0.00265255160219342	0.00132627580109671
410	0.99823578776015	0.00352842447969968	0.00176421223984984
411	0.997703109213082	0.00459378157383623	0.00229689078691812
412	0.997243014762769	0.00551397047446209	0.00275698523723104
413	0.99660630874654	0.0067873825069194	0.0033936912534597
414	0.995529186508664	0.00894162698267252	0.00447081349133626
415	0.998588878795122	0.00282224240975667	0.00141112120487834
416	0.998142842524387	0.00371431495122655	0.00185715747561327
417	0.997823965014025	0.00435206997195036	0.00217603498597518
418	0.99750593012281	0.0049881397543812	0.0024940698771906
419	0.996769438166477	0.0064611236670456	0.0032305618335228
420	0.996003334062567	0.0079933318748665	0.00399666593743325
421	0.99474239025641	0.0105152194871808	0.00525760974359042
422	0.993011247400217	0.0139775051995659	0.00698875259978294
423	0.990794853910905	0.0184102921781908	0.00920514608909539
424	0.98843445305294	0.0231310938941181	0.0115655469470591
425	0.985018386310869	0.0299632273782629	0.0149816136891314
426	0.98071335883936	0.0385732823212811	0.0192866411606405
427	0.996487367895377	0.00702526420924507	0.00351263210462253
428	0.995261233065073	0.00947753386985348	0.00473876693492674
429	0.99560941511436	0.00878116977128166	0.00439058488564083
430	0.994134079872245	0.0117318402555094	0.00586592012775468
431	0.992072901831692	0.015854196336616	0.00792709816830801
432	0.990426935319358	0.0191461293612838	0.0095730646806419
433	0.987287024738127	0.0254259505237469	0.0127129752618735
434	0.98351443867274	0.0329711226545198	0.0164855613272599
435	0.978396237891789	0.0432075242164226	0.0216037621082113
436	0.972088845730127	0.0558223085397459	0.0279111542698729
437	0.96447503198054	0.0710499360389213	0.0355249680194607
438	0.955091347494245	0.089817305011511	0.0449086525057555
439	0.99445882125194	0.0110823574961196	0.00554117874805978
440	0.992850276213956	0.0142994475720877	0.00714972378604385
441	0.995899804974814	0.0082003900503717	0.00410019502518585
442	0.99432612979943	0.0113477404011407	0.00567387020057037
443	0.992766112426195	0.0144677751476098	0.00723388757380489
444	0.99019813895449	0.0196037220910178	0.00980186104550892
445	0.98891245992972	0.0221750801405587	0.0110875400702794
446	0.984729454677371	0.0305410906452576	0.0152705453226288
447	0.979506848916696	0.0409863021666088	0.0204931510833044
448	0.973631805678187	0.0527363886436252	0.0263681943218126
449	0.967828635964647	0.064342728070706	0.032171364035353
450	0.959197210757482	0.0816055784850364	0.0408027892425182
451	0.997929206240733	0.00414158751853407	0.00207079375926704
452	0.99681708616082	0.00636582767836136	0.00318291383918068
453	0.99584461332118	0.00831077335764199	0.00415538667882099
454	0.995870407379371	0.00825918524125732	0.00412959262062866
455	0.993773975138505	0.0124520497229891	0.00622602486149457
456	0.993351002910182	0.0132979941796357	0.00664899708981787
457	0.990496610683641	0.019006778632717	0.0095033893163585
458	0.98592848972235	0.0281430205553008	0.0140715102776504
459	0.980128247647779	0.0397435047044425	0.0198717523522212
460	0.97135127030925	0.0572974593814984	0.0286487296907492
461	0.988858768766973	0.0222824624660537	0.0111412312330268
462	0.983388279044854	0.0332234419102923	0.0166117209551461
463	0.986510486195153	0.0269790276096943	0.0134895138048472
464	0.987270719819904	0.0254585603601927	0.0127292801800964
465	0.981219326165296	0.037561347669408	0.018780673834704
466	0.972346157666584	0.0553076846668317	0.0276538423334159
467	0.968034446971291	0.0639311060574176	0.0319655530287088
468	0.9538030413489	0.0923939173022013	0.0461969586511006
469	0.942748070472424	0.114503859055153	0.0572519295275764
470	0.927986141836404	0.144027716327192	0.072013858163596
471	0.906770341840749	0.186459316318502	0.0932296581592508
472	0.892670554237142	0.214658891525717	0.107329445762858
473	0.96645043451495	0.067099130970099	0.0335495654850495
474	0.978661890362231	0.0426762192755377	0.0213381096377689
475	0.96654180891482	0.0669163821703609	0.0334581910851805
476	0.95373583033336	0.0925283393332786	0.0462641696666393
477	0.923619665779524	0.152760668440953	0.0763803342204764
478	0.874239157784243	0.251521684431513	0.125760842215757
479	0.806509446168517	0.386981107662967	0.193490553831483
480	0.835740178260343	0.328519643479315	0.164259821739657
481	0.788951758682998	0.422096482634005	0.211048241317002
482	0.74118906474778	0.517621870504439	0.258810935252219
483	0.62051934860189	0.75896130279622	0.37948065139811

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	413	0.869473684210526	NOK
5% type I error level	446	0.938947368421053	NOK
10% type I error level	459	0.966315789473684	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/10dhe61296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/10dhe61296726865.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/1q0qf1296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/1q0qf1296726865.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/2k3ct1296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/2k3ct1296726865.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/3jcwx1296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/3jcwx1296726865.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/4al2h1296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/4al2h1296726865.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/5lee41296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/5lee41296726865.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/6y5t41296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/6y5t41296726865.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/7h0ep1296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/7h0ep1296726865.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/8ug2e1296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/8ug2e1296726865.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/9iqh61296726865.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727039r7sh0980vowsa9x/9iqh61296726865.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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