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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 10:03:20 +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/t1296727260y14reiqovf4965q.htm/, Retrieved Thu, 03 Feb 2011 11:01:04 +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/t1296727260y14reiqovf4965q.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 0 336 349 0 331 336 349 327 331 336 323 327 331 322 323 327 385 322 323 405 385 322 412 405 385 411 412 405 410 411 412 415 410 411 414 415 410 411 414 415 408 411 414 410 408 411 411 410 408 416 411 410 479 416 411 498 479 416 502 498 479 498 502 498 499 498 502 506 499 498 510 506 499 509 510 506 502 509 510 495 502 509 490 495 502 490 490 495 553 490 490 570 553 490 573 570 553 572 573 570 575 572 573 580 575 572 580 580 575 574 580 580 563 574 580 556 563 574 546 556 563 545 546 556 605 545 546 628 605 545 631 628 605 626 631 628 614 626 631 606 614 626 602 606 614 589 602 606 574 589 602 558 574 589 552 558 574 546 552 558 607 546 552 636 607 546 631 636 607 623 631 636 618 623 631 605 618 623 619 605 618 596 619 605 570 596 619 546 570 596 528 546 570 506 528 546 555 506 528 568 555 506 564 568 555 553 564 568 541 553 564 542 541 553 540 542 541 521 540 542 505 521 540 491 505 521 482 491 505 478 482 491 523 478 482 531 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	15 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

werkloosheid[t] = + 60.2605865582908 + 0.964324886793272y1[t] -0.0782903635158953y2[t] + 0.0016988590288664t + 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)	60.2605865582908	7.403572	8.1394	0	0
y1	0.964324886793272	0.044789	21.5304	0	0
y2	-0.0782903635158953	0.042504	-1.842	0.066089	0.033044
t	0.0016988590288664	0.007017	0.2421	0.808792	0.404396

Multiple Linear Regression - Regression Statistics
Multiple R	0.944079082312587
R-squared	0.891285313660176
Adjusted R-squared	0.890616985670382
F-TEST (value)	1333.6046481233
F-TEST (DF numerator)	3
F-TEST (DF denominator)	488
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	21.4168668541479
Sum Squared Residuals	223836.906693971

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	349	60.2622854173196	288.73771458268
2	336	396.8133697672	-60.8133697672004
3	331	356.955508230869	-25.9555082308694
4	327	353.153357381639	-26.1533573816386
5	323	349.689208511074	-26.6892085110738
6	322	346.146769276993	-24.1467692769932
7	385	345.497304703292	39.5026952967077
8	405	406.329761793813	-1.32976179381327
9	412	420.685665487206	-8.68566548720617
10	411	425.87183128347	-14.87183128347
11	410	424.361172711094	-14.3611727110944
12	415	423.476837046846	-8.47683704684587
13	414	428.378450703357	-14.378450703357
14	411	427.024372858013	-16.0243728580131
15	408	424.211387420178	-16.211387420178
16	410	421.554982709375	-11.5549827093748
17	411	423.720202432538	-12.7202024325379
18	416	424.529645451328	-8.52964545132822
19	479	429.274678380808	49.7253216191925
20	498	489.637393290233	8.36260670976691
21	502	503.028972096833	-1.02897209683272
22	498	505.400453596233	-7.40045359623266
23	499	501.231691454025	-2.23169145402486
24	506	502.510876653911	3.48912334608942
25	510	509.184559356976	0.815440643023541
26	509	512.495525218567	-3.49552521856715
27	502	511.219737736739	-9.21973773673916
28	495	504.549452751731	-9.54945275173102
29	490	498.348909947818	-8.34890994781824
30	490	494.077016917492	-4.07701691749202
31	553	494.4701675941	58.5298324058996
32	570	555.224334321105	14.7756656788946
33	573	566.687263354118	6.31273664588153
34	572	568.251000693757	3.74899930624306
35	575	567.053503575445	7.94649642455516
36	580	570.026467458369	9.97353254163058
37	580	574.614919660817	5.38508033918303
38	574	574.225166702266	-0.225166702266356
39	563	568.440916240536	-5.44091624053558
40	556	558.304783525934	-2.30478352593383
41	546	552.417402176085	-6.41740217608463
42	545	543.323884711792	1.67611528820796
43	605	543.144162319187	61.8558376808134
44	628	601.083644749328	26.9163552506723
45	631	618.567394193648	12.4326058063519
46	626	619.661389352191	6.33861064780881
47	614	614.606592686706	-0.606592686706019
48	606	603.427844721795	2.5721552782049
49	602	596.654428848669	5.34557115133148
50	589	593.425151068651	-4.42515106865146
51	574	581.203787853431	-7.20378785343136
52	558	567.758388136268	-9.75838813626779
53	552	553.505244259343	-1.50524425934272
54	546	548.973639613866	-2.97363961386628
55	607	543.659131333231	63.3408686667691
56	636	602.954390467745	33.0456095322552
57	631	626.145798869309	4.85420113069112
58	623	619.05545275241	3.94454724758958
59	618	611.734004334673	6.2659956653274
60	605	607.540401667862	-2.54040166786227
61	619	595.397328816158	23.6026711838419
62	596	609.917350816	-13.9173508159994
63	570	586.64351218956	-16.6435121895604
64	546	563.37344235283	-17.3734423528298
65	528	542.266893380233	-14.2668933802334
66	506	526.789713001365	-20.7897130013649
67	555	506.985490894228	48.0145091057721
68	568	555.961497203477	12.0385027965232
69	564	564.663191778539	-0.663191778539324
70	553	559.789816364688	-6.78981636468845
71	541	549.497102923055	-8.49710292305491
72	542	538.788097139239	3.21190286076064
73	540	540.693605247252	-0.693605247252237
74	521	538.688363969179	-17.6883639691787
75	505	520.524470706167	-15.5244707061672
76	491	506.584488283306	-15.5844882833057
77	482	494.338284543483	-12.3382845434831
78	478	486.757124510595	-8.757124510595
79	523	483.606137094094	39.3938629059062
80	531	527.315617312884	3.68438268711646
81	532	531.508848908043	0.491151091956703
82	540	531.848549745738	8.15145025426173
83	525	539.486557335597	-14.4865573355974
84	533	524.3970599846	8.60294001539996
85	531	533.287713390714	-2.28771339071351
86	508	530.734439568029	-22.7344395680287
87	495	508.713246757844	-13.7132467578441
88	482	497.979400449426	-15.979400449426
89	470	486.462650505849	-16.462650505849
90	466	475.910225449065	-9.9102254490652
91	515	472.994109123112	42.0058908768883
92	518	520.560888889074	-2.5608888890745
93	516	519.619334596204	-3.61933459620432
94	511	517.457512591099	-6.45751259109895
95	500	512.794167743193	-12.7941677431932
96	498	502.579744665076	-4.57974466507559
97	494	501.513987749193	-7.51398774919276
98	476	497.81496778808	-21.8149677880803
99	458	480.771980138894	-22.7719801388939
100	443	464.82505757893	-21.82505757893
101	430	451.771109679346	-21.7711096793459
102	424	440.410940462801	-16.4109404628006
103	476	435.644464726777	40.3555352732235
104	481	486.260799880151	-5.26079988015089
105	470	487.01302427032	-17.0130242703196
106	460	476.015697557043	-16.0156975570429
107	451	467.235341546814	-16.235341546814
108	450	459.341020059862	-9.34102005986232
109	444	459.083007303741	-15.083007303741
110	429	453.377047205526	-24.3770472055261
111	421	439.383614943751	-18.3836149437513
112	400	432.845070161172	-32.8450701611724
113	389	413.22226930567	-24.2222693056697
114	384	404.260492043806	-20.2604920438064
115	432	400.301760467544	31.6982395324563
116	446	446.982505710229	-0.982505710229133
117	431	456.726815535601	-25.7268155356008
118	423	441.167576003508	-18.1675760035081
119	416	434.629031220929	-18.6290312209292
120	416	428.506778780532	-12.5067787805323
121	413	429.056510184172	-16.0565101841725
122	399	426.165234382821	-27.1652343828215
123	386	412.901255917292	-26.9012559172922
124	374	401.462796337231	-27.4627963372311
125	365	390.910371280447	-25.9103712804474
126	365	383.172630520527	-18.1726305205275
127	418	383.878942651199	34.1210573488006
128	428	434.989860510272	-6.98986051027172
129	424	440.485418970891	-16.4854189708909
130	421	435.846914647588	-14.8469146475877
131	417	433.2688003003	-16.2688003003003
132	423	429.648070702704	-6.64807070270378
133	423	435.748880336556	-12.7488803365559
134	419	435.280837014489	-16.2808370144894
135	406	431.425236326345	-25.4252363263451
136	398	419.203873111125	-21.203873111125
137	390	412.508747601514	-22.5087476015144
138	391	405.422170274324	-14.4221702743242
139	444	407.014516928274	36.9854830717265
140	460	458.04714442383	1.95285557617009
141	455	469.328652205209	-14.3286522052087
142	456	463.256080814017	-7.25608081401686
143	452	464.613556377418	-12.6135563774185
144	459	460.679665325758	-1.67966532575836
145	461	467.744799846404	-6.7447998464037
146	451	469.127115934408	-18.1271159344079
147	443	459.328985198472	-16.3289851984722
148	439	452.398988598314	-13.3989885983139
149	430	449.169710818297	-19.1697108182968
150	436	440.80564715025	-4.8056471502498
151	488	447.297908601681	40.7020913983187
152	506	496.974759392865	9.025240607135
153	502	510.263207311346	-8.2632073113462
154	501	504.998380079916	-3.99838007991587
155	501	504.348915506215	-3.34891550621504
156	515	504.42890472876	10.5710952712402
157	521	517.931152002894	3.06884799710552
158	520	522.622735093461	-2.62273509346045
159	512	521.190366884601	-9.19036688460067
160	509	513.555757012799	-4.55575701279925
161	505	511.290804119575	-6.29080411957546
162	511	507.670074521979	3.32992547802107
163	570	513.770884155831	56.229115844169
164	592	570.198009154568	21.8019908454324
165	594	586.79572407561	7.20427592438939
166	586	587.003684710876	-1.00368471087633
167	586	579.134203748527	6.86579625147278
168	592	579.762225515683	12.2377744843167
169	594	585.549873695472	8.45012630452825
170	594	587.010480146992	6.98951985300821
171	586	586.855598278989	-0.855598278988866
172	586	579.142698043672	6.85730195632845
173	572	579.770719810828	-7.77071981082758
174	572	566.271870254751	5.72812974524937
175	563	567.369634203002	-4.36963420300203
176	563	558.692409080891	4.30759091910855
177	555	559.398721211563	-4.39872121156337
178	555	551.685820976246	3.31417902375395
179	554	552.313842743402	1.68615725659792
180	554	551.351216715638	2.64878328436232
181	601	551.431205938182	49.5687940618175
182	601	596.756174476495	4.24382552350488
183	622	593.078226250277	28.9217737497231
184	622	613.330747731964	8.66925226803551
185	617	611.68834895716	5.31165104284045
186	617	606.868423382222	10.131576617778
187	606	607.26157405883	-1.26157405883039
188	606	596.655699163133	9.34430083686674
189	595	597.518592020837	-2.51859202083698
190	595	586.91271712514	8.08728287486014
191	599	587.775609982844	11.2243900171564
192	599	591.634608389046	7.36539161095449
193	600	591.323145794011	8.6768542059892
194	600	592.289169539833	7.71083046016705
195	592	592.212578035346	-0.212578035345925
196	592	584.499677800029	7.50032219997139
197	575	585.127699567185	-10.1276995671846
198	575	568.735875350728	6.26412464927213
199	567	570.068510389527	-3.06851038952696
200	567	562.35561015421	4.64438984579036
201	555	562.983631921366	-7.98363192136567
202	555	551.413432138875	3.58656786112474
203	555	552.354615360095	2.64538463990512
204	555	552.356314219124	2.64368578087626
205	608	552.358013078153	55.6419869218474
206	608	603.468930937225	4.53106906277508
207	631	599.321240529911	31.6787594700887
208	631	621.502411785185	9.49758821481454
209	629	619.703432283349	9.29656771665127
210	629	617.776481368791	11.223518631209
211	624	617.934760954852	6.0652390451483
212	624	613.114835379914	10.8851646200858
213	610	613.507986056523	-3.50798605652255
214	610	600.009136500446	9.9908634995544
215	616	601.106900448697	14.893099551303
216	616	606.894548628485	9.10545137151449
217	621	606.426505306419	14.573494693581
218	621	611.249828599414	9.75017140058577
219	604	610.860075640864	-6.86007564086362
220	604	594.468251424407	9.53174857559313
221	584	595.800886463206	-11.800886463206
222	584	576.516087586369	7.48391241363062
223	574	578.083593715716	-4.08359371571614
224	574	568.442043706812	5.55795629318772
225	555	569.226646201	-14.2266462010001
226	555	550.906172210957	4.09382778904321
227	545	552.395387976788	-7.39538797678767
228	545	542.753837967884	2.24616203211618
229	599	543.538440462072	55.4615595379284
230	599	595.613683207937	3.3863167920628
231	620	591.387702437108	28.6122975628923
232	620	611.640223918795	8.35977608120469
233	608	609.99782514399	-1.99782514399037
234	608	598.4276253615	9.57237463850002
235	590	599.36880858272	-9.3688085827196
236	590	582.01265947947	7.98734052053044
237	579	583.423584881785	-4.42358488178453
238	579	572.817709986087	6.1822900139126
239	580	573.680602843791	6.31939715620888
240	580	574.646626589613	5.35337341038674
241	579	574.570035085126	4.42996491487377
242	579	573.607409057362	5.39259094263818
243	572	573.687398279907	-1.68739827990658
244	572	566.938822931383	5.06117706861746
245	560	567.488554335023	-7.48855433502268
246	560	555.918354552532	4.08164544746773
247	551	556.859537773752	-5.85953777375188
248	551	548.182312651641	2.8176873483587
249	537	548.888624782313	-11.8886247823132
250	537	535.389775226236	1.61022477376372
251	541	536.487539174488	4.51246082551232
252	541	540.34653758069	0.653462419310366
253	588	540.035074985655	47.9649250143451
254	588	585.360043523968	2.6399564760324
255	607	581.682095297749	25.3179047022506
256	607	600.00596700585	6.99403299414957
257	599	598.520148958077	0.479851041922717
258	599	590.80724872276	8.19275127724005
259	578	591.435270489916	-13.435270489916
260	578	571.186146726286	6.81385327371387
261	563	572.831943219149	-9.8319432191488
262	563	558.368768776279	4.63123122372142
263	566	559.544823088046	6.45517691195412
264	566	562.439496607455	3.56050339254543
265	561	562.206324375936	-1.20632437593575
266	561	557.386398800998	3.61360119900175
267	554	557.779549477607	-3.77954947760659
268	554	551.030974129083	2.96902587091745
269	540	551.580705532723	-11.5807055327227
270	540	538.081855976646	1.91814402335426
271	526	539.179619924897	-13.1796199248971
272	526	525.68077036882	0.319229631179804
273	512	526.778534317072	-14.7785343170716
274	512	513.279684760995	-1.27968476099465
275	505	514.377448709246	-9.37744870924605
276	505	507.628873360722	-2.62887336072201
277	554	508.178604764362	45.8213952356379
278	554	555.432223076261	-1.43222307626135
279	584	551.597694123011	32.4023058769886
280	584	580.529139585838	3.4708604141616
281	569	578.18212753939	-9.1821275393904
282	569	563.71895309652	5.28104690347982
283	540	564.895007408287	-24.8950074082875
284	540	536.931284550311	3.06871544968856
285	522	539.203403951301	-17.2034039513013
286	522	521.847254848051	0.152745151948761
287	526	523.258180250366	2.74181974963378
288	526	527.117178656568	-1.11717865656817
289	527	526.805716061533	0.194283938466541
290	527	527.771739807356	-0.771739807355599
291	516	527.695148302869	-11.6951483028686
292	516	517.089273407171	-1.08927340717144
293	503	517.952166264875	-14.9521662648752
294	503	505.417641595591	-2.41764159559148
295	489	506.437115180327	-17.437115180327
296	489	492.93826562425	-3.93826562425004
297	479	494.036029572501	-15.0360295725014
298	479	484.394479563598	-5.39447956359759
299	475	485.179082057785	-10.1790820577854
300	475	481.323481369641	-6.32348136964119
301	524	481.638341682734	42.3616583172664
302	524	528.891959994633	-4.89195999463284
303	552	525.057431041383	26.9425689586172
304	552	552.060226730623	-0.0602267306233231
305	532	549.869795411207	-17.8697954112071
306	532	530.58499653437	1.41500346562946
307	511	532.152502663717	-21.1525026637173
308	511	511.903378900087	-0.903378900087465
309	492	513.54917539295	-21.5491753929501
310	492	495.228701402907	-3.22870140290682
311	492	496.717917168738	-4.7179171687377
312	492	496.719616027767	-4.71961602776657
313	493	496.721314886795	-3.72131488679544
314	493	497.687338632618	-4.68733863261757
315	481	497.610747128131	-16.6107471281305
316	481	486.04054734564	-5.04054734564015
317	462	486.98173056686	-24.9817305668598
318	462	468.661256576816	-6.66125657681645
319	457	470.150472342647	-13.1504723426473
320	457	465.33054676771	-8.33054676770983
321	442	465.723697444318	-23.7236974443182
322	442	451.260523001448	-9.26052300144796
323	439	452.436577313215	-13.4365773132153
324	439	449.545301511864	-10.5453015118643
325	488	449.781871461441	38.2181285385591
326	488	497.03548977334	-9.03548977334006
327	521	493.20096082009	27.7990391799099
328	521	525.025380943297	-4.02538094329691
329	501	522.443497806301	-21.4434978063012
330	501	503.158698929465	-2.15869892946465
331	485	504.726205058811	-19.7262050588114
332	485	489.298705729148	-4.29870572914794
333	464	490.553050404431	-26.5530504044311
334	464	470.303926640801	-6.30392664080127
335	460	471.949723133664	-11.9497231336639
336	460	468.09412244552	-8.09412244551972
337	467	468.408982758612	-1.40898275861217
338	467	475.160955825194	-8.16095582519394
339	460	474.614622139612	-14.6146221396115
340	460	467.866046791088	-7.8660467910875
341	448	468.415778194728	-20.4157781947276
342	448	456.845578412237	-8.84557841223724
343	443	457.786761633457	-14.7867616334569
344	443	452.966836058519	-9.96683605851936
345	436	453.359986735128	-17.3599867351277
346	436	446.611411386604	-10.6114113866037
347	431	447.161142790244	-16.1611427902438
348	431	442.341217215306	-11.3412172153063
349	484	442.734367891915	41.2656321080854
350	484	493.845285750987	-9.84528575098693
351	510	489.697595343673	20.3024046563267
352	510	514.771741259327	-4.77174125932729
353	513	512.737890666943	0.262109333057123
354	513	515.632564186352	-2.63256418635156
355	503	515.399391954833	-12.3993919548327
356	503	505.757841945929	-2.75784194592889
357	471	506.542444440117	-35.5424444401167
358	471	475.685746921761	-4.68574692176086
359	471	478.192737413298	-7.19273741329837
360	471	478.194436272327	-7.19443627232724
361	476	478.196135131356	-2.19613513135611
362	476	483.019458424351	-7.01945842435134
363	475	482.629705465801	-7.62970546580073
364	475	481.667079438036	-6.66707943803632
365	470	481.747068660581	-11.7470686605811
366	470	476.927143085644	-6.92714308564358
367	461	477.320293762252	-16.3202937622519
368	461	468.643068640141	-7.64306864014134
369	455	469.349380770813	-14.3493807708133
370	455	463.565130309083	-8.5651303090825
371	456	464.036571349207	-8.03657134920674
372	456	465.002595095029	-9.00259509502888
373	517	464.926003590542	52.0739964094581
374	517	523.75152054396	-6.75152054396033
375	525	518.97750722852	6.02249277148042
376	525	526.693805181895	-1.69380518189462
377	523	526.069181132796	-3.06918113279632
378	523	524.142230218239	-1.14223021823865
379	519	524.300509804299	-5.3005098042993
380	519	520.444909116155	-1.44490911615508
381	509	520.759769429248	-11.7597694292475
382	509	511.118219420344	-2.11821942034367
383	512	511.902821914532	0.0971780854685071
384	512	514.79749543394	-2.79749543394018
385	519	514.564323202421	4.43567679757864
386	519	521.316296269003	-2.31629626900313
387	517	520.769962583421	-3.76996258342073
388	517	518.843011668863	-1.84301166886305
389	510	519.001291254924	-9.0012912549237
390	510	512.2527159064	-2.25271590639966
391	509	512.80244731004	-3.8024473100398
392	509	511.839821282275	-2.83982128227539
393	501	511.91981050482	-10.9198105048202
394	501	504.206910269503	-3.20691026950285
395	507	504.834932036659	2.16506796334113
396	507	510.622580216447	-3.62258021644737
397	569	510.154536894381	58.8454631056191
398	569	569.944378734593	-0.94437873459262
399	580	565.092075055636	14.907924944364
400	580	575.701347669391	4.29865233060916
401	578	574.841852529745	3.15814747025514
402	578	572.914901615187	5.08509838481283
403	565	573.073181201248	-8.07318120124783
404	565	560.538656531964	4.46134346803585
405	547	561.5581301167	-14.5581301166997
406	547	544.20198101345	2.79801898655037
407	555	545.612906415765	9.3870935842354
408	555	553.32920436914	1.67079563086035
409	562	552.704580320041	9.29541967995865
410	561	559.456553386623	1.54344661337688
411	555	557.945894814247	-2.94589481424746
412	544	552.239934716033	-8.23993471603258
413	537	542.103802001431	-5.10380200143082
414	543	536.216420651582	6.78357934841836
415	594	542.552101375981	51.4478986240186
416	611	591.264627280372	19.7353727196282
417	613	603.667040675576	9.33295932442437
418	611	604.266453128421	6.73354687157919
419	594	602.182921486831	-8.18292148683135
420	595	585.947677997406	9.05232200259363
421	591	588.244637922999	2.75536207700126
422	589	584.310746871339	4.68925312866138
423	584	582.696957410845	1.30304258915548
424	573	578.033612562939	-5.03361256293881
425	567	567.819189484821	-0.81918948482116
426	569	562.896133021765	6.10386697823476
427	621	565.296223835476	55.703776164524
428	629	615.286236080723	13.7137639192767
429	628	618.931435131272	9.06856486872826
430	612	617.34248619538	-5.34248619538017
431	595	601.993277229233	-6.99327722923258
432	597	586.85409882903	10.1459011709698
433	593	590.115383641416	2.88461635858422
434	590	586.10320222624	3.89679777376023
435	580	583.525087878952	-3.52508787895239
436	574	574.118408960596	-0.118408960596226
437	573	569.117062134024	3.88293786597559
438	573	568.624178287355	4.37582171264462
439	620	568.7041675099	51.2958324900999
440	626	614.029136048213	11.9708639517872
441	620	616.137137142754	3.86286285724577
442	588	609.883144499928	-21.8831444999281
443	566	579.496189162668	-13.4961891626676
444	557	560.788032144753	-3.78803214475313
445	561	553.833195019992	7.16680498000776
446	549	558.396806697837	-9.39680669783725
447	532	546.513445461283	-14.5134454612833
448	526	531.061105607017	-5.06110560701726
449	511	526.607791325057	-15.6077913250567
450	499	512.614359063282	-13.6143590632819
451	555	502.21851473353	52.7814852664701
452	565	557.161891615173	7.83810838482725
453	542	562.422578985244	-20.4225789852442
454	527	539.461901812869	-12.4619018128688
455	510	526.799405730864	-16.7994057308642
456	514	511.581936967146	2.41806303285411
457	517	516.771871553118	0.228128446881931
458	508	519.353383618463	-11.3533836184632
459	493	510.441287405805	-17.4412874058049
460	490	496.682726234578	-6.68272623457775
461	469	494.965805885965	-25.9658058859652
462	478	474.951553212883	3.04844678711694
463	528	485.276273686885	42.7237263131148
464	534	532.789603613935	1.2103963860654
465	518	534.662733617928	-16.6627336179283
466	506	518.76549210717	-12.7654921071695
467	502	508.447938140933	-6.4479381409334
468	516	505.53182181498	10.4681781850201
469	528	519.347230543178	8.65276945682183
470	533	529.824762954504	3.17523704549622
471	536	533.708601885308	2.29139811469175
472	537	536.211823587137	0.788176412862536
473	524	536.942976242412	-12.9429762424119
474	536	524.330161209612	11.6698387903877
475	587	536.921533435867	50.0784665641329
476	597	585.164317159162	11.8356828408379
477	581	590.816456346813	-9.81645634681307
478	564	574.60605338199	-10.6060533819906
479	558	559.466874981788	-1.46687498178817
480	575	555.013560699828	19.9864393001724
481	580	571.878524815437	8.1214751845625
482	575	575.370911928663	-0.370911928662515
483	563	570.159534536146	-7.15953453614554
484	552	558.980786571235	-6.98078657123461
485	537	549.314396037728	-12.3143960377282
486	545	535.712415593533	9.28758440646715
487	601	544.603068999646	56.3969310003537
488	604	597.980638610971	6.01936138902871
489	586	596.49105177349	-10.4910517734898
490	564	578.900031579692	-14.9000315796921
491	549	559.095809472555	-10.0958094725551
492	551	546.355023027035	4.64497697296543

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.935300136838617	0.129399726322766	0.064699863161383
8	0.968240021110742	0.0635199577785168	0.0317599788892584
9	0.960860530286116	0.0782789394277677	0.0391394697138839
10	0.930932096299042	0.138135807401916	0.0690679037009578
11	0.913621962725824	0.172756074548352	0.0863780372741759
12	0.909566777763975	0.18086644447205	0.0904332222360249
13	0.931752590996212	0.136494818007576	0.068247409003788
14	0.961731276163353	0.0765374476732946	0.0382687238366473
15	0.982652676612995	0.0346946467740101	0.017347323387005
16	0.99005158528364	0.0198968294327219	0.00994841471636093
17	0.993402276489617	0.0131954470207654	0.0065977235103827
18	0.993895581784546	0.0122088364309072	0.00610441821545362
19	0.997988119317435	0.0040237613651295	0.00201188068256475
20	0.999273771210149	0.00145245757970183	0.000726228789850914
21	0.999523227938175	0.000953544123649247	0.000476772061824624
22	0.999341153940045	0.00131769211991023	0.000658846059955117
23	0.998933329902918	0.00213334019416485	0.00106667009708243
24	0.998295066972778	0.003409866054444	0.001704933027222
25	0.997349414091765	0.00530117181646909	0.00265058590823454
26	0.996411652535312	0.0071766949293767	0.00358834746468835
27	0.99688602168014	0.00622795663972136	0.00311397831986068
28	0.998497587437234	0.00300482512553163	0.00150241256276581
29	0.999559730373704	0.000880539252592854	0.000440269626296427
30	0.999873445486306	0.000253109027387714	0.000126554513693857
31	0.999921133126946	0.000157733746107371	7.88668730536853e-05
32	0.999917420436611	0.000165159126778247	8.25795633891235e-05
33	0.999898626118485	0.000202747763030226	0.000101373881515113
34	0.999847261920286	0.000305476159427939	0.00015273807971397
35	0.999763920721814	0.000472158556372085	0.000236079278186043
36	0.999639217171011	0.000721565657977369	0.000360782828988684
37	0.999478314620382	0.00104337075923651	0.000521685379618257
38	0.999434303988522	0.001131392022957	0.000565696011478499
39	0.999701120513182	0.000597758973636619	0.00029887948681831
40	0.999914406954084	0.000171186091831651	8.55930459158253e-05
41	0.999990886884175	1.82262316505764e-05	9.11311582528819e-06
42	0.999998945957809	2.10808438264121e-06	1.0540421913206e-06
43	0.999999338737315	1.32252536954198e-06	6.6126268477099e-07
44	0.999999128592757	1.74281448515812e-06	8.71407242579061e-07
45	0.999998666352266	2.66729546759515e-06	1.33364773379758e-06
46	0.999997826246393	4.34750721358602e-06	2.17375360679301e-06
47	0.999997461073778	5.07785244387753e-06	2.53892622193877e-06
48	0.999998099231961	3.80153607722032e-06	1.90076803861016e-06
49	0.999999003021001	1.99395799748941e-06	9.96978998744704e-07
50	0.999999833676676	3.32646647097225e-07	1.66323323548612e-07
51	0.999999992449505	1.51009890617588e-08	7.5504945308794e-09
52	0.99999999991294	1.74118589696294e-10	8.7059294848147e-11
53	0.99999999999848	3.03908490112928e-12	1.51954245056464e-12
54	0.999999999999968	6.39638380335422e-14	3.19819190167711e-14
55	0.99999999999999	1.82556912339264e-14	9.12784561696319e-15
56	0.999999999999986	2.77280257632201e-14	1.38640128816101e-14
57	0.999999999999977	4.52489481135045e-14	2.26244740567523e-14
58	0.999999999999972	5.58953068585722e-14	2.79476534292861e-14
59	0.999999999999972	5.69150943344844e-14	2.84575471672422e-14
60	0.999999999999987	2.63401693155923e-14	1.31700846577961e-14
61	0.999999999999986	2.7493135721311e-14	1.37465678606555e-14
62	0.999999999999998	4.34347193385371e-15	2.17173596692685e-15
63	1	1.30332523256764e-16	6.51662616283819e-17
64	1	1.15298710053319e-18	5.76493550266595e-19
65	1	1.01476551228923e-20	5.07382756144614e-21
66	1	4.171699896092e-23	2.085849948046e-23
67	1	1.04015668157852e-23	5.20078340789258e-24
68	1	1.20567990330249e-23	6.02839951651246e-24
69	1	8.41797556680856e-24	4.20898778340428e-24
70	1	3.45116360588152e-24	1.72558180294076e-24
71	1	1.08631677494048e-24	5.4315838747024e-25
72	1	7.74496836678931e-25	3.87248418339465e-25
73	1	5.65733608103594e-25	2.82866804051797e-25
74	1	1.05653248009875e-25	5.28266240049376e-26
75	1	2.07027165394815e-26	1.03513582697408e-26
76	1	4.44402639756527e-27	2.22201319878263e-27
77	1	1.54015637754513e-27	7.70078188772563e-28
78	1	8.9656213205011e-28	4.48281066025055e-28
79	1	3.63015720020818e-28	1.81507860010409e-28
80	1	7.43599957372021e-28	3.71799978686011e-28
81	1	1.33522904531451e-27	6.67614522657254e-28
82	1	2.62514004843033e-27	1.31257002421517e-27
83	1	2.47316902790126e-27	1.23658451395063e-27
84	1	4.52392613553221e-27	2.26196306776611e-27
85	1	8.06192299001944e-27	4.03096149500972e-27
86	1	3.86290702689267e-27	1.93145351344633e-27
87	1	3.24743675316492e-27	1.62371837658246e-27
88	1	2.45960233099969e-27	1.22980116549985e-27
89	1	1.84415655078532e-27	9.2207827539266e-28
90	1	2.1646659469963e-27	1.08233297349815e-27
91	1	3.94132333006636e-28	1.97066166503318e-28
92	1	8.35274547167048e-28	4.17637273583524e-28
93	1	1.63866598583707e-27	8.19332992918535e-28
94	1	2.94369228409748e-27	1.47184614204874e-27
95	1	4.02173261959511e-27	2.01086630979755e-27
96	1	7.27899596376355e-27	3.63949798188178e-27
97	1	1.25240431980576e-26	6.26202159902879e-27
98	1	9.79965225146524e-27	4.89982612573262e-27
99	1	6.69880004467212e-27	3.34940002233606e-27
100	1	4.99158374324716e-27	2.49579187162358e-27
101	1	3.9226979298254e-27	1.9613489649127e-27
102	1	4.5850311393975e-27	2.29251556969875e-27
103	1	5.58858130031918e-28	2.79429065015959e-28
104	1	1.18116055578362e-27	5.90580277891812e-28
105	1	1.64236345479035e-27	8.21181727395176e-28
106	1	2.34111243496164e-27	1.17055621748082e-27
107	1	3.32607774675141e-27	1.6630388733757e-27
108	1	6.13549908378202e-27	3.06774954189101e-27
109	1	9.59984098032374e-27	4.79992049016187e-27
110	1	8.89849724750246e-27	4.44924862375123e-27
111	1	1.18530310715898e-26	5.92651553579489e-27
112	1	5.72364668818852e-27	2.86182334409426e-27
113	1	5.6077821109708e-27	2.8038910554854e-27
114	1	7.41117165965812e-27	3.70558582982906e-27
115	1	1.55976178408377e-27	7.79880892041883e-28
116	1	3.1972534163538e-27	1.5986267081769e-27
117	1	3.31299613330351e-27	1.65649806665175e-27
118	1	5.22060081966253e-27	2.61030040983127e-27
119	1	8.18457190940207e-27	4.09228595470103e-27
120	1	1.57766745636217e-26	7.88833728181087e-27
121	1	2.78115467828839e-26	1.3905773391442e-26
122	1	2.79696184207666e-26	1.39848092103833e-26
123	1	2.88135680525385e-26	1.44067840262692e-26
124	1	2.91081071988586e-26	1.45540535994293e-26
125	1	3.31504470171478e-26	1.65752235085739e-26
126	1	5.60042536326733e-26	2.80021268163367e-26
127	1	6.21880185882872e-27	3.10940092941436e-27
128	1	1.20950210268319e-26	6.04751051341595e-27
129	1	2.14565945967539e-26	1.07282972983769e-26
130	1	4.02054223624415e-26	2.01027111812208e-26
131	1	7.21939234281644e-26	3.60969617140822e-26
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386	0.9997480409507	0.000503918098600209	0.000251959049300104
387	0.999676473180684	0.000647053638632134	0.000323526819316067
388	0.999576716395166	0.000846567209668042	0.000423283604834021
389	0.999516955241326	0.000966089517348344	0.000483044758674172
390	0.999376447545327	0.00124710490934611	0.000623552454673053
391	0.999237454882314	0.00152509023537283	0.000762545117686416
392	0.99906296268	0.00187407464000138	0.000937037320000688
393	0.999063653502508	0.00187269299498354	0.00093634649749177
394	0.99887709883194	0.00224580233612024	0.00112290116806012
395	0.998607191018988	0.00278561796202481	0.00139280898101241
396	0.998503211900046	0.00299357619990822	0.00149678809995411
397	0.999732419458525	0.000535161082950899	0.000267580541475449
398	0.999755619372772	0.000488761254455346	0.000244380627227673
399	0.999693049467605	0.000613901064790033	0.000306950532395016
400	0.999581958960545	0.000836082078910056	0.000418041039455028
401	0.999428285998177	0.00114342800364628	0.000571714001823138
402	0.99922376994584	0.00155246010832128	0.00077623005416064
403	0.999073159748712	0.00185368050257515	0.000926840251287573
404	0.99875481352151	0.00249037295698253	0.00124518647849126
405	0.99879668353307	0.0024066329338611	0.00120331646693055
406	0.998381307299208	0.00323738540158296	0.00161869270079148
407	0.9978607311554	0.00427853768919933	0.00213926884459967
408	0.997287397407028	0.00542520518594378	0.00271260259297189
409	0.996455208281365	0.00708958343727058	0.00354479171863529
410	0.995519846445646	0.00896030710870721	0.00448015355435361
411	0.994518703573398	0.0109625928532043	0.00548129642660214
412	0.993831741412308	0.0123365171753846	0.0061682585876923
413	0.99269719656509	0.0146056068698205	0.00730280343491027
414	0.990644266468332	0.0187114670633363	0.00935573353166813
415	0.996556309659445	0.00688738068111047	0.00344369034055524
416	0.995605284380604	0.00878943123879175	0.00439471561939587
417	0.994264501937511	0.0114709961249773	0.00573549806248867
418	0.992590849482133	0.0148183010357341	0.00740915051786704
419	0.990941324228319	0.0181173515433623	0.00905867577168116
420	0.989067278104898	0.0218654437902045	0.0109327218951022
421	0.985911843533465	0.0281763129330704	0.0140881564665352
422	0.982076481954145	0.0358470360917096	0.0179235180458548
423	0.977243712309922	0.0455125753801563	0.0227562876900782
424	0.972090773712643	0.0558184525747148	0.0279092262873574
425	0.965117876014744	0.0697642479705119	0.0348821239852559
426	0.956918222149678	0.0861635557006448	0.0430817778503224
427	0.990545808619862	0.0189083827602769	0.00945419138013846
428	0.987784569297938	0.0244308614041243	0.0122154307020622
429	0.985212059816937	0.0295758803661257	0.0147879401830629
430	0.980851389229197	0.0382972215416066	0.0191486107708033
431	0.975417980644695	0.0491640387106091	0.0245820193553046
432	0.97350970122486	0.0529805975502794	0.0264902987751397
433	0.966850810653145	0.066298378693709	0.0331491893468545
434	0.959748687538532	0.080502624922935	0.0402513124614675
435	0.949500428901987	0.100999142196026	0.0504995710980128
436	0.93814553887771	0.123708922244579	0.0618544611222894
437	0.926318351702356	0.147363296595287	0.0736816482976437
438	0.912491761121165	0.17501647775767	0.087508238878835
439	0.986177127706105	0.0276457445877897	0.0138228722938948
440	0.984466281738654	0.0310674365226928	0.0155337182613464
441	0.986038276733195	0.0279234465336095	0.0139617232668047
442	0.981764793105322	0.0364704137893551	0.0182352068946776
443	0.978705481212064	0.042589037575872	0.021294518787936
444	0.977924448131403	0.0441511037371938	0.0220755518685969
445	0.982874395763417	0.0342512084731654	0.0171256042365827
446	0.9785083569426	0.0429832861147988	0.0214916430573994
447	0.972562071136055	0.0548758577278896	0.0274379288639448
448	0.96845388511298	0.0630922297740411	0.0315461148870205
449	0.958946704698167	0.082106590603665	0.0410532953018325
450	0.947146673747269	0.105706652505463	0.0528533262527313
451	0.997850911180713	0.00429817763857338	0.00214908881928669
452	0.997035669563457	0.00592866087308669	0.00296433043654334
453	0.99568132339532	0.00863735320936138	0.00431867660468069
454	0.995945446510722	0.00810910697855685	0.00405455348927842
455	0.99439931564542	0.01120136870916	0.00560068435458
456	0.995410800491187	0.00917839901762668	0.00458919950881334
457	0.994168372733298	0.0116632545334036	0.00583162726670181
458	0.991269282967317	0.0174614340653654	0.0087307170326827
459	0.987423186214045	0.0251536275719097	0.0125768137859549
460	0.981588880491377	0.0368222390172449	0.0184111195086225
461	0.99146077535249	0.0170784492950216	0.00853922464751082
462	0.987999259196476	0.0240014816070489	0.0120007408035245
463	0.990429086077187	0.0191418278456254	0.00957091392281268
464	0.99069541131209	0.0186091773758187	0.00930458868790933
465	0.988986521386979	0.0220269572260428	0.0110134786130214
466	0.984436260377754	0.0311274792444921	0.0155637396222461
467	0.98093098503995	0.0381380299201018	0.0190690149600509
468	0.971985239308082	0.0560295213838366	0.0280147606919183
469	0.965773479326887	0.0684530413462264	0.0342265206731132
470	0.961335409694691	0.0773291806106172	0.0386645903053086
471	0.953061047181124	0.093877905637751	0.0469389528188755
472	0.947825725958437	0.104348548083126	0.0521742740415628
473	0.98688198599563	0.0262360280087402	0.0131180140043701
474	0.988797497079	0.0224050058419996	0.0112025029209998
475	0.987072140457317	0.0258557190853654	0.0129278595426827
476	0.980787222282496	0.0384255554350069	0.0192127777175035
477	0.965661005985813	0.0686779880283738	0.0343389940141869
478	0.942221823747352	0.115556352505296	0.057778176252648
479	0.904489757249793	0.191020485500414	0.0955102427502072
480	0.889007077450344	0.221985845099311	0.110992922549656
481	0.822015826455765	0.35596834708847	0.177984173544235
482	0.738810168591027	0.522379662817947	0.261189831408973
483	0.623162016774749	0.753675966450502	0.376837983225251
484	0.477209774653758	0.954419549307517	0.522790225346242
485	0.499191120916134	0.998382241832268	0.500808879083866

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	399	0.832985386221294	NOK
5% type I error level	443	0.924843423799583	NOK
10% type I error level	460	0.960334029227557	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/10lt431296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/10lt431296727380.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/1l72j1296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/1l72j1296727380.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/2wwhe1296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/2wwhe1296727380.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/3hcj61296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/3hcj61296727380.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/4mz7r1296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/4mz7r1296727380.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/52br51296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/52br51296727380.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/628r01296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/628r01296727380.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/7cxqg1296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/7cxqg1296727380.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/8tms01296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/8tms01296727380.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/93aud1296727380.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Feb/03/t1296727260y14reiqovf4965q/93aud1296727380.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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