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W7-interactie_gender

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 25 Nov 2010 13:07:11 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td.htm/, Retrieved Thu, 25 Nov 2010 14:07:21 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0 24 14 0 11 0 12 0 24 0 26 0 0 25 11 0 7 0 8 0 25 0 23 0 0 17 6 0 17 0 8 0 30 0 25 0 1 18 12 12 10 10 8 8 19 19 23 23 1 18 8 8 12 12 9 9 22 22 19 19 1 16 10 10 12 12 7 7 22 22 29 29 1 20 10 10 11 11 4 4 25 25 25 25 1 16 11 11 11 11 11 11 23 23 21 21 1 18 16 16 12 12 7 7 17 17 22 22 1 17 11 11 13 13 7 7 21 21 25 25 0 23 13 0 14 0 12 0 19 0 24 0 0 30 12 0 16 0 10 0 19 0 18 0 1 23 8 8 11 11 10 10 15 15 22 22 1 18 12 12 10 10 8 8 16 16 15 15 1 15 11 11 11 11 8 8 23 23 22 22 1 12 4 4 15 15 4 4 27 27 28 28 0 21 9 0 9 0 9 0 22 0 20 0 1 15 8 8 11 11 8 8 14 14 12 12 1 20 8 8 17 17 7 7 22 22 24 24 0 31 14 0 17 0 11 0 23 0 20 0 0 27 15 0 11 0 9 0 23 0 21 0 1 34 16 16 18 18 11 11 21 21 20 20 1 21 9 9 14 14 13 13 19 19 21 21 1 31 14 14 10 10 8 8 18 18 23 23 1 19 11 11 11 11 8 8 20 20 28 28 0 16 8 0 15 0 9 0 23 0 24 0 1 20 9 9 15 15 6 6 25 25 24 24 1 21 9 9 13 13 9 9 19 19 24 24 1 22 9 9 16 16 9 9 24 24 23 23 1 17 9 9 13 13 6 6 22 22 23 23 1 24 10 10 9 9 6 6 25 25 29 29 0 25 16 0 18 0 16 0 2 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	14 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation

YT[t] = + 18.5439219142398 + 0.0622101960213318G[t] + 0.730621258268541X1[t] -0.0488988052248012X1_G[t] -0.23865725478243X2[t] -0.105245492279363X2_G[t] + 0.247229909963782X3[t] -0.501008474019651X3_G[t] + 0.334674427648451X4[t] + 0.172083243211216X4_G[t] -0.52672682818197X5[t] + 0.133226980233375`X5_G `[t] -0.0211658458013016t + 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)	18.5439219142398	1.197091	15.4908	0	0
G	0.0622101960213318	0.074509	0.8349	0.405121	0.20256
X1	0.730621258268541	0.117388	6.224	0	0
X1_G	-0.0488988052248012	0.121642	-0.402	0.688281	0.34414
X2	-0.23865725478243	0.137157	-1.74	0.083961	0.041981
X2_G	-0.105245492279363	0.166405	-0.6325	0.528071	0.264036
X3	0.247229909963782	0.186211	1.3277	0.186354	0.093177
X3_G	-0.501008474019651	0.097517	-5.1376	1e-06	0
X4	0.334674427648451	0.089326	3.7467	0.000257	0.000129
X4_G	0.172083243211216	0.086576	1.9877	0.04872	0.02436
X5	-0.52672682818197	0.093813	-5.6146	0	0
`X5_G `	0.133226980233375	0.091853	1.4504	0.149082	0.074541
t	-0.0211658458013016	0.010826	-1.9551	0.052482	0.026241

Multiple Linear Regression - Regression Statistics
Multiple R	0.849135511888232
R-squared	0.72103111754969
Adjusted R-squared	0.6981021683072
F-TEST (value)	31.4463218494766
F-TEST (DF numerator)	12
F-TEST (DF denominator)	146
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.65450296177352
Sum Squared Residuals	1949.88721705127

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	24	23.4302715319882	0.56972846801179
2	25	23.0978062028504	1.90219379714961
3	17	17.6568799997603	-0.656879999760318
4	18	21.8107814240319	-3.81078142403191
5	18	21.2154141122496	-3.21541411224959
6	16	19.1302518211615	-3.13025182116155
7	20	23.3085968189630	-3.30859681896303
8	16	22.7531875278894	-6.75318752788943
9	18	23.3777995833619	-5.3777995833619
10	17	20.450649864873	-3.45064986487299
11	23	21.1521015694812	1.84789843051883
12	30	22.5889011050107	7.41109889498928
13	23	16.4084082889816	6.59159171101837
14	18	23.2268487370287	-5.22684873702865
15	15	22.9728624514993	-7.97286245149932
16	12	17.4851742981152	-5.48517429811525
17	21	21.6651486012932	-0.665148601293227
18	15	20.2383769967131	-5.23837699671314
19	20	17.7396364240912	2.26036357590884
20	31	24.1746335645486	6.82536643545139
21	27	25.2948458576009	1.70515414239911
22	34	22.8381432286866	11.1618567713134
23	21	17.5059588820466	3.49404111795336
24	31	22.2441517432337	8.75584825676631
25	19	18.8799318932157	0.120068106784265
26	16	17.5398583170390	-1.53985831703897
27	20	20.7138891814829	-0.713889181482937
28	21	17.5786471124796	3.42135288752039
29	22	19.4530612277399	2.54693877226014
30	17	20.2114239735722	-3.21142397357221
31	24	21.4068654939492	2.59313450605075
32	25	25.2764941967240	-0.276494196724022
33	26	23.0470773020156	2.95292269798441
34	25	23.8157393330287	1.18426066697133
35	17	20.2759207726271	-3.27592077262712
36	32	28.5989012104896	3.40109878951037
37	33	26.2363648204043	6.76363517959566
38	13	23.2715471832359	-10.2715471832359
39	32	22.2579594739541	9.74204052604586
40	25	25.682360753714	-0.682360753714005
41	29	19.9251156912427	9.07488430875734
42	22	20.3724683973062	1.62753160269381
43	18	17.7295655297779	0.270434470222058
44	17	20.6574454678035	-3.65744546780348
45	20	21.075756238427	-1.07575623842701
46	15	20.2976711282779	-5.29767112827793
47	20	19.4806693254963	0.519330674503749
48	33	29.2887388632627	3.71126113673728
49	29	20.1791039180085	8.8208960819915
50	23	23.8746024789380	-0.87460247893803
51	26	24.0061052361601	1.99389476383986
52	18	18.6818634660854	-0.681863466085368
53	20	15.7874342556512	4.21256574434882
54	6	12.7423353580560	-6.74233535805596
55	8	8.95533275311095	-0.95533275311095
56	13	11.8826975477405	1.11730245225953
57	10	9.03059763752766	0.969402362472339
58	8	9.25079194001516	-1.25079194001516
59	7	11.2077571202430	-4.20775712024302
60	15	18.6321323173743	-3.63213231737432
61	9	9.98827725926862	-0.988277259268624
62	10	10.5877077277315	-0.587707727731456
63	12	10.3392180242187	1.66078197578125
64	13	14.2811157957265	-1.28111579572652
65	10	11.1648831659835	-1.16488316598348
66	11	7.74921664951315	3.25078335048685
67	8	10.7012600102935	-2.70126001029350
68	9	7.0287360071376	1.9712639928624
69	13	13.2245830565789	-0.224583056578933
70	11	11.4248953476096	-0.424895347609600
71	8	11.0423808240166	-3.04238082401656
72	9	10.4619036874179	-1.46190368741792
73	9	8.13136506745948	0.868634932540524
74	15	9.17858579184867	5.82141420815133
75	9	8.98586277343772	0.0141372265622786
76	10	10.6642081481749	-0.664208148174871
77	14	12.8393820597583	1.16061794024171
78	12	12.1813619972361	-0.181361997236140
79	12	11.1044636105117	0.895536389488292
80	11	11.9157682572962	-0.915768257296198
81	14	12.5701349520778	1.42986504792223
82	6	8.23618032037408	-2.23618032037408
83	12	11.0158197475292	0.984180252470836
84	8	8.13754185188919	-0.137541851889191
85	14	14.2113340587138	-0.211334058713820
86	11	10.7550011099163	0.244998890083714
87	10	10.1590211922866	-0.159021192286559
88	14	12.4139287907590	1.58607120924102
89	12	12.3964359720643	-0.396435972064284
90	10	12.5284532104547	-2.52845321045466
91	14	14.3681853126345	-0.368185312634538
92	5	7.75153745777384	-2.75153745777384
93	11	12.1785421983973	-1.1785421983973
94	10	11.1487411707221	-1.14874117072209
95	9	9.61231434093077	-0.612314340930776
96	10	6.8684424356047	3.1315575643953
97	16	10.7016883506144	5.29831164938556
98	13	13.3591147417237	-0.359114741723721
99	9	11.0358153509783	-2.03581535097827
100	10	12.9008068217531	-2.90080682175314
101	10	10.9765235052967	-0.976523505296659
102	7	10.1944870675154	-3.19448706751538
103	9	8.49290088628732	0.507099113712681
104	8	9.25198395925403	-1.25198395925403
105	14	12.7596498347755	1.24035016522452
106	14	17.6169946212989	-3.61699462129891
107	8	12.6591641208644	-4.65916412086439
108	9	13.6614883179975	-4.6614883179975
109	14	14.2956476082921	-0.295647608292068
110	14	15.0938846105181	-1.09388461051811
111	8	11.4577560650174	-3.45775606501737
112	8	9.35765991735726	-1.35765991735726
113	8	7.41149426103054	0.588505738969455
114	7	7.33580614820812	-0.335806148208119
115	6	4.68274638193216	1.31725361806784
116	8	8.02947627924661	-0.0294762792466125
117	6	8.8905271822193	-2.89052718221930
118	11	13.4729791874093	-2.47297918740928
119	14	12.7360895599980	1.26391044000205
120	11	16.9130651429320	-5.91306514293199
121	11	12.3852122537675	-1.38521225376751
122	11	14.0439343478287	-3.04393434782866
123	14	14.3105362698450	-0.310536269844989
124	8	9.63127963571698	-1.63127963571697
125	20	10.8684230798688	9.13157692013115
126	11	9.75566799788626	1.24433200211374
127	8	9.12008080529924	-1.12008080529924
128	11	10.0089667525346	0.99103324746545
129	10	8.91107763319837	1.08892236680163
130	14	12.9892068762432	1.01079312375677
131	11	12.7578794199781	-1.75787941997814
132	9	11.0305543045231	-2.03055430452308
133	9	7.99564521224491	1.00435478775509
134	8	9.37714962101867	-1.37714962101867
135	10	11.5059762561231	-1.50597625612307
136	13	6.14523896089694	6.85476103910306
137	13	13.5433031111217	-0.543303111121712
138	12	15.0797502021508	-3.07975020215077
139	8	8.46036575258291	-0.460365752582913
140	13	15.9967530673342	-2.99675306733424
141	14	17.5537031156223	-3.55370311562232
142	12	5.00855327423951	6.99144672576049
143	14	16.2710673551707	-2.27106735517067
144	15	14.58756874484	0.412431255160004
145	13	11.4581253657349	1.54187463426507
146	16	13.7672957791049	2.23270422089512
147	9	9.5627044280725	-0.562704428072497
148	9	10.4760595709918	-1.47605957099177
149	9	12.0785763545246	-3.07857635452458
150	8	8.55535352463767	-0.555353524637675
151	7	7.82755793085548	-0.827557930855484
152	16	12.9232811827220	3.07671881727798
153	11	5.55323115112761	5.44676884887239
154	9	8.01601463887984	0.983985361120157
155	11	9.55404099384758	1.44595900615242
156	9	9.26027996129633	-0.260279961296329
157	14	12.7939897372146	1.20601026278541
158	13	11.7114143348072	1.28858566519280
159	16	9.41858413864334	6.58141586135666

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.471565632767165	0.94313126553433	0.528434367232835
17	0.302719521533864	0.605439043067728	0.697280478466136
18	0.338696139148210	0.677392278296419	0.66130386085179
19	0.517641720554905	0.96471655889019	0.482358279445095
20	0.430253266316234	0.860506532632469	0.569746733683766
21	0.375424525862644	0.750849051725289	0.624575474137356
22	0.816895577379259	0.366208845241483	0.183104422620741
23	0.758099389787249	0.483801220425502	0.241900610212751
24	0.982287559812034	0.035424880375933	0.0177124401879665
25	0.976865276724916	0.0462694465501686	0.0231347232750843
26	0.971381427778162	0.0572371444436767	0.0286185722218384
27	0.958306153173498	0.0833876936530047	0.0416938468265024
28	0.942613608023947	0.114772783952105	0.0573863919760526
29	0.920339821320133	0.159320357359735	0.0796601786798673
30	0.902303474920801	0.195393050158399	0.0976965250791993
31	0.930827608559836	0.138344782880328	0.0691723914401639
32	0.944350126694476	0.111299746611049	0.0556498733055245
33	0.9492434633051	0.101513073389801	0.0507565366949007
34	0.95806131319671	0.0838773736065813	0.0419386868032907
35	0.970977652555442	0.0580446948891166	0.0290223474445583
36	0.968290935395978	0.0634181292080434	0.0317090646040217
37	0.99094088282874	0.0181182343425197	0.00905911717125983
38	0.999670321350113	0.000659357299774613	0.000329678649887306
39	0.999908404955813	0.000183190088373985	9.15950441869923e-05
40	0.999862616385429	0.000274767229142499	0.000137383614571249
41	0.999931787788208	0.000136424423583824	6.82122117919118e-05
42	0.999914999750516	0.000170000498968391	8.50002494841957e-05
43	0.999880769648178	0.000238460703644889	0.000119230351822444
44	0.999847123210018	0.000305753579963921	0.000152876789981961
45	0.99981011243373	0.000379775132541563	0.000189887566270782
46	0.99994713997251	0.000105720054978440	5.28600274892201e-05
47	0.999928232641518	0.000143534716964763	7.17673584823816e-05
48	0.999935225607809	0.000129548784381794	6.47743921908971e-05
49	0.999999948443614	1.03112771071824e-07	5.15563855359121e-08
50	0.999999910023482	1.79953035256824e-07	8.99765176284121e-08
51	0.99999999121209	1.75758217465388e-08	8.7879108732694e-09
52	0.999999984590745	3.08185105395242e-08	1.54092552697621e-08
53	1	4.13042206721116e-20	2.06521103360558e-20
54	1	1.61407830244153e-21	8.07039151220767e-22
55	1	5.41881111997112e-21	2.70940555998556e-21
56	1	9.0346137332179e-21	4.51730686660895e-21
57	1	2.93737393785734e-21	1.46868696892867e-21
58	1	9.41630729015494e-22	4.70815364507747e-22
59	1	1.84883782631852e-22	9.24418913159262e-23
60	1	3.73408504858449e-22	1.86704252429224e-22
61	1	7.98758032810882e-22	3.99379016405441e-22
62	1	2.39152756674451e-21	1.19576378337226e-21
63	1	8.0204340155742e-21	4.0102170077871e-21
64	1	8.08119412246517e-21	4.04059706123258e-21
65	1	1.68583341087901e-20	8.42916705439504e-21
66	1	2.37751439084313e-20	1.18875719542157e-20
67	1	7.75201220833773e-20	3.87600610416886e-20
68	1	1.06412017355714e-19	5.3206008677857e-20
69	1	2.53580484305394e-19	1.26790242152697e-19
70	1	6.34077608395306e-19	3.17038804197653e-19
71	1	1.99086374697876e-18	9.95431873489381e-19
72	1	5.85144612171303e-18	2.92572306085652e-18
73	1	2.15685416246753e-18	1.07842708123376e-18
74	1	1.11650308536899e-19	5.58251542684495e-20
75	1	2.18913775673321e-19	1.09456887836660e-19
76	1	6.40664076159268e-19	3.20332038079634e-19
77	1	1.52880768868587e-18	7.64403844342937e-19
78	1	4.72195226910635e-18	2.36097613455318e-18
79	1	1.47965742767146e-17	7.39828713835732e-18
80	1	3.47951318368306e-17	1.73975659184153e-17
81	1	1.07578157052363e-16	5.37890785261814e-17
82	1	3.28861076059634e-16	1.64430538029817e-16
83	1	9.34709095341143e-16	4.67354547670571e-16
84	0.999999999999999	2.70491200723316e-15	1.35245600361658e-15
85	0.999999999999996	7.95887286825814e-15	3.97943643412907e-15
86	0.999999999999988	2.32208433615442e-14	1.16104216807721e-14
87	0.99999999999997	6.00192793533862e-14	3.00096396766931e-14
88	0.999999999999932	1.36875964486213e-13	6.84379822431067e-14
89	0.99999999999985	2.98711975569631e-13	1.49355987784816e-13
90	0.999999999999662	6.75153401008661e-13	3.37576700504331e-13
91	0.999999999999087	1.82555563785638e-12	9.12777818928189e-13
92	0.99999999999831	3.38139596094978e-12	1.69069798047489e-12
93	0.999999999995657	8.6862205832746e-12	4.3431102916373e-12
94	0.999999999989553	2.08929926882147e-11	1.04464963441073e-11
95	0.999999999972697	5.4606338191621e-11	2.73031690958105e-11
96	0.999999999934003	1.31994866958659e-10	6.59974334793295e-11
97	0.99999999994684	1.06318730825807e-10	5.31593654129034e-11
98	0.99999999987422	2.51559896088936e-10	1.25779948044468e-10
99	0.999999999799308	4.01383144919376e-10	2.00691572459688e-10
100	0.999999999610057	7.79885850501357e-10	3.89942925250678e-10
101	0.999999999168937	1.66212566333608e-09	8.31062831668041e-10
102	0.999999999167214	1.66557246761023e-09	8.32786233805115e-10
103	0.999999998107268	3.78546322949954e-09	1.89273161474977e-09
104	0.999999995423013	9.15397310637772e-09	4.57698655318886e-09
105	0.99999998892495	2.21501004832171e-08	1.10750502416086e-08
106	0.999999982393963	3.52120739764536e-08	1.76060369882268e-08
107	0.999999971244219	5.75115623426902e-08	2.87557811713451e-08
108	0.999999954106066	9.17878683520928e-08	4.58939341760464e-08
109	0.999999892534296	2.14931407832742e-07	1.07465703916371e-07
110	0.999999751545686	4.96908627531213e-07	2.48454313765607e-07
111	0.999999465096892	1.06980621522865e-06	5.34903107614326e-07
112	0.99999903857288	1.92285424098849e-06	9.61427120494247e-07
113	0.99999845118423	3.09763154039969e-06	1.54881577019984e-06
114	0.999997052317907	5.8953641850845e-06	2.94768209254225e-06
115	0.99999961181205	7.7637590059848e-07	3.8818795029924e-07
116	0.999999075043383	1.84991323416342e-06	9.24956617081712e-07
117	0.999997939398952	4.12120209575748e-06	2.06060104787874e-06
118	0.999995487881246	9.02423750692174e-06	4.51211875346087e-06
119	0.99999000287711	1.99942457814962e-05	9.99712289074812e-06
120	0.999984020347549	3.19593049019695e-05	1.59796524509847e-05
121	0.999966189666797	6.7620666406061e-05	3.38103332030305e-05
122	0.999931744623365	0.000136510753269679	6.82553766348395e-05
123	0.99985501803189	0.000289963936220684	0.000144981968110342
124	0.99970082358491	0.000598352830182549	0.000299176415091275
125	0.999999999999019	1.96256737557483e-12	9.81283687787415e-13
126	0.999999999994856	1.02872480078326e-11	5.14362400391629e-12
127	0.99999999998581	2.83778742639275e-11	1.41889371319637e-11
128	0.999999999919653	1.60693740333872e-10	8.03468701669359e-11
129	0.999999999560665	8.78669633664959e-10	4.39334816832479e-10
130	0.999999997879271	4.24145745854309e-09	2.12072872927154e-09
131	0.999999989132007	2.17359862647687e-08	1.08679931323843e-08
132	0.999999945712513	1.08574974077297e-07	5.42874870386484e-08
133	0.999999745848078	5.08303843869452e-07	2.54151921934726e-07
134	0.999998844689406	2.31062118806774e-06	1.15531059403387e-06
135	0.99999672870746	6.54258508140731e-06	3.27129254070366e-06
136	0.99999413158513	1.17368297411935e-05	5.86841487059675e-06
137	0.999973687856766	5.26242864681908e-05	2.63121432340954e-05
138	0.99990841783814	0.000183164323720003	9.15821618600016e-05
139	0.999623056037505	0.000753887924989432	0.000376943962494716
140	0.998561960795781	0.00287607840843798	0.00143803920421899
141	0.996009835341279	0.00798032931744284	0.00399016465872142
142	0.999621904080245	0.000756191839510462	0.000378095919755231
143	0.99765234756648	0.0046953048670387	0.00234765243351935

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	106	0.828125	NOK
5% type I error level	109	0.8515625	NOK
10% type I error level	114	0.890625	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/10g9as1290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/10g9as1290690415.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/1r8vg1290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/1r8vg1290690415.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/2r8vg1290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/2r8vg1290690415.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/32hc11290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/32hc11290690415.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/42hc11290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/42hc11290690415.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/52hc11290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/52hc11290690415.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/6dqt41290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/6dqt41290690415.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/7dqt41290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/7dqt41290690415.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/860b71290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/860b71290690415.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/960b71290690415.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906904275vrzsuj1kfex7td/960b71290690415.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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