Home » date » 2010 » Nov » 25 »

workshop 7: 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 12:40:04 +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/t12906887494uzlzntelv34wdp.htm/, Retrieved Thu, 25 Nov 2010 13:39:30 +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/t12906887494uzlzntelv34wdp.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	11 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Concernovermistakes[t] = + 17.7079010523209 + 0.0365943321020009Gender[t] + 0.695145667491058Doubtsaboutactions[t] -0.0783765564644857DoubtsaboutactionsMale[t] -0.223159928154307Parentalexpectations[t] -0.173559424326886ParentalexpectationsMale[t] + 0.323644568786632Parentalcritism[t] -0.511763726976633ParentalcritismMale[t] + 0.320852405582859Personalstandards[t] + 0.182877097980758PersonalstandarsMale[t] -0.529139795925976Organization[t] + 0.187133930397755OrganizationMale[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	17.7079010523209	1.128795	15.6874	0	0
Gender	0.0365943321020009	0.074049	0.4942	0.62191	0.310955
Doubtsaboutactions	0.695145667491058	0.117085	5.9371	0	0
DoubtsaboutactionsMale	-0.0783765564644857	0.121858	-0.6432	0.521109	0.260554
Parentalexpectations	-0.223159928154307	0.138236	-1.6143	0.108598	0.054299
ParentalexpectationsMale	-0.173559424326886	0.16425	-1.0567	0.29239	0.146195
Parentalcritism	0.323644568786632	0.183802	1.7608	0.080347	0.040173
ParentalcritismMale	-0.511763726976633	0.098292	-5.2065	1e-06	0
Personalstandards	0.320852405582859	0.089897	3.5691	0.000484	0.000242
PersonalstandarsMale	0.182877097980758	0.087225	2.0966	0.037741	0.01887
Organization	-0.529139795925976	0.094701	-5.5875	0	0
OrganizationMale	0.187133930397755	0.088454	2.1156	0.036064	0.018032

Multiple Linear Regression - Regression Statistics
Multiple R	0.844823945222742
R-squared	0.713727498421718
Adjusted R-squared	0.692305746602935
F-TEST (value)	33.3178866256824
F-TEST (DF numerator)	11
F-TEST (DF denominator)	147
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.68941928829607
Sum Squared Residuals	2000.93675867311

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	24	22.811739052851	1.18826094714901
2	25	22.2326352812095	2.76736471879052
3	17	17.0712900982734	-0.0712900982733651
4	18	21.3783035869694	-3.37830358696944
5	18	20.8088812525145	-2.8088812525145
6	16	18.9985991356654	-2.99859913566543
7	20	22.8388879355204	-2.83888793552037
8	16	22.4993873942026	-6.49938739420258
9	18	22.5746073427043	-4.57460734270433
10	17	20.0829428527601	-3.08294285276007
11	23	20.9011311648348	2.0988688351652
12	30	22.2872152790177	7.71278472098228
13	23	16.4653573252757	6.5346426747243
14	18	22.6031620005044	-4.60316200050435
15	15	22.7217390032444	-7.72173900324436
16	12	17.5328372699787	-5.53283726997872
17	21	21.3445308297347	-0.344530829734687
18	15	19.7579247933743	-4.7579247933743
19	20	17.4914934788474	2.50850652115258
20	31	24.0031212851116	6.99687871488835
21	27	24.8607975880293	2.13920241197069
22	34	22.1407443403681	11.8592556596319
23	21	17.6845347840714	3.31546521592862
24	31	22.1081123054590	8.89188769454104
25	19	19.1585152993842	-0.158515299384178
26	16	17.5147188151967	-1.51471881519674
27	20	20.6010089637172	-0.601008963717234
28	21	17.8077131727279	3.19228682727209
29	22	19.4782084986306	2.52179150136936
30	17	20.225265023517	-3.22526502351699
31	24	21.8880648619899	2.11193513801014
32	25	25.6344735689173	-0.634473568917283
33	26	22.7360509671135	3.26394903288652
34	25	24.1703794113613	0.829620588638669
35	17	20.2692303807695	-3.26923038076951
36	32	28.6296151389884	3.37038486101158
37	33	26.4100635424048	6.58993645759517
38	13	22.9508456590843	-9.95084565908433
39	32	22.3276435319607	9.67235646803934
40	25	25.8102790758332	-0.810279075833216
41	29	20.1939141390408	8.8060858609592
42	22	20.7753767071566	1.22462329284336
43	18	17.6945562778616	0.305443722138430
44	17	20.7344146349543	-3.73441463495426
45	20	21.1888622348383	-1.18886223483831
46	15	20.8013020069954	-5.80130200699542
47	20	19.8029907136260	0.197009286374031
48	33	29.5912771586826	3.40872284131743
49	29	20.4216899729468	8.57831002705316
50	23	23.7293142522824	-0.729314252282376
51	26	23.6892605927351	2.31073940726488
52	18	19.4000106905263	-1.40001069052634
53	20	16.1717289654027	3.82827103459733
54	6	12.3826532627888	-6.3826532627888
55	8	7.5930752791294	0.406924720870598
56	13	10.6550007507472	2.34499924925282
57	10	8.00294096086172	1.99705903913828
58	8	8.37051905183334	-0.370519051833336
59	7	10.9530501253590	-3.95305012535897
60	15	17.9022858335098	-2.90228583350984
61	9	9.26733895850332	-0.267338958503316
62	10	9.70333273434956	0.296667265650435
63	12	9.48393327318776	2.51606672681224
64	13	13.4497906665922	-0.44979066659219
65	10	10.8265675715985	-0.826567571598504
66	11	7.00472188700895	3.99527811299105
67	8	9.77691669093432	-1.77691669093432
68	9	6.20575509323006	2.79424490676994
69	13	12.8625411144788	0.137458885521231
70	11	10.7279533085567	0.27204669144329
71	8	10.2898601042278	-2.28986010422777
72	9	9.94202337083844	-0.942023370838442
73	9	7.00523176860224	1.99476823139776
74	15	7.88481253241727	7.11518746758273
75	9	8.2201830691474	0.779816930852602
76	10	10.0128550669390	-0.0128550669389737
77	14	12.4176320137339	1.5823679862661
78	12	11.6241656608797	0.375834339120323
79	12	10.6848129795712	1.31518702042877
80	11	11.1562528010513	-0.156252801051291
81	14	11.9277452311274	2.07225476887264
82	6	8.17293024970463	-2.17293024970463
83	12	10.5559306712279	1.44406932877212
84	8	7.78126686985428	0.218733130145723
85	14	13.8304698602852	0.169530139714764
86	11	10.4075993780191	0.592400621980913
87	10	9.77449791633986	0.225502083660142
88	14	12.2302634707641	1.76973652923586
89	12	11.7656475275764	0.234352472423609
90	10	12.4569330428162	-2.45693304281618
91	14	14.0019001188809	-0.00190011888091830
92	5	8.10017184343734	-3.10017184343734
93	11	12.4135480614905	-1.41354806149050
94	10	10.9037275317824	-0.903727531782402
95	9	9.67235782007674	-0.672357820076742
96	10	6.15054514773932	3.84945485226068
97	16	10.1950418514455	5.80495814855448
98	13	12.8987409649225	0.101259035077521
99	9	11.0635646168111	-2.06356461681112
100	10	12.9168787734598	-2.91687877345979
101	10	10.8163544919866	-0.81635449198664
102	7	10.3918490553556	-3.39184905535562
103	9	8.09293183682401	0.907068163175988
104	8	9.4657853717346	-1.46578537173460
105	14	12.7928494114434	1.20715058855663
106	14	17.5360240887630	-3.53602408876303
107	8	12.9370428116627	-4.93704281166275
108	9	13.7892315294793	-4.78923152947933
109	14	14.1982562224062	-0.198256222406151
110	14	15.3960645469042	-1.39606454690421
111	8	12.1123350752938	-4.11233507529382
112	8	9.52343713494843	-1.52343713494843
113	8	7.42418579326049	0.575814206739514
114	7	7.14369677937642	-0.143696779376416
115	6	5.05324302356007	0.946756976439928
116	8	8.55271855857902	-0.552718558579022
117	6	9.21530140868406	-3.21530140868406
118	11	13.9532057872090	-2.95320578720898
119	14	12.859297839651	1.14070216034900
120	11	17.478310416642	-6.47831041664202
121	11	12.5109844305528	-1.51098443055278
122	11	14.7004205395233	-3.70042053952332
123	14	14.5098011424281	-0.509801142428057
124	8	10.4344794563191	-2.43447945631912
125	20	11.5968155076155	8.40318449238454
126	11	10.1877711519117	0.812228848088271
127	8	10.0961950603485	-2.09619506034849
128	11	10.5726019320053	0.427398067994736
129	10	9.6224772550949	0.377522744905107
130	14	13.0982053069621	0.901794693037882
131	11	13.2630746439127	-2.26307464391267
132	9	11.9930008448726	-2.99300084487264
133	9	8.81318348873168	0.186816511268319
134	8	10.3406420843232	-2.34064208432323
135	10	12.1653415762053	-2.16534157620527
136	13	6.26080149899628	6.73919850100372
137	13	14.3201306238853	-1.32013062388528
138	12	15.8157201479836	-3.81572014798365
139	8	9.4911899531388	-1.4911899531388
140	13	16.6756834890563	-3.67568348905627
141	14	17.9360701355344	-3.93607013553442
142	12	5.26377042879681	6.73622957120319
143	14	16.7984008276639	-2.79840082766391
144	15	15.4109287992392	-0.410928799239194
145	13	12.2164862379305	0.783513762069489
146	16	14.1973004011682	1.80269959883181
147	9	10.1586420511149	-1.15864205111490
148	9	11.4954016740183	-2.49540167401828
149	9	13.0506218734206	-4.0506218734206
150	8	9.28951167527347	-1.28951167527347
151	7	8.9453325554941	-1.9453325554941
152	16	13.5259278990730	2.47407210092696
153	11	5.88537128799132	5.11462871200868
154	9	9.03249631571158	-0.0324963157115857
155	11	10.4996415500084	0.500358449991586
156	9	10.5737950934929	-1.57379509349289
157	14	13.6433189871849	0.356681012815117
158	13	12.7098457858064	0.290154214193630
159	16	9.53377858643988	6.46622141356012

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
15	0.299197157899065	0.598394315798131	0.700802842100935
16	0.360950803501773	0.721901607003547	0.639049196498227
17	0.226825081819398	0.453650163638795	0.773174918180602
18	0.264718522878353	0.529437045756707	0.735281477121647
19	0.401486521201951	0.802973042403902	0.598513478798049
20	0.334343178118498	0.668686356236996	0.665656821881502
21	0.3957985377829	0.7915970755658	0.6042014622171
22	0.77599767219407	0.448004655611861	0.224002327805931
23	0.730507026319836	0.538985947360328	0.269492973680164
24	0.98011514849604	0.0397697030079209	0.0198848515039604
25	0.969339714859622	0.0613205702807553	0.0306602851403776
26	0.95657311724774	0.0868537655045219	0.0434268827522609
27	0.94200695150303	0.115986096993939	0.0579930484969697
28	0.924345076827568	0.151309846344863	0.0756549231724317
29	0.899890875966248	0.200218248067503	0.100109124033752
30	0.873685275557068	0.252629448885864	0.126314724442932
31	0.944773200708683	0.110453598582635	0.0552267992913175
32	0.942309576771944	0.115380846456111	0.0576904232280555
33	0.943995082215729	0.112009835568542	0.0560049177842712
34	0.964622607782795	0.0707547844344108	0.0353773922172054
35	0.969573061708357	0.0608538765832859	0.0304269382916429
36	0.972839133059946	0.0543217338801081	0.0271608669400540
37	0.99514610670107	0.00970778659786023	0.00485389329893011
38	0.99946627906408	0.00106744187184137	0.000533720935920684
39	0.999848197095487	0.000303605809025677	0.000151802904512839
40	0.999761317974885	0.000477364050230385	0.000238682025115193
41	0.999872869097483	0.000254261805034778	0.000127130902517389
42	0.99982810281389	0.00034379437221869	0.000171897186109345
43	0.999818842601438	0.000362314797123243	0.000181157398561621
44	0.999747751900233	0.000504496199533586	0.000252248099766793
45	0.999633004053636	0.000733991892728707	0.000366995946364354
46	0.999857107956848	0.000285784086304917	0.000142892043152459
47	0.999803644297014	0.00039271140597142	0.00019635570298571
48	0.999818477771806	0.000363044456387451	0.000181522228193726
49	0.999999888797058	2.22405883035720e-07	1.11202941517860e-07
50	0.99999980644745	3.87105097687285e-07	1.93552548843642e-07
51	0.999999989348302	2.13033968404892e-08	1.06516984202446e-08
52	0.99999998122387	3.75522618160516e-08	1.87761309080258e-08
53	1	2.74828117060058e-20	1.37414058530029e-20
54	1	1.01788784506053e-21	5.08943922530267e-22
55	1	3.3425615886261e-21	1.67128079431305e-21
56	1	3.40769362733608e-21	1.70384681366804e-21
57	1	1.07793536907340e-21	5.38967684536702e-22
58	1	2.71366330445564e-22	1.35683165222782e-22
59	1	5.24240722160819e-23	2.62120361080409e-23
60	1	1.24869286440906e-22	6.2434643220453e-23
61	1	2.61993146770147e-22	1.30996573385073e-22
62	1	7.7774554357041e-22	3.88872771785205e-22
63	1	2.66417157228660e-21	1.33208578614330e-21
64	1	2.92207973755596e-21	1.46103986877798e-21
65	1	5.81844174948077e-21	2.90922087474038e-21
66	1	7.45790655643037e-21	3.72895327821519e-21
67	1	2.42777939756005e-20	1.21388969878003e-20
68	1	3.33302339836282e-20	1.66651169918141e-20
69	1	8.88025583734845e-20	4.44012791867423e-20
70	1	2.37510068848758e-19	1.18755034424379e-19
71	1	7.54513598720578e-19	3.77256799360289e-19
72	1	2.0537725367021e-18	1.02688626835105e-18
73	1	6.09396464965295e-19	3.04698232482648e-19
74	1	4.07170550909182e-20	2.03585275454591e-20
75	1	6.43540978424014e-20	3.21770489212007e-20
76	1	1.7534995379027e-19	8.7674976895135e-20
77	1	4.48521124842474e-19	2.24260562421237e-19
78	1	1.46522656784661e-18	7.32613283923307e-19
79	1	4.61760723273354e-18	2.30880361636677e-18
80	1	1.15910106462230e-17	5.79550532311148e-18
81	1	3.66931106294925e-17	1.83465553147462e-17
82	1	1.14824313964726e-16	5.74121569823631e-17
83	1	3.15203312976219e-16	1.57601656488109e-16
84	1	9.54140649788758e-16	4.77070324894379e-16
85	0.999999999999999	2.83006484819311e-15	1.41503242409656e-15
86	0.999999999999996	8.27291935542827e-15	4.13645967771414e-15
87	0.999999999999988	2.32952545756093e-14	1.16476272878047e-14
88	0.99999999999997	5.80764493293664e-14	2.90382246646832e-14
89	0.999999999999931	1.38276515680589e-13	6.91382578402947e-14
90	0.999999999999828	3.43925654901153e-13	1.71962827450577e-13
91	0.999999999999548	9.03420139449364e-13	4.51710069724682e-13
92	0.999999999999025	1.94945155771048e-12	9.74725778855238e-13
93	0.999999999997395	5.20944245973803e-12	2.60472122986902e-12
94	0.999999999993436	1.31273950513607e-11	6.56369752568036e-12
95	0.999999999982888	3.42248059097691e-11	1.71124029548845e-11
96	0.999999999958774	8.24518333837681e-11	4.12259166918841e-11
97	0.999999999961513	7.69745851801688e-11	3.84872925900844e-11
98	0.999999999904977	1.90046252294446e-10	9.50231261472228e-11
99	0.999999999871297	2.57405809342816e-10	1.28702904671408e-10
100	0.999999999740833	5.18334593187104e-10	2.59167296593552e-10
101	0.999999999394054	1.21189227739082e-09	6.0594613869541e-10
102	0.999999999525379	9.49243041686492e-10	4.74621520843246e-10
103	0.999999998986317	2.02736603236428e-09	1.01368301618214e-09
104	0.99999999751072	4.97856209707567e-09	2.48928104853783e-09
105	0.99999999398873	1.20225387934872e-08	6.01126939674358e-09
106	0.999999990215115	1.95697692571343e-08	9.78488462856713e-09
107	0.999999984402011	3.11959778579486e-08	1.55979889289743e-08
108	0.99999997651703	4.69659405075203e-08	2.34829702537601e-08
109	0.99999994493399	1.10132019573977e-07	5.50660097869887e-08
110	0.99999987270321	2.54593579443315e-07	1.27296789721657e-07
111	0.999999733376903	5.33246194055071e-07	2.66623097027535e-07
112	0.99999949416659	1.01166682213634e-06	5.0583341106817e-07
113	0.99999913734545	1.72530909959696e-06	8.6265454979848e-07
114	0.9999984910356	3.01792880118358e-06	1.50896440059179e-06
115	0.999999820312659	3.59374682333377e-07	1.79687341166689e-07
116	0.9999995613981	8.77203798736352e-07	4.38601899368176e-07
117	0.999999046184704	1.90763059101826e-06	9.5381529550913e-07
118	0.999997986618959	4.02676208274442e-06	2.01338104137221e-06
119	0.999995363978086	9.27204382839599e-06	4.63602191419800e-06
120	0.999992996734184	1.40065316323677e-05	7.00326581618383e-06
121	0.999984615439634	3.07691207314873e-05	1.53845603657437e-05
122	0.999969270114656	6.14597706886996e-05	3.07298853443498e-05
123	0.99993519829215	0.000129603415698172	6.4801707849086e-05
124	0.999868396916523	0.000263206166954876	0.000131603083477438
125	0.999999999999821	3.58009495111676e-13	1.79004747555838e-13
126	0.999999999998965	2.07084826342282e-12	1.03542413171141e-12
127	0.999999999997468	5.0630867341022e-12	2.5315433670511e-12
128	0.999999999985192	2.96165388901380e-11	1.48082694450690e-11
129	0.999999999915927	1.68145768138336e-10	8.40728840691681e-11
130	0.999999999555379	8.89242404685857e-10	4.44621202342928e-10
131	0.999999997738778	4.52244452085649e-09	2.26122226042825e-09
132	0.999999988639976	2.27200479962714e-08	1.13600239981357e-08
133	0.999999946930512	1.06138975131048e-07	5.30694875655242e-08
134	0.999999744555546	5.10888907686234e-07	2.55444453843117e-07
135	0.999999348533744	1.30293251296212e-06	6.51466256481061e-07
136	0.999998252894217	3.49421156526313e-06	1.74710578263157e-06
137	0.99999277504072	1.44499185601954e-05	7.22495928009771e-06
138	0.99997652335206	4.69532958798633e-05	2.34766479399317e-05
139	0.999893456461803	0.000213087076394792	0.000106543538197396
140	0.999559134262038	0.000881731475924102	0.000440865737962051
141	0.998559811786628	0.00288037642674358	0.00144018821337179
142	0.999959809124748	8.03817505041603e-05	4.01908752520802e-05
143	0.999630915452091	0.00073816909581703	0.000369084547908515
144	0.997451538016132	0.00509692396773644	0.00254846198386822

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	108	0.830769230769231	NOK
5% type I error level	109	0.838461538461538	NOK
10% type I error level	114	0.876923076923077	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/1075821290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/1075821290688791.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/1i3b81290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/1i3b81290688791.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/2susb1290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/2susb1290688791.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/3susb1290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/3susb1290688791.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/4susb1290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/4susb1290688791.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/534ae1290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/534ae1290688791.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/634ae1290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/634ae1290688791.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/7wd9h1290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/7wd9h1290688791.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/8wd9h1290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/8wd9h1290688791.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/975821290688791.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/25/t12906887494uzlzntelv34wdp/975821290688791.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)

library(lmtest)

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

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

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

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

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

x <- x1

if (par3 == 'First Differences'){

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

for (i in 1:n-1) {

for (j in 1:k) {

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

}

}

x <- x2

}

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

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

for (i in 1:11){

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

}

x <- cbind(x, x2)

}

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

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

for (i in 1:3){

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

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

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

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

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

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

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

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

j <- j + 1

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

}

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

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

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

}

gqarr

}

bitmap(file='test0.png')

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

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

grid()

dev.off()

bitmap(file='test1.png')

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

grid()

dev.off()

bitmap(file='test2.png')

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

grid()

dev.off()

bitmap(file='test3.png')

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

dev.off()

bitmap(file='test4.png')

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

qqline(mysum$resid)

grid()

dev.off()

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

bitmap(file='test5.png')

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

dum

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

dum1

z <- as.data.frame(dum1)

z

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

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

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

grid()

dev.off()

bitmap(file='test7.png')

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

grid()

dev.off()

bitmap(file='test8.png')

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

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

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

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

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

myeq <- colnames(x)[1]

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

for (i in 1:k){

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

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

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

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

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

}

}

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

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant1)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant5)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant10)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

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

}

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by