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

Title produced by software: Multiple Regression

Date of computation: Mon, 22 Dec 2008 06:18:22 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7.htm/, Retrieved Mon, 22 Dec 2008 14:20:02 +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/2008/Dec/22/t1229951992jivrkdjv5rqa1e7.htm/},
    year = {2008},
}
@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 = {2008},
    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 «

9.2 0 9.1 0 9.1 0 9.1 0 9.1 0 9.2 0 9.3 0 9.3 0 9.3 0 9.3 0 9.3 0 9.4 0 9.4 0 9.4 0 9.5 0 9.5 0 9.4 0 9.4 0 9.3 0 9.4 0 9.4 0 9.2 0 9.1 0 9.1 0 9.1 0 9.1 0 9 0 8.9 0 8.8 0 8.7 0 8.5 0 8.3 0 8.1 0 7.8 0 7.6 0 7.5 0 7.4 0 7.3 0 7.1 0 6.9 0 6.8 0 6.8 0 6.8 0 6.9 0 6.7 0 6.6 0 6.5 0 6.4 0 6.3 0 6.3 0 6.3 0 6.5 0 6.6 0 6.5 0 6.4 0 6.5 0 6.7 0 7.1 0 7.1 0 7.2 1 7.2 1 7.3 1 7.3 1 7.3 1 7.4 1 7.4 1 7.6 1 7.6 1 7.6 1 7.7 1 7.8 1 7.9 1 8.1 1 8.1 1 8.1 1 8.2 1 8.2 1 8.2 1 8.2 1 8.2 1 8.2 1 8.3 1 8.3 1 8.4 1 8.4 1 8.4 1 8.3 1 8 1 8 1 8.2 1 8.6 1 8.7 1 8.7 1 8.5 1 8.4 1 8.4 1 8.4 1 8.5 1 8.5 1 8.5 1 8.5 1 8.5 1 8.4 1 8.4 1 8.4 1 8.5 1 8.5 1 8.6 1 8.6 1 8.6 1 8.5 1 8.4 1 8.4 1 8.3 1 8.2 1 8.1 1 8.2 1 8.1 1 8 1 7.9 1 7.8 1 7.7 1 7.7 1 7.9 1 7.8 1 7.6 1 7.4 1 7.3 1 7.1 1 7.1 1 7 1 7 1

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	4 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Werkloosheid[t] = + 8.76672357226612 + 1.36175819286055SabenaFailliet[t] -0.0217721416744073t + 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)	8.76672357226612	0.145792	60.1317	0	0
SabenaFailliet	1.36175819286055	0.267915	5.0828	1e-06	1e-06
t	-0.0217721416744073	0.003496	-6.2282	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.482262997343590
R-squared	0.232577598606824
Adjusted R-squared	0.220679576879798
F-TEST (value)	19.5475856358987
F-TEST (DF numerator)	2
F-TEST (DF denominator)	129
p-value	3.84346970916383e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.777927240618144
Sum Squared Residuals	78.067032128753

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9.2	8.7449514305917	0.455048569408303
2	9.1	8.7231792889173	0.376820711082695
3	9.1	8.7014071472429	0.3985928527571
4	9.1	8.67963500556849	0.420364994431510
5	9.1	8.65786286389408	0.442137136105918
6	9.2	8.63609072221968	0.563909277780325
7	9.3	8.61431858054527	0.685681419454733
8	9.3	8.59254643887086	0.707453561129141
9	9.3	8.57077429719645	0.729225702803548
10	9.3	8.54900215552204	0.750997844477955
11	9.3	8.52723001384764	0.772769986152363
12	9.4	8.50545787217323	0.89454212782677
13	9.4	8.48368573049882	0.916314269501177
14	9.4	8.46191358882442	0.938086411175584
15	9.5	8.44014144715001	1.05985855284999
16	9.5	8.4183693054756	1.08163069452440
17	9.4	8.3965971638012	1.00340283619881
18	9.4	8.37482502212679	1.02517497787321
19	9.3	8.35305288045238	0.946947119547621
20	9.4	8.33128073877797	1.06871926122203
21	9.4	8.30950859710356	1.09049140289644
22	9.2	8.28773645542916	0.912263544570842
23	9.1	8.26596431375475	0.83403568624525
24	9.1	8.24419217208034	0.855807827919657
25	9.1	8.22242003040594	0.877579969594064
26	9.1	8.20064788873153	0.899352111268472
27	9	8.17887574705712	0.82112425294288
28	8.9	8.15710360538271	0.742896394617287
29	8.8	8.1353314637083	0.664668536291695
30	8.7	8.1135593220339	0.586440677966101
31	8.5	8.09178718035949	0.408212819640509
32	8.3	8.07001503868508	0.229984961314917
33	8.1	8.04824289701068	0.051757102989323
34	7.8	8.02647075533627	-0.226470755336269
35	7.6	8.00469861366186	-0.404698613661862
36	7.5	7.98292647198745	-0.482926471987455
37	7.4	7.96115433031305	-0.561154330313047
38	7.3	7.93938218863864	-0.63938218863864
39	7.1	7.91761004696423	-0.817610046964233
40	6.9	7.89583790528983	-0.995837905289825
41	6.8	7.87406576361542	-1.07406576361542
42	6.8	7.852293621941	-1.05229362194101
43	6.8	7.8305214802666	-1.03052148026660
44	6.9	7.8087493385922	-0.908749338592196
45	6.7	7.78697719691779	-1.08697719691779
46	6.6	7.76520505524338	-1.16520505524338
47	6.5	7.74343291356897	-1.24343291356897
48	6.4	7.72166077189457	-1.32166077189457
49	6.3	7.69988863022016	-1.39988863022016
50	6.3	7.67811648854575	-1.37811648854575
51	6.3	7.65634434687134	-1.35634434687134
52	6.5	7.63457220519694	-1.13457220519694
53	6.6	7.61280006352253	-1.01280006352253
54	6.5	7.59102792184812	-1.09102792184812
55	6.4	7.56925578017372	-1.16925578017371
56	6.5	7.5474836384993	-1.04748363849931
57	6.7	7.5257114968249	-0.8257114968249
58	7.1	7.50393935515049	-0.403939355150494
59	7.1	7.48216721347609	-0.382167213476086
60	7.2	8.82215326466223	-1.62215326466223
61	7.2	8.80038112298782	-1.60038112298782
62	7.3	8.77860898131341	-1.47860898131341
63	7.3	8.756836839639	-1.45683683963900
64	7.3	8.7350646979646	-1.43506469796460
65	7.4	8.71329255629019	-1.31329255629019
66	7.4	8.69152041461578	-1.29152041461578
67	7.6	8.66974827294137	-1.06974827294137
68	7.6	8.64797613126697	-1.04797613126697
69	7.6	8.62620398959256	-1.02620398959256
70	7.7	8.60443184791815	-0.904431847918152
71	7.8	8.58265970624375	-0.782659706243745
72	7.9	8.56088756456934	-0.660887564569337
73	8.1	8.53911542289493	-0.439115422894931
74	8.1	8.51734328122052	-0.417343281220523
75	8.1	8.49557113954612	-0.395571139546116
76	8.2	8.47379899787171	-0.273798997871709
77	8.2	8.4520268561973	-0.252026856197302
78	8.2	8.43025471452289	-0.230254714522894
79	8.2	8.40848257284849	-0.208482572848487
80	8.2	8.38671043117408	-0.186710431174080
81	8.2	8.36493828949967	-0.164938289499672
82	8.3	8.34316614782526	-0.0431661478252636
83	8.3	8.32139400615086	-0.0213940061508563
84	8.4	8.29962186447645	0.100378135523551
85	8.4	8.27784972280204	0.122150277197958
86	8.4	8.25607758112763	0.143922418872365
87	8.3	8.23430543945323	0.065694560546773
88	8	8.21253329777882	-0.212533297778820
89	8	8.19076115610441	-0.190761156104413
90	8.2	8.16898901443	0.0310109855699937
91	8.6	8.1472168727556	0.452783127244401
92	8.7	8.12544473108119	0.574555268918808
93	8.7	8.10367258940678	0.596327410593216
94	8.5	8.08190044773238	0.418099552267624
95	8.4	8.06012830605797	0.339871693942031
96	8.4	8.03835616438356	0.361643835616439
97	8.4	8.01658402270915	0.383415977290846
98	8.5	7.99481188103475	0.505188118965253
99	8.5	7.97303973936034	0.526960260639660
100	8.5	7.95126759768593	0.548732402314068
101	8.5	7.92949545601152	0.570504543988475
102	8.5	7.90772331433712	0.592276685662882
103	8.4	7.88595117266271	0.51404882733729
104	8.4	7.8641790309883	0.535820969011698
105	8.4	7.8424068893139	0.557593110686105
106	8.5	7.82063474763949	0.679365252360512
107	8.5	7.79886260596508	0.701137394034919
108	8.6	7.77709046429067	0.822909535709326
109	8.6	7.75531832261627	0.844681677383733
110	8.6	7.73354618094186	0.86645381905814
111	8.5	7.71177403926745	0.788225960732548
112	8.4	7.69000189759304	0.709998102406956
113	8.4	7.66822975591864	0.731770244081363
114	8.3	7.64645761424423	0.653542385755771
115	8.2	7.62468547256982	0.575314527430177
116	8.1	7.60291333089542	0.497086669104585
117	8.2	7.58114118922101	0.618858810778992
118	8.1	7.5593690475466	0.5406309524534
119	8	7.53759690587219	0.462403094127807
120	7.9	7.51582476419779	0.384175235802215
121	7.8	7.49405262252338	0.305947377476622
122	7.7	7.47228048084897	0.227719519151029
123	7.7	7.45050833917456	0.249491660825437
124	7.9	7.42873619750016	0.471263802499844
125	7.8	7.40696405582575	0.393035944174251
126	7.6	7.38519191415134	0.214808085848658
127	7.4	7.36341977247693	0.0365802275230664
128	7.3	7.34164763080253	-0.0416476308025268
129	7.1	7.31987548912812	-0.219875489128120
130	7.1	7.29810334745371	-0.198103347453712
131	7	7.2763312057793	-0.276331205779305
132	7	7.2545590641049	-0.254559064104897

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.000832760326742889	0.00166552065348578	0.999167239673257
7	0.000256800626748302	0.000513601253496603	0.999743199373252
8	3.0317832725678e-05	6.0635665451356e-05	0.999969682167274
9	2.77696860098558e-06	5.55393720197115e-06	0.999997223031399
10	2.39161023844817e-07	4.78322047689634e-07	0.999999760838976
11	2.13117527970927e-08	4.26235055941855e-08	0.999999978688247
12	2.26160899285285e-09	4.52321798570570e-09	0.99999999773839
13	1.85892393311179e-10	3.71784786622358e-10	0.999999999814108
14	1.52069795747574e-11	3.04139591495147e-11	0.999999999984793
15	2.05626502774265e-12	4.1125300554853e-12	0.999999999997944
16	1.88327959158439e-13	3.76655918316877e-13	0.999999999999812
17	7.57735739731868e-14	1.51547147946374e-13	0.999999999999924
18	3.01115154543953e-14	6.02230309087905e-14	0.99999999999997
19	1.52813053842851e-13	3.05626107685702e-13	0.999999999999847
20	4.08643277717611e-14	8.17286555435222e-14	0.99999999999996
21	1.24893871323192e-14	2.49787742646384e-14	0.999999999999988
22	2.46835500849802e-13	4.93671001699603e-13	0.999999999999753
23	5.27615232312097e-12	1.05523046462419e-11	0.999999999994724
24	2.00181914365171e-11	4.00363828730342e-11	0.999999999979982
25	3.94135361905903e-11	7.88270723811806e-11	0.999999999960586
26	5.94051493375531e-11	1.18810298675106e-10	0.999999999940595
27	1.86880673754482e-10	3.73761347508964e-10	0.99999999981312
28	1.04527244740078e-09	2.09054489480156e-09	0.999999998954728
29	8.498646593197e-09	1.6997293186394e-08	0.999999991501353
30	8.47719021953042e-08	1.69543804390608e-07	0.999999915228098
31	1.85620395463694e-06	3.71240790927389e-06	0.999998143796045
32	4.69630787876911e-05	9.39261575753822e-05	0.999953036921212
33	0.000856249923919575	0.00171249984783915	0.99914375007608
34	0.0125620893123275	0.0251241786246549	0.987437910687673
35	0.073992326314496	0.147984652628992	0.926007673685504
36	0.197730239509989	0.395460479019978	0.802269760490011
37	0.350002713708179	0.700005427416357	0.649997286291821
38	0.492307075770955	0.98461415154191	0.507692924229045
39	0.629160767029817	0.741678465940366	0.370839232970183
40	0.744663190157356	0.510673619685288	0.255336809842644
41	0.815984530365799	0.368030939268402	0.184015469634201
42	0.847284756576613	0.305430486846774	0.152715243423387
43	0.857752110925196	0.284495778149608	0.142247889074804
44	0.850599501474376	0.298800997051248	0.149400498525624
45	0.84633526239728	0.307329475205441	0.153664737602720
46	0.839472401222898	0.321055197554205	0.160527598777102
47	0.830984091802547	0.338031816394905	0.169015908197453
48	0.822246358713978	0.355507282572044	0.177753641286022
49	0.815277791613093	0.369444416773814	0.184722208386907
50	0.800514853086083	0.398970293827835	0.199485146913917
51	0.780267749321657	0.439464501356686	0.219732250678343
52	0.742721065815084	0.514557868369833	0.257278934184916
53	0.702200189856675	0.59559962028665	0.297799810143325
54	0.661885703452471	0.676228593095058	0.338114296547529
55	0.628605982342937	0.742788035314126	0.371394017657063
56	0.600079998682862	0.799840002634276	0.399920001317138
57	0.586036140002672	0.827927719994656	0.413963859997328
58	0.626569715843228	0.746860568313545	0.373430284156772
59	0.66218694116392	0.675626117672161	0.337813058836080
60	0.679064419993911	0.641871160012177	0.320935580006089
61	0.706305450189084	0.587389099621832	0.293694549810916
62	0.72984240710705	0.540315185785901	0.270157592892950
63	0.763051018128902	0.473897963742195	0.236948981871098
64	0.80596418281996	0.38807163436008	0.19403581718004
65	0.844312511302947	0.311374977394105	0.155687488697053
66	0.88705813011025	0.225883739779498	0.112941869889749
67	0.915503037064863	0.168993925870274	0.0844969629351368
68	0.942416701852802	0.115166596294395	0.0575832981471975
69	0.96576979069404	0.0684604186119199	0.0342302093059599
70	0.980960754116343	0.0380784917673149	0.0190392458836574
71	0.989991828320224	0.0200163433595515	0.0100081716797757
72	0.994940478720763	0.0101190425584746	0.00505952127923729
73	0.997318011801848	0.00536397639630444	0.00268198819815222
74	0.998600303297267	0.00279939340546628	0.00139969670273314
75	0.999292268808692	0.00141546238261637	0.000707731191308187
76	0.999628278335257	0.000743443329486889	0.000371721664743445
77	0.999804595837766	0.000390808324467548	0.000195404162233774
78	0.999898360819444	0.000203278361111934	0.000101639180555967
79	0.999948317012694	0.000103365974611717	5.16829873058585e-05
80	0.999974652707431	5.06945851381023e-05	2.53472925690512e-05
81	0.999988198481766	2.36030364677302e-05	1.18015182338651e-05
82	0.999993803290582	1.2393418836392e-05	6.196709418196e-06
83	0.999996756411583	6.48717683330002e-06	3.24358841665001e-06
84	0.999998028277725	3.94344454896978e-06	1.97172227448489e-06
85	0.999998746109122	2.50778175657188e-06	1.25389087828594e-06
86	0.999999169765073	1.66046985398103e-06	8.30234926990516e-07
87	0.99999953985688	9.20286240459554e-07	4.60143120229777e-07
88	0.999999940380762	1.19238476638176e-07	5.96192383190878e-08
89	0.999999996952792	6.0944159592419e-09	3.04720797962095e-09
90	0.99999999972222	5.55561161151066e-10	2.77780580575533e-10
91	0.999999999816743	3.66514092565076e-10	1.83257046282538e-10
92	0.999999999827314	3.45372590938391e-10	1.72686295469195e-10
93	0.99999999981038	3.79238919278069e-10	1.89619459639035e-10
94	0.999999999857013	2.85973054334954e-10	1.42986527167477e-10
95	0.999999999934075	1.31849606555294e-10	6.59248032776472e-11
96	0.999999999972729	5.45427742535976e-11	2.72713871267988e-11
97	0.99999999999031	1.93794030067456e-11	9.6897015033728e-12
98	0.9999999999935	1.29993616604464e-11	6.49968083022318e-12
99	0.999999999995482	9.03624919794547e-12	4.51812459897274e-12
100	0.999999999996724	6.5527347112379e-12	3.27636735561895e-12
101	0.999999999997495	5.00977681897016e-12	2.50488840948508e-12
102	0.99999999999795	4.10182113185773e-12	2.05091056592887e-12
103	0.999999999999407	1.18643618807864e-12	5.93218094039322e-13
104	0.999999999999885	2.30382232842056e-13	1.15191116421028e-13
105	0.999999999999989	2.28232027145019e-14	1.14116013572510e-14
106	0.999999999999995	9.0564647095737e-15	4.52823235478685e-15
107	0.999999999999998	3.83648551667369e-15	1.91824275833685e-15
108	0.999999999999994	1.13393936899691e-14	5.66969684498457e-15
109	0.999999999999973	5.38863143946469e-14	2.69431571973235e-14
110	0.999999999999837	3.26872910639725e-13	1.63436455319863e-13
111	0.999999999999055	1.88905227790934e-12	9.4452613895467e-13
112	0.999999999995417	9.16641854840985e-12	4.58320927420493e-12
113	0.999999999971325	5.73495398482354e-11	2.86747699241177e-11
114	0.999999999838121	3.23758614541506e-10	1.61879307270753e-10
115	0.99999999927425	1.45149886592726e-09	7.25749432963632e-10
116	0.99999999795203	4.09594220726671e-09	2.04797110363336e-09
117	0.999999986455148	2.70897036738809e-08	1.35448518369405e-08
118	0.999999914586237	1.7082752485275e-07	8.5413762426375e-08
119	0.99999951240687	9.75186259400643e-07	4.87593129700321e-07
120	0.999997681369786	4.63726042742004e-06	2.31863021371002e-06
121	0.999992225917161	1.55481656773885e-05	7.77408283869425e-06
122	0.999989182320145	2.16353597095569e-05	1.08176798547784e-05
123	0.999987540374698	2.49192506034947e-05	1.24596253017474e-05
124	0.999915378766973	0.000169242466054790	8.46212330273948e-05
125	0.999748610304228	0.000502779391544305	0.000251389695772152
126	0.99904349862294	0.00191300275412044	0.000956501377060222

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	82	0.677685950413223	NOK
5% type I error level	86	0.710743801652893	NOK
10% type I error level	87	0.71900826446281	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/101vui1229951896.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/101vui1229951896.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/1xesm1229951896.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/2jasb1229951896.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/2jasb1229951896.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/3bj6p1229951896.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/3bj6p1229951896.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/43i181229951896.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/43i181229951896.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/5mw6l1229951896.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/5mw6l1229951896.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/6u6mt1229951896.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/6u6mt1229951896.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/7dmro1229951896.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/7dmro1229951896.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/8v91x1229951896.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/8v91x1229951896.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/9r4js1229951896.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229951992jivrkdjv5rqa1e7/9r4js1229951896.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly 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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