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WS10 Multiple Regression Celebrity

*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: Mon, 13 Dec 2010 09:30:41 +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/Dec/13/t1292232556jqilguz7qqgeh3z.htm/, Retrieved Mon, 13 Dec 2010 10:29:35 +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/Dec/13/t1292232556jqilguz7qqgeh3z.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 «

5 2 1 3 11 16 14 6 12 1 1 1 11 13 11 4 11 1 1 3 15 16 11 5 6 1 1 3 11 6 9 4 12 1 2 3 9 11 11 4 11 1 1 3 14 13 16 6 12 1 1 1 12 15 13 6 7 2 4 3 6 9 11 4 8 1 1 3 4 6 4 4 13 1 1 1 13 11 15 6 12 1 1 1 12 9 13 4 13 1 1 3 10 4 13 6 12 1 1 1 12 8 13 5 12 1 3 3 9 11 11 4 11 2 1 3 16 16 15 6 12 2 1 1 13 5 12 3 12 1 1 1 12 6 14 5 12 1 6 1 11 7 13 6 11 2 1 3 12 16 13 4 13 2 1 1 12 12 12 6 9 1 1 3 11 7 13 2 11 2 1 3 16 13 14 7 11 1 1 1 9 12 13 5 11 2 1 3 8 10 15 2 9 1 1 1 11 12 12 4 11 2 1 4 9 8 10 4 12 2 1 3 16 15 14 6 12 1 1 3 14 15 13 6 10 2 1 3 10 10 11 5 12 1 4 3 14 13 15 6 12 2 1 1 16 16 14 6 12 1 1 3 12 10 13 4 9 2 1 3 13 14 14 6 9 1 1 3 16 16 16 6 12 1 1 3 15 13 13 6 14 2 1 1 5 4 5 2 12 2 1 3 12 7 11 4 11 1 1 1 11 15 10 5 9 1 1 2 15 5 11 3 11 2 1 3 15 14 15 7 7 1 1 1 12 11 15 5 15 1 1 1 5 8 12 3 11 1 1 3 16 14 15 8 12 1 1 3 16 12 15 8 12 2 2 1 12 12 14 5 9 2 1 3 6 15 11 6 12 2 1 3 7 8 12 3 11 2 1 3 14 16 12 5 11 2 2 3 8 9 12 4 8 1 4 3 12 13 13 5 7 2 1 1 10 8 9 5 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	9 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Celebrity[t] = + 0.500464733012455 -0.026175955077005FF[t] -0.142179883084269Geslacht[t] + 0.0812069443486666Opvoeding[t] + 0.0920112671738348Huwelijksstatus[t] + 0.169119780174780Popularity[t] + 0.095325030211679KnowingPeople[t] + 0.129653715719074Liked[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)	0.500464733012455	0.812915	0.6156	0.539145	0.269572
FF	-0.026175955077005	0.049327	-0.5307	0.596506	0.298253
Geslacht	-0.142179883084269	0.18642	-0.7627	0.446951	0.223476
Opvoeding	0.0812069443486666	0.102256	0.7941	0.428472	0.214236
Huwelijksstatus	0.0920112671738348	0.089266	1.0308	0.304457	0.152228
Popularity	0.169119780174780	0.041238	4.1011	7e-05	3.5e-05
KnowingPeople	0.095325030211679	0.032005	2.9784	0.003424	0.001712
Liked	0.129653715719074	0.050154	2.5851	0.010772	0.005386

Multiple Linear Regression - Regression Statistics
Multiple R	0.680555914549806
R-squared	0.463156352828723
Adjusted R-squared	0.435925153334528
F-TEST (value)	17.0082978874086
F-TEST (DF numerator)	7
F-TEST (DF denominator)	138
p-value	4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.04735303859423
Sum Squared Residuals	151.378877468454

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6	5.64313602270552	0.356863977294483
2	4	4.74312544811084	-0.743125448110845
3	5	5.91577814886968	-0.915778148869678
4	4	4.15762107000065	-0.157621070000648
5	4	4.47946530603427	-0.479465306034268
6	6	6.10895185665523	-0.108951856655234
7	6	5.36220272014713	0.637797279852869
8	4	3.93256968608466	0.0674303139153404
9	4	2.27316212002781	1.72683787997219
10	6	5.38315385583634	0.616846144163657
11	4	4.79025253887706	-0.790252538877061
12	6	4.13323440673977	1.86676559326023
13	5	4.69492750866538	0.305072491334618
14	4	4.56067225038293	-0.560672250382934
15	6	6.46133290883649	-0.461332908836488
16	3	4.30623859940178	-1.30623859940178
17	5	4.6339311639611	0.366068836038901
18	6	4.83651742002226	1.16348257997774
19	4	5.52554635669922	-1.52554635669922
20	6	4.77821807563175	1.22178192436825
21	2	4.69303309785761	-2.69303309785761
22	7	6.04570410248238	0.954295897517624
23	5	4.59504424406476	0.404955755935236
24	2	4.53642448616818	-2.53642448616818
25	4	4.85598199884926	-0.85598199884926
26	4	3.95863689449806	0.0413631055019396
27	6	6.21017820782873	-0.210178207828729
28	6	5.88446481484436	0.115535185155636
29	5	4.38222513871844	0.617774861281557
30	6	6.19674301890516	-0.196743018905155
31	6	6.12148070369274	-0.121480703692738
32	4	5.06960010343641	-1.06960010343641
33	6	5.68602170232373	0.313978297676273
34	6	6.78551841779384	-0.785518417793842
35	6	5.86293453459579	0.137065465404214
36	2	1.89802740760433	0.101972592395666
37	4	4.38213769827895	-0.382137698278955
38	5	4.83029774789214	0.169702252107863
39	3	4.82754345952139	-1.82754345952139
40	7	6.10156306823835	0.89843693176165
41	5	5.37108980612359	-0.371089806123594
42	3	3.30290746649183	-0.302907466491834
43	8	6.4128627314974	1.5871372685026
44	8	6.19603671599704	1.80396328400296
45	5	5.14490840649557	-0.144908406495570
46	6	4.20854712415472	1.79145287584528
47	3	3.76151754333581	-0.76151754333581
48	5	5.7341322013297	-0.734132201329705
49	4	4.13334525314794	-0.133345253147940
50	5	5.70389984742547	-0.703899847425468
51	5	3.82677297774028	1.17322702225972
52	6	5.34889270856268	0.651107291437319
53	5	5.76236627331117	-0.762366273311166
54	6	6.02490002734767	-0.0249000273476717
55	6	4.68622753475429	1.31377246524571
56	4	3.47437820385203	0.525621796147968
57	8	6.51634049213947	1.48365950786053
58	6	4.85379593691222	1.14620406308778
59	4	4.06029103922966	-0.0602910392296643
60	6	5.6879161131315	0.3120838868685
61	5	5.50361181277025	-0.503611812770247
62	5	5.63290585015621	-0.632905850156211
63	6	6.52885166062481	-0.528851660624815
64	6	6.20126153538483	-0.201261535384834
65	6	5.55163487133674	0.448365128663263
66	6	4.41997343938831	1.58002656061169
67	6	5.63124862001474	0.368751379985263
68	6	5.79378545946111	0.206214540538888
69	7	5.36107225135287	1.63892774864713
70	4	4.09046575402852	-0.0904657540285153
71	4	4.09738051687767	-0.097380516877674
72	3	5.26764163194896	-2.26764163194896
73	6	6.51719187459246	-0.517191874592462
74	5	5.98443551988447	-0.98443551988447
75	5	5.34067243989856	-0.340672439898557
76	3	3.63103585113242	-0.631035851132423
77	5	5.50979222007334	-0.509792220073337
78	4	4.67235420006202	-0.672354200062024
79	3	4.51179141399803	-1.51179141399803
80	7	6.73316650763983	0.266833492360168
81	4	3.39571699977351	0.604283000226489
82	4	5.5805539400022	-1.5805539400022
83	5	5.45870715422913	-0.458707154229126
84	6	5.1500223794752	0.849977620524801
85	2	3.41427387010287	-1.41427387010287
86	2	2.48188002633295	-0.481880026332947
87	6	4.86615163775424	1.13384836224576
88	4	3.48216997877646	0.517830021223539
89	5	4.91926748992175	0.0807325100782491
90	6	5.31260207401378	0.687397925986222
91	7	6.52296801821516	0.477031981784844
92	8	6.15504544718663	1.84495455281337
93	6	6.44325204508449	-0.443252045084487
94	6	5.23689626770072	0.763103732299276
95	3	4.52589516893009	-1.52589516893009
96	7	6.64446900350384	0.355530996496159
97	3	4.38419581518341	-1.38419581518341
98	6	5.80610333538234	0.19389666461766
99	4	4.20635533480116	-0.206355334801164
100	4	4.83766036264688	-0.837660362646881
101	6	4.6063525021962	1.39364749780380
102	6	6.36441596012699	-0.364415960126991
103	6	3.86003930967841	2.13996069032159
104	4	5.68508287943125	-1.68508287943125
105	7	5.46690654045516	1.53309345954484
106	5	5.27313600955807	-0.273136009558073
107	7	6.39000053945824	0.609999460541762
108	4	4.36343797205832	-0.363437972058323
109	6	5.58463164505355	0.41536835494645
110	6	6.21463908520302	-0.214639085203021
111	6	5.11879648588937	0.881203514110628
112	5	5.11104801913938	-0.111048019139377
113	5	5.13483785928873	-0.134837859288726
114	6	5.54622525449481	0.453774745505195
115	7	5.45554589911155	1.54445410088845
116	4	5.16880954613532	-1.16880954613532
117	4	5.26025016385977	-1.26025016385977
118	8	5.77062485586619	2.22937514413381
119	6	5.46217342518724	0.537826574812759
120	3	4.39110407426271	-1.39110407426271
121	4	4.69577398779247	-0.69577398779247
122	5	4.5653612333858	0.434638766614204
123	5	4.18994475494874	0.810055245051261
124	6	5.85040836723059	0.149591632769408
125	8	6.77100224874919	1.22899775125081
126	2	5.21793921444517	-3.21793921444517
127	4	3.96344080466328	0.0365591953367169
128	7	6.23777759589	0.762222404110003
129	5	4.1298147915773	0.870185208422704
130	6	5.54348214090635	0.456517859093653
131	6	6.25703306070132	-0.257033060701320
132	4	5.34684331908169	-1.34684331908169
133	5	5.52545891625973	-0.525458916259732
134	6	5.72121676916396	0.278783230836037
135	6	5.8229767660202	0.177023233979802
136	6	5.67595791554338	0.324042084456617
137	6	5.85111467013871	0.148885329861289
138	5	5.47707617936013	-0.477076179360131
139	5	5.00573897156748	-0.00573897156747614
140	6	6.80534861280874	-0.805348612808742
141	4	4.59597189217495	-0.595971892174952
142	6	4.25473539202846	1.74526460797154
143	3	4.1963000431308	-1.19630004313080
144	6	4.54847662796564	1.45152337203436
145	8	6.51634049213947	1.48365950786053
146	4	6.34722071196469	-2.34722071196469

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.477026702854834	0.954053405709667	0.522973297145166
12	0.407866691851486	0.815733383702971	0.592133308148514
13	0.269925232372258	0.539850464744516	0.730074767627742
14	0.166654662633863	0.333309325267726	0.833345337366137
15	0.099100651985631	0.198201303971262	0.900899348014369
16	0.104899017956924	0.209798035913849	0.895100982043076
17	0.0622434876158856	0.124486975231771	0.937756512384114
18	0.285898424735698	0.571796849471396	0.714101575264302
19	0.248105045849833	0.496210091699666	0.751894954150167
20	0.454923422330366	0.909846844660732	0.545076577669634
21	0.835615755273062	0.328768489453877	0.164384244726938
22	0.881822420403215	0.236355159193571	0.118177579596785
23	0.841421267052371	0.317157465895257	0.158578732947629
24	0.932640610100932	0.134718779798136	0.0673593898990681
25	0.923535137289182	0.152929725421637	0.0764648627108185
26	0.896729370468012	0.206541259063976	0.103270629531988
27	0.863602779194153	0.272794441611695	0.136397220805848
28	0.826461849864964	0.347076300270073	0.173538150135037
29	0.801526595940363	0.396946808119274	0.198473404059637
30	0.752988307317186	0.494023385365628	0.247011692682814
31	0.699435082907644	0.601129834184713	0.300564917092356
32	0.676960101776724	0.646079796446552	0.323039898223276
33	0.64698933115686	0.70602133768628	0.35301066884314
34	0.597312225929027	0.805375548141945	0.402687774070973
35	0.541468256368548	0.917063487262904	0.458531743631452
36	0.506701103497713	0.986597793004575	0.493298896502287
37	0.453023421635716	0.906046843271433	0.546976578364283
38	0.395397546216909	0.790795092433817	0.604602453783091
39	0.494393126830778	0.988786253661556	0.505606873169222
40	0.508023433798123	0.983953132403755	0.491976566201877
41	0.455310859606834	0.910621719213668	0.544689140393166
42	0.404009267388704	0.808018534777408	0.595990732611296
43	0.510637008913052	0.978725982173895	0.489362991086948
44	0.623217516760651	0.753564966478698	0.376782483239349
45	0.570159440420598	0.859681119158803	0.429840559579402
46	0.667530813789252	0.664938372421497	0.332469186210748
47	0.6372952186807	0.7254095626386	0.3627047813193
48	0.616403447322409	0.767193105355181	0.383596552677591
49	0.564516149210735	0.87096770157853	0.435483850789265
50	0.537414085446595	0.92517182910681	0.462585914553405
51	0.550296189614432	0.899407620771136	0.449703810385568
52	0.514031530280931	0.971936939438138	0.485968469719069
53	0.482138176450283	0.964276352900566	0.517861823549717
54	0.433116600851799	0.866233201703599	0.566883399148201
55	0.461583203804797	0.923166407609594	0.538416796195203
56	0.427591102986589	0.855182205973178	0.572408897013411
57	0.483116147234489	0.966232294468978	0.516883852765511
58	0.49865899990689	0.99731799981378	0.50134100009311
59	0.44766108531014	0.89532217062028	0.55233891468986
60	0.400952302507731	0.801904605015462	0.599047697492269
61	0.363632731298664	0.727265462597328	0.636367268701336
62	0.331840475726608	0.663680951453215	0.668159524273392
63	0.298191480259836	0.596382960519671	0.701808519740164
64	0.25770423619692	0.51540847239384	0.74229576380308
65	0.224346930465847	0.448693860931693	0.775653069534153
66	0.278029495297516	0.556058990595032	0.721970504702484
67	0.247763729576289	0.495527459152578	0.752236270423711
68	0.211583818157161	0.423167636314322	0.788416181842839
69	0.260398019426745	0.520796038853491	0.739601980573255
70	0.225878563598548	0.451757127197095	0.774121436401452
71	0.191037831059903	0.382075662119806	0.808962168940097
72	0.333783424138906	0.667566848277812	0.666216575861094
73	0.303299143699323	0.606598287398647	0.696700856300677
74	0.297009892546493	0.594019785092985	0.702990107453507
75	0.258016489662158	0.516032979324317	0.741983510337842
76	0.232643255031601	0.465286510063202	0.767356744968399
77	0.202852968896818	0.405705937793637	0.797147031103182
78	0.188166954699754	0.376333909399509	0.811833045300246
79	0.225788318671188	0.451576637342376	0.774211681328812
80	0.192520228057153	0.385040456114306	0.807479771942847
81	0.172830312136027	0.345660624272054	0.827169687863973
82	0.207144822238433	0.414289644476866	0.792855177761567
83	0.184408170995582	0.368816341991165	0.815591829004418
84	0.179228846261683	0.358457692523367	0.820771153738317
85	0.191864456159964	0.383728912319928	0.808135543840036
86	0.163850707524888	0.327701415049777	0.836149292475112
87	0.171778550083259	0.343557100166517	0.828221449916741
88	0.151138530407831	0.302277060815662	0.848861469592169
89	0.123681783149396	0.247363566298792	0.876318216850604
90	0.108358773241002	0.216717546482005	0.891641226758998
91	0.0910685407041654	0.182137081408331	0.908931459295835
92	0.146593380615687	0.293186761231375	0.853406619384313
93	0.122612825843683	0.245225651687367	0.877387174156317
94	0.112855561800831	0.225711123601662	0.887144438199169
95	0.130765430482317	0.261530860964633	0.869234569517683
96	0.110396545632808	0.220793091265617	0.889603454367192
97	0.128264499391441	0.256528998782883	0.871735500608559
98	0.102761665338059	0.205523330676119	0.89723833466194
99	0.081758794480539	0.163517588961078	0.918241205519461
100	0.075508064116437	0.151016128232874	0.924491935883563
101	0.0859245646642248	0.171849129328450	0.914075435335775
102	0.0681278767045737	0.136255753409147	0.931872123295426
103	0.117135559566266	0.234271119132532	0.882864440433734
104	0.17049671393346	0.34099342786692	0.82950328606654
105	0.243855816953983	0.487711633907967	0.756144183046017
106	0.206549351775907	0.413098703551814	0.793450648224093
107	0.183222615491473	0.366445230982947	0.816777384508527
108	0.157561894206007	0.315123788412013	0.842438105793993
109	0.130512782879766	0.261025565759532	0.869487217120234
110	0.103404054549714	0.206808109099427	0.896595945450286
111	0.114065917176548	0.228131834353095	0.885934082823452
112	0.0951753487619623	0.190350697523925	0.904824651238038
113	0.0756478485764305	0.151295697152861	0.92435215142357
114	0.061531945260391	0.123063890520782	0.938468054739609
115	0.0783537439474996	0.156707487894999	0.9216462560525
116	0.0745178684030472	0.149035736806094	0.925482131596953
117	0.0793992626669348	0.158798525333870	0.920600737333065
118	0.143401801454101	0.286803602908202	0.856598198545899
119	0.157182214457163	0.314364428914326	0.842817785542837
120	0.146674126491606	0.293348252983212	0.853325873508394
121	0.114949359639428	0.229898719278855	0.885050640360572
122	0.111848278577301	0.223696557154602	0.888151721422699
123	0.0835713559299472	0.167142711859894	0.916428644070053
124	0.0599762770298752	0.119952554059750	0.940023722970125
125	0.100317181521463	0.200634363042925	0.899682818478537
126	0.339917985877327	0.679835971754655	0.660082014122673
127	0.264336149961124	0.528672299922248	0.735663850038876
128	0.263035685593573	0.526071371187146	0.736964314406427
129	0.197215822933236	0.394431645866473	0.802784177066764
130	0.141066261385966	0.282132522771933	0.858933738614034
131	0.0927751678938345	0.185550335787669	0.907224832106166
132	0.180004854099909	0.360009708199818	0.819995145900091
133	0.152508780529239	0.305017561058477	0.847491219470761
134	0.0893802370276545	0.178760474055309	0.910619762972345
135	0.0442177717652122	0.0884355435304244	0.955782228234788

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.008	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/10jhs61292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/10jhs61292232630.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/1cyvc1292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/1cyvc1292232630.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/2n7ux1292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/2n7ux1292232630.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/3n7ux1292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/3n7ux1292232630.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/4n7ux1292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/4n7ux1292232630.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/5xzci1292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/5xzci1292232630.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/6xzci1292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/6xzci1292232630.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/78qt31292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/78qt31292232630.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/88qt31292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/88qt31292232630.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/9jhs61292232630.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292232556jqilguz7qqgeh3z/9jhs61292232630.ps (open in new window)

Parameters (Session):

par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 8 ; 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

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