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*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: Sat, 20 Nov 2010 13:21:24 +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/20/t1290259192e0z586baco3v4et.htm/, Retrieved Sat, 20 Nov 2010 14:19:54 +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/20/t1290259192e0z586baco3v4et.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 «

38 23 10 11 35 37 12 36 15 10 11 35 37 12 23 25 10 11 35 37 12 30 18 10 11 35 37 12 26 21 10 11 35 37 12 26 19 10 11 35 37 12 30 15 13 12 38 34 12 27 22 10 11 35 37 12 34 19 10 11 35 37 14 28 20 13 9 34 32 12 36 26 10 11 35 37 12 42 26 10 11 35 37 12 31 21 10 11 35 37 14 26 19 10 11 35 37 12 16 19 13 12 38 34 12 23 19 10 11 35 37 14 45 28 10 11 35 37 12 30 27 10 11 35 37 15 45 18 10 11 35 37 12 30 19 10 11 35 37 15 24 24 10 11 35 37 12 29 21 13 12 38 34 12 30 22 13 9 34 32 12 31 25 10 11 35 37 14 34 15 10 11 35 37 14 41 34 10 11 35 37 12 37 23 10 11 35 37 12 33 19 10 11 35 37 12 48 15 10 11 35 37 14 44 15 10 11 35 37 15 29 17 10 11 35 37 14 44 30 13 9 34 32 12 43 28 10 11 35 37 14 31 23 10 11 35 37 14 28 23 10 11 35 37 12 26 21 10 11 35 37 14 30 18 10 11 35 37 12 27 19 15 11 33 36 12 34 24 10 11 35 37 12 47 15 10 11 35 37 12 37 24 13 16 34 36 12 27 20 10 11 35 37 12 30 20 10 11 35 37 12 36 44 10 11 35 37 14 39 20 10 11 35 37 12 32 20 10 11 35 37 12 25 20 10 11 35 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	8 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

CM+D[t] = + 44.9403520933643 + 0.355766125652141`PE+PC`[t] -0.231653308417249happiness[t] + 0.263610054527117depression[t] -0.466508840964572connected[t] -0.147481738970619separated[t] + 0.0813939733229003populariteit[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)	44.9403520933643	50.087201	0.8972	0.371401	0.185701
`PE+PC`	0.355766125652141	0.11166	3.1862	0.001841	0.000921
happiness	-0.231653308417249	1.290405	-0.1795	0.857835	0.428917
depression	0.263610054527117	0.587324	0.4488	0.654369	0.327184
connected	-0.466508840964572	0.666857	-0.6996	0.485565	0.242783
separated	-0.147481738970619	1.023207	-0.1441	0.885636	0.442818
populariteit	0.0813939733229003	0.590047	0.1379	0.890517	0.445259

Multiple Linear Regression - Regression Statistics
Multiple R	0.293830789401748
R-squared	0.0863365328004547
Adjusted R-squared	0.0402694672273682
F-TEST (value)	1.87414873785438
F-TEST (DF numerator)	6
F-TEST (DF denominator)	119
p-value	0.0907825194364746
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.9205070293393
Sum Squared Residuals	5699.31668763302

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	38	32.8982444031912	5.10175559680876
2	36	30.0521153979741	5.94788460202587
3	23	33.6097766544955	-10.6097766544955
4	30	31.1194137749306	-1.11941377493055
5	26	32.186712151887	-6.18671215188697
6	26	31.4751799005827	-5.47517990058269
7	30	28.6636842212676	1.33631577873236
8	27	32.5424782775391	-5.54247827753911
9	34	31.6379678472285	2.36203215277151
10	28	31.8126835277465	-3.81268352774652
11	36	33.9655427801477	2.03445721985232
12	42	33.9655427801477	8.03445721985232
13	31	32.3495000985328	-1.34950009853277
14	26	31.4751799005827	-5.47517990058269
15	16	30.0867487238762	-14.0867487238762
16	23	31.6379678472285	-8.6379678472285
17	45	34.677075031452	10.322924968548
18	30	34.5654908257685	-4.56549082576852
19	45	31.1194137749305	13.8805862250695
20	30	31.7193618205514	-1.71936182055139
21	24	33.2540105288434	-9.2540105288434
22	29	30.7982809751805	-1.79828097518049
23	30	32.5242157790508	-2.5242157790508
24	31	33.7725646011413	-2.77256460114134
25	34	30.2149033446199	3.78509665538007
26	41	36.8116717853648	4.18832821463519
27	37	32.8982444031913	4.10175559680874
28	33	31.4751799005827	1.52482009941731
29	48	30.2149033446199	17.7850966553801
30	44	30.2962973179428	13.7037026820572
31	29	30.9264355959242	-1.92643559592421
32	44	35.3703447842679	8.62965521573207
33	43	34.8398629780978	8.16013702190224
34	31	33.0610323498371	-2.06103234983706
35	28	32.8982444031913	-4.89824440319126
36	26	32.3495000985328	-6.34950009853277
37	30	31.1194137749305	-1.11941377493055
38	27	31.3974127793962	-4.39741277939621
39	34	33.2540105288434	0.745989471156603
40	47	30.0521153979741	16.9478846020259
41	37	34.4910914561624	2.50890854383757
42	27	31.8309460262348	-4.83094602623483
43	30	31.8309460262348	-1.83094602623483
44	36	40.532120988532	-4.53212098853202
45	39	31.8309460262348	7.16905397376517
46	32	31.8309460262348	0.169053973765168
47	25	31.8309460262348	-6.83094602623483
48	19	28.6290508953656	-9.62905089536556
49	29	32.186712151887	-3.18671215188697
50	26	32.1684496533987	-6.16844965339866
51	31	30.0867487238762	0.913251276123796
52	31	32.186712151887	-1.18671215188697
53	31	31.0078295692471	-0.00782956924710881
54	39	31.4751799005827	7.5248200994173
55	28	32.186712151887	-4.18671215188697
56	22	30.4078815236263	-8.40788152362627
57	31	31.4751799005827	-0.475179900582691
58	36	31.6379678472285	4.36203215277151
59	28	30.4078815236263	-2.40788152362627
60	39	33.2540105288434	5.7459894711566
61	35	32.186712151887	2.81328784811303
62	33	31.8309460262348	1.16905397376517
63	27	31.4751799005827	-4.47517990058269
64	33	32.8982444031913	0.101755596808744
65	31	31.1194137749305	-0.119413774930549
66	39	31.6379678472285	7.36203215277151
67	37	33.0610323498371	3.93896765016294
68	24	31.7193618205514	-7.71936182055139
69	28	32.5771116034412	-4.57711160344119
70	37	27.9521519699634	9.04784803003665
71	32	33.0610323498371	-1.06103234983706
72	31	29.0194503469198	1.98054965308022
73	29	29.3752164725719	-0.37521647257192
74	40	35.3886072827562	4.61139271724376
75	40	32.7052662241849	7.29473377581509
76	15	29.696349272322	-14.696349272322
77	27	29.6780867738337	-2.67808677383367
78	32	32.1684496533987	-0.168449653398659
79	28	32.186712151887	-4.18671215188697
80	41	36.6186936063585	4.38130639364153
81	47	32.8982444031913	14.1017555968087
82	42	35.3886072827562	6.61139271724376
83	32	33.3651738076091	-1.36517380760909
84	33	33.8539585744642	-0.85395857446424
85	29	35.0328411571041	-6.0328411571041
86	37	32.3495000985328	4.65049990146723
87	39	30.652063443595	8.34793655640503
88	29	30.7636476492784	-1.76364764927841
89	33	32.8982444031913	0.101755596808744
90	31	31.1011512764422	-0.101151276442236
91	21	31.7193618205514	-10.7193618205514
92	36	34.8398629780978	1.16013702190224
93	32	35.1956291037499	-3.1956291037499
94	15	31.4751799005827	-16.4751799005827
95	25	33.5915141560072	-8.59151415600723
96	28	30.0521153979741	-2.05211539797413
97	39	33.2540105288434	5.7459894711566
98	31	28.9665545225294	2.03344547747061
99	40	28.6290508953656	11.3709491046344
100	25	31.4751799005827	-6.4751799005827
101	36	33.7725646011413	2.22743539885866
102	23	29.1476049676635	-6.1476049676635
103	39	30.0521153979741	8.94788460202588
104	31	33.7725646011413	-2.77256460114134
105	23	29.696349272322	-6.69634927232199
106	31	31.6379678472285	-0.637967847228491
107	28	32.8799819047029	-4.87998190470294
108	47	31.4569174020944	15.5430825979056
109	25	32.0751279462035	-7.07512794620353
110	26	30.389619025138	-4.38961902513795
111	24	32.4170369123601	-8.41703691236012
112	30	32.7866601975078	-2.78666019750782
113	25	33.423793079206	-8.423793079206
114	44	31.8397666166868	12.1602333833132
115	38	40.6135149618549	-2.61351496185492
116	36	28.9848170210177	7.0151829789823
117	34	33.2886438547455	0.711356145254524
118	45	32.0007285765974	12.9992714234026
119	29	31.2822017215763	-2.28220172157635
120	25	32.186712151887	-7.18671215188697
121	30	33.2540105288434	-3.2540105288434
122	27	32.1565219195264	-5.15652191952643
123	44	33.4167984754892	10.5832015245108
124	31	36.4559056597127	-5.45590565971267
125	35	33.6097766544955	1.39022334550446
126	47	37.1674379110169	9.83256208898305

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.630865749551737	0.738268500896526	0.369134250448263
11	0.649010379098078	0.701979241803843	0.350989620901922
12	0.750322233252325	0.49935553349535	0.249677766747675
13	0.653013055425938	0.693973889148124	0.346986944574062
14	0.581752019758591	0.836495960482818	0.418247980241409
15	0.70376917999455	0.592461640010899	0.29623082000545
16	0.709546451005659	0.580907097988682	0.290453548994341
17	0.783843172668688	0.432313654662624	0.216156827331312
18	0.721071345835328	0.557857308329344	0.278928654164672
19	0.864294800005245	0.27141039998951	0.135705199994755
20	0.8184980555564	0.3630038888872	0.1815019444436
21	0.846794451639475	0.306411096721051	0.153205548360525
22	0.811730484202915	0.37653903159417	0.188269515797085
23	0.758533954076168	0.482932091847665	0.241466045923832
24	0.698540050209592	0.602919899580817	0.301459949790409
25	0.659486299563134	0.681027400873732	0.340513700436866
26	0.623980497408082	0.752039005183835	0.376019502591918
27	0.57349695456031	0.853006090879381	0.42650304543969
28	0.505386732636266	0.989226534727468	0.494613267363734
29	0.803270614967656	0.393458770064689	0.196729385032344
30	0.871164357571934	0.257671284856131	0.128835642428066
31	0.845839392132	0.308321215735999	0.154160607867999
32	0.882452193598322	0.235095612803355	0.117547806401678
33	0.885383305338714	0.229233389322572	0.114616694661286
34	0.859450731343907	0.281098537312185	0.140549268656093
35	0.841837996933999	0.316324006132002	0.158162003066001
36	0.840892889810558	0.318214220378884	0.159107110189442
37	0.80367566177942	0.39264867644116	0.19632433822058
38	0.777328479588577	0.445343040822846	0.222671520411423
39	0.731229564466456	0.537540871067088	0.268770435533544
40	0.883536439378766	0.232927121242468	0.116463560621234
41	0.852832705587927	0.294334588824146	0.147167294412073
42	0.839523369799656	0.320953260400687	0.160476630200344
43	0.807080319929575	0.385839360140849	0.192919680070425
44	0.77530233543019	0.449395329139619	0.22469766456981
45	0.770421229365045	0.459157541269909	0.229578770634955
46	0.726650660836609	0.546698678326782	0.273349339163391
47	0.730627074503713	0.538745850992574	0.269372925496287
48	0.785589769837111	0.428820460325778	0.214410230162889
49	0.752291680214057	0.495416639571886	0.247708319785943
50	0.744008219149793	0.511983561700415	0.255991780850207
51	0.712494131547876	0.575011736904247	0.287505868452124
52	0.665570071871698	0.668859856256605	0.334429928128302
53	0.616753460978762	0.766493078042477	0.383246539021238
54	0.621744007032523	0.756511985934954	0.378255992967477
55	0.58836529337214	0.823269413255721	0.411634706627861
56	0.613316898291463	0.773366203417074	0.386683101708537
57	0.560914805623974	0.878170388752052	0.439085194376026
58	0.526234761911479	0.947530476177043	0.473765238088521
59	0.479201733192756	0.958403466385512	0.520798266807244
60	0.462063204436375	0.92412640887275	0.537936795563625
61	0.417459280097373	0.834918560194747	0.582540719902627
62	0.367186894486898	0.734373788973796	0.632813105513102
63	0.337477636503059	0.674955273006117	0.662522363496941
64	0.28981459180458	0.57962918360916	0.71018540819542
65	0.245579540271585	0.491159080543169	0.754420459728415
66	0.247796102359473	0.495592204718945	0.752203897640527
67	0.219347387632947	0.438694775265895	0.780652612367053
68	0.231603041927502	0.463206083855005	0.768396958072498
69	0.218804508561655	0.43760901712331	0.781195491438345
70	0.243049264548327	0.486098529096655	0.756950735451673
71	0.203750044555963	0.407500089111925	0.796249955444037
72	0.170481750467042	0.340963500934084	0.829518249532958
73	0.142862381121044	0.285724762242089	0.857137618878956
74	0.126948569636984	0.253897139273967	0.873051430363016
75	0.129764043673037	0.259528087346074	0.870235956326963
76	0.247825725079296	0.495651450158592	0.752174274920704
77	0.21259121143932	0.425182422878641	0.78740878856068
78	0.175616748802775	0.35123349760555	0.824383251197225
79	0.153947304267063	0.307894608534126	0.846052695732937
80	0.136330664985008	0.272661329970016	0.863669335014992
81	0.244562834040582	0.489125668081165	0.755437165959418
82	0.244879299918458	0.489758599836916	0.755120700081542
83	0.214437777637476	0.428875555274951	0.785562222362524
84	0.177196016969967	0.354392033939934	0.822803983030033
85	0.162133695054087	0.324267390108174	0.837866304945913
86	0.146103503659681	0.292207007319361	0.85389649634032
87	0.172339487834737	0.344678975669474	0.827660512165263
88	0.13894341617379	0.277886832347581	0.86105658382621
89	0.108830924015225	0.21766184803045	0.891169075984775
90	0.0843645959014932	0.168729191802986	0.915635404098507
91	0.103671612403562	0.207343224807125	0.896328387596438
92	0.081738151782618	0.163476303565236	0.918261848217382
93	0.0631177282464942	0.126235456492988	0.936882271753506
94	0.187661271320266	0.375322542640532	0.812338728679734
95	0.232131546251456	0.464263092502912	0.767868453748544
96	0.192483099420433	0.384966198840866	0.807516900579567
97	0.171231080416248	0.342462160832496	0.828768919583752
98	0.137792451850565	0.27558490370113	0.862207548149435
99	0.192497068147115	0.384994136294229	0.807502931852885
100	0.181547026851871	0.363094053703741	0.818452973148129
101	0.147936242211387	0.295872484422774	0.852063757788613
102	0.127134886998847	0.254269773997693	0.872865113001153
103	0.146482904659686	0.292965809319372	0.853517095340314
104	0.109320868807463	0.218641737614925	0.890679131192537
105	0.0995427907576756	0.199085581515351	0.900457209242324
106	0.0696144287645245	0.139228857529049	0.930385571235475
107	0.0899089805646203	0.179817961129241	0.91009101943538
108	0.13261475008314	0.26522950016628	0.86738524991686
109	0.110443103993595	0.220886207987191	0.889556896006405
110	0.179826018846332	0.359652037692664	0.820173981153668
111	0.126258424343333	0.252516848686666	0.873741575656667
112	0.0831391952468005	0.166278390493601	0.9168608047532
113	0.233852160070004	0.467704320140007	0.766147839929996
114	0.169587355589543	0.339174711179086	0.830412644410457
115	0.10291455949842	0.20582911899684	0.89708544050158
116	0.143409025663522	0.286818051327044	0.856590974336478

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	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/10hdut1290259272.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/10hdut1290259272.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/13lek1290259272.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/13lek1290259272.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/23lek1290259272.png (open in new window)

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http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/33lek1290259272.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/43lek1290259272.png (open in new window)

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http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/5wcw51290259272.ps (open in new window)

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http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/773v81290259272.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/873v81290259272.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/873v81290259272.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/9hdut1290259272.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290259192e0z586baco3v4et/9hdut1290259272.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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