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MR - Happiness

*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, 18 Dec 2010 18:05:27 +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/18/t1292695452uljk5yoyj2dt2tm.htm/, Retrieved Sat, 18 Dec 2010 19:04:15 +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/18/t1292695452uljk5yoyj2dt2tm.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 «

14 11 11 26 9 2 1 18 12 8 20 9 1 1 11 15 12 21 9 4 1 12 10 10 31 14 1 1 16 12 7 21 8 5 2 18 11 6 18 8 1 1 14 5 8 26 11 1 1 14 16 16 22 10 1 1 15 11 8 22 9 1 1 15 15 16 29 15 1 1 17 12 7 15 14 2 1 19 9 11 16 11 1 1 10 11 16 24 14 3 2 18 15 16 17 6 1 1 14 12 12 19 20 1 1 14 16 13 22 9 1 1 17 14 19 31 10 1 1 14 11 7 28 8 1 1 16 10 8 38 11 2 1 18 7 12 26 14 4 2 14 11 13 25 11 1 1 12 10 11 25 16 2 1 17 11 8 29 14 1 1 9 16 16 28 11 2 4 16 14 15 15 11 3 1 14 12 11 18 12 1 1 11 12 12 21 9 1 2 16 11 7 25 7 1 2 13 6 9 23 13 1 1 17 14 15 23 10 1 1 15 9 6 19 9 2 1 14 15 14 18 9 1 1 16 12 14 18 13 1 1 9 12 7 26 16 1 1 15 9 15 18 12 1 1 17 13 14 18 6 1 1 13 15 17 28 14 1 1 15 11 14 17 14 1 1 16 10 5 29 10 2 2 16 13 14 12 4 1 1 12 16 8 28 12 1 1 11 13 8 20 14 1 1 15 14 13 17 9 2 1 17 14 14 17 9 1 1 13 16 16 20 10 1 1 16 9 11 31 14 1 1 14 8 10 21 10 1 1 11 8 10 19 9 1 1 12 12 10 23 14 1 1 12 10 8 15 8 4 1 15 16 14 24 9 2 1 16 13 14 28 8 1 1 15 11 12 16 9 1 1 12 14 13 19 9 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

Happiness[t] = + 17.5662446474836 + 0.00713222387829059Popularity[t] + 0.0344298760876943KnowingPeople[t] + 0.00620232457943602CMistakes[t] -0.306971082504576DAction[t] + 0.173787200187903Tobacco[t] -0.826460409772176Drugs[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)	17.5662446474836	1.379172	12.7368	0	0
Popularity	0.00713222387829059	0.076841	0.0928	0.926183	0.463091
KnowingPeople	0.0344298760876943	0.066563	0.5173	0.605806	0.302903
CMistakes	0.00620232457943602	0.035267	0.1759	0.860654	0.430327
DAction	-0.306971082504576	0.07295	-4.208	4.6e-05	2.3e-05
Tobacco	0.173787200187903	0.203693	0.8532	0.395037	0.197519
Drugs	-0.826460409772176	0.368906	-2.2403	0.02667	0.013335

Multiple Linear Regression - Regression Statistics
Multiple R	0.404266973125832
R-squared	0.163431785560322
Adjusted R-squared	0.127059254497727
F-TEST (value)	4.49327502886909
F-TEST (DF numerator)	6
F-TEST (DF denominator)	138
p-value	0.000345024051561227
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.21953728874425
Sum Squared Residuals	679.835717105414

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	14	14.9430624342372	-0.943062434237218
2	18	14.635903882188	3.36409611781205
3	11	15.3225839833167	-4.32258398331675
4	12	13.2238693444577	-1.22386934445768
5	16	14.7833358041637	1.2166641958363
6	18	14.85447833948	3.14552166052002
7	14	14.0092500975074	-0.00925009750738331
8	14	14.645305353057	-0.645305353056965
9	15	14.6411763074685	0.358823692531467
10	15	13.1467339887118	1.85326601128815
11	17	13.2093941708681	3.7906058291319
12	19	14.0790453754893	4.92095462451073
13	10	12.9152785434097	-2.91527854340971
14	18	15.8350458362998	2.1649541637002
15	14	11.390739154409	2.60926084559104
16	14	14.8489868072985	-0.848986807298458
17	17	14.7901514547784	2.20984854522161
18	14	14.950931461362	-0.95093146136203
19	16	14.29312631204	1.70687368796003
20	18	12.9352219928925	5.06477800710745
21	14	14.2179904966362	-0.217990496636162
22	12	12.7809303082475	-0.780930308247507
23	17	13.1497371670017	3.85026283299829
24	9	12.0699541889004	-3.06995418890038
25	16	14.5937980750279	1.40620192497213
26	14	13.8058756137784	0.194124386221564
27	11	13.953365301346	-2.95336530134599
28	16	14.4128351603561	1.58716483964388
29	13	13.4182630587259	-0.418263058725908
30	17	14.6028133537921	2.39718664620787
31	15	14.7132323339862	0.286767666013842
32	14	14.8514751611901	-0.851475161190117
33	16	13.6021941595369	2.39780584046306
34	9	12.4898903760448	-3.48989037604485
35	15	13.9221984464943	1.07780155350566
36	17	15.7581239609473	1.24187603905274
37	13	13.4819326227247	-0.481932622724682
38	15	13.2818885285746	1.71811147142536
39	16	13.6145264352944	2.38547356470564
40	16	16.3348521784798	-0.334852178479798
41	12	13.7931381268229	-1.79313812682288
42	11	13.1081806935434	-2.10818069354336
43	15	14.9774979368326	0.0225020631674011
44	17	14.8381406127324	2.16185938726761
45	13	14.6329007038981	-1.63290070389809
46	16	13.2511669966671	2.74883300333292
47	14	14.375465980925	-0.375465980925039
48	11	14.6700324142707	-3.67003241427074
49	12	13.1885151955788	-1.18851519557877
50	12	15.4189604946025	-3.41896049460247
51	15	15.0696085327329	-0.0696085327329265
52	16	15.2062050417325	0.793794958267528
53	15	14.7416818643427	0.258318135657306
54	12	13.6845561668229	-1.68455616682292
55	12	13.9075400405549	-1.9075400405549
56	8	12.8406105684022	-4.84061056840216
57	13	14.8526185408823	-1.85261854088227
58	11	14.5807937533709	-3.58079375337095
59	14	15.0902094203056	-1.09020942030561
60	15	13.4456429480393	1.55435705196072
61	10	14.2759984277378	-4.27599842773784
62	11	14.6599763772592	-3.65997637725922
63	12	13.6802594145816	-1.68025941458163
64	15	13.3882579457239	1.61174205427609
65	15	15.004795589042	-0.00479558904200265
66	14	13.5262078099909	0.473792190009095
67	16	12.2347775105721	3.76522248942791
68	15	15.348056346232	-0.348056346231982
69	15	15.6859279317339	-0.68592793173387
70	13	14.6391030284775	-1.63910302847753
71	17	14.5640409517227	2.4359590482773
72	13	13.1713682921833	-0.171368292183255
73	15	14.336468845447	0.663531154552962
74	13	14.9217796370626	-1.92177963706256
75	15	13.5878216073923	1.41217839260775
76	16	14.5596105621294	1.44038943787063
77	15	14.541085825495	0.458914174504973
78	16	14.2214087498267	1.77859125017332
79	15	14.2499376453398	0.750062354660162
80	14	14.4958097548413	-0.495809754841266
81	15	13.1728508799782	1.82714912002178
82	7	12.7157107881109	-5.71571078811091
83	17	15.0573436253184	1.94265637468163
84	13	15.868958016042	-2.86895801604195
85	15	14.1941110976173	0.805888902382728
86	14	13.737015861603	0.262984138396953
87	13	12.3074594813878	0.692540518612214
88	16	14.5056438130045	1.49435618699549
89	12	14.1897685716355	-2.18976857163555
90	14	15.2149013467112	-1.21490134671121
91	17	14.9481417634655	2.05185823653453
92	15	15.322124052563	-0.322124052563006
93	17	12.5224604535348	4.47753954646517
94	12	13.6387795517196	-1.63877955171961
95	16	14.005992702779	1.99400729722102
96	11	13.1679023111923	-2.16790231119232
97	15	13.9103330932458	1.08966690675417
98	9	14.3366935788556	-5.33669357885562
99	16	14.6329007038981	1.36709929610191
100	10	12.9940336176989	-2.99403361769893
101	10	11.6542629489503	-1.65426294895033
102	15	14.4517593009836	0.548240699016378
103	11	14.0245266816668	-3.02452668166678
104	13	14.9736674806888	-1.97366748068877
105	14	12.7678233651457	1.2321766348543
106	18	15.0135945154656	2.98640548453438
107	16	14.8440415933069	1.15595840669308
108	14	12.6248201824991	1.37517981750094
109	14	13.8494849818869	0.150515018113135
110	14	13.9573073602367	0.0426926397632883
111	14	13.8296213754453	0.170378624554713
112	12	13.3671682446016	-1.36716824460158
113	14	14.2629708497927	-0.262970849792661
114	15	14.6489370866408	0.351062913359226
115	15	13.9518249479592	1.04817505204078
116	13	12.0349596324652	0.965040367534796
117	17	14.5566130103472	2.44338698965285
118	17	14.326412064637	2.67358793536297
119	19	15.4849541992364	3.51504580076357
120	15	14.122467274053	0.877532725947038
121	13	14.2524259992315	-1.2524259992315
122	9	12.2990960142057	-3.29909601420574
123	15	15.0074391748881	-0.00743917488812934
124	15	15.1136301037947	-0.11363010379467
125	16	15.1504289763706	0.849571023629369
126	11	13.5264156638766	-2.52641566387656
127	14	13.9934328216656	0.00656717833436187
128	11	13.175180647121	-2.17518064712104
129	15	12.1719627179104	2.82803728208958
130	13	13.2840440446696	-0.284044044669612
131	16	13.3404991476861	2.65950085231387
132	14	15.419478190948	-1.41947819094801
133	15	14.4572248223166	0.542775177683419
134	16	14.0872893417035	1.91271065829649
135	16	14.9392432311087	1.06075676889128
136	11	12.8369732083107	-1.83697320831071
137	13	13.229709204646	-0.22970920464604
138	16	14.5937980750279	1.40620192497213
139	12	13.8874413591176	-1.8874413591176
140	9	13.0713705679521	-4.07137056795212
141	13	12.8635018196834	0.136498180316632
142	13	15.868958016042	-2.86895801604195
143	14	14.4581490951078	-0.458149095107793
144	19	15.4849541992364	3.51504580076357
145	13	15.0213552946379	-2.02135529463787

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.437859426670834	0.875718853341667	0.562140573329166
11	0.283147384109154	0.566294768218307	0.716852615890846
12	0.226691245941355	0.45338249188271	0.773308754058645
13	0.584632541098815	0.830734917802371	0.415367458901185
14	0.502946108147804	0.994107783704392	0.497053891852196
15	0.41545130034789	0.83090260069578	0.58454869965211
16	0.40916674441979	0.818333488839579	0.59083325558021
17	0.689193166236234	0.621613667527532	0.310806833763766
18	0.631778443343365	0.73644311331327	0.368221556656635
19	0.74458001447218	0.510839971055639	0.255419985527819
20	0.894117224173506	0.211765551652988	0.105882775826494
21	0.866551820595342	0.266896358809316	0.133448179404658
22	0.843878856091272	0.312242287817456	0.156121143908728
23	0.863129450900812	0.273741098198375	0.136870549099188
24	0.891748456364024	0.216503087271951	0.108251543635976
25	0.860164523399543	0.279670953200915	0.139835476600457
26	0.83994142167799	0.320117156644019	0.160058578322009
27	0.880834151922183	0.238331696155635	0.119165848077817
28	0.859888962414435	0.28022207517113	0.140111037585565
29	0.865674454027625	0.268651091944749	0.134325545972375
30	0.86064804954334	0.278703900913319	0.13935195045666
31	0.835473754498112	0.329052491003776	0.164526245501888
32	0.809080158670592	0.381839682658815	0.190919841329407
33	0.787555953096279	0.424888093807443	0.212444046903721
34	0.87467158981052	0.25065682037896	0.12532841018948
35	0.84957534223415	0.300849315531701	0.15042465776585
36	0.816240417033727	0.367519165932546	0.183759582966273
37	0.776244753953209	0.447510492093581	0.223755246046791
38	0.743778869280482	0.512442261439035	0.256221130719518
39	0.751775452058599	0.496449095882803	0.248224547941401
40	0.720507711163988	0.558984577672023	0.279492288836012
41	0.693725339058582	0.612549321882837	0.306274660941418
42	0.703911633025068	0.592176733949865	0.296088366974932
43	0.655890637067345	0.68821872586531	0.344109362932655
44	0.640293284389954	0.719413431220091	0.359706715610046
45	0.614722786653058	0.770554426693883	0.385277213346942
46	0.619345056052216	0.761309887895568	0.380654943947784
47	0.603288304092007	0.793423391815986	0.396711695907993
48	0.757425921071317	0.485148157857365	0.242574078928683
49	0.73281242022238	0.534375159555241	0.267187579777621
50	0.800648632401915	0.39870273519617	0.199351367598085
51	0.76296489985738	0.474070200285241	0.23703510014262
52	0.724937373693982	0.550125252612037	0.275062626306018
53	0.682467171796542	0.635065656406916	0.317532828203458
54	0.661303136754887	0.677393726490226	0.338696863245113
55	0.642183350725865	0.71563329854827	0.357816649274135
56	0.822955196421345	0.354089607157311	0.177044803578655
57	0.814707924834021	0.370584150331957	0.185292075165979
58	0.85751749991144	0.284965000177119	0.142482500088559
59	0.839554363682934	0.320891272634132	0.160445636317066
60	0.821848514896167	0.356302970207666	0.178151485103833
61	0.901318959717582	0.197362080564836	0.0986810402824178
62	0.931785751640255	0.136428496719491	0.0682142483597453
63	0.923692310008846	0.152615379982308	0.0763076899911539
64	0.914246530333761	0.171506939332477	0.0857534696662385
65	0.8931044064727	0.213791187054601	0.1068955935273
66	0.870201971113347	0.259596057773307	0.129798028886654
67	0.915399918997962	0.169200162004076	0.0846000810020378
68	0.895312243585666	0.209375512828667	0.104687756414334
69	0.873520896650199	0.252958206699603	0.126479103349801
70	0.86180625539954	0.276387489200918	0.138193744600459
71	0.865892345324696	0.268215309350608	0.134107654675304
72	0.838132363501116	0.323735272997769	0.161867636498884
73	0.807968576895147	0.384062846209706	0.192031423104853
74	0.802560121918265	0.39487975616347	0.197439878081735
75	0.78382194211939	0.43235611576122	0.21617805788061
76	0.762979876142828	0.474040247714343	0.237020123857172
77	0.724162837895513	0.551674324208973	0.275837162104487
78	0.710792946622446	0.578414106755108	0.289207053377554
79	0.672004469784537	0.655991060430925	0.327995530215463
80	0.627804115200948	0.744391769598104	0.372195884799052
81	0.617377314107143	0.765245371785713	0.382622685892857
82	0.821469388600387	0.357061222799225	0.178530611399613
83	0.815258670967131	0.369482658065738	0.184741329032869
84	0.839629333155049	0.320741333689903	0.160370666844951
85	0.811860626225002	0.376278747549995	0.188139373774998
86	0.777400046297653	0.445199907404695	0.222599953702347
87	0.743472221041765	0.51305555791647	0.256527778958235
88	0.727287571669229	0.545424856661543	0.272712428330771
89	0.722041702491705	0.55591659501659	0.277958297508295
90	0.69657650563024	0.60684698873952	0.30342349436976
91	0.69698622550775	0.6060275489845	0.30301377449225
92	0.651074442057225	0.69785111588555	0.348925557942775
93	0.848335321913425	0.303329356173149	0.151664678086575
94	0.828970243411526	0.342059513176948	0.171029756588474
95	0.832624890131104	0.334750219737793	0.167375109868896
96	0.825208102052667	0.349583795894666	0.174791897947333
97	0.807493753535018	0.385012492929964	0.192506246464982
98	0.93439881577335	0.1312023684533	0.0656011842266499
99	0.92049961604602	0.15900076790796	0.0795003839539798
100	0.921101748710195	0.15779650257961	0.0788982512898052
101	0.907481570646046	0.185036858707907	0.0925184293539537
102	0.883749075564157	0.232501848871685	0.116250924435843
103	0.906080228269433	0.187839543461134	0.093919771730567
104	0.922941628944107	0.154116742111786	0.0770583710558929
105	0.925935983105162	0.148128033789677	0.0740640168948385
106	0.938740185624855	0.12251962875029	0.0612598143751449
107	0.92115440030203	0.15769119939594	0.0788455996979701
108	0.92708787792188	0.145824244156239	0.0729121220781193
109	0.904544813980598	0.190910372038805	0.0954551860194024
110	0.901527178253015	0.196945643493971	0.0984728217469853
111	0.881868241065353	0.236263517869294	0.118131758934647
112	0.854174859183898	0.291650281632205	0.145825140816102
113	0.81473476906238	0.370530461875241	0.18526523093762
114	0.76918137932748	0.461637241345041	0.230818620672521
115	0.729489639622466	0.541020720755069	0.270510360377534
116	0.673648594691154	0.652702810617693	0.326351405308846
117	0.736462991780735	0.52707401643853	0.263537008219265
118	0.716933629307985	0.566132741384031	0.283066370692015
119	0.742244662685978	0.515510674628044	0.257755337314022
120	0.765149159222504	0.469701681554992	0.234850840777496
121	0.705693752909748	0.588612494180503	0.294306247090252
122	0.740075644069735	0.51984871186053	0.259924355930265
123	0.684474771665825	0.63105045666835	0.315525228334175
124	0.609564794180176	0.780870411639648	0.390435205819824
125	0.528547238589466	0.942905522821068	0.471452761410534
126	0.454124750044363	0.908249500088727	0.545875249955637
127	0.42250937457428	0.84501874914856	0.57749062542572
128	0.341698131678046	0.683396263356091	0.658301868321954
129	0.301283993301769	0.602567986603539	0.69871600669823
130	0.22302103921352	0.446042078427039	0.77697896078648
131	0.415301055505612	0.830602111011223	0.584698944494388
132	0.645998566866687	0.708002866266625	0.354001433133313
133	0.51748681640825	0.9650263671835	0.48251318359175
134	0.604647672648657	0.790704654702686	0.395352327351343
135	0.545686171311208	0.908627657377583	0.454313828688792

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/Dec/18/t1292695452uljk5yoyj2dt2tm/100xuc1292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/100xuc1292695515.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/1bwf01292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/1bwf01292695515.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/2bwf01292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/2bwf01292695515.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/34oxl1292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/34oxl1292695515.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/44oxl1292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/44oxl1292695515.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/54oxl1292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/54oxl1292695515.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/6wfwo1292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/6wfwo1292695515.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/7povr1292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/7povr1292695515.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/8povr1292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/8povr1292695515.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/9povr1292695515.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292695452uljk5yoyj2dt2tm/9povr1292695515.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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