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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: Tue, 14 Dec 2010 16:06:05 +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/14/t129234265858bwhm4isbm1rgk.htm/, Retrieved Tue, 14 Dec 2010 17:04:19 +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/14/t129234265858bwhm4isbm1rgk.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0 13 13 14 13 3 0 12 12 8 13 5 1 15 10 12 16 6 1 12 9 7 12 6 1 10 10 10 11 5 1 12 12 7 12 3 0 15 13 16 18 8 1 9 12 11 11 4 0 12 12 14 14 4 0 11 6 6 9 4 1 11 5 16 14 6 0 11 12 11 12 6 0 15 11 16 11 5 1 7 14 12 12 4 0 11 14 7 13 6 1 11 12 13 11 4 1 10 12 11 12 6 0 14 11 15 16 6 0 10 11 7 9 4 0 6 7 9 11 4 0 11 9 7 13 2 0 15 11 14 15 7 0 11 11 15 10 5 0 12 12 7 11 4 1 14 12 15 13 6 1 15 11 17 16 6 0 9 11 15 15 7 1 13 8 14 14 5 1 13 9 14 14 6 1 16 12 8 14 4 1 13 10 8 8 4 0 12 10 14 13 7 1 14 12 14 15 7 1 11 8 8 13 4 0 9 12 11 11 4 0 16 11 16 15 6 1 12 12 10 15 6 0 10 7 8 9 5 1 13 11 14 13 6 1 16 11 16 16 7 1 14 12 13 13 6 1 15 9 5 11 3 1 5 15 8 12 3 0 8 11 10 12 4 0 11 11 8 12 6 1 16 11 13 14 7 1 17 11 15 14 5 1 9 15 6 8 4 1 9 11 12 13 5 1 13 12 16 16 6 1 10 12 5 13 6 0 6 9 15 11 6 1 12 12 12 14 5 1 8 12 8 13 4 1 14 13 13 13 5 1 12 11 14 13 5 0 11 9 12 12 4 0 16 9 16 16 6 1 8 11 10 15 2 0 15 11 15 15 8 1 7 12 8 12 3 0 16 12 16 14 6 1 14 9 19 12 6 1 16 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Celebrity[t] = + 0.14910336567032 -0.191454250730584Gender[t] + 0.143311716917591Popularity[t] -0.018666060010054FindingFriends[t] + 0.124670081000215KnowingPeople[t] + 0.168144777231389Liked[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)	0.14910336567032	0.815107	0.1829	0.855194	0.427597
Gender	-0.191454250730584	0.19316	-0.9912	0.323778	0.161889
Popularity	0.143311716917591	0.042085	3.4053	0.000924	0.000462
FindingFriends	-0.018666060010054	0.049828	-0.3746	0.708671	0.354336
KnowingPeople	0.124670081000215	0.03452	3.6115	0.00046	0.00023
Liked	0.168144777231389	0.057812	2.9085	0.004395	0.002197

Multiple Linear Regression - Regression Statistics
Multiple R	0.717481957162345
R-squared	0.514780358853509
Adjusted R-squared	0.492724920619577
F-TEST (value)	23.3402915595456
F-TEST (DF numerator)	5
F-TEST (DF denominator)	110
p-value	6.66133814775094e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.01137430658735
Sum Squared Residuals	112.516578682754

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	3	5.70076014347937	-2.70076014347937
2	5	4.82809400057054	0.171905999429456
3	6	6.10702167630787	-0.107021676307870
4	6	4.39982307163852	1.60017692836148
5	5	4.30039904356254	0.699600956437462
6	3	4.34382489160836	-1.34382489160836
7	8	7.07744762547193	0.92255237452807
8	4	4.24442528762505	-0.244425287625055
9	4	5.74425926380323	-1.74425926380322
10	4	3.87485937278729	0.12514062721271
11	6	5.78949587822586	0.210504121774141
12	6	4.89064774942221	1.10935225057779
13	5	5.93776630487231	-0.937766304872315
14	4	4.21328459200137	-0.21328459200137
15	6	4.52278008263263	1.47721991736737
16	4	4.78038888346067	-0.780388883460668
17	6	4.55588178177404	1.44411821822596
18	6	6.51050839311145	-0.510508393111455
19	4	3.76288743681964	0.237112563180356
20	4	3.84993452565271	0.150065474347294
21	2	4.6161103826829	-2.6161103826829
22	7	6.36100525179744	0.638994748202559
23	5	5.07170457897035	-0.0717045789703467
24	4	4.36713436510755	-0.367134365107550
25	6	5.79595375067665	0.204046249323351
26	6	6.71170602129889	-0.711706021298892
27	7	5.62580503129211	1.37419496870789
28	5	5.77078097003045	-0.770780970030448
29	6	5.75211491002039	0.247885089979606
30	4	5.37803139474171	-1.37803139474171
31	4	3.97655970062071	0.0234402993792867
32	7	5.61344660659194	1.38655339340806
33	7	6.00757322413921	0.992426775860788
34	4	4.56799227296259	-0.567992272962586
35	4	4.43587953835564	-0.435879538355639
36	6	6.75365713071546	-0.753657130715463
37	6	5.22226946630317	0.77773053369683
38	5	3.96222175786008	1.03777824213992
39	6	5.5466380127689	0.453361987231103
40	7	6.73034765721627	0.269652342783732
41	6	5.54661358867622	0.453386411323782
42	3	4.41227328315947	-1.41227328315947
43	3	3.40931477415527	-0.409314774155273
44	4	4.35470857767928	-0.354708577679276
45	6	4.53530356643162	1.46469643356838
46	7	6.02004785975284	0.979952140247157
47	5	6.41269973867086	-1.41269973867087
48	4	3.06064237089965	0.93935762910035
49	5	4.7240509830981	0.275949016901897
50	6	6.28174644645344	-0.281746446453441
51	6	3.97600607300413	2.02399392699587
52	6	4.56062289163389	1.43937710836611
53	5	5.30346485107221	-0.303464851072211
54	4	4.0633928821696	-0.0633928821695968
55	5	5.52794752866616	-0.527947528666164
56	5	5.40332629585131	-0.403326295851306
57	4	5.07131601045259	-1.07131601045259
58	6	6.95913402796696	-0.95913402796696
59	2	4.66768865864286	-2.66768865864286
60	8	6.48567533279766	1.51432466720234
61	3	3.75193638802062	-0.751936388020617
62	6	6.56684629347402	-0.566846293474019
63	6	6.18248747747628	-0.182487477476283
64	6	6.31286271798445	-0.312862717984448
65	5	3.84521783988553	1.15478216011447
66	5	5.7380678394068	-0.738067839406802
67	6	5.19787382269249	0.802126177307513
68	5	4.48713660852595	0.512863391474053
69	6	5.68992530559381	0.310074694406190
70	2	1.94374132027532	0.0562586797246752
71	5	5.83326144660408	-0.833261446604079
72	5	5.59475612248921	-0.594756122489211
73	5	5.24560336389504	-0.245603363895042
74	6	6.02592398790954	-0.0259239879095420
75	6	5.36410644459151	0.635893555408486
76	6	5.70078456757205	0.299215432427948
77	5	5.38930364933039	-0.389303649330393
78	5	5.33465439488125	-0.334654394881253
79	4	5.09140796090653	-1.09140796090653
80	2	3.19113973187121	-1.19113973187121
81	4	3.81872055775099	0.181279442249015
82	6	5.68191942223574	0.318080577764263
83	6	6.26116823111102	-0.261168231111024
84	5	4.59321390179414	0.406786098205857
85	3	4.4292506972561	-1.4292506972561
86	6	5.13996348732996	0.860036512670038
87	4	3.91239057813366	0.0876094218663422
88	5	5.44026984735365	-0.440269847353655
89	8	6.31286271798445	1.68713728201555
90	4	4.51666193051424	-0.516661930514242
91	6	5.6957037373798	0.304296262620206
92	6	5.51977658620927	0.480223413790729
93	7	6.71168159720621	0.288318402793786
94	6	6.29272191934515	-0.292721919345147
95	5	4.86117127561927	0.138828724380729
96	4	4.20518101227258	-0.205181012272584
97	6	3.84949710894959	2.15050289105041
98	3	3.41562240946516	-0.41562240946516
99	5	5.65230231342666	-0.652302313426656
100	6	5.34541596048878	0.654584039511219
101	7	6.85501773821648	0.144982261783517
102	7	6.61032098970519	0.389679010294807
103	6	6.84868567881392	-0.848685678813918
104	3	4.34810416067242	-1.34810416067242
105	2	2.52436347882431	-0.524363478824312
106	8	5.65837161735969	2.34162838264031
107	3	4.61804696210794	-1.61804696210794
108	8	6.34236361588006	1.65763638411993
109	3	4.70966419215211	-1.70966419215211
110	4	4.61768281768286	-0.61768281768286
111	5	5.19748525417473	-0.197485254174728
112	7	5.58399455688168	1.41600544311832
113	6	4.36865795192215	1.63134204807785
114	6	5.882903143139	0.117096856861003
115	7	6.06968955628776	0.930310443712238
116	6	6.14471794075306	-0.144717940753059

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.750754136758933	0.498491726482133	0.249245863241067
10	0.620096073668	0.759807852664	0.379903926332
11	0.72169480320833	0.556610393583339	0.278305196791669
12	0.83497344461523	0.330053110769542	0.165026555384771
13	0.92622771313515	0.147544573729701	0.0737722868648506
14	0.884596426482231	0.230807147035538	0.115403573517769
15	0.88831112287907	0.223377754241858	0.111688877120929
16	0.845319441987704	0.309361116024591	0.154680558012296
17	0.885714535823558	0.228570928352883	0.114285464176442
18	0.84563031746048	0.308739365079039	0.154369682539520
19	0.7926464040252	0.414707191949599	0.207353595974800
20	0.76318840359958	0.473623192800839	0.236811596400420
21	0.972657889939267	0.0546842201214658	0.0273421100607329
22	0.970422817248568	0.0591543655028632	0.0295771827514316
23	0.960674164403517	0.0786516711929654	0.0393258355964827
24	0.944445352248533	0.111109295502934	0.0555546477514668
25	0.924047380203922	0.151905239592155	0.0759526197960777
26	0.906541114911915	0.186917770176169	0.0934588850880845
27	0.91936063348236	0.161278733035279	0.0806393665176393
28	0.90447517717512	0.191049645649759	0.0955248228248794
29	0.876380473241351	0.247239053517298	0.123619526758649
30	0.882456160836749	0.235087678326502	0.117543839163251
31	0.856312178204998	0.287375643590003	0.143687821795002
32	0.88648812258237	0.227023754835261	0.113511877417631
33	0.887107388873118	0.225785222253764	0.112892611126882
34	0.868001713631564	0.263996572736871	0.131998286368436
35	0.840924441802518	0.318151116394963	0.159075558197482
36	0.813450565359284	0.373098869281432	0.186549434640716
37	0.789131614561582	0.421736770876837	0.210868385438418
38	0.79066645337151	0.41866709325698	0.20933354662849
39	0.756636026060911	0.486727947878178	0.243363973939089
40	0.715731082737475	0.568537834525049	0.284268917262525
41	0.678113741017066	0.643772517965868	0.321886258982934
42	0.710630419645321	0.578739160709358	0.289369580354679
43	0.688650875339606	0.622698249320787	0.311349124660394
44	0.645922925436486	0.708154149127028	0.354077074563514
45	0.698911489295041	0.602177021409917	0.301088510704959
46	0.7004071293786	0.5991857412428	0.2995928706214
47	0.736184702489212	0.527630595021577	0.263815297510788
48	0.72702671706343	0.545946565873141	0.272973282936571
49	0.682941217841219	0.634117564317562	0.317058782158781
50	0.634639275063101	0.730721449873798	0.365360724936899
51	0.752535299030388	0.494929401939224	0.247464700969612
52	0.804668286209309	0.390663427581382	0.195331713790691
53	0.768456305140387	0.463087389719226	0.231543694859613
54	0.733581568491769	0.532836863016462	0.266418431508231
55	0.698288432397786	0.603423135204428	0.301711567602214
56	0.654829751108388	0.690340497783223	0.345170248891612
57	0.660658765832256	0.678682468335488	0.339341234167744
58	0.659966973803926	0.680066052392147	0.340033026196074
59	0.889355961421988	0.221288077156025	0.110644038578012
60	0.918772759709722	0.162454480580556	0.081227240290278
61	0.90705416123856	0.185891677522880	0.0929458387614402
62	0.891617002609863	0.216765994780275	0.108382997390138
63	0.864655549537122	0.270688900925756	0.135344450462878
64	0.840641915252999	0.318716169494003	0.159358084747001
65	0.849947839723658	0.300104320552684	0.150052160276342
66	0.839233825984109	0.321532348031782	0.160766174015891
67	0.829947001210482	0.340105997579035	0.170052998789518
68	0.798652958451085	0.40269408309783	0.201347041548915
69	0.759425785017252	0.481148429965496	0.240574214982748
70	0.728464980188574	0.543070039622853	0.271535019811426
71	0.724366089394457	0.551267821211085	0.275633910605543
72	0.695833239353545	0.60833352129291	0.304166760646455
73	0.645936242337671	0.708127515324658	0.354063757662329
74	0.59005876335154	0.81988247329692	0.40994123664846
75	0.550948401895792	0.898103196208415	0.449051598104208
76	0.49789908800878	0.99579817601756	0.50210091199122
77	0.445577494123247	0.891154988246495	0.554422505876753
78	0.396246357727827	0.792492715455655	0.603753642272173
79	0.442746804369575	0.88549360873915	0.557253195630425
80	0.440257432282532	0.880514864565065	0.559742567717468
81	0.396109250961498	0.792218501922996	0.603890749038502
82	0.339624350254655	0.67924870050931	0.660375649745345
83	0.293250896603089	0.586501793206177	0.706749103396912
84	0.251056576214305	0.502113152428611	0.748943423785695
85	0.432789093342424	0.865578186684848	0.567210906657576
86	0.414356840864601	0.828713681729201	0.5856431591354
87	0.353848182981817	0.707696365963634	0.646151817018183
88	0.336952391536392	0.673904783072784	0.663047608463608
89	0.35946931054203	0.71893862108406	0.64053068945797
90	0.309233046709559	0.618466093419119	0.690766953290441
91	0.270785591143905	0.54157118228781	0.729214408856095
92	0.280421937195749	0.560843874391499	0.719578062804251
93	0.225466582885049	0.450933165770097	0.774533417114951
94	0.183421969394506	0.366843938789013	0.816578030605493
95	0.138793863366731	0.277587726733462	0.861206136633269
96	0.101172629692049	0.202345259384098	0.898827370307951
97	0.229532253715493	0.459064507430987	0.770467746284507
98	0.183165276939428	0.366330553878857	0.816834723060572
99	0.136594976991605	0.273189953983209	0.863405023008396
100	0.100807398881843	0.201614797763687	0.899192601118157
101	0.0744636120110535	0.148927224022107	0.925536387988947
102	0.0471889890184688	0.0943779780369377	0.952811010981531
103	0.0576139119224171	0.115227823844834	0.942386088077583
104	0.09674380657875	0.1934876131575	0.90325619342125
105	0.0628402911801444	0.125680582360289	0.937159708819856
106	0.107818123822267	0.215636247644534	0.892181876177733
107	0.0804995285774989	0.160999057154998	0.919500471422501

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	4	0.0404040404040404	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/10n09u1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/10n09u1292342755.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/1hzci1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/1hzci1292342755.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/2r8tl1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/2r8tl1292342755.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/3r8tl1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/3r8tl1292342755.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/4r8tl1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/4r8tl1292342755.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/52iao1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/52iao1292342755.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/62iao1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/62iao1292342755.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/7v9rr1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/7v9rr1292342755.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/8v9rr1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/8v9rr1292342755.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/9n09u1292342755.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234265858bwhm4isbm1rgk/9n09u1292342755.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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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