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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 13:09:49 +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/t1290258639rvxia5u73iteyd8.htm/, Retrieved Sat, 20 Nov 2010 14:10:50 +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/t1290258639rvxia5u73iteyd8.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	9 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

happiness[t] = + 32.7257088461299 -0.00116875052912139`CM+D`[t] -0.000623856008098606`PE+PC`[t] + 0.257591438280578depression[t] + 0.0551379081268023connected[t] -0.729313863506522separated[t] -0.0312671114293483populariteit[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)	32.7257088461299	1.934693	16.9152	0	0
`CM+D`	-0.00116875052912139	0.00651	-0.1795	0.857835	0.428917
`PE+PC`	-0.000623856008098606	0.008262	-0.0755	0.939939	0.46997
depression	0.257591438280578	0.034434	7.4807	0	0
connected	0.0551379081268023	0.047194	1.1683	0.245013	0.122507
separated	-0.729313863506522	0.028519	-25.573	0	0
populariteit	-0.0312671114293483	0.041816	-0.7477	0.4561	0.22805

Multiple Linear Regression - Regression Statistics
Multiple R	0.9302787057485
R-squared	0.865418470369105
Adjusted R-squared	0.85863284702637
F-TEST (value)	127.537062795529
F-TEST (DF numerator)	6
F-TEST (DF denominator)	119
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.491563435503502
Sum Squared Residuals	28.7545187237566

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	10	10.0704619564674	-0.0704619564673938
2	10	10.0777903055909	-0.0777903055909233
3	10	10.0867455023885	-0.086745502388514
4	10	10.0829312407414	-0.0829312407413582
5	10	10.0857346748335	-0.0857346748335431
6	10	10.0869823868497	-0.0869823868497424
7	13	12.6957495619462	0.304250438053798
8	10	10.0839420682963	-0.0839420682963243
9	10	10.0150981597581	-0.0150981597580739
10	13	13.1602695626281	-0.160269562628054
11	10	10.0709278895018	-0.0709278895018374
12	10	10.0639153863271	-0.0639153863271092
13	10	10.0173566993292	-0.0173566993292408
14	10	10.0869823868497	-0.0869823868497414
15	13	12.7096166453215	0.290383354678493
16	10	10.0279544155784	-0.027954415578409
17	10	10.0591614227235	-0.0591614227235479
18	10	9.98351520238042	0.0164847976195778
19	10	10.0653999828045	-0.0653999828045337
20	10	9.98850605044521	0.011493949554789
21	10	10.0862006078675	-0.0862006078674913
22	13	12.6931751764267	0.306824823573268
23	13	13.1566843495536	-0.156684349553614
24	10	10.0148612752968	-0.0148612752968464
25	10	10.0175935837905	-0.0175935837904685
26	10	10.0600932887914	-0.0600932887914417
27	10	10.0716307069970	-0.0716307069970119
28	10	10.0788011331459	-0.0788011331458917
29	10	10.0012310763828	-0.00123107638276915
30	10	9.9746389670699	0.0253610329300939
31	10	10.0221896244199	-0.022189624419878
32	13	13.1353309940811	-0.135330994081126
33	10	9.9989647009231	0.00103529907690594
34	10	10.0161089873130	-0.0161089873130436
35	10	10.0821494617591	-0.0821494617591044
36	10	10.0232004519748	-0.0232004519748476
37	10	10.0829312407414	-0.0829312407413545
38	15	10.7048516835735	4.29514831642646
39	10	10.0745131025763	-0.0745131025762774
40	10	10.0649340497706	-0.064934049770587
41	13	12.0331399977715	0.966860002228478
42	10	10.0851897803125	-0.0851897803125216
43	10	10.0816835287252	-0.0816835287251572
44	10	9.99716425849737	0.00283574150263418
45	10	10.0711647739631	-0.071164773963065
46	10	10.0793460276669	-0.0793460276669146
47	10	10.0875272813708	-0.0875272813707642
48	10	10.1001544886184	-0.100154488618380
49	10	10.0822284232462	-0.0822284232461802
50	13	13.1619832076782	-0.161983207678198
51	13	12.6920853873847	0.307914612615313
52	10	10.0798909221879	-0.0798909221879374
53	10	9.98858501193229	0.0114149880677129
54	10	10.0717886299712	-0.0717886299711634
55	10	10.0833971737753	-0.0833971737753014
56	10	10.0935289569905	-0.0935289569905232
57	10	10.0811386342041	-0.0811386342041346
58	10	10.0127606586998	-0.0127606586998311
59	10	10.0865164538158	-0.0865164538157945
60	10	10.0686693499307	-0.0686693499306705
61	10	10.0752159200715	-0.075215920071452
62	10	10.0781772771378	-0.0781772771377933
63	10	10.0858136363206	-0.08581363632062
64	10	10.0763057091135	-0.0763057091134973
65	10	10.0817624902122	-0.0817624902122332
66	10	10.0092544071125	-0.00925440711246686
67	10	10.0090964841383	-0.00909648413831532
68	10	9.99551855361994	0.00448144638006073
69	13	12.6912246469154	0.30877535308464
70	13	12.6888160202585	0.31118397974145
71	10	10.0149402367839	-0.0149402367839222
72	13	12.6939569554090	0.306043044591017
73	13	12.6956706004591	0.304329399540874
74	10	10.0637574633530	-0.0637574633529576
75	10	10.0062140885590	-0.00621408855904971
76	10	10.1029579227106	-0.102957922710569
77	13	13.1651814492058	-0.165181449205767
78	13	13.1549707045035	-0.154970704503469
79	10	10.0833971737753	-0.0833971737753014
80	10	9.99818292194084	0.00181707805915638
81	10	10.0599432017058	-0.0599432017057981
82	10	10.0614199622947	-0.0614199622947148
83	11	13.1380364364821	-2.13803643648213
84	10	9.98125666280925	0.0187433371907447
85	10	10.0772375751814	-0.0772375751813913
86	10	10.0103441961545	-0.0103441961545127
87	10	9.97985886370742	0.0201411362925855
88	10	10.0847238472786	-0.0847238472785746
89	10	10.0763057091135	-0.0763057091134973
90	13	13.1580110230569	-0.158011023056887
91	10	9.9990248052073	0.000975194792696533
92	10	10.0071459546269	-0.00714595462694355
93	10	10.0111971007353	-0.0111971007353305
94	10	10.0998386426701	-0.0998386426700767
95	13	13.1606565341749	-0.160656534174925
96	10	10.0871403098239	-0.0871403098238931
97	10	10.0686693499307	-0.0686693499306705
98	13	13.1617541591055	-0.161754159105478
99	10	10.0756107275068	-0.075610727506831
100	10	10.0881511373789	-0.0881511373788627
101	10	10.0090175226512	-0.00901752265123949
102	10	10.0323214076351	-0.0323214076350992
103	10	10.0742840540036	-0.074284054003558
104	10	10.0148612752968	-0.0148612752968464
105	10	10.0936079184776	-0.0936079184775984
106	10	10.0186044113454	-0.018604411345438
107	13	13.1583979946038	-0.158397994603758
108	13	13.1386871585828	-0.138687158582846
109	10	9.99372594708272	0.00627405291728074
110	13	13.1651024877187	-0.165102487718691
111	12	13.1894170945533	-1.18941709455326
112	10	9.98663448242091	0.0133655175790848
113	13	12.0490365721453	0.950963427854726
114	15	14.9024968828071	0.097503117192856
115	10	9.96355964600978	0.0364403539902252
116	10	10.0796618736152	-0.079661873615218
117	13	12.6829644317244	0.317035568275565
118	13	12.0281569855952	0.97184301440476
119	10	10.0215657684118	-0.0215657684117795
120	10	10.0869034253627	-0.0869034253626654
121	10	10.0791881046928	-0.0791881046927629
122	10	9.96012133459513	0.0398786654048715
123	10	10.0002913744264	-0.000291374426367053
124	10	10.0724046500908	-0.0724046500907541
125	10	10.0727204960391	-0.0727204960390575
126	10	10.0524569296086	-0.0524569296086148

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	8.83013093180728e-43	1.76602618636146e-42	1
11	1.08465622154546e-58	2.16931244309092e-58	1
12	1.09659168800411e-69	2.19318337600821e-69	1
13	2.98455877552724e-79	5.96911755105449e-79	1
14	5.10804753786775e-95	1.02160950757355e-94	1
15	5.99434363674468e-110	1.19886872734894e-109	1
16	5.66025037969224e-132	1.13205007593845e-131	1
17	2.01321513114805e-148	4.0264302622961e-148	1
18	1.54733406108098e-148	3.09466812216196e-148	1
19	1.03139027202500e-157	2.06278054405000e-157	1
20	3.88378685574442e-179	7.76757371148885e-179	1
21	2.79428443935583e-183	5.58856887871167e-183	1
22	1.16892314522556e-196	2.33784629045111e-196	1
23	1.81249244387596e-211	3.62498488775193e-211	1
24	2.69161520820935e-230	5.3832304164187e-230	1
25	5.91256365877058e-250	1.18251273175412e-249	1
26	1.87431952670911e-250	3.74863905341822e-250	1
27	2.8844229536109e-258	5.7688459072218e-258	1
28	9.10235168140344e-276	1.82047033628069e-275	1
29	1.09847985134083e-290	2.19695970268167e-290	1
30	3.55917581701102e-302	7.11835163402204e-302	1
31	2.28196275643009e-305	4.56392551286019e-305	1
32	9.82839848615533e-319	1.96567969723107e-318	1
33	0	0	1
34	0	0	1
35	0	0	1
36	0	0	1
37	0	0	1
38	0	0	1
39	0	0	1
40	0	0	1
41	0.999999924507301	1.50985397448662e-07	7.5492698724331e-08
42	0.99999985750647	2.84987058412926e-07	1.42493529206463e-07
43	0.9999997235713	5.52857401190861e-07	2.76428700595431e-07
44	0.999999566432592	8.67134815165369e-07	4.33567407582685e-07
45	0.999999143473812	1.71305237614954e-06	8.56526188074769e-07
46	0.999998373237483	3.25352503334584e-06	1.62676251667292e-06
47	0.999997117849537	5.76430092619818e-06	2.88215046309909e-06
48	0.999995603041819	8.79391636256788e-06	4.39695818128394e-06
49	0.999991987594095	1.60248118104190e-05	8.01240590520948e-06
50	0.999989178318649	2.16433627028353e-05	1.08216813514177e-05
51	0.999986005771879	2.79884562428831e-05	1.39942281214415e-05
52	0.999975127610084	4.97447798321957e-05	2.48723899160979e-05
53	0.999956051989948	8.7896020104203e-05	4.39480100521015e-05
54	0.999924343930588	0.000151312138822932	7.56560694114658e-05
55	0.999873098193564	0.000253803612871895	0.000126901806435947
56	0.999798318792344	0.000403362415311543	0.000201681207655772
57	0.999670253460022	0.000659493079956061	0.000329746539978030
58	0.999464176699784	0.00107164660043254	0.000535823300216271
59	0.999162104149033	0.00167579170193331	0.000837895850966655
60	0.998687581394603	0.00262483721079345	0.00131241860539672
61	0.997977085608648	0.00404582878270319	0.00202291439135159
62	0.996937243636144	0.00612551272771162	0.00306275636385581
63	0.995472707608716	0.00905458478256903	0.00452729239128451
64	0.993337811306546	0.0133243773869074	0.00666218869345368
65	0.990413745116294	0.0191725097674117	0.00958625488370583
66	0.98636501638295	0.0272699672340994	0.0136349836170497
67	0.980867746018381	0.0382645079632372	0.0191322539816186
68	0.973698644178589	0.0526027116428226	0.0263013558214113
69	0.969373198765957	0.0612536024680869	0.0306268012340434
70	0.962223258699357	0.0755534826012865	0.0377767413006432
71	0.949413222364753	0.101173555270494	0.050586777635247
72	0.94113447554696	0.117731048906079	0.0588655244530397
73	0.941492234013814	0.117015531972373	0.0585077659861863
74	0.924047984522571	0.151904030954857	0.0759520154774286
75	0.902451495616986	0.195097008766029	0.0975485043830144
76	0.879790061126095	0.240419877747809	0.120209938873905
77	0.858665473974913	0.282669052050175	0.141334526025087
78	0.829380612139558	0.341238775720883	0.170619387860442
79	0.791498175508363	0.417003648983274	0.208501824491637
80	0.74999914963579	0.500001700728419	0.250000850364210
81	0.706872891527878	0.586254216944243	0.293127108472122
82	0.659074378612428	0.681851242775144	0.340925621387572
83	0.999997375304845	5.24939030904417e-06	2.62469515452208e-06
84	0.999994396502726	1.12069945487489e-05	5.60349727437443e-06
85	0.999988300258748	2.33994825040600e-05	1.16997412520300e-05
86	0.999976159710004	4.76805799912004e-05	2.38402899956002e-05
87	0.999952547202627	9.4905594745477e-05	4.74527973727385e-05
88	0.999907252157487	0.000185495685026412	9.2747842513206e-05
89	0.999822429686086	0.000355140627826988	0.000177570313913494
90	0.999683127911635	0.000633744176730043	0.000316872088365022
91	0.999420447892973	0.00115910421405375	0.000579552107026875
92	0.9989507313243	0.00209853735140014	0.00104926867570007
93	0.998133974417308	0.00373205116538373	0.00186602558269186
94	0.99684461838184	0.00631076323632036	0.00315538161816018
95	0.995041758050863	0.0099164838982749	0.00495824194913745
96	0.991756187496236	0.0164876250075279	0.00824381250376395
97	0.98682357536297	0.0263528492740580	0.0131764246370290
98	0.979926581989647	0.0401468360207057	0.0200734180103529
99	0.969997479349275	0.0600050413014505	0.0300025206507252
100	0.954354547914065	0.0912909041718701	0.0456454520859351
101	0.932515623304531	0.134968753390938	0.0674843766954688
102	0.902674218327454	0.194651563345091	0.0973257816725456
103	0.867634252140791	0.264731495718418	0.132365747859209
104	0.817811601659946	0.364376796680108	0.182188398340054
105	0.756804909487002	0.486390181025997	0.243195090512998
106	0.684207070841713	0.631585858316575	0.315792929158287
107	0.622093547165782	0.755812905668436	0.377906452834218
108	0.53295716146889	0.93408567706222	0.46704283853111
109	0.440338297422117	0.880676594844234	0.559661702577883
110	0.438812568335797	0.877625136671593	0.561187431664203
111	1	3.72951415095656e-109	1.86475707547828e-109
112	1	4.03736646539224e-104	2.01868323269612e-104
113	1	2.09856276210415e-90	1.04928138105207e-90
114	1	6.31932594813159e-72	3.15966297406580e-72
115	1	2.32062277524296e-56	1.16031138762148e-56
116	1	7.37155670038881e-46	3.68577835019441e-46

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	73	0.682242990654206	NOK
5% type I error level	80	0.747663551401869	NOK
10% type I error level	85	0.794392523364486	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290258639rvxia5u73iteyd8/10vnj31290258579.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290258639rvxia5u73iteyd8/10vnj31290258579.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290258639rvxia5u73iteyd8/1o43q1290258579.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290258639rvxia5u73iteyd8/1o43q1290258579.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t1290258639rvxia5u73iteyd8/2o43q1290258579.png (open in new window)

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Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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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