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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: Thu, 19 Nov 2009 10:47:58 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy.htm/, Retrieved Thu, 19 Nov 2009 18:49:06 +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/2009/Nov/19/t1258652934tzxstiz0uybq3gy.htm/},
    year = {2009},
}
@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 = {2009},
    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:

ws7l5

Dataseries X:

» Textbox « » Textfile « » CSV «

6,1 110,4 6,1 6,3 6,3 96,4 6,1 6,1 6,3 101,9 6,3 6,1 6 106,2 6,3 6,3 6,2 81 6 6,3 6,4 94,7 6,2 6 6,8 101 6,4 6,2 7,5 109,4 6,8 6,4 7,5 102,3 7,5 6,8 7,6 90,7 7,5 7,5 7,6 96,2 7,6 7,5 7,4 96,1 7,6 7,6 7,3 106 7,4 7,6 7,1 103,1 7,3 7,4 6,9 102 7,1 7,3 6,8 104,7 6,9 7,1 7,5 86 6,8 6,9 7,6 92,1 7,5 6,8 7,8 106,9 7,6 7,5 8 112,6 7,8 7,6 8,1 101,7 8 7,8 8,2 92 8,1 8 8,3 97,4 8,2 8,1 8,2 97 8,3 8,2 8 105,4 8,2 8,3 7,9 102,7 8 8,2 7,6 98,1 7,9 8 7,6 104,5 7,6 7,9 8,3 87,4 7,6 7,6 8,4 89,9 8,3 7,6 8,4 109,8 8,4 8,3 8,4 111,7 8,4 8,4 8,4 98,6 8,4 8,4 8,6 96,9 8,4 8,4 8,9 95,1 8,6 8,4 8,8 97 8,9 8,6 8,3 112,7 8,8 8,9 7,5 102,9 8,3 8,8 7,2 97,4 7,5 8,3 7,4 111,4 7,2 7,5 8,8 87,4 7,4 7,2 9,3 96,8 8,8 7,4 9,3 114,1 9,3 8,8 8,7 110,3 9,3 9,3 8,2 103,9 8,7 9,3 8,3 101,6 8,2 8,7 8,5 94,6 8,3 8,2 8,6 95,9 8,5 8,3 8,5 104,7 8,6 8,5 8,2 102,8 8,5 8,6 8,1 98,1 8,2 8,5 7,9 113,9 8,1 8,2 8,6 80,9 7,9 8,1 8,7 95,7 8,6 7,9 8,7 113,2 8,7 8,6 8,5 105,9 8,7 8,7 8,4 108,8 8,5 8,7 8 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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 1.67058536444880 -0.00940480827711062X[t] + 1.4664080020596Y1[t] -0.578726593157617Y2[t] + 0.0892563503721176M1[t] + 0.0340631621848652M2[t] + 0.00541120381002455M3[t] + 0.174002531815138M4[t] + 0.646315973961958M5[t] -0.199110925542800M6[t] + 0.148855305434789M7[t] + 0.174528724554786M8[t] + 0.0891393379223372M9[t] + 0.27550884188137M10[t] + 0.244894451851841M11[t] + 0.000867678569418552t + 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)	1.67058536444880	0.602183	2.7742	0.006903	0.003451
X	-0.00940480827711062	0.005569	-1.6887	0.09523	0.047615
Y1	1.4664080020596	0.094486	15.5198	0	0
Y2	-0.578726593157617	0.093488	-6.1904	0	0
M1	0.0892563503721176	0.1261	0.7078	0.481139	0.24057
M2	0.0340631621848652	0.1159	0.2939	0.769605	0.384802
M3	0.00541120381002455	0.116226	0.0466	0.962983	0.481492
M4	0.174002531815138	0.130926	1.329	0.187668	0.093834
M5	0.646315973961958	0.13704	4.7163	1e-05	5e-06
M6	-0.199110925542800	0.12375	-1.609	0.111612	0.055806
M7	0.148855305434789	0.123293	1.2073	0.230907	0.115453
M8	0.174528724554786	0.129559	1.3471	0.181801	0.090901
M9	0.0891393379223372	0.113498	0.7854	0.434579	0.217289
M10	0.27550884188137	0.113471	2.428	0.017455	0.008728
M11	0.244894451851841	0.110375	2.2187	0.029374	0.014687
t	0.000867678569418552	0.001064	0.8156	0.417181	0.20859

Multiple Linear Regression - Regression Statistics
Multiple R	0.96797577325124
R-squared	0.936977097601335
Adjusted R-squared	0.925010723728171
F-TEST (value)	78.3008376248883
F-TEST (DF numerator)	15
F-TEST (DF denominator)	79
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.210508612002221
Sum Squared Residuals	3.50079618244103

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6.1	6.0215298352679	0.0784701647320955
2	6.3	6.21461696016114	0.0853830398388554
3	6.3	6.42838783524353	-0.128387835243527
4	6	6.44166084759497	-0.441660847594966
5	6.2	6.71192073627651	-0.511920736276512
6	6.4	6.20541522030396	0.194584779696037
7	6.8	6.67253511948557	0.127464880514429
8	7.5	7.09089370983957	0.409106290160427
9	7.5	7.8681411047227	-0.368141104722704
10	7.6	7.7593654480553	-0.159365448055307
11	7.6	7.82453309127705	-0.224533091277046
12	7.4	7.52357413950658	-0.123574139506574
13	7.3	7.2273089660928	0.0726910339072044
14	7.1	7.16936191890414	-0.0693619189041445
15	6.9	6.91651398710739	-0.0165139871073851
16	6.8	6.88304372955332	-0.0830437295533228
17	7.5	7.5011992834771	-0.00119928347709286
18	7.6	7.68362899280886	-0.0836289928088626
19	7.8	7.63480392485026	0.165196075149740
20	8	7.8431465564563	0.156853443543697
21	8.1	8.03867354039418	0.0613264596058233
22	8.2	8.34803284478504	-0.148032844785038
23	8.3	8.35626830951873	-0.056268309518726
24	8.2	8.20477160043735	-0.00477160043735051
25	8	8.01138178032943	-0.0113817803294315
26	7.9	7.74704031196364	0.152959688036361
27	7.6	7.73162266865849	-0.13162266865849
28	7.6	7.4588411609574	0.141158839042606
29	8.3	8.26646248115951	0.0335375188404909
30	8.4	8.42487684097312	-0.0248768409731164
31	8.4	8.32808725080125	0.0719127491987509
32	8.4	8.27888655344839	0.121113446551607
33	8.4	8.31756783381551	0.082432166184488
34	8.6	8.52079319041505	0.0792068095849475
35	8.9	8.80125673426566	0.0987432657343409
36	8.8	8.86353790724309	-0.0635379072430851
37	8.3	8.48574766808074	-0.185747668080739
38	7.5	7.84825793786455	-0.348257937864551
39	7.2	6.98843699851436	0.211563001485637
40	7.4	7.04928756311756	0.350712436882440
41	8.8	8.21508366084366	0.584916339156341
42	9.3	9.2193451263554	0.0806548736446009
43	9.3	9.32846262331753	-0.0284626233175294
44	8.7	9.10137869588116	-0.401378695881158
45	8.2	8.19720295955587	0.00279704044412738
46	8.3	8.02010315598645	0.279896844013552
47	8.5	8.49219419925088	0.0078058007491174
48	8.6	8.47135011630437	0.128649883695627
49	8.5	8.50960731398177	-0.0096073139817724
50	8.2	8.26863748056873	-0.0686374805687286
51	8.1	7.9030060583636	0.196993941636394
52	7.9	7.95084627190112	-0.0508462719011148
53	8.6	8.49897712466585	0.101022875334152
54	8.7	8.65745766130251	0.0425423386974851
55	8.7	8.58323961099571	0.116760389004286
56	8.5	8.62056314979227	-0.120563149792274
57	8.4	8.2154858973137	0.184514102686296
58	8.5	8.43295885206894	0.0670411479310616
59	8.7	8.63876146744501	0.0612385325549858
60	8.7	8.61415546118766	0.085844538812338
61	8.6	8.44934300899644	0.150656991003563
62	8.5	8.38756786167388	0.112432138326119
63	8.3	8.18449120482884	0.115508795171158
64	8	8.0743386714048	-0.0743386714047969
65	8.2	8.49796311182631	-0.297963111826309
66	8.1	7.9782908642658	0.121709135734197
67	8.1	7.93024989661265	0.169750103387354
68	8	7.99867547954673	0.0013245204532663
69	7.9	7.80137028107534	0.0986297189246578
70	7.9	8.0023517329341	-0.102351732934102
71	8	7.99285844768131	0.00714155231869007
72	8	7.90205584039883	0.097944159601175
73	7.9	7.8073422982836	0.0926577017163944
74	8	7.72581705357912	0.274182946420882
75	7.7	7.87245084680866	-0.172450846808662
76	7.2	7.46981680806038	-0.269816808060378
77	7.5	7.61571067013873	-0.115710670138734
78	7.3	7.39040088955789	-0.0904008895578896
79	7	7.19427531204567	-0.194275312045673
80	7	6.80353172580534	0.196468274194664
81	7	6.9782117510113	0.0217882489887020
82	7.2	7.29053288362532	-0.0905328836253206
83	7.3	7.45343632401205	-0.153436324012047
84	7.1	7.22055493492213	-0.120554934922129
85	6.8	6.98773912896731	-0.187739128967314
86	6.4	6.5387004752848	-0.138700475284793
87	6.1	6.17509040047512	-0.0750904004751244
88	6.5	6.07216494741047	0.427835052589532
89	7.7	7.49268293161234	0.207317068387664
90	7.9	8.14058440443245	-0.240584404432451
91	7.5	7.92834626189136	-0.428346261891358
92	6.9	7.26292412923023	-0.362924129230230
93	6.6	6.68334663211139	-0.0833466321113905
94	6.9	6.8258618921298	0.0741381078702057
95	7.7	7.44069142654932	0.259308573450685

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.376152869936109	0.752305739872218	0.623847130063891
20	0.455051595033538	0.910103190067077	0.544948404966462
21	0.31413201187733	0.62826402375466	0.68586798812267
22	0.442399620921897	0.884799241843794	0.557600379078103
23	0.350929498532526	0.701858997065052	0.649070501467474
24	0.259905737282412	0.519811474564825	0.740094262717588
25	0.196804932892412	0.393609865784825	0.803195067107587
26	0.12967627511142	0.25935255022284	0.87032372488858
27	0.133473453148402	0.266946906296804	0.866526546851598
28	0.108057211764215	0.216114423528431	0.891942788235785
29	0.0832357593977387	0.166471518795477	0.916764240602261
30	0.0574771124577065	0.114954224915413	0.942522887542294
31	0.0519236401473644	0.103847280294729	0.948076359852636
32	0.110672925121807	0.221345850243615	0.889327074878193
33	0.0886705955594159	0.177341191118832	0.911329404440584
34	0.0864686904551533	0.172937380910307	0.913531309544847
35	0.0599140764097585	0.119828152819517	0.940085923590241
36	0.0571092761439248	0.114218552287850	0.942890723856075
37	0.116648974901392	0.233297949802784	0.883351025098608
38	0.585583539069906	0.828832921860188	0.414416460930094
39	0.523094628595548	0.953810742808904	0.476905371404452
40	0.457396164581599	0.914792329163198	0.542603835418401
41	0.521373269486454	0.957253461027092	0.478626730513546
42	0.48138039435303	0.96276078870606	0.51861960564697
43	0.420954223476029	0.841908446952058	0.579045776523971
44	0.71452023609684	0.570959527806321	0.285479763903160
45	0.66881214569404	0.66237570861192	0.33118785430596
46	0.626480712601596	0.747038574796807	0.373519287398404
47	0.721111327857574	0.557777344284851	0.278888672142426
48	0.691725826205221	0.616548347589558	0.308274173794779
49	0.7012320523259	0.597535895348199	0.298767947674099
50	0.758709080176043	0.482581839647913	0.241290919823957
51	0.699847393641677	0.600305212716647	0.300152606358323
52	0.757028922431136	0.485942155137728	0.242971077568864
53	0.710416811612965	0.57916637677407	0.289583188387035
54	0.694749819720998	0.610500360558004	0.305250180279002
55	0.651086305572136	0.697827388855728	0.348913694427864
56	0.76899301556745	0.462013968865099	0.231006984432549
57	0.711779937016761	0.576440125966478	0.288220062983239
58	0.672417614911572	0.655164770176855	0.327582385088428
59	0.628112936131121	0.743774127737759	0.371887063868879
60	0.567637877400048	0.864724245199903	0.432362122599952
61	0.525013060673614	0.949973878652771	0.474986939326386
62	0.457070932080749	0.914141864161498	0.542929067919251
63	0.484103501486185	0.96820700297237	0.515896498513815
64	0.428424281656845	0.85684856331369	0.571575718343155
65	0.670075801118775	0.65984839776245	0.329924198881225
66	0.639186410516107	0.721627178967786	0.360813589483893
67	0.658553822922088	0.682892354155823	0.341446177077912
68	0.662952323535541	0.674095352928919	0.337047676464459
69	0.605240169803533	0.789519660392935	0.394759830196467
70	0.534242021475399	0.931515957049201	0.465757978524601
71	0.436870608962228	0.873741217924455	0.563129391037772
72	0.331185947947650	0.662371895895299	0.66881405205235
73	0.565997077032793	0.868005845934414	0.434002922967207
74	0.607015214184824	0.785969571630351	0.392984785815176
75	0.776325894037877	0.447348211924246	0.223674105962123
76	0.643776035780444	0.712447928439113	0.356223964219556

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/2009/Nov/19/t1258652934tzxstiz0uybq3gy/10mt981258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/10mt981258652872.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/1rbsb1258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/1rbsb1258652872.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/2jroj1258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/2jroj1258652872.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/30d7o1258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/30d7o1258652872.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/4kmg71258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/4kmg71258652872.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/59pc41258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/59pc41258652872.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/6x92q1258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/6x92q1258652872.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/7sgkj1258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/7sgkj1258652872.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/83v081258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/83v081258652872.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/9qfyh1258652872.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258652934tzxstiz0uybq3gy/9qfyh1258652872.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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