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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 09:08:19 -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/t1258647099rpqmf1n3gb11ipz.htm/, Retrieved Thu, 19 Nov 2009 17:11:51 +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/t1258647099rpqmf1n3gb11ipz.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:

ws7l1

Dataseries X:

» Textbox « » Textfile « » CSV «

98,8 6,3 100,5 6,1 110,4 6,1 96,4 6,3 101,9 6,3 106,2 6 81 6,2 94,7 6,4 101 6,8 109,4 7,5 102,3 7,5 90,7 7,6 96,2 7,6 96,1 7,4 106 7,3 103,1 7,1 102 6,9 104,7 6,8 86 7,5 92,1 7,6 106,9 7,8 112,6 8 101,7 8,1 92 8,2 97,4 8,3 97 8,2 105,4 8 102,7 7,9 98,1 7,6 104,5 7,6 87,4 8,3 89,9 8,4 109,8 8,4 111,7 8,4 98,6 8,4 96,9 8,6 95,1 8,9 97 8,8 112,7 8,3 102,9 7,5 97,4 7,2 111,4 7,4 87,4 8,8 96,8 9,3 114,1 9,3 110,3 8,7 103,9 8,2 101,6 8,3 94,6 8,5 95,9 8,6 104,7 8,5 102,8 8,2 98,1 8,1 113,9 7,9 80,9 8,6 95,7 8,7 113,2 8,7 105,9 8,5 108,8 8,4 102,3 8,5 99 8,7 100,7 8,7 115,5 8,6 100,7 8,5 109,9 8,3 114,6 8 85,4 8,2 100,5 8,1 114,8 8,1 116,5 8 112,9 7,9 102 7,9 106 8 105,3 8 118,8 7,9 106,1 8 109,3 7,7 117,2 7,2 92,5 7,5 104,2 7,3 112,5 7 122,4 7 113,3 7 100 7,2 110,7 7,3 112,8 7,1 109,8 6,8 117,3 6,4 109,1 6,1 115,9 6,5 96 7,7 99,8 7,9 116,8 7,5 115,7 6,9 99,4 6,6 94,3 6,9 91 7,7

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] = + 113.617161955274 -1.35094011427636X[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)	113.617161955274	8.874609	12.8025	0	0
X	-1.35094011427636	1.142461	-1.1825	0.239965	0.119983

Multiple Linear Regression - Regression Statistics
Multiple R	0.120437052145418
R-squared	0.0145050835294782
Adjusted R-squared	0.00413145282978844
F-TEST (value)	1.39826488424270
F-TEST (DF numerator)	1
F-TEST (DF denominator)	95
p-value	0.239965421501360
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.87412361611837
Sum Squared Residuals	7481.25664564423

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	98.8	105.106239235332	-6.30623923533175
2	100.5	105.376427258188	-4.87642725818804
3	110.4	105.376427258188	5.02357274181197
4	96.4	105.106239235333	-8.70623923533275
5	101.9	105.106239235333	-3.20623923533275
6	106.2	105.511521269616	0.688478730384335
7	81	105.241333246760	-24.2413332467604
8	94.7	104.971145223905	-10.2711452239051
9	101	104.430769178195	-3.43076917819458
10	109.4	103.485111098201	5.91488890179888
11	102.3	103.485111098201	-1.18511109820112
12	90.7	103.350017086773	-12.6500170867735
13	96.2	103.350017086773	-7.15001708677348
14	96.1	103.620205109629	-7.52020510962876
15	106	103.755299121056	2.24470087894361
16	103.1	104.025487143912	-0.925487143911673
17	102	104.295675166767	-2.29567516676694
18	104.7	104.430769178195	0.269230821805427
19	86	103.485111098201	-17.4851110982011
20	92.1	103.350017086773	-11.2500170867735
21	106.9	103.079829063918	3.82017093608179
22	112.6	102.809641041063	9.79035895893706
23	101.7	102.674547029635	-0.9745470296353
24	92	102.539453018208	-10.5394530182077
25	97.4	102.40435900678	-5.00435900678002
26	97	102.539453018208	-5.53945301820767
27	105.4	102.809641041063	2.59035895893707
28	102.7	102.944735052491	-0.244735052490571
29	98.1	103.350017086773	-5.25001708677349
30	104.5	103.350017086773	1.14998291322652
31	87.4	102.40435900678	-15.0043590067800
32	89.9	102.269264995352	-12.3692649953524
33	109.8	102.269264995352	7.5307350046476
34	111.7	102.269264995352	9.43073500464761
35	98.6	102.269264995352	-3.6692649953524
36	96.9	101.999076972497	-5.09907697249711
37	95.1	101.593794938214	-6.49379493821422
38	97	101.728888949642	-4.72888894964185
39	112.7	102.40435900678	10.2956409932200
40	102.9	103.485111098201	-0.585111098201115
41	97.4	103.890393132484	-6.49039313248402
42	111.4	103.620205109629	7.77979489037125
43	87.4	101.728888949642	-14.3288889496418
44	96.8	101.053418892504	-4.25341889250367
45	114.1	101.053418892504	13.0465811074963
46	110.3	101.863982961069	8.43601703893051
47	103.9	102.539453018208	1.36054698179234
48	101.6	102.40435900678	-0.804359006780033
49	94.6	102.134170983925	-7.53417098392476
50	95.9	101.999076972497	-6.09907697249711
51	104.7	102.134170983925	2.56582901607525
52	102.8	102.539453018208	0.260546981792331
53	98.1	102.674547029635	-4.57454702963531
54	113.9	102.944735052491	10.9552649475094
55	80.9	101.999076972497	-21.0990769724971
56	95.7	101.863982961069	-6.16398296106948
57	113.2	101.863982961069	11.3360170389305
58	105.9	102.134170983925	3.76582901607525
59	108.8	102.269264995352	6.5307350046476
60	102.3	102.134170983925	0.165829016075242
61	99	101.863982961069	-2.86398296106948
62	100.7	101.863982961069	-1.16398296106948
63	115.5	101.999076972497	13.5009230275029
64	100.7	102.134170983925	-1.43417098392475
65	109.9	102.40435900678	7.49564099321998
66	114.6	102.809641041063	11.7903589589371
67	85.4	102.539453018208	-17.1394530182077
68	100.5	102.674547029635	-2.1745470296353
69	114.8	102.674547029635	12.1254529703647
70	116.5	102.809641041063	13.6903589589371
71	112.9	102.944735052491	9.95526494750943
72	102	102.944735052491	-0.944735052490574
73	106	102.809641041063	3.19035895893706
74	105.3	102.809641041063	2.49035895893706
75	118.8	102.944735052491	15.8552649475094
76	106.1	102.809641041063	3.29035895893706
77	109.3	103.214923075346	6.08507692465415
78	117.2	103.890393132484	13.3096068675160
79	92.5	103.485111098201	-10.9851110982011
80	104.2	103.755299121056	0.444700878943609
81	112.5	104.160581155339	8.3394188446607
82	122.4	104.160581155339	18.2394188446607
83	113.3	104.160581155339	9.1394188446607
84	100	103.890393132484	-3.89039313248403
85	110.7	103.755299121056	6.94470087894361
86	112.8	104.025487143912	8.77451285608833
87	109.8	104.430769178195	5.36923082180542
88	117.3	104.971145223905	12.3288547760949
89	109.1	105.376427258188	3.72357274181196
90	115.9	104.836051212477	11.0639487875225
91	96	103.214923075346	-7.21492307534585
92	99.8	102.944735052491	-3.14473505249058
93	116.8	103.485111098201	13.3148889017989
94	115.7	104.295675166767	11.4043248332331
95	99.4	104.700957201050	-5.30095720104984
96	94.3	104.295675166767	-9.99567516676694
97	91	103.214923075346	-12.2149230753458

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.149074967562460	0.298149935124921	0.85092503243754
6	0.0646632869836963	0.129326573967393	0.935336713016304
7	0.673032902609737	0.653934194780525	0.326967097390263
8	0.570488341644719	0.859023316710562	0.429511658355281
9	0.581675289451948	0.836649421096105	0.418324710548052
10	0.556497314184382	0.887005371631237	0.443502685815618
11	0.45968332482314	0.91936664964628	0.54031667517686
12	0.53762015411245	0.9247596917751	0.46237984588755
13	0.463839859321882	0.927679718643763	0.536160140678118
14	0.393772915540482	0.787545831080964	0.606227084459518
15	0.360590649746759	0.721181299493518	0.639409350253241
16	0.296106195093588	0.592212390187176	0.703893804906412
17	0.235302702875044	0.470605405750087	0.764697297124956
18	0.194692173415574	0.389384346831148	0.805307826584426
19	0.336313686593808	0.672627373187616	0.663686313406192
20	0.328350696273129	0.656701392546258	0.671649303726871
21	0.328745770754477	0.657491541508954	0.671254229245523
22	0.410410842810594	0.820821685621188	0.589589157189406
23	0.342454034989333	0.684908069978666	0.657545965010667
24	0.355613400161163	0.711226800322325	0.644386599838837
25	0.301263399062191	0.602526798124383	0.698736600937809
26	0.253996547874179	0.507993095748358	0.746003452125821
27	0.223201253552761	0.446402507105523	0.776798746447239
28	0.180696693644651	0.361393387289301	0.81930330635535
29	0.149586692462504	0.299173384925008	0.850413307537496
30	0.122904264285905	0.245808528571809	0.877095735714095
31	0.188028420781455	0.376056841562910	0.811971579218545
32	0.211993444623134	0.423986889246267	0.788006555376866
33	0.241289479877703	0.482578959755406	0.758710520122297
34	0.287843028439013	0.575686056878026	0.712156971560987
35	0.241844532098043	0.483689064196085	0.758155467901957
36	0.205371673580851	0.410743347161703	0.794628326419148
37	0.178510679685576	0.357021359371152	0.821489320314424
38	0.146486465471045	0.29297293094209	0.853513534528955
39	0.191026047474547	0.382052094949094	0.808973952525453
40	0.158286638267557	0.316573276535115	0.841713361732443
41	0.146749245748461	0.293498491496921	0.85325075425154
42	0.158491774685074	0.316983549370148	0.841508225314926
43	0.221313306135168	0.442626612270335	0.778686693864832
44	0.183875288675882	0.367750577351763	0.816124711324118
45	0.277457555018997	0.554915110037994	0.722542444981003
46	0.291240289707267	0.582480579414534	0.708759710292733
47	0.246717456017296	0.493434912034592	0.753282543982704
48	0.203495713252598	0.406991426505196	0.796504286747402
49	0.191712281685747	0.383424563371495	0.808287718314253
50	0.170522818735105	0.34104563747021	0.829477181264895
51	0.140938802867773	0.281877605735546	0.859061197132227
52	0.112088789822736	0.224177579645472	0.887911210177264
53	0.0947818612012325	0.189563722402465	0.905218138798767
54	0.117486079229421	0.234972158458842	0.882513920770579
55	0.350242348500643	0.700484697001286	0.649757651499357
56	0.332932456257888	0.665864912515776	0.667067543742112
57	0.374994605503446	0.749989211006893	0.625005394496554
58	0.33035636751575	0.6607127350315	0.66964363248425
59	0.306403589465761	0.612807178931522	0.693596410534239
60	0.257172837997714	0.514345675995427	0.742827162002286
61	0.219274841224003	0.438549682448006	0.780725158775997
62	0.180603791497174	0.361207582994348	0.819396208502826
63	0.237776358522254	0.475552717044508	0.762223641477746
64	0.195902498002144	0.391804996004289	0.804097501997856
65	0.181859830122120	0.363719660244241	0.81814016987788
66	0.212422371228964	0.424844742457928	0.787577628771036
67	0.406403334721975	0.81280666944395	0.593596665278025
68	0.364040310124507	0.728080620249015	0.635959689875493
69	0.39665568946552	0.79331137893104	0.60334431053448
70	0.469441607504538	0.938883215009076	0.530558392495462
71	0.477225718044941	0.954451436089882	0.522774281955059
72	0.414765276089259	0.829530552178518	0.585234723910741
73	0.355295094672182	0.710590189344364	0.644704905327818
74	0.296345797004483	0.592691594008967	0.703654202995517
75	0.450249458940694	0.900498917881387	0.549750541059306
76	0.401671247630167	0.803342495260334	0.598328752369833
77	0.376372373237533	0.752744746475067	0.623627626762467
78	0.44200084883167	0.88400169766334	0.55799915116833
79	0.48454817392826	0.96909634785652	0.51545182607174
80	0.408896383280771	0.817792766561541	0.59110361671923
81	0.364639961990375	0.729279923980751	0.635360038009624
82	0.544042793570597	0.911914412858807	0.455957206429403
83	0.512750100586546	0.974499798826908	0.487249899413454
84	0.451398437952159	0.902796875904319	0.548601562047841
85	0.405827829219458	0.811655658438916	0.594172170780542
86	0.377883502658051	0.755767005316101	0.622116497341950
87	0.294076578804641	0.588153157609282	0.705923421195359
88	0.272509149796650	0.545018299593301	0.72749085020335
89	0.19088346359509	0.38176692719018	0.80911653640491
90	0.174283473135003	0.348566946270005	0.825716526864997
91	0.117762250622366	0.235524501244732	0.882237749377634
92	0.0617803322374873	0.123560664474975	0.938219667762513

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/t1258647099rpqmf1n3gb11ipz/104lhv1258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/104lhv1258646894.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/1694v1258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/1694v1258646894.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/22mr91258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/22mr91258646894.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/33gpu1258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/33gpu1258646894.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/4ww511258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/4ww511258646894.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/5j59q1258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/5j59q1258646894.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/67awd1258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/67awd1258646894.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/7kv1x1258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/7kv1x1258646894.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/8kawt1258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/8kawt1258646894.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/9ybu61258646894.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647099rpqmf1n3gb11ipz/9ybu61258646894.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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