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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, 23 Nov 2010 16:41:37 +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/23/t1290530412fcsxp8tpp7sqhbm.htm/, Retrieved Tue, 23 Nov 2010 17:40:15 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm.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 «

10 14 0 11 0 24 0 26 0 14 11 11 7 7 25 25 23 23 18 6 6 17 17 30 30 25 25 15 12 0 10 0 19 0 23 0 18 8 8 12 12 22 22 19 19 11 10 10 12 12 22 22 29 29 17 10 10 11 11 25 25 25 25 19 11 11 11 11 23 23 21 21 7 16 16 12 12 17 17 22 22 12 11 11 13 13 21 21 25 25 13 13 0 14 0 19 0 24 0 15 12 12 16 16 19 19 18 18 14 8 8 11 11 15 15 22 22 14 12 12 10 10 16 16 15 15 16 11 0 11 0 23 0 22 0 16 4 0 15 0 27 0 28 0 12 9 9 9 9 22 22 20 20 12 8 0 11 0 14 0 12 0 13 8 0 17 0 22 0 24 0 16 14 14 17 17 23 23 20 20 9 15 15 11 11 23 23 21 21 11 11 0 11 0 20 0 28 0 14 8 8 15 15 23 23 24 24 11 9 0 13 0 19 0 24 0 17 9 9 13 13 22 22 23 23 14 8 8 12 12 32 32 25 25 15 9 9 17 17 25 25 21 21 11 16 0 9 0 29 0 26 0 15 11 0 9 0 28 0 22 0 14 16 0 12 0 17 0 22 0 11 12 12 18 18 28 28 22 22 12 12 0 12 0 29 0 23 0 9 10 0 15 0 14 0 17 0 16 9 9 16 16 25 25 23 23 13 10 0 10 0 26 0 23 0 15 12 0 11 0 20 0 25 0 10 14 0 9 0 32 0 24 0 13 14 14 17 17 25 25 21 21 16 10 10 12 12 20 20 28 28 15 6 6 6 6 15 15 16 16 13 13 13 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	7 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

Perceived_happiness[t] = + 18.0703506993062 -0.345268804897921Doubts_about_actions[t] -0.169898748342036`Doubts_about_actions*G`[t] -0.0551277931462227Parental_expectations[t] + 0.246451657746884`Parental_expectations*G`[t] -0.0770701169689628Personal_standards[t] + 0.172410480676388`Personal_standards*G`[t] + 0.0569201045809098Organization[t] -0.200013667756433`Organization*G`[t] + 0.00150951858246073t + 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)	18.0703506993062	2.565253	7.0443	0	0
Doubts_about_actions	-0.345268804897921	0.147741	-2.337	0.022307	0.011153
`Doubts_about_actions*G`	-0.169898748342036	0.182982	-0.9285	0.35634	0.17817
Parental_expectations	-0.0551277931462227	0.138559	-0.3979	0.691941	0.345971
`Parental_expectations*G`	0.246451657746884	0.17258	1.428	0.157725	0.078862
Personal_standards	-0.0770701169689628	0.10012	-0.7698	0.444021	0.22201
`Personal_standards*G`	0.172410480676388	0.132078	1.3054	0.19604	0.09802
Organization	0.0569201045809098	0.123354	0.4614	0.645916	0.322958
`Organization*G`	-0.200013667756433	0.14675	-1.363	0.177266	0.088633
t	0.00150951858246073	0.011175	0.1351	0.892935	0.446468

Multiple Linear Regression - Regression Statistics
Multiple R	0.518248205118805
R-squared	0.268581202108863
Adjusted R-squared	0.174541642380002
F-TEST (value)	2.85604486966176
F-TEST (DF numerator)	9
F-TEST (DF denominator)	70
p-value	0.00631789732661947
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.25228355117066
Sum Squared Residuals	355.094683641174

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	10	12.2619311365579	-2.26193113655788
2	14	12.8381508426848	1.16184915731519
3	18	17.5192514656597	0.48074853434025
4	15	13.2267153663494	1.77328463365062
5	18	15.6311545427352	2.36884545726481
6	11	13.1713933230825	-2.17139332308251
7	17	13.8399743208887	3.16002567911133
8	19	13.7080098115184	5.29199018848158
9	7	10.6098696830817	-3.60986968308168
10	12	13.3306215977677	-1.33062159776772
11	13	12.7284221235247	0.271577876475294
12	15	14.2034188903085	0.796581109691524
13	14	14.3552435913157	-0.355243591315667
14	14	13.2017543382737	0.798245661726278
15	16	13.1682605100514	2.83173948994861
16	16	15.399380649944	0.600619350055991
17	12	14.4170360555073	-2.41703605550725
18	12	14.3330254874041	-2.3330254874041
19	13	14.0702485663284	-1.07024856632844
20	16	13.4716581255676	2.52834187443244
21	9	11.6669633401306	-2.66696334013058
22	11	13.751558118521	-2.75155811852096
23	14	15.6121700188513	-1.61217001885127
24	11	14.1842488778346	-3.1842488778346
25	17	14.765126973043	2.23487302695698
26	14	15.757696690988	-1.75769669098798
27	15	16.1056496860839	-1.1056496860839
28	11	11.3370555299361	-0.337055529936082
29	15	12.9142987716535	2.08570122834653
30	14	11.8718521729662	2.12814782703375
31	11	14.9004364932413	-3.90043649324129
32	12	12.3880251306762	-0.38802513067621
33	9	13.7292200066648	-4.72922000666483
34	16	15.6387053252094	0.361294674790579
35	13	13.4245572334188	-0.424557233418767
36	15	13.2566622600348	1.74333773996524
37	10	11.6961282469054	-1.69612824690536
38	13	13.5464166242912	-0.546416624291184
39	16	13.1736202720644	2.82637972793562
40	15	15.3282777555719	-0.328277755571851
41	13	11.8694045411636	1.13059545883639
42	16	13.2090175117778	2.79098248822217
43	15	15.2307041669913	-0.230704166991284
44	16	11.9417345392393	4.05826546076072
45	15	13.9426460809486	1.0573539190514
46	13	13.486838896906	-0.486838896906002
47	11	13.2332835849634	-2.2332835849634
48	17	13.9285440006273	3.07145599937273
49	10	13.1686396290431	-3.16863962904307
50	17	14.4478104012621	2.55218959873794
51	14	13.0662341439889	0.933765856011064
52	15	13.3927351038594	1.60726489614055
53	16	15.2374623515638	0.762537648436204
54	12	13.3116306536396	-1.31163065363964
55	11	13.0813973300353	-2.0813973300353
56	16	16.043988724088	-0.0439887240879911
57	9	12.95411824604	-3.95411824604005
58	15	13.3025814034348	1.69741859656521
59	15	13.9303209366642	1.06967906333577
60	13	13.8657852389493	-0.865785238949253
61	15	15.1162305074995	-0.116230507499534
62	15	14.2788948194315	0.721105180568487
63	18	15.7680571610859	2.2319428389141
64	16	12.8360715888715	3.16392841112854
65	12	12.5350689078074	-0.535068907807392
66	15	15.3917013639606	-0.391701363960622
67	13	16.0847926361749	-3.08479263617491
68	13	15.1067698984458	-2.10676989844584
69	13	12.1843672523564	0.815632747643603
70	14	13.1792320331751	0.82076796682491
71	15	14.1378589804472	0.862141019552834
72	11	13.3099557886439	-2.30995578864391
73	14	13.1361061674756	0.863893832524391
74	17	14.637195779846	2.36280422015396
75	13	14.7164163389786	-1.7164163389786
76	12	14.2106230607561	-2.21062306075612
77	13	13.7363763671747	-0.736376367174665
78	16	14.4927526365224	1.50724736347762
79	13	14.9421683791614	-1.94216837916136
80	19	16.0202928903622	2.97970710963783

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.437772229535027	0.875544459070053	0.562227770464973
14	0.582381482466691	0.835237035066617	0.417618517533309
15	0.458596636478108	0.917193272956216	0.541403363521892
16	0.385873374959679	0.771746749919359	0.61412662504032
17	0.82927803614241	0.341443927715181	0.170721963857591
18	0.8857715815869	0.2284568368262	0.1142284184131
19	0.82982253521735	0.340354929565299	0.170177464782649
20	0.801935751780917	0.396128496438166	0.198064248219083
21	0.831227796950172	0.337544406099657	0.168772203049828
22	0.81405867113397	0.371882657732059	0.185941328866029
23	0.753254934036226	0.493490131927548	0.246745065963774
24	0.739019850524853	0.521960298950294	0.260980149475147
25	0.797064835304757	0.405870329390487	0.202935164695243
26	0.770770556933691	0.458458886132618	0.229229443066309
27	0.715912460797774	0.568175078404452	0.284087539202226
28	0.647491374013458	0.705017251973084	0.352508625986542
29	0.621965191974821	0.756069616050358	0.378034808025179
30	0.760481976285808	0.479036047428384	0.239518023714192
31	0.827365264777457	0.345269470445085	0.172634735222543
32	0.775558063115986	0.448883873768027	0.224441936884014
33	0.906479417605451	0.187041164789097	0.0935205823945485
34	0.886298282145543	0.227403435708914	0.113701717854457
35	0.846709090897876	0.306581818204248	0.153290909102124
36	0.850373152994412	0.299253694011176	0.149626847005588
37	0.827706548248824	0.344586903502353	0.172293451751176
38	0.80611754167383	0.387764916652339	0.193882458326169
39	0.86381981880009	0.27236036239982	0.13618018119991
40	0.827393350502887	0.345213298994227	0.172606649497113
41	0.79511716938782	0.409765661224359	0.204882830612179
42	0.856778966780601	0.286442066438797	0.143221033219399
43	0.845925357013542	0.308149285972916	0.154074642986458
44	0.935424041277188	0.129151917445624	0.0645759587228121
45	0.91134890375691	0.177302192486179	0.0886510962430897
46	0.876752360041717	0.246495279916567	0.123247639958283
47	0.871622243064106	0.256755513871789	0.128377756935894
48	0.889865901564334	0.220268196871331	0.110134098435666
49	0.893054791548015	0.21389041690397	0.106945208451985
50	0.890477076842412	0.219045846315176	0.109522923157588
51	0.892275100349663	0.215449799300673	0.107724899650337
52	0.890292946601157	0.219414106797685	0.109707053398843
53	0.85428028440245	0.2914394311951	0.14571971559755
54	0.808222689074557	0.383554621850885	0.191777310925443
55	0.783849666691044	0.432300666617913	0.216150333308956
56	0.712959305816612	0.574081388366776	0.287040694183388
57	0.775958977868756	0.448082044262488	0.224041022131244
58	0.718634778871835	0.56273044225633	0.281365221128165
59	0.640467323077022	0.719065353845955	0.359532676922977
60	0.545165623311738	0.909668753376524	0.454834376688262
61	0.442511744211054	0.88502348842211	0.557488255788946
62	0.358737402194881	0.717474804389763	0.641262597805119
63	0.43405296914623	0.86810593829246	0.56594703085377
64	0.34624707689513	0.69249415379026	0.65375292310487
65	0.28967419837679	0.57934839675358	0.71032580162321
66	0.216754533551072	0.433509067102144	0.783245466448928
67	0.28773722362668	0.57547444725336	0.71226277637332

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/104mvn1290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/104mvn1290530486.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/1x3gb1290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/1x3gb1290530486.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/2x3gb1290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/2x3gb1290530486.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/38cfw1290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/38cfw1290530486.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/48cfw1290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/48cfw1290530486.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/58cfw1290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/58cfw1290530486.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/6jmez1290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/6jmez1290530486.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/7cdw21290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/7cdw21290530486.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/8cdw21290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/8cdw21290530486.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/9cdw21290530486.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290530412fcsxp8tpp7sqhbm/9cdw21290530486.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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