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R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sat, 09 Jan 2010 08:14:00 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69.htm/, Retrieved Sat, 09 Jan 2010 16:14:57 +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/Jan/09/t1263050085ulry3dzlechgn69.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 «

8.8 8.9 8.6 8.4 8.4 8.4 99.4 8.3 8.8 8.9 8.6 8.4 8.4 99.8 7.5 8.3 8.8 8.9 8.6 8.4 99.9 7.2 7.5 8.3 8.8 8.9 8.6 100 7.4 7.2 7.5 8.3 8.8 8.9 100.1 8.8 7.4 7.2 7.5 8.3 8.8 100.1 9.3 8.8 7.4 7.2 7.5 8.3 100.2 9.3 9.3 8.8 7.4 7.2 7.5 100.3 8.7 9.3 9.3 8.8 7.4 7.2 100 8.2 8.7 9.3 9.3 8.8 7.4 99.9 8.3 8.2 8.7 9.3 9.3 8.8 99.4 8.5 8.3 8.2 8.7 9.3 9.3 99.8 8.6 8.5 8.3 8.2 8.7 9.3 99.6 8.5 8.6 8.5 8.3 8.2 8.7 100 8.2 8.5 8.6 8.5 8.3 8.2 99.9 8.1 8.2 8.5 8.6 8.5 8.3 100.3 7.9 8.1 8.2 8.5 8.6 8.5 100.6 8.6 7.9 8.1 8.2 8.5 8.6 100.7 8.7 8.6 7.9 8.1 8.2 8.5 100.8 8.7 8.7 8.6 7.9 8.1 8.2 100.8 8.5 8.7 8.7 8.6 7.9 8.1 100.6 8.4 8.5 8.7 8.7 8.6 7.9 101.1 8.5 8.4 8.5 8.7 8.7 8.6 101.1 8.7 8.5 8.4 8.5 8.7 8.7 100.9 8.7 8.7 8.5 8.4 8.5 8.7 101.1 8.6 8.7 8.7 8.5 8.4 8.5 101.2 8.5 8.6 8.7 8.7 8.5 8.4 101.4 8.3 8.5 8.6 8.7 8.7 8.5 101.9 8 8.3 8.5 8.6 8.7 8.7 102.1 8.2 8 8.3 8.5 8.6 8.7 102.1 8.1 8.2 8 8.3 8.5 8.6 103 8.1 8.1 8.2 8 8.3 8.5 103.4 8 8.1 8.1 8.2 8 8.3 103.2 7.9 8 8.1 8.1 8.2 8 103.1 7. 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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

werkl[t] = + 19.4250848391952 + 1.38018027566590`werkl-1`[t] -0.694005422132068`werkl-2`[t] -0.200396397847844`werkl-3`[t] + 0.217499603220255`werkl-4`[t] + 0.0508481331699843`werkl-5`[t] -0.174579752192536afzetp[t] -0.226257796357584M1[t] -0.129506295557380M2[t] -0.148491155978233M3[t] -0.169728355178839M4[t] -0.119227957982638M5[t] + 0.489046736630819M6[t] -0.415557532986574M7[t] -0.10456110042014M8[t] + 0.0158936263692312M9[t] -0.0350934389340346M10[t] + 0.0506195813463781M11[t] + 0.0162133957639823t + 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)	19.4250848391952	6.034377	3.2191	0.002284	0.001142
`werkl-1`	1.38018027566590	0.146104	9.4465	0	0
`werkl-2`	-0.694005422132068	0.247281	-2.8065	0.007166	0.003583
`werkl-3`	-0.200396397847844	0.270402	-0.7411	0.462167	0.231084
`werkl-4`	0.217499603220255	0.248385	0.8757	0.385489	0.192745
`werkl-5`	0.0508481331699843	0.134054	0.3793	0.706096	0.353048
afzetp	-0.174579752192536	0.055399	-3.1513	0.002771	0.001386
M1	-0.226257796357584	0.102885	-2.1991	0.032619	0.01631
M2	-0.129506295557380	0.116627	-1.1104	0.272234	0.136117
M3	-0.148491155978233	0.113998	-1.3026	0.19881	0.099405
M4	-0.169728355178839	0.108223	-1.5683	0.123242	0.061621
M5	-0.119227957982638	0.106469	-1.1198	0.26824	0.13412
M6	0.489046736630819	0.103489	4.7256	2e-05	1e-05
M7	-0.415557532986574	0.11831	-3.5124	0.000965	0.000482
M8	-0.10456110042014	0.152152	-0.6872	0.495186	0.247593
M9	0.0158936263692312	0.162364	0.0979	0.92242	0.46121
M10	-0.0350934389340346	0.131428	-0.267	0.790577	0.395289
M11	0.0506195813463781	0.107891	0.4692	0.641027	0.320513
t	0.0162133957639823	0.005522	2.9362	0.005048	0.002524

Multiple Linear Regression - Regression Statistics
Multiple R	0.980228972060803
R-squared	0.96084883766738
Adjusted R-squared	0.946466778034988
F-TEST (value)	66.8088481223753
F-TEST (DF numerator)	18
F-TEST (DF denominator)	49
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.161355997571108
Sum Squared Residuals	1.2757521396562

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8.8	8.74776213751048	0.0522378624895221
2	8.3	8.40459619942178	-0.104596199421779
3	7.5	7.74705816521561	-0.247058165215610
4	7.2	7.0628940244779	0.137105975522094
5	7.4	7.34700277577762	0.0529972242223853
6	8.8	8.502211051279	0.297788948720997
7	9.3	9.25050867390534	0.0494913260946615
8	9.3	9.13273540679292	0.167264593207078
9	8.7	8.72246526764408	-0.0224652676440766
10	8.2	8.09151228014294	0.108487719857062
11	8.3	8.186978875778	0.113021124222003
12	8.5	8.71342343324491	-0.213423433244914
13	8.6	8.71462893300156	-0.114628933001559
14	8.5	8.597680550532	-0.097680550532003
15	8.2	8.36119510548206	-0.161195105482055
16	8.1	7.97023095485812	0.129769045141880
17	7.9	8.10671364797438	-0.206713647974380
18	8.6	8.55056202256192	0.0494379774380806
19	8.7	8.6993453963835	0.000654603616493099
20	8.7	8.68184433608461	0.0181556639153870
21	8.5	8.59516565440873	-0.0951656544087262
22	8.4	8.3191065094754	0.080893490524606
23	8.5	8.47915963592063	0.0208403640793728
24	8.7	8.7322520634431	-0.0322520634431003
25	8.7	8.6704669444717	0.0295330555283018
26	8.6	8.57521355464941	0.0247864453505907
27	8.5	8.3760939794229	0.123906020577099
28	8.3	8.26374754849768	0.0362524515023253
29	8	8.11911914451816	-0.119119144518163
30	8.2	8.466643916085	-0.266643916085004
31	8.1	7.91861345296165	0.181386547038345
32	8.1	7.91070645381536	0.189293546184643
33	8	8.03619228185078	-0.0361922818507823
34	7.9	7.929143680442	-0.0291436804420043
35	7.9	7.96833025266424	-0.0683302526642403
36	8	7.89607360922806	0.103926390771938
37	8	7.87471084132157	0.125289158678432
38	7.9	7.87398244681427	0.0260175531857289
39	8	7.65569457583127	0.344305424168731
40	7.7	7.84492335205796	-0.144923352057959
41	7.2	7.41839102276844	-0.218391022768442
42	7.5	7.48428505140723	0.0157149485927650
43	7.3	7.43373503767901	-0.133735037679008
44	7	7.2294504394152	-0.229450439415200
45	7	6.83691050190271	0.163089498097291
46	7	7.10770152817295	-0.107701528172952
47	7.2	7.20658543244014	-0.00658543244013645
48	7.3	7.37279579439085	-0.0727957943908475
49	7.1	7.12925592176717	-0.0292559217671732
50	6.8	6.8217889909769	-0.0217889909768962
51	6.4	6.5846827841252	-0.184682784125192
52	6.1	6.27287141314891	-0.172871413148915
53	6.5	6.29466900516647	0.205330994833533
54	7.7	7.6667119087697	0.033288091230301
55	7.9	8.06242590932003	-0.162425909320034
56	7.5	7.61474366719418	-0.11474366719418
57	6.9	6.90926629419371	-0.009266294193706
58	6.6	6.65253600176671	-0.0525360017667118
59	6.9	6.958945803197	-0.0589458031969987
60	7.7	7.48545509969308	0.214544900306925
61	8	8.06317522192752	-0.0631752219275241
62	8	7.82673825760564	0.173261742394358
63	7.7	7.57527538992297	0.124724610077028
64	7.3	7.28533270695943	0.0146672930405738
65	7.4	7.11410440379493	0.285895596205067
66	8.1	8.22958604989714	-0.12958604989714
67	8.3	8.23537152975046	0.0646284702495425
68	8.2	8.23051969669773	-0.0305196966977268

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.071080176356782	0.142160352713564	0.928919823643218
23	0.0331918658114294	0.0663837316228588	0.96680813418857
24	0.0163363979757382	0.0326727959514764	0.983663602024262
25	0.00605337929270302	0.0121067585854060	0.993946620707297
26	0.00514048473153522	0.0102809694630704	0.994859515268465
27	0.139111179960755	0.278222359921509	0.860888820039245
28	0.0914821074073654	0.182964214814731	0.908517892592635
29	0.087107570229665	0.17421514045933	0.912892429770335
30	0.158212515208783	0.316425030417566	0.841787484791217
31	0.122116623791342	0.244233247582684	0.877883376208658
32	0.169047755432358	0.338095510864716	0.830952244567642
33	0.235731315757121	0.471462631514243	0.764268684242879
34	0.20833474723282	0.41666949446564	0.79166525276718
35	0.256393032532873	0.512786065065746	0.743606967467127
36	0.191962711413402	0.383925422826805	0.808037288586598
37	0.150430187494302	0.300860374988604	0.849569812505698
38	0.125460807334195	0.250921614668390	0.874539192665805
39	0.382323427862769	0.764646855725537	0.617676572137231
40	0.468394709634720	0.936789419269441	0.53160529036528
41	0.764225821282755	0.47154835743449	0.235774178717245
42	0.665277535684001	0.669444928631998	0.334722464315999
43	0.674366110165412	0.651267779669176	0.325633889834588
44	0.664693881669431	0.670612236661138	0.335306118330569
45	0.560692153242982	0.878615693514035	0.439307846757018
46	0.436604016129902	0.873208032259803	0.563395983870098

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	3	0.12	NOK
10% type I error level	4	0.16	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/10dlzk1263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/10dlzk1263050033.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/1awvy1263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/1awvy1263050033.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/28f3w1263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/28f3w1263050033.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/3i86x1263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/3i86x1263050033.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/46h6a1263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/46h6a1263050033.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/5hfjy1263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/5hfjy1263050033.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/6xikd1263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/6xikd1263050033.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/7sjvu1263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/7sjvu1263050033.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/8822k1263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/8822k1263050033.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/9ya641263050033.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263050085ulry3dzlechgn69/9ya641263050033.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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