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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 10:49:26 -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/t1258653083c90mh28uqbcgewu.htm/, Retrieved Thu, 19 Nov 2009 18:51:35 +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/t1258653083c90mh28uqbcgewu.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

1,4 2 1,2 2 1 2 1,7 2 2,4 2 2 2 2,1 2 2 2 1,8 2 2,7 2 2,3 2 1,9 2 2 2 2,3 2 2,8 2 2,4 2 2,3 2 2,7 2 2,7 2 2,9 2 3 2 2,2 2 2,3 2 2,8 2,21 2,8 2,25 2,8 2,25 2,2 2,45 2,6 2,5 2,8 2,5 2,5 2,64 2,4 2,75 2,3 2,93 1,9 3 1,7 3,17 2 3,25 2,1 3,39 1,7 3,5 1,8 3,5 1,8 3,65 1,8 3,75 1,3 3,75 1,3 3,9 1,3 4 1,2 4 1,4 4 2,2 4 2,9 4 3,1 4 3,5 4 3,6 4 4,4 4 4,1 4 5,1 4 5,8 4 5,9 4,18 5,4 4,25 5,5 4,25 4,8 3,97 3,2 3,42 2,7 2,75 2,1 2,31 1,9 2 0,6 1,66 0,7 1,31 -0,2 1,09 -1 1 -1,7 1 -0,7 1 -1 1

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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = -0.207439244018707 + 0.921102813688366X[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)	-0.207439244018707	0.406456	-0.5104	0.611476	0.305738
X	0.921102813688366	0.141354	6.5163	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.6228277326174
R-squared	0.387914384517331
Adjusted R-squared	0.378778778316097
F-TEST (value)	42.461811068973
F-TEST (DF numerator)	1
F-TEST (DF denominator)	67
p-value	1.10074940273819e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.15053961016272
Sum Squared Residuals	88.6906734350775

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.4	1.63476638335802	-0.234766383358022
2	1.2	1.63476638335802	-0.434766383358021
3	1	1.63476638335802	-0.634766383358023
4	1.7	1.63476638335802	0.0652336166419766
5	2.4	1.63476638335802	0.765233616641977
6	2	1.63476638335802	0.365233616641977
7	2.1	1.63476638335802	0.465233616641977
8	2	1.63476638335802	0.365233616641977
9	1.8	1.63476638335802	0.165233616641977
10	2.7	1.63476638335802	1.06523361664198
11	2.3	1.63476638335802	0.665233616641977
12	1.9	1.63476638335802	0.265233616641977
13	2	1.63476638335802	0.365233616641977
14	2.3	1.63476638335802	0.665233616641977
15	2.8	1.63476638335802	1.16523361664198
16	2.4	1.63476638335802	0.765233616641977
17	2.3	1.63476638335802	0.665233616641977
18	2.7	1.63476638335802	1.06523361664198
19	2.7	1.63476638335802	1.06523361664198
20	2.9	1.63476638335802	1.26523361664198
21	3	1.63476638335802	1.36523361664198
22	2.2	1.63476638335802	0.565233616641977
23	2.3	1.63476638335802	0.665233616641977
24	2.8	1.82819797423258	0.97180202576742
25	2.8	1.86504208678011	0.934957913219885
26	2.8	1.86504208678011	0.934957913219885
27	2.2	2.04926264951779	0.150737350482212
28	2.6	2.09531779020221	0.504682209797794
29	2.8	2.09531779020221	0.704682209797794
30	2.5	2.22427218411858	0.275727815881422
31	2.4	2.32559349362430	0.0744065063757022
32	2.3	2.49139200008820	-0.191392000088204
33	1.9	2.55586919704639	-0.655869197046389
34	1.7	2.71245667537341	-1.01245667537341
35	2	2.78614490046848	-0.78614490046848
36	2.1	2.91509929438485	-0.815099294384852
37	1.7	3.01642060389057	-1.31642060389057
38	1.8	3.01642060389057	-1.21642060389057
39	1.8	3.15458602594383	-1.35458602594383
40	1.8	3.24669630731266	-1.44669630731266
41	1.3	3.24669630731266	-1.94669630731266
42	1.3	3.38486172936592	-2.08486172936592
43	1.3	3.47697201073476	-2.17697201073475
44	1.2	3.47697201073475	-2.27697201073475
45	1.4	3.47697201073475	-2.07697201073475
46	2.2	3.47697201073476	-1.27697201073475
47	2.9	3.47697201073475	-0.576972010734755
48	3.1	3.47697201073475	-0.376972010734755
49	3.5	3.47697201073475	0.023027989265245
50	3.6	3.47697201073475	0.123027989265245
51	4.4	3.47697201073475	0.923027989265245
52	4.1	3.47697201073475	0.623027989265245
53	5.1	3.47697201073476	1.62302798926524
54	5.8	3.47697201073475	2.32302798926525
55	5.9	3.64277051719866	2.25722948280134
56	5.4	3.70724771415685	1.69275228584315
57	5.5	3.70724771415685	1.79275228584315
58	4.8	3.44933892632410	1.35066107367590
59	3.2	2.94273237879550	0.257267621204497
60	2.7	2.32559349362430	0.374406506375702
61	2.1	1.92030825560142	0.179691744398583
62	1.9	1.63476638335802	0.265233616641977
63	0.6	1.32159142670398	-0.721591426703979
64	0.7	0.999205441913051	-0.299205441913051
65	-0.2	0.79656282290161	-0.99656282290161
66	-1	0.713663569669658	-1.71366356966966
67	-1.7	0.713663569669657	-2.41366356966966
68	-0.7	0.713663569669657	-1.41366356966966
69	-1	0.713663569669658	-1.71366356966966

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.161594524268047	0.323189048536095	0.838405475731953
6	0.0847774589575892	0.169554917915178	0.915222541042411
7	0.045665412779086	0.091330825558172	0.954334587220914
8	0.0206990496412050	0.0413980992824099	0.979300950358795
9	0.00783724148119887	0.0156744829623977	0.992162758518801
10	0.0116752109441808	0.0233504218883615	0.98832478905582
11	0.00645803425019947	0.0129160685003989	0.9935419657498
12	0.00259312218906569	0.00518624437813138	0.997406877810934
13	0.00101880091269824	0.00203760182539649	0.998981199087302
14	0.000513221520186991	0.00102644304037398	0.999486778479813
15	0.000707706515875194	0.00141541303175039	0.999292293484125
16	0.000379582269404654	0.000759164538809308	0.999620417730595
17	0.000175623448813135	0.000351246897626271	0.999824376551187
18	0.000153294584607831	0.000306589169215663	0.999846705415392
19	0.000126089711709868	0.000252179423419735	0.99987391028829
20	0.000153931410750643	0.000307862821501287	0.99984606858925
21	0.000219993318616656	0.000439986637233311	0.999780006681383
22	0.000104873633948759	0.000209747267897517	0.999895126366051
23	5.21118188679677e-05	0.000104223637735935	0.999947888181132
24	2.78884727553605e-05	5.57769455107209e-05	0.999972111527245
25	1.52077590766730e-05	3.04155181533461e-05	0.999984792240923
26	8.5563622785853e-06	1.71127245571706e-05	0.999991443637721
27	8.95087227484142e-06	1.79017445496828e-05	0.999991049127725
28	4.44112337581569e-06	8.88224675163139e-06	0.999995558876624
29	2.39567838302280e-06	4.79135676604559e-06	0.999997604321617
30	1.24355448277283e-06	2.48710896554566e-06	0.999998756445517
31	6.41559802630996e-07	1.28311960526199e-06	0.999999358440197
32	3.30520310454488e-07	6.61040620908977e-07	0.99999966947969
33	2.48909037153164e-07	4.97818074306327e-07	0.999999751090963
34	2.01382843070875e-07	4.0276568614175e-07	0.999999798617157
35	8.27061479452403e-08	1.65412295890481e-07	0.999999917293852
36	3.12397390421145e-08	6.2479478084229e-08	0.999999968760261
37	1.88120398222335e-08	3.7624079644467e-08	0.99999998118796
38	9.06790128969788e-09	1.81358025793958e-08	0.999999990932099
39	4.79180007751672e-09	9.58360015503343e-09	0.9999999952082
40	2.87388881849845e-09	5.74777763699689e-09	0.99999999712611
41	5.56287434702816e-09	1.11257486940563e-08	0.999999994437126
42	1.57685481131404e-08	3.15370962262808e-08	0.999999984231452
43	8.80750051018436e-08	1.76150010203687e-07	0.999999911924995
44	1.67573717102815e-06	3.35147434205631e-06	0.999998324262829
45	6.21467321689057e-05	0.000124293464337811	0.99993785326783
46	0.00109378534221262	0.00218757068442525	0.998906214657787
47	0.0128329690721842	0.0256659381443683	0.987167030927816
48	0.0917837014043981	0.183567402808796	0.908216298595602
49	0.317380423649401	0.634760847298801	0.6826195763506
50	0.658357087255145	0.68328582548971	0.341642912744855
51	0.827230918741317	0.345538162517367	0.172769081258683
52	0.934539737675536	0.130920524648927	0.0654602623244635
53	0.960988540917116	0.0780229181657672	0.0390114590828836
54	0.98777989328679	0.0244402134264212	0.0122201067132106
55	0.993364243460448	0.0132715130791036	0.00663575653955181
56	0.990866521307978	0.0182669573840431	0.00913347869202156
57	0.986498341044595	0.0270033179108101	0.0135016589554051
58	0.975959648840381	0.0480807023192372	0.0240403511596186
59	0.984440691184228	0.0311186176315446	0.0155593088157723
60	0.97830544000974	0.0433891199805204	0.0216945599902602
61	0.96454367565167	0.07091264869666	0.03545632434833
62	0.920938137121122	0.158123725757755	0.0790618628788775
63	0.963103806016237	0.0737923879675251	0.0368961939837626
64	0.920902815726502	0.158194368546995	0.0790971842734976

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	35	0.583333333333333	NOK
5% type I error level	47	0.783333333333333	NOK
10% type I error level	51	0.85	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/107n8z1258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/107n8z1258652961.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/1p58a1258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/1p58a1258652961.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/26t6b1258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/26t6b1258652961.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/3pn4w1258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/3pn4w1258652961.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/4indi1258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/4indi1258652961.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/5k9yz1258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/5k9yz1258652961.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/6zxlh1258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/6zxlh1258652961.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/7l48q1258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/7l48q1258652961.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/8hzuv1258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/8hzuv1258652961.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/94ft31258652961.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653083c90mh28uqbcgewu/94ft31258652961.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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