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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 08:21:37 -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/t1263051584534mslfiam7osjd.htm/, Retrieved Sat, 09 Jan 2010 16:39:57 +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/t1263051584534mslfiam7osjd.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 «

8.4 8.4 8.4 98.6 8.6 8.4 8.4 98.5 8.9 8.6 8.4 98.9 8.8 8.9 8.6 99.4 8.3 8.8 8.9 99.8 7.5 8.3 8.8 99.9 7.2 7.5 8.3 100 7.4 7.2 7.5 100.1 8.8 7.4 7.2 100.1 9.3 8.8 7.4 100.2 9.3 9.3 8.8 100.3 8.7 9.3 9.3 100 8.2 8.7 9.3 99.9 8.3 8.2 8.7 99.4 8.5 8.3 8.2 99.8 8.6 8.5 8.3 99.6 8.5 8.6 8.5 100 8.2 8.5 8.6 99.9 8.1 8.2 8.5 100.3 7.9 8.1 8.2 100.6 8.6 7.9 8.1 100.7 8.7 8.6 7.9 100.8 8.7 8.7 8.6 100.8 8.5 8.7 8.7 100.6 8.4 8.5 8.7 101.1 8.5 8.4 8.5 101.1 8.7 8.5 8.4 100.9 8.7 8.7 8.5 101.1 8.6 8.7 8.7 101.2 8.5 8.6 8.7 101.4 8.3 8.5 8.6 101.9 8 8.3 8.5 102.1 8.2 8 8.3 102.1 8.1 8.2 8 103 8.1 8.1 8.2 103.4 8 8.1 8.1 103.2 7.9 8 8.1 103.1 7.9 7.9 8 103 8 7.9 7.9 103.7 8 8 7.9 103.4 7.9 8 8 103.5 8 7.9 8 103.8 7.7 8 7.9 104 7.2 7.7 8 104.2 7.5 7.2 7.7 104.4 7.3 7.5 7.2 104.4 7 7.3 7.5 104.9 7 7 7.3 105.3 7 7 7 105.2 7.2 7 7 105.4 7.3 7.2 7 105.4 7.1 7.3 7.2 105.5 6.8 7.1 7.3 105.7 6.4 6.8 7.1 105.6 6.1 6.4 6.8 105.8 6.5 6.1 6.4 105.4 7.7 6.5 6.1 105.5 7.9 7.7 6.5 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

werkl[t] = + 26.3106019568154 + 1.31669964010823`werkl-1`[t] -0.717070137986305`werkl-2`[t] -0.232618213537436afzetp[t] + 0.0457433670576205M1[t] + 0.193077155005919M2[t] + 0.213469490726063M3[t] -0.0394471352569172M4[t] -0.00298783299766641M5[t] -0.0692285262308115M6[t] -0.0376573725565223M7[t] + 0.0442625747595174M8[t] + 0.664799937984984M9[t] -0.203343875163899M10[t] + 0.078324253693556M11[t] + 0.0195867962194246t + 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)	26.3106019568154	5.272524	4.9901	6e-06	3e-06
`werkl-1`	1.31669964010823	0.0903	14.5814	0	0
`werkl-2`	-0.717070137986305	0.087976	-8.1507	0	0
afzetp	-0.232618213537436	0.04956	-4.6937	1.8e-05	9e-06
M1	0.0457433670576205	0.104131	0.4393	0.662175	0.331087
M2	0.193077155005919	0.109927	1.7564	0.084585	0.042292
M3	0.213469490726063	0.108866	1.9609	0.054968	0.027484
M4	-0.0394471352569172	0.108836	-0.3624	0.718408	0.359204
M5	-0.00298783299766641	0.101962	-0.0293	0.976729	0.488364
M6	-0.0692285262308115	0.102329	-0.6765	0.501541	0.250771
M7	-0.0376573725565223	0.104668	-0.3598	0.720389	0.360194
M8	0.0442625747595174	0.109779	0.4032	0.688366	0.344183
M9	0.664799937984984	0.113401	5.8624	0	0
M10	-0.203343875163899	0.127001	-1.6011	0.115079	0.057539
M11	0.078324253693556	0.103561	0.7563	0.452691	0.226346
t	0.0195867962194246	0.005247	3.7329	0.000451	0.000225

Multiple Linear Regression - Regression Statistics
Multiple R	0.977494074989697
R-squared	0.955494666639963
Adjusted R-squared	0.943356848450863
F-TEST (value)	78.7204629163045
F-TEST (DF numerator)	15
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.166646632358458
Sum Squared Residuals	1.52741050420283

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8.4	8.47666408312547	-0.0766640831254713
2	8.6	8.66684648864691	-0.0668464886469122
3	8.9	8.87711826319315	0.0228817368068487
4	8.8	8.77907519109608	0.0209248089039169
5	8.3	8.39528299875307	-0.0952829987530737
6	7.5	7.73872447413013	-0.238724474130125
7	7.2	7.07179595957667	0.128204040423333
8	7.4	7.32868710011497	0.0713128998850345
9	8.8	8.4472722289774	0.352727771022605
10	9.3	9.27541885924845	0.0245811407515535
11	9.3	9.20786358984487	0.0921364101551311
12	8.7	8.86037652743881	-0.160376527438816
13	8.2	8.15894872800467	0.0410512719953341
14	8.3	8.21407068167878	0.085929318321224
15	8.5	8.65120756120735	-0.151207561207351
16	8.6	8.6560342883743	-0.0560342883742955
17	8.5	8.60728903785156	-0.107289037851557
18	8.2	8.38051998438213	-0.180519984382127
19	8.1	8.01532777062703	0.0846722293729708
20	7.9	8.13050012748633	-0.230500127486333
21	8.6	8.55572955135447	0.0442704486455342
22	8.7	8.74901448874428	-0.0490144887442831
23	8.7	8.67999028124157	0.0200097187584284
24	8.5	8.5960694526763	-0.096069452676297
25	8.4	8.28175058116298	0.118249418837021
26	8.5	8.46041522891714	0.0395847710828588
27	8.7	8.75029498137365	-0.0502949813736478
28	8.7	8.66207442312562	0.0379255768743785
29	8.6	8.55144467265329	0.0485553273467098
30	8.5	8.32659716892126	0.173402831078740
31	8.3	8.20148306183406	0.0985169381659373
32	8	8.06483324843903	-0.0648332484390293
33	8.2	8.45336154344871	-0.253361543448713
34	8.1	7.8739091037531	0.226090896246903
35	8.1	7.80703275180692	0.292967248193081
36	8	7.8665259508389	0.133474049161095
37	7.9	7.82344797145887	0.0765520285411273
38	7.9	7.95366742676815	-0.0536674267681462
39	8	7.90252082303014	0.0974791769698596
40	8	7.87064642133864	0.129353578661364
41	7.9	7.83172368466494	0.0682763153350615
42	8	7.58361435957917	0.416385640420833
43	7.7	7.79162564457484	-0.0916256445748447
44	7.2	7.37989183957172	-0.179891839571723
45	7.5	7.5302635776509	-0.0302635776509057
46	7.3	7.43525152174707	-0.135251521747068
47	7	7.1417363706377	-0.141736370637692
48	7	6.73835576331338	0.261644236686618
49	7	7.04206878934006	-0.0420687893400602
50	7.2	7.1624657308003	0.0375342691997045
51	7.3	7.46578479076151	-0.165784790761510
52	7.1	7.19744907605777	-0.0974490760577733
53	6.8	6.87192459000868	-0.0719245900086847
54	6.4	6.5969366499135	-0.196936649913503
55	6.1	6.29001214245233	-0.190012142452331
56	6.5	6.37638433456482	0.123615665435180
57	7.7	7.73504757009515	-0.0350475700951526
58	7.9	8.10991660203981	-0.209916602039813
59	7.5	7.74424182549354	-0.244241825493542
60	6.9	7.0386723057326	-0.138672305732600
61	6.6	6.71711984690795	-0.117119846907950
62	6.9	6.94253444318873	-0.0425344431887289
63	7.7	7.4530735804342	0.246926419565801
64	8	8.0347206000076	-0.0347206000075903
65	8	7.84233501606846	0.157664983931544
66	7.7	7.67360736307382	0.0263926369261809
67	7.3	7.32975542093507	-0.0297554209350653
68	7.4	7.11970334982313	0.280296650176870
69	8.1	8.17832552847337	-0.078325528473368
70	8.3	8.1564894244673	0.143510575532708
71	8.2	8.2191351809754	-0.0191351809754066

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.0708558767589141	0.141711753517828	0.929144123241086
20	0.284203277795617	0.568406555591234	0.715796722204383
21	0.321253293316592	0.642506586633184	0.678746706683408
22	0.205007919205788	0.410015838411576	0.794992080794212
23	0.129879369746952	0.259758739493905	0.870120630253048
24	0.0838253068968951	0.167650613793790	0.916174693103105
25	0.0458643231207309	0.0917286462414617	0.95413567687927
26	0.028565918964774	0.057131837929548	0.971434081035226
27	0.0165374856663115	0.0330749713326231	0.983462514333688
28	0.008295586925367	0.016591173850734	0.991704413074633
29	0.007786004743401	0.015572009486802	0.992213995256599
30	0.0338425849261086	0.0676851698522173	0.966157415073891
31	0.0216544274312256	0.0433088548624511	0.978345572568774
32	0.0154049450760977	0.0308098901521954	0.984595054923902
33	0.121377779994996	0.242755559989992	0.878622220005004
34	0.113450290826441	0.226900581652883	0.886549709173559
35	0.164728129558683	0.329456259117367	0.835271870441317
36	0.126474779702595	0.252949559405191	0.873525220297405
37	0.123087043958945	0.246174087917891	0.876912956041055
38	0.150386893103996	0.300773786207993	0.849613106896004
39	0.102728783865495	0.20545756773099	0.897271216134505
40	0.0697409270202281	0.139481854040456	0.930259072979772
41	0.0553428565095963	0.110685713019193	0.944657143490404
42	0.212577491039254	0.425154982078507	0.787422508960747
43	0.240879624810273	0.481759249620545	0.759120375189727
44	0.376424973087704	0.752849946175408	0.623575026912296
45	0.330503536377132	0.661007072754264	0.669496463622868
46	0.334930401836526	0.669860803673053	0.665069598163474
47	0.351560570863202	0.703121141726404	0.648439429136798
48	0.685083054644923	0.629833890710154	0.314916945355077
49	0.691944717129525	0.616110565740951	0.308055282870475
50	0.838060747705874	0.323878504588252	0.161939252294126
51	0.799369259536432	0.401261480927136	0.200630740463568
52	0.755898563837322	0.488202872325355	0.244101436162678

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	5	0.147058823529412	NOK
10% type I error level	8	0.235294117647059	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/10ffkq1263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/10ffkq1263050491.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/1kp6e1263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/1kp6e1263050491.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/2zsee1263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/2zsee1263050491.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/3261a1263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/3261a1263050491.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/4xtzs1263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/4xtzs1263050491.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/5kymy1263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/5kymy1263050491.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/6bnit1263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/6bnit1263050491.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/717k51263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/717k51263050491.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/87ouj1263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/87ouj1263050491.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/9s79d1263050491.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/09/t1263051584534mslfiam7osjd/9s79d1263050491.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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