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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 03:25:48 -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/t1258626902efx5uwayxdsm7k0.htm/, Retrieved Thu, 19 Nov 2009 11:35:14 +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/t1258626902efx5uwayxdsm7k0.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 «

8.9 426 8.6 8.4 8.4 8.4 8.8 428 8.9 8.6 8.4 8.4 8.3 430 8.8 8.9 8.6 8.4 7.5 424 8.3 8.8 8.9 8.6 7.2 423 7.5 8.3 8.8 8.9 7.4 427 7.2 7.5 8.3 8.8 8.8 441 7.4 7.2 7.5 8.3 9.3 449 8.8 7.4 7.2 7.5 9.3 452 9.3 8.8 7.4 7.2 8.7 462 9.3 9.3 8.8 7.4 8.2 455 8.7 9.3 9.3 8.8 8.3 461 8.2 8.7 9.3 9.3 8.5 461 8.3 8.2 8.7 9.3 8.6 463 8.5 8.3 8.2 8.7 8.5 462 8.6 8.5 8.3 8.2 8.2 456 8.5 8.6 8.5 8.3 8.1 455 8.2 8.5 8.6 8.5 7.9 456 8.1 8.2 8.5 8.6 8.6 472 7.9 8.1 8.2 8.5 8.7 472 8.6 7.9 8.1 8.2 8.7 471 8.7 8.6 7.9 8.1 8.5 465 8.7 8.7 8.6 7.9 8.4 459 8.5 8.7 8.7 8.6 8.5 465 8.4 8.5 8.7 8.7 8.7 468 8.5 8.4 8.5 8.7 8.7 467 8.7 8.5 8.4 8.5 8.6 463 8.7 8.7 8.5 8.4 8.5 460 8.6 8.7 8.7 8.5 8.3 462 8.5 8.6 8.7 8.7 8,00 461 8.3 8.5 8.6 8.7 8.2 476 8,00 8.3 8.5 8.6 8.1 476 8.2 8,00 8.3 8.5 8.1 471 8.1 8.2 8,00 8.3 8,00 453 8.1 8.1 8.2 8,00 7.9 443 8,00 8.1 8.1 8.2 7.9 442 7.9 8,00 8.1 8.1 8,00 444 7.9 7.9 8,00 8.1 8,00 438 8,00 7.9 7.9 8,00 7.9 427 8,00 8,00 7.9 7.9 8,00 424 7.9 8,00 8,00 7.9 7.7 41 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

wgb[t] = + 0.451954428011678 + 0.00587256561771003nwwz[t] + 1.38809036116834Y1[t] -0.788796684232674Y2[t] -0.114052438796979Y3[t] + 0.167309468978011Y4[t] -0.0520241177582429M1[t] -0.28014740714743M2[t] -0.197063572681837M3[t] -0.202127216051015M4[t] -0.213586051913450M5[t] -0.158146680840925M6[t] + 0.337282596270055M7[t] -0.617032999128151M8[t] -0.235284837988997M9[t] -0.148948216253043M10[t] -0.118843550292108M11[t] -0.00344913911679573t + 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)	0.451954428011678	0.493516	0.9158	0.36409	0.182045
nwwz	0.00587256561771003	0.002115	2.7769	0.007658	0.003829
Y1	1.38809036116834	0.144931	9.5776	0	0
Y2	-0.788796684232674	0.244577	-3.2251	0.002199	0.0011
Y3	-0.114052438796979	0.242952	-0.4694	0.640753	0.320377
Y4	0.167309468978011	0.139028	1.2034	0.23437	0.117185
M1	-0.0520241177582429	0.103645	-0.5019	0.617866	0.308933
M2	-0.28014740714743	0.110452	-2.5364	0.014305	0.007152
M3	-0.197063572681837	0.110807	-1.7784	0.081293	0.040647
M4	-0.202127216051015	0.105266	-1.9202	0.060439	0.03022
M5	-0.213586051913450	0.101665	-2.1009	0.040615	0.020307
M6	-0.158146680840925	0.099475	-1.5898	0.118057	0.059028
M7	0.337282596270055	0.108874	3.0979	0.003167	0.001583
M8	-0.617032999128151	0.131268	-4.7006	2e-05	1e-05
M9	-0.235284837988997	0.154413	-1.5237	0.133752	0.066876
M10	-0.148948216253043	0.135101	-1.1025	0.275423	0.137712
M11	-0.118843550292108	0.106089	-1.1202	0.267865	0.133932
t	-0.00344913911679573	0.001895	-1.8204	0.074572	0.037286

Multiple Linear Regression - Regression Statistics
Multiple R	0.979707023698603
R-squared	0.959825852284375
Adjusted R-squared	0.9464344697125
F-TEST (value)	71.6748884689645
F-TEST (DF numerator)	17
F-TEST (DF denominator)	51
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.162688886495414
Sum Squared Residuals	1.349851363245

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8.9	8.65723813629504	0.242761863704964
2	8.8	8.69607861052845	0.103921389471551
3	8.3	8.38919990796663	-0.0891999079666332
4	7.5	7.72953238177001	-0.229532381770010
5	7.2	7.05427597892783	0.145724021072168
6	7.4	7.38466198489073	0.0153380151092726
7	8.8	8.4807023355849	0.319297664415100
8	9.3	9.2558534512574	0.0441465487425966
9	9.3	9.16849666433852	0.131503335661480
10	8.7	8.78949994049827	-0.089499940498275
11	8.2	8.11940032848816	0.0805996715118354
12	8.3	8.13291769781418	0.167082302185824
13	8.5	8.6790832824505	-0.179083282450499
14	8.6	8.61463492700202	-0.0146349270020170
15	8.5	8.5743867776347	-0.0743867776347
16	8.2	8.30687035604077	-0.106870356040772
17	8.1	7.9705990254325	0.129400974567501
18	7.9	8.1544279829364	-0.254427982936405
19	8.6	8.55911555174484	0.040884448255156
20	8.7	8.6919858100805	0.00801418991949257
21	8.7	8.65714316450071	0.0428568354992862
22	8.5	8.51261698403686	-0.0126169840368568
23	8.4	8.33213042934598	0.0678695706540217
24	8.5	8.51844148185505	-0.0184414818550525
25	8.7	8.72108511413264	-0.0210851141326407
26	8.7	8.66032187390344	0.0396781260965566
27	8.6	8.53057077915737	0.0694292208426325
28	8.5	8.35955172283984	0.140448277160165
29	8.3	8.32992140519806	-0.0299214051980597
30	8	8.18870591160538	-0.188705911605377
31	8.2	8.50478105934314	-0.304781059343141
32	8.1	8.0673529431932	0.0326470568067971
33	8.1	8.12047460200714	-0.0204746020071360
34	8	8.10353224347798	-0.103532243477981
35	7.9	7.97752021570349	-0.0775202157034859
36	7.9	8.01038174666972	-0.110381746669721
37	8	8.05693853333307	-0.056938533333067
38	8	7.92361404421955	0.076385955780446
39	7.9	7.84303990245247	0.0569600975475273
40	8	7.66669514311684	0.333304856883162
41	7.7	7.83922629463383	-0.139226294633828
42	7.2	7.34858933751839	-0.148589337518386
43	7.5	7.50184124986346	-0.00184124986345491
44	7.3	7.42346634120532	-0.123466341205316
45	7	7.20038061454593	-0.200380614545931
46	7	6.87149446582676	0.128505534173237
47	7	7.16081180145182	-0.160811801451823
48	7.2	7.30632287855918	-0.106322878559176
49	7.3	7.49589255007753	-0.195892550077533
50	7.1	7.21013442713559	-0.110134427135588
51	6.8	6.86348036912637	-0.0634803691263744
52	6.4	6.61248389943463	-0.212483899434628
53	6.1	6.24804943250254	-0.148049432502541
54	6.5	6.22924789573288	0.270752104267123
55	7.7	7.64360032749692	0.0563996725030805
56	7.9	8.00331729673874	-0.103317296738745
57	7.5	7.54639376629085	-0.0463937662908484
58	6.9	6.82285636616013	0.0771436338398752
59	6.6	6.51013722501055	0.0898627749894512
60	6.9	6.83193619510188	0.0680638048981248
61	7.7	7.48976238371123	0.210237616288775
62	8	8.09521611721095	-0.0952161172109485
63	8	7.89932226366245	0.100677736337548
64	7.7	7.62486649679792	0.075133503202083
65	7.3	7.25792786330524	0.0420721366947602
66	7.4	7.09436688731623	0.305633112683773
67	8.1	8.20995947596674	-0.109959475966741
68	8.3	8.15802415752483	0.141975842475174
69	8.2	8.10711118831685	0.0928888116831484

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.200624948633456	0.401249897266913	0.799375051366544
22	0.115918717699965	0.231837435399930	0.884081282300035
23	0.167113512830856	0.334227025661713	0.832886487169144
24	0.248698189250866	0.497396378501732	0.751301810749134
25	0.154583726275144	0.309167452550288	0.845416273724856
26	0.0939695655172887	0.187939131034577	0.906030434482711
27	0.130828940676327	0.261657881352655	0.869171059323673
28	0.268123444388773	0.536246888777546	0.731876555611227
29	0.228285241551681	0.456570483103362	0.771714758448319
30	0.220927221671497	0.441854443342995	0.779072778328503
31	0.501278439084922	0.997443121830156	0.498721560915078
32	0.42266389338462	0.84532778676924	0.57733610661538
33	0.361098105020522	0.722196210041044	0.638901894979478
34	0.357502500639959	0.715005001279918	0.64249749936004
35	0.356308381484550	0.712616762969099	0.64369161851545
36	0.309416239428517	0.618832478857035	0.690583760571483
37	0.234831969100777	0.469663938201554	0.765168030899223
38	0.197318714814874	0.394637429629747	0.802681285185126
39	0.15402500767187	0.30805001534374	0.84597499232813
40	0.727419475353733	0.545161049292534	0.272580524646267
41	0.82420722379609	0.351585552407821	0.175792776203911
42	0.746216700887357	0.507566598225286	0.253783299112643
43	0.835775905016414	0.328448189967172	0.164224094983586
44	0.785406427718363	0.429187144563273	0.214593572281637
45	0.720416923213301	0.559166153573399	0.279583076786699
46	0.699513837913065	0.60097232417387	0.300486162086935
47	0.59434922579039	0.81130154841922	0.40565077420961
48	0.673510925410328	0.652978149179345	0.326489074589672

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/t1258626902efx5uwayxdsm7k0/10mzfu1258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/10mzfu1258626342.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/1lbsd1258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/1lbsd1258626342.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/24z4a1258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/24z4a1258626342.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/3r5gs1258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/3r5gs1258626342.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/4cdqy1258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/4cdqy1258626342.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/5zigd1258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/5zigd1258626342.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/6bvv01258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/6bvv01258626342.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/7x6911258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/7x6911258626342.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/8sp0j1258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/8sp0j1258626342.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/9o79i1258626342.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626902efx5uwayxdsm7k0/9o79i1258626342.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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