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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: Sun, 20 Dec 2009 13:40:51 -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/Dec/20/t1261341719e23936tx2ow3702.htm/, Retrieved Sun, 20 Dec 2009 21:42:12 +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/Dec/20/t1261341719e23936tx2ow3702.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 «

87.4 0 104.5 98.1 102.7 105.4 97 97.4 89.9 0 87.4 104.5 98.1 102.7 105.4 97 109.8 0 89.9 87.4 104.5 98.1 102.7 105.4 111.7 0 109.8 89.9 87.4 104.5 98.1 102.7 98.6 0 111.7 109.8 89.9 87.4 104.5 98.1 96.9 0 98.6 111.7 109.8 89.9 87.4 104.5 95.1 0 96.9 98.6 111.7 109.8 89.9 87.4 97 0 95.1 96.9 98.6 111.7 109.8 89.9 112.7 0 97 95.1 96.9 98.6 111.7 109.8 102.9 0 112.7 97 95.1 96.9 98.6 111.7 97.4 0 102.9 112.7 97 95.1 96.9 98.6 111.4 0 97.4 102.9 112.7 97 95.1 96.9 87.4 0 111.4 97.4 102.9 112.7 97 95.1 96.8 0 87.4 111.4 97.4 102.9 112.7 97 114.1 0 96.8 87.4 111.4 97.4 102.9 112.7 110.3 0 114.1 96.8 87.4 111.4 97.4 102.9 103.9 0 110.3 114.1 96.8 87.4 111.4 97.4 101.6 0 103.9 110.3 114.1 96.8 87.4 111.4 94.6 0 101.6 103.9 110.3 114.1 96.8 87.4 95.9 0 94.6 101.6 103.9 110.3 114.1 96.8 104.7 0 95.9 94.6 101.6 103.9 110.3 114.1 102.8 0 104.7 95.9 94.6 101.6 103.9 110.3 98.1 0 102.8 104.7 95.9 94.6 101.6 103.9 113.9 0 98.1 102.8 104.7 95.9 94.6 101.6 80.9 0 113.9 98.1 102.8 104.7 95.9 94.6 95.7 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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Productie[t] = + 70.843171659092 -12.1409361201919Dummy[t] -0.170535875011005`Yt-1`[t] + 0.127341252226174`Yt-2`[t] + 0.406670131624456`Yt-3`[t] -0.115932126741984`Yt-4`[t] -0.0322030815182641`Yt-5`[t] + 0.125704389082812`Yt-6`[t] -19.0624672402896M1[t] -15.1805417856902M2[t] -0.526451480378921M3[t] + 14.7571552687538M4[t] -1.15645418805891M5[t] -16.4847096765184M6[t] -11.7529419581613M7[t] -7.21825953770693M8[t] + 2.70776198327648M9[t] -3.68093564071264M10[t] -7.4642631479732M11[t] + 0.128245177155806t + 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)	70.843171659092	15.456906	4.5833	2.7e-05	1.3e-05
Dummy	-12.1409361201919	2.59814	-4.6729	2e-05	1e-05
`Yt-1`	-0.170535875011005	0.127664	-1.3358	0.18711	0.093555
`Yt-2`	0.127341252226174	0.127842	0.9961	0.323573	0.161786
`Yt-3`	0.406670131624456	0.131509	3.0923	0.003117	0.001558
`Yt-4`	-0.115932126741984	0.121414	-0.9548	0.343833	0.171916
`Yt-5`	-0.0322030815182641	0.120282	-0.2677	0.789909	0.394955
`Yt-6`	0.125704389082812	0.128168	0.9808	0.330995	0.165498
M1	-19.0624672402896	2.382462	-8.0012	0	0
M2	-15.1805417856902	3.051581	-4.9746	7e-06	3e-06
M3	-0.526451480378921	3.479885	-0.1513	0.880305	0.440153
M4	14.7571552687538	4.455422	3.3122	0.00164	0.00082
M5	-1.15645418805891	4.751963	-0.2434	0.808629	0.404315
M6	-16.4847096765184	3.512189	-4.6936	1.8e-05	9e-06
M7	-11.7529419581613	3.371446	-3.486	0.000971	0.000486
M8	-7.21825953770693	3.443448	-2.0962	0.040676	0.020338
M9	2.70776198327648	4.281735	0.6324	0.529747	0.264873
M10	-3.68093564071264	4.096295	-0.8986	0.372782	0.186391
M11	-7.4642631479732	2.88189	-2.5901	0.012259	0.006129
t	0.128245177155806	0.038619	3.3208	0.001599	0.000799

Multiple Linear Regression - Regression Statistics
Multiple R	0.956037433079494
R-squared	0.914007573449228
Adjusted R-squared	0.884301098822598
F-TEST (value)	30.7679583301975
F-TEST (DF numerator)	19
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.24878213411558
Sum Squared Residuals	580.502194522171

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	87.4	85.2458124493166	2.15418755068342
2	89.9	91.1086770543519	-1.20867705435185
3	109.8	107.565979250028	2.23402074997159
4	111.7	112.015227856697	-0.315227856696888
5	98.6	100.654711118671	-2.05471111867122
6	96.9	97.0887552358055	-0.188755235805451
7	95.1	96.8060798854834	-1.70607988548339
8	97	95.6852618147323	1.31473818526770
9	112.7	108.453999220765	4.24600077923484
10	102.9	99.8838590011728	3.01614099882724
11	97.4	99.288654725945	-1.88865472594506
12	111.4	112.579884202804	-1.17988420280436
13	87.4	84.46482756727	2.93517243273008
14	96.8	92.9833358080133	3.81666419198670
15	114.1	111.726601659251	2.37339834074884
16	110.3	112.947271720740	-2.64727172073952
17	103.9	105.475800427504	-1.57580042750357
18	101.6	99.361689647137	2.23831035286297
19	94.6	96.9283644438593	-2.32836444385928
20	95.9	100.954519472762	-5.05451947276176
21	104.7	111.999382717118	-7.29938271711795
22	102.8	101.552134211419	1.24786578858060
23	98.1	99.9514281190936	-1.85142811909361
24	113.9	111.467793546813	2.43220645318740
25	80.9	86.5259300780725	-5.62593007807252
26	95.7	96.3647263809863	-0.664726380986282
27	113.2	112.558523143994	0.641476856006124
28	105.9	111.531321988285	-5.6313219882853
29	108.8	107.964200324016	0.835799675983572
30	102.3	99.7898076991875	2.51019230081246
31	99	96.5052387886815	2.49476121131850
32	100.7	104.225735573141	-3.52573557314125
33	115.5	113.025215432337	2.47478456766280
34	100.7	102.857828576880	-2.15782857688021
35	109.9	105.259105730105	4.64089426989528
36	114.6	114.508858444738	0.0911415552623357
37	85.4	87.7405741431553	-2.34057414315528
38	100.5	102.523148752392	-2.02314875239176
39	114.8	114.193832574693	0.60616742530706
40	116.5	114.513532249424	1.98646775057574
41	112.9	110.790299873904	2.10970012609582
42	102	102.016647218873	-0.0166472188729180
43	106	103.153747763199	2.84625223680116
44	105.3	105.523047331630	-0.223047331629558
45	118.8	113.933532898049	4.86646710195109
46	106.1	108.501676524901	-2.40167652490070
47	109.3	107.881586901235	1.41841309876503
48	117.2	117.383355621630	-0.183355621630165
49	92.5	91.3049574837798	1.19504251622018
50	104.2	102.784307878861	1.41569212113938
51	112.5	117.373744315871	-4.87374431587117
52	122.4	120.199929476019	2.20007052398130
53	113.3	111.552606198834	1.74739380116604
54	100	102.972587744027	-2.97258774402668
55	110.7	108.524045568942	2.17595443105776
56	112.8	106.023630172466	6.77636982753436
57	109.8	113.453128457005	-3.65312845700514
58	117.3	115.402489452666	1.89751054733407
59	109.1	108.984288867112	0.115711132888437
60	115.9	115.450341550949	0.449658449051261
61	96	98.9875101288926	-2.98751012889258
62	99.8	103.413667620248	-3.61366762024811
63	116.8	118.111239914068	-1.31123991406762
64	115.7	111.292716708835	4.40728329116467
65	99.4	100.462382057071	-1.06238205707065
66	94.3	95.8705124549704	-1.57051245497038
67	91	94.4825235498348	-3.48252354983475
68	93.2	92.4878056352695	0.712194364730514
69	103.1	103.734741274726	-0.634741274725637
70	94.1	95.702012232961	-1.60201223296099
71	91.8	94.23493565651	-2.43493565651008
72	102.7	104.309766633066	-1.60976663306648
73	82.6	77.9303881495133	4.66961185048669
74	89.1	86.8221365051481	2.27786349485193
75	104.5	104.170079142095	0.329920857905177

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
23	0.758949783020708	0.482100433958584	0.241050216979292
24	0.694807790770859	0.610384418458281	0.305192209229141
25	0.715913417574933	0.568173164850134	0.284086582425067
26	0.796156071973752	0.407687856052496	0.203843928026248
27	0.758485152113029	0.483029695773942	0.241514847886971
28	0.833294573628002	0.333410852743997	0.166705426371998
29	0.794873108169919	0.410253783660162	0.205126891830081
30	0.804728424177996	0.390543151644009	0.195271575822004
31	0.783591832843186	0.432816334313628	0.216408167156814
32	0.811852217865904	0.376295564268193	0.188147782134096
33	0.840649130742443	0.318701738515115	0.159350869257557
34	0.809950258232644	0.380099483534711	0.190049741767356
35	0.871952830444972	0.256094339110056	0.128047169555028
36	0.81545271093365	0.369094578132699	0.184547289066350
37	0.8143689313626	0.371262137274801	0.185631068637401
38	0.787750497960839	0.424499004078323	0.212249502039161
39	0.722180352784343	0.555639294431314	0.277819647215657
40	0.692418712105648	0.615162575788704	0.307581287894352
41	0.618566699244185	0.76286660151163	0.381433300755815
42	0.524826286006806	0.950347427986389	0.475173713993194
43	0.458174090762654	0.916348181525308	0.541825909237346
44	0.557865700180263	0.884268599639475	0.442134299819737
45	0.560157547468241	0.879684905063518	0.439842452531759
46	0.57808103669867	0.84383792660266	0.42191896330133
47	0.577470666416466	0.845058667167069	0.422529333583534
48	0.493802345092007	0.987604690184014	0.506197654907993
49	0.43688314972895	0.8737662994579	0.56311685027105
50	0.327195985053999	0.654391970107998	0.672804014946001
51	0.260382091085208	0.520764182170415	0.739617908914792
52	0.222440506984859	0.444881013969718	0.777559493015141

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/Dec/20/t1261341719e23936tx2ow3702/10pwta1261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/10pwta1261341644.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/19cvg1261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/19cvg1261341644.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/2ywbb1261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/2ywbb1261341644.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/33htw1261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/33htw1261341644.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/41ps21261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/41ps21261341644.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/5ngmb1261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/5ngmb1261341644.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/6dyko1261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/6dyko1261341644.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/7lnrc1261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/7lnrc1261341644.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/85ys41261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/85ys41261341644.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/9q3481261341644.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261341719e23936tx2ow3702/9q3481261341644.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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