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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 03:39:42 -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/t125862725086yvrkcb7jmfymx.htm/, Retrieved Thu, 19 Nov 2009 11:41:03 +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/t125862725086yvrkcb7jmfymx.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 «

8.4 410 8.4 8.4 8.6 418 8.4 8.4 8.9 426 8.6 8.4 8.8 428 8.9 8.6 8.3 430 8.8 8.9 7.5 424 8.3 8.8 7.2 423 7.5 8.3 7.4 427 7.2 7.5 8.8 441 7.4 7.2 9.3 449 8.8 7.4 9.3 452 9.3 8.8 8.7 462 9.3 9.3 8.2 455 8.7 9.3 8.3 461 8.2 8.7 8.5 461 8.3 8.2 8.6 463 8.5 8.3 8.5 462 8.6 8.5 8.2 456 8.5 8.6 8.1 455 8.2 8.5 7.9 456 8.1 8.2 8.6 472 7.9 8.1 8.7 472 8.6 7.9 8.7 471 8.7 8.6 8.5 465 8.7 8.7 8.4 459 8.5 8.7 8.5 465 8.4 8.5 8.7 468 8.5 8.4 8.7 467 8.7 8.5 8.6 463 8.7 8.7 8.5 460 8.6 8.7 8.3 462 8.5 8.6 8.00 461 8.3 8.5 8.2 476 8.00 8.3 8.1 476 8.2 8.00 8.1 471 8.1 8.2 8.00 453 8.1 8.1 7.9 443 8.00 8.1 7.9 442 7.9 8.00 8.00 444 7.9 7.9 8.00 438 8.00 7.9 7.9 427 8.00 8.00 8.00 424 7.9 8.00 7.7 416 8.00 7.9 7.2 406 7.7 8.00 7.5 431 7.2 7.7 7.3 434 7.5 7.2 7.00 418 7.3 7.5 7.00 412 7.00 7.3 7.00 404 7.00 7.00 7.2 409 7.00 7.00 7.3 412 7.2 7.00 7.1 406 7.3 7.2 6.8 398 7.1 7.3 6.4 397 6.8 7.1 6.1 385 6.4 6.8 6.5 390 6.1 6.4 7.7 413 6.5 6.1 7.9 413 7.7 6.5 7.5 401 7.9 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	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

wgb[t] = + 0.653200943441071 + 0.00702202501777568nwwz[t] + 1.34862494351242Y1[t] -0.805559669404799Y2[t] + 0.169468018952212M1[t] + 0.306393307864775M2[t] + 0.228420712145096M3[t] -0.00583118897583866M4[t] + 0.0433429091353213M5[t] + 0.0295890799380566M6[t] + 0.0559292644629458M7[t] + 0.120208514339465M8[t] + 0.592741231781824M9[t] -0.353046985245862M10[t] + 0.0176212393133223M11[t] -0.00567653245168045t + 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)	0.653200943441071	0.453948	1.4389	0.155836	0.077918
nwwz	0.00702202501777568	0.001412	4.9732	7e-06	3e-06
Y1	1.34862494351242	0.086747	15.5467	0	0
Y2	-0.805559669404799	0.090653	-8.8862	0	0
M1	0.169468018952212	0.100487	1.6865	0.097371	0.048685
M2	0.306393307864775	0.102123	3.0002	0.004048	0.002024
M3	0.228420712145096	0.106125	2.1524	0.035772	0.017886
M4	-0.00583118897583866	0.106016	-0.055	0.956335	0.478168
M5	0.0433429091353213	0.100087	0.4331	0.66667	0.333335
M6	0.0295890799380566	0.099418	0.2976	0.767111	0.383556
M7	0.0559292644629458	0.100908	0.5543	0.581647	0.290824
M8	0.120208514339465	0.104058	1.1552	0.252998	0.126499
M9	0.592741231781824	0.116655	5.0811	5e-06	2e-06
M10	-0.353046985245862	0.131114	-2.6927	0.009376	0.004688
M11	0.0176212393133223	0.101819	0.1731	0.863236	0.431618
t	-0.00567653245168045	0.001411	-4.0229	0.000177	8.9e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.97826517898424
R-squared	0.957002760413066
Adjusted R-squared	0.94527624052572
F-TEST (value)	81.6101255621272
F-TEST (DF numerator)	15
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.163798831759909
Sum Squared Residuals	1.47565315072511

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8.4	8.25777098973366	0.142229010266339
2	8.6	8.44519594633676	0.154804053663241
3	8.9	8.68744800701009	0.212551992989909
4	8.8	8.7050391726458	0.0949608273542069
5	8.3	8.38605039316814	-0.086050393168142
6	7.5	7.73073137659681	-0.230731376596812
7	7.2	7.0682528835447	0.131747116455296
8	7.4	7.39480395351076	0.00519604648924152
9	8.8	8.47136137827422	0.328638621725780
10	9.3	9.3030358159735	-0.00303581597349203
11	9.3	9.23562251772382	0.0643774822761842
12	8.7	8.87976516143417	-0.179765161434172
13	8.2	8.18522750670282	0.0147724932971820
14	8.3	8.16763174315702	0.132368256842979
15	8.5	8.6216249440393	-0.121624944039307
16	8.6	8.58490958226425	0.015090417735754
17	8.5	8.59513568337623	-0.0951356833762322
18	8.2	8.31815471032891	-0.118154710328912
19	8.1	8.0077648212711	0.0922351787289031
20	7.9	8.1801949701839	-0.280194970183909
21	8.6	8.570234533697	0.0297654663030053
22	8.7	8.72391917855728	-0.0239191785572828
23	8.7	8.6528595714149	0.0471404285851058
24	8.5	8.50687368260276	-0.00687368260275717
25	8.4	8.35880803029415	0.0411919697058491
26	8.5	8.5584383763914	-0.0584383763914046
27	8.7	8.7112737845651	-0.0112737845650952
28	8.7	8.65349234773671	0.046507652263292
29	8.6	8.50778987944413	0.0922101205558746
30	8.5	8.33243094839061	0.167569051609389
31	8.3	8.31283212308861	-0.0128321230886081
32	8	8.17524379373367	-0.175243793733668
33	8.2	8.50395480481821	-0.303954804818213
34	8.1	8.06388294486277	0.0361170551372293
35	8.1	8.0977900836492	0.00220991635080508
36	8	8.0286518285047	-0.0286518285047095
37	7.9	7.98736057047624	-0.0873605704762423
38	7.9	8.05728077450859	-0.157280774508586
39	8	8.06823166331326	-0.0682316633132592
40	8	7.92103357398523	0.0789664260147686
41	7.9	7.8067328975087	0.0932671024913011
42	8	7.63137396645519	0.368626033544815
43	7.7	7.81127987967791	-0.111279879677910
44	7.2	7.31451889693079	-0.114518896930786
45	7.5	7.52428113643108	-0.0242811364310837
46	7.3	7.40124977976117	-0.101249779761171
47	7	7.14249618206034	-0.142496182060340
48	7	6.83359071101592	0.166409288984084
49	7	7.18287389819568	-0.182873898195682
50	7.2	7.34923277974544	-0.149232779745442
51	7.3	7.5563747153299	-0.256374715329895
52	7.1	7.24806469212091	-0.148064692120908
53	6.8	6.88510510199522	-0.0851051019952178
54	6.4	6.61517716615573	-0.21517716615573
55	6.1	6.2537944414321	-0.153794441432102
56	6.5	6.26514366865401	0.234856331345990
57	7.7	7.67462430727994	0.0253756927200614
58	7.9	8.01928562225356	-0.11928562225356
59	7.5	7.60306639956448	-0.103066399564482
60	6.9	6.85111861644245	0.048881383557553
61	6.6	6.52795900459745	0.072040995402554
62	6.9	6.82222037986079	0.0777796201392137
63	7.7	7.45504688574235	0.244953114257648
64	8	8.08746063124711	-0.0874606312471133
65	8	7.91918604450758	0.0808139554924165
66	7.7	7.67213183207275	0.0278681679272501
67	7.3	7.24607585098558	0.0539241490144214
68	7.4	7.07009471698687	0.329905283013132
69	8.1	8.15554383949955	-0.0555438394995496
70	8.3	8.08862665859172	0.211373341408276
71	8.2	8.06816524558727	0.131834754412727

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.146143530184029	0.292287060368058	0.853856469815971
20	0.182786164307958	0.365572328615915	0.817213835692042
21	0.176478698613797	0.352957397227595	0.823521301386203
22	0.319342283036999	0.638684566073998	0.680657716963001
23	0.221995027357922	0.443990054715845	0.778004972642078
24	0.159118893150005	0.31823778630001	0.840881106849995
25	0.102935817961775	0.205871635923551	0.897064182038225
26	0.0902050997577756	0.180410199515551	0.909794900242224
27	0.054889168128782	0.109778336257564	0.945110831871218
28	0.0358874148601435	0.071774829720287	0.964112585139857
29	0.0496928379063984	0.0993856758127968	0.950307162093602
30	0.150339662837324	0.300679325674648	0.849660337162676
31	0.124239748495893	0.248479496991787	0.875760251504107
32	0.110502554668844	0.221005109337688	0.889497445331156
33	0.341647015584612	0.683294031169225	0.658352984415388
34	0.2751511010539	0.5503022021078	0.7248488989461
35	0.245133308428945	0.490266616857891	0.754866691571055
36	0.234963718118039	0.469927436236077	0.765036281881961
37	0.242224447172859	0.484448894345719	0.757775552827141
38	0.253382545954380	0.506765091908759	0.74661745404562
39	0.199397544905286	0.398795089810571	0.800602455094714
40	0.161520943272291	0.323041886544582	0.83847905672771
41	0.132609848996512	0.265219697993023	0.867390151003488
42	0.768360039411322	0.463279921177356	0.231639960588678
43	0.836060910096215	0.32787817980757	0.163939089903785
44	0.766245185921292	0.467509628157415	0.233754814078708
45	0.858852056583823	0.282295886832355	0.141147943416177
46	0.81579368500552	0.368412629988960	0.184206314994480
47	0.76189697137667	0.476206057246661	0.238103028623331
48	0.771559547458315	0.456880905083371	0.228440452541685
49	0.709491394937432	0.581017210125136	0.290508605062568
50	0.68937639555081	0.62124720889838	0.31062360444919
51	0.713160123759352	0.573679752481295	0.286839876240648
52	0.603317257608773	0.793365484782454	0.396682742391227

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	2	0.0588235294117647	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/102dw81258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/102dw81258627174.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/156s31258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/156s31258627174.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/2qi101258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/2qi101258627174.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/3e5gs1258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/3e5gs1258627174.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/4ddkh1258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/4ddkh1258627174.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/5b4ed1258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/5b4ed1258627174.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/6n67k1258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/6n67k1258627174.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/7ms4x1258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/7ms4x1258627174.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/838051258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/838051258627174.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/9dea71258627174.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862725086yvrkcb7jmfymx/9dea71258627174.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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