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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: Mon, 23 Nov 2009 07:58:43 -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/23/t1258988498apmyp2rw6b9oqlp.htm/, Retrieved Mon, 23 Nov 2009 16:01:50 +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/23/t1258988498apmyp2rw6b9oqlp.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 «

6.7 510 6.9 7.0 6.7 509 6.7 6.9 6.5 501 6.7 6.7 6.4 507 6.5 6.7 6.5 569 6.4 6.5 6.5 580 6.5 6.4 6.5 578 6.5 6.5 6.7 565 6.5 6.5 6.8 547 6.7 6.5 7.2 555 6.8 6.7 7.6 562 7.2 6.8 7.6 561 7.6 7.2 7.2 555 7.6 7.6 6.4 544 7.2 7.6 6.1 537 6.4 7.2 6.3 543 6.1 6.4 7.1 594 6.3 6.1 7.5 611 7.1 6.3 7.4 613 7.5 7.1 7.1 611 7.4 7.5 6.8 594 7.1 7.4 6.9 595 6.8 7.1 7.2 591 6.9 6.8 7.4 589 7.2 6.9 7.3 584 7.4 7.2 6.9 573 7.3 7.4 6.9 567 6.9 7.3 6.8 569 6.9 6.9 7.1 621 6.8 6.9 7.2 629 7.1 6.8 7.1 628 7.2 7.1 7.0 612 7.1 7.2 6.9 595 7.0 7.1 7.1 597 6.9 7.0 7.3 593 7.1 6.9 7.5 590 7.3 7.1 7.5 580 7.5 7.3 7.5 574 7.5 7.5 7.3 573 7.5 7.5 7.0 573 7.3 7.5 6.7 620 7.0 7.3 6.5 626 6.7 7.0 6.5 620 6.5 6.7 6.5 588 6.5 6.5 6.6 566 6.5 6.5 6.8 557 6.6 6.5 6.9 561 6.8 6.6 6.9 549 6.9 6.8 6.8 532 6.9 6.9 6.8 526 6.8 6.9 6.5 511 6.8 6.8 6.1 499 6.5 6.8 6.1 555 6.1 6.5 5.9 565 6.1 6.1 5.7 542 5.9 6.1 5.9 527 5.7 5.9 5.9 510 5.9 5.7 6.1 514 5.9 5.9 6.3 517 6.1 5.9 6.2 508 6.3 6.1 5.9 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

wkgo[t] = + 0.753076660402044 + 0.00609879281421756werkl[t] + 1.20867003086177Y1[t] -0.797725081680719Y2[t] + 0.020267931887861M1[t] + 0.0775591481335028M2[t] + 0.059439407605469M3[t] + 0.0444861284488672M4[t] -0.0970493024554228M5[t] -0.487497044140113M6[t] -0.394445509798076M7[t] -0.198209424553334M8[t] -0.16031660909194M9[t] + 0.0804244309999566M10[t] + 0.0178292881987102M11[t] -0.00381852004158168t + 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)	0.753076660402044	0.32193	2.3393	0.023051	0.011526
werkl	0.00609879281421756	0.000941	6.4827	0	0
Y1	1.20867003086177	0.086044	14.0472	0	0
Y2	-0.797725081680719	0.088342	-9.03	0	0
M1	0.020267931887861	0.084654	0.2394	0.811686	0.405843
M2	0.0775591481335028	0.090232	0.8596	0.393833	0.196917
M3	0.059439407605469	0.091228	0.6515	0.517458	0.258729
M4	0.0444861284488672	0.089102	0.4993	0.619617	0.309808
M5	-0.0970493024554228	0.100152	-0.969	0.336855	0.168428
M6	-0.487497044140113	0.09886	-4.9312	8e-06	4e-06
M7	-0.394445509798076	0.090963	-4.3363	6.4e-05	3.2e-05
M8	-0.198209424553334	0.089461	-2.2156	0.030955	0.015478
M9	-0.16031660909194	0.086313	-1.8574	0.068712	0.034356
M10	0.0804244309999566	0.087945	0.9145	0.364527	0.182264
M11	0.0178292881987102	0.085436	0.2087	0.835478	0.417739
t	-0.00381852004158168	0.000975	-3.9149	0.000256	0.000128

Multiple Linear Regression - Regression Statistics
Multiple R	0.979231420750028
R-squared	0.958894175384118
Adjusted R-squared	0.947475890768595
F-TEST (value)	83.9788293664123
F-TEST (DF numerator)	15
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.130855591573846
Sum Squared Residuals	0.924652035691628

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6.7	6.63565804868046	0.0643419513195365
2	6.7	6.52107045406602	0.178929545933979
3	6.5	6.60988686731881	-0.109886867318807
4	6.4	6.38597381883358	0.0140261811664228
5	6.5	6.65742303561916	-0.157423035619160
6	6.5	6.53088300610353	-0.0308830061035303
7	6.5	6.52814592660748	-0.0281459266074778
8	6.7	6.64127918522581	0.0587208147741899
9	6.8	6.80730921616206	-0.00730921616206051
10	7.2	7.05434406547615	0.145655934523851
11	7.6	7.43431745650948	0.165682543490520
12	7.6	7.57094883512739	0.0290511648726091
13	7.2	7.23171545741608	-0.0317154574160772
14	6.4	6.73463342031904	-0.334633420319037
15	6.1	6.02215761803277	0.0778423819672291
16	6.3	6.31555763180593	-0.0155576318059354
17	7.1	6.96229364506173	0.137706354938271
18	7.5	7.47909786953043	0.0209021304695729
19	7.4	7.42581641645945	-0.0258164164594509
20	7.1	7.16607936027571	-0.0660793602757127
21	6.8	6.81364567676337	-0.0136456767633662
22	6.9	6.93338350487358	-0.0333835048735842
23	7.2	7.20275919836428	-0.00275919836427855
24	7.4	7.45174230558601	-0.0517423055860097
25	7.3	7.44011423502934	-0.140114235029341
26	6.9	7.14608819085469	-0.246088190854686
27	6.9	6.68386166922313	0.21613833077687
28	6.8	6.99637748832567	-0.196377488325669
29	7.1	7.04729376063293	0.0527062393670671
30	7.2	7.144191358847	0.0558086411529964
31	7.1	7.1088750589152	-0.00887505891520399
32	7	7.00307242783663	-0.00307242783663344
33	6.9	6.89237275049664	0.00762724950335739
34	7.1	7.10039836125729	-0.000398361257288924
35	7.3	7.33109604149801	-0.0310960414980147
36	7.5	7.37334084465128	0.126659155348719
37	7.5	7.4109913181916	0.0890086818084047
38	7.5	7.2683262411742	0.231673758825794
39	7.3	7.24028918779037	0.0597108122096268
40	7	6.97978338241984	0.0202166175801644
41	6.7	6.9180167008198	-0.218016700819802
42	6.5	6.43705971122452	0.0629402887754798
43	6.5	6.48728348697153	0.012716513028469
44	6.5	6.64408469845587	-0.144084698455874
45	6.6	6.5439855519629	0.0560144480371001
46	6.8	6.84688593977143	-0.0468859397714336
47	6.9	6.96682894618976	-0.0668289461897575
48	6.9	6.83331761092889	0.0666823890711113
49	6.8	6.6663150367654	0.133684963234602
50	6.8	6.56232797299798	0.237672027002024
51	6.5	6.52868032838317	-0.0286803283831683
52	6.1	6.07412200615584	0.0258779938441561
53	6.1	6.02614996496566	0.0738500350343375
54	5.9	6.01196166405385	-0.111961664053853
55	5.7	5.71918843745495	-0.0191884374549512
56	5.9	5.73793512060864	0.162064879391363
57	5.9	6.06960896069525	-0.169608960695249
58	6.1	6.17138163566629	-0.0713816356662916
59	6.3	6.36499835743847	-0.0649983574384686
60	6.2	6.37065040370643	-0.170650403706430
61	5.9	6.01520590391713	-0.115205903917125
62	5.7	5.76755372058807	-0.0675537205880738
63	5.4	5.61512432925175	-0.215124329251750
64	5.6	5.44818567245914	0.151814327540861
65	6.2	6.08882289290071	0.111177107099287
66	6.3	6.29680639024067	0.00319360975933404
67	6	5.93069067359138	0.069309326408615
68	5.6	5.60754920759733	-0.00754920759733302
69	5.5	5.37307784391978	0.126922156080218
70	5.9	5.89360649295525	0.00639350704474632

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.930335931185668	0.139328137628665	0.0696640688143324
20	0.88013753507597	0.239724929848061	0.119862464924030
21	0.820869300029411	0.358261399941178	0.179130699970589
22	0.77030897580878	0.459382048382441	0.229691024191221
23	0.741459393014469	0.517081213971062	0.258540606985531
24	0.66661825042738	0.66676349914524	0.33338174957262
25	0.626976224637655	0.74604755072469	0.373023775362345
26	0.669320212948605	0.66135957410279	0.330679787051395
27	0.848958844541089	0.302082310917823	0.151041155458911
28	0.863421540736285	0.273156918527430	0.136578459263715
29	0.826811414705644	0.346377170588711	0.173188585294356
30	0.767357705284791	0.465284589430418	0.232642294715209
31	0.691867813310952	0.616264373378096	0.308132186689048
32	0.613801803476271	0.772396393047458	0.386198196523729
33	0.523481431830323	0.953037136339354	0.476518568169677
34	0.449319547848516	0.898639095697031	0.550680452151484
35	0.392311064898738	0.784622129797475	0.607688935101262
36	0.366701105351303	0.733402210702606	0.633298894648697
37	0.301719192767275	0.603438385534551	0.698280807232725
38	0.390710146048839	0.781420292097678	0.609289853951161
39	0.353307550589538	0.706615101179075	0.646692449410462
40	0.279489395384166	0.558978790768332	0.720510604615834
41	0.581710485075704	0.836579029848592	0.418289514924296
42	0.487249438652058	0.974498877304117	0.512750561347942
43	0.406672953291785	0.81334590658357	0.593327046708215
44	0.768350616339989	0.463298767320021	0.231649383660011
45	0.720293479018962	0.559413041962075	0.279706520981038
46	0.657274474611101	0.685451050777799	0.342725525388899
47	0.693723645372962	0.612552709254077	0.306276354627038
48	0.581326031433791	0.837347937132417	0.418673968566209
49	0.472943500704097	0.945887001408195	0.527056499295903
50	0.499826202148503	0.999652404297005	0.500173797851497
51	0.465474566937131	0.930949133874263	0.534525433062869

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/23/t1258988498apmyp2rw6b9oqlp/10rbi81258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/10rbi81258988318.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/1cbzq1258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/1cbzq1258988318.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/24tr41258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/24tr41258988318.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/3i7si1258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/3i7si1258988318.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/468gl1258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/468gl1258988318.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/59rot1258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/59rot1258988318.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/6g6571258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/6g6571258988318.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/73fk41258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/73fk41258988318.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/8ryow1258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/8ryow1258988318.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/9vrxi1258988318.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258988498apmyp2rw6b9oqlp/9vrxi1258988318.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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