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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 07:38:52 -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/t1258987601mrxhs1o41bmukft.htm/, Retrieved Mon, 23 Nov 2009 15:46:54 +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/t1258987601mrxhs1o41bmukft.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 «

6.5 501 6.7 6.7 6.9 7.0 6.4 507 6.5 6.7 6.7 6.9 6.5 569 6.4 6.5 6.7 6.7 6.5 580 6.5 6.4 6.5 6.7 6.5 578 6.5 6.5 6.4 6.5 6.7 565 6.5 6.5 6.5 6.4 6.8 547 6.7 6.5 6.5 6.5 7.2 555 6.8 6.7 6.5 6.5 7.6 562 7.2 6.8 6.7 6.5 7.6 561 7.6 7.2 6.8 6.7 7.2 555 7.6 7.6 7.2 6.8 6.4 544 7.2 7.6 7.6 7.2 6.1 537 6.4 7.2 7.6 7.6 6.3 543 6.1 6.4 7.2 7.6 7.1 594 6.3 6.1 6.4 7.2 7.5 611 7.1 6.3 6.1 6.4 7.4 613 7.5 7.1 6.3 6.1 7.1 611 7.4 7.5 7.1 6.3 6.8 594 7.1 7.4 7.5 7.1 6.9 595 6.8 7.1 7.4 7.5 7.2 591 6.9 6.8 7.1 7.4 7.4 589 7.2 6.9 6.8 7.1 7.3 584 7.4 7.2 6.9 6.8 6.9 573 7.3 7.4 7.2 6.9 6.9 567 6.9 7.3 7.4 7.2 6.8 569 6.9 6.9 7.3 7.4 7.1 621 6.8 6.9 6.9 7.3 7.2 629 7.1 6.8 6.9 6.9 7.1 628 7.2 7.1 6.8 6.9 7.0 612 7.1 7.2 7.1 6.8 6.9 595 7.0 7.1 7.2 7.1 7.1 597 6.9 7.0 7.1 7.2 7.3 593 7.1 6.9 7.0 7.1 7.5 590 7.3 7.1 6.9 7.0 7.5 580 7.5 7.3 7.1 6.9 7.5 574 7.5 7.5 7.3 7.1 7.3 573 7.5 7.5 7.5 7.3 7.0 573 7.3 7.5 7.5 7.5 6.7 620 7.0 7.3 7.5 7.5 6.5 626 6.7 7.0 7.3 7.5 6.5 620 6.5 6.7 7.0 7.3 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] = + 0.611568123924592 + 0.00675489918161896werkl[t] + 1.12474072934668Y1[t] -0.594296879745087Y2[t] -0.239259215469524Y3[t] + 0.0912719771670837Y4[t] + 0.0368974823988274M1[t] + 0.00252819351432243M2[t] -0.176381285134097M3[t] -0.566367872612399M4[t] -0.51709672722601M5[t] -0.260759394182951M6[t] -0.209771369325065M7[t] + 0.00660363244903975M8[t] -0.0258802322194126M9[t] -0.0749365444845493M10[t] -0.0486698370192032M11[t] -0.003230062272187t + 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)	0.611568123924592	0.352134	1.7367	0.088589	0.044295
werkl	0.00675489918161896	0.001471	4.5908	3e-05	1.5e-05
Y1	1.12474072934668	0.134875	8.3392	0	0
Y2	-0.594296879745087	0.207644	-2.8621	0.006131	0.003066
Y3	-0.239259215469524	0.20774	-1.1517	0.254913	0.127456
Y4	0.0912719771670837	0.124034	0.7359	0.465252	0.232626
M1	0.0368974823988274	0.084448	0.4369	0.664047	0.332024
M2	0.00252819351432243	0.087198	0.029	0.976985	0.488493
M3	-0.176381285134097	0.12184	-1.4476	0.153958	0.076979
M4	-0.566367872612399	0.13089	-4.3271	7.2e-05	3.6e-05
M5	-0.51709672722601	0.134149	-3.8546	0.000332	0.000166
M6	-0.260759394182951	0.119739	-2.1777	0.034171	0.017086
M7	-0.209771369325065	0.097609	-2.1491	0.036492	0.018246
M8	0.00660363244903975	0.102593	0.0644	0.948935	0.474467
M9	-0.0258802322194126	0.102453	-0.2526	0.801608	0.400804
M10	-0.0749365444845493	0.094754	-0.7909	0.432765	0.216382
M11	-0.0486698370192032	0.085601	-0.5686	0.572198	0.286099
t	-0.003230062272187	0.001117	-2.8924	0.005648	0.002824

Multiple Linear Regression - Regression Statistics
Multiple R	0.980936126345125
R-squared	0.962235683968979
Adjusted R-squared	0.949395816518431
F-TEST (value)	74.9412474603048
F-TEST (DF numerator)	17
F-TEST (DF denominator)	50
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.130328008145194
Sum Squared Residuals	0.849269485354691

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6.5	6.57142907980288	-0.0714290798028816
2	6.4	6.38813562324378	0.0118643767562195
3	6.5	6.61293073916448	-0.112930739164481
4	6.5	6.51377358441488	-0.0137735844148818
5	6.5	6.49254670730487	0.00745329269512729
6	6.7	6.62478716945104	0.0752128305489624
7	6.8	6.78503229035364	0.0149677096463594
8	7.2	7.04583112029416	0.154168879705840
9	7.6	7.40001624829511	0.199983751704885
10	7.6	7.54748098830327	0.0525190116967248
11	7.2	7.20569299803758	-0.00569299803758433
12	6.4	6.66773769472714	-0.267737694727143
13	6.1	6.02855577986997	0.0714442201300285
14	6.3	6.26520479498287	0.0347952050171339
15	7.1	6.98570090362648	0.114299096373524
16	7.5	7.48701093039903	0.0129890696009710
17	7.4	7.44578716357504	-0.045787163575043
18	7.1	7.16203883420777	-0.062038834207772
19	6.8	6.79428487542231	0.00571512457769177
20	6.9	6.91548627163915	-0.0154862716391519
21	7.2	7.20616645175438	-0.00616645175438092
22	7.4	7.46275898117405	-0.0627589811740473
23	7.3	7.44737269770784	-0.147372697707845
24	6.9	7.12452456564922	-0.224524565649217
25	6.9	6.7067257369782	0.1932742630218
26	6.8	6.96253525306315	-0.162535253063151
27	7.1	7.10575288512316	-0.00575288512316178
28	7.2	7.1269185447373	0.073081455262695
29	7.1	7.12431565922798	-0.0243156592279838
30	7	7.01653581982621	-0.0165358198262085
31	6.9	6.8998717829674	0.000128217032601591
32	7.1	7.10653525513606	-0.00653525513605626
33	7.3	7.34297828914303	-0.0429782891430294
34	7.5	7.39131471081141	0.108685289188587
35	7.5	7.39591209329809	0.104087906701911
36	7.5	7.25236564934589	0.247634350654114
37	7.3	7.24968072263042	0.0503192773695807
38	7	7.0053876210378	-0.0053876210378067
39	6.7	6.9221654987983	-0.222165498798303
40	6.5	6.45819693235095	0.0418030676490457
41	6.5	6.47057290763707	0.0294270923629282
42	6.5	6.67077895203589	-0.170778952035887
43	6.6	6.59039938256975	0.00960061743025129
44	6.8	6.83696990693835	-0.0369699069383472
45	6.9	6.99379403461901	-0.0937940346190116
46	6.9	6.83013764534096	0.0698623546590403
47	6.8	6.6401866710949	0.159813328905108
48	6.8	6.52695145170399	0.273048548296011
49	6.5	6.52785226979756	-0.0278522697975629
50	6.1	6.09569783120439	0.00430216879560911
51	6.1	6.01109821892259	0.0889017810774102
52	5.9	5.99492707752718	-0.094927077527182
53	5.7	5.7289794266325	-0.0289794266324966
54	5.9	5.73816564889193	0.161834351108070
55	5.9	6.06274969030237	-0.162749690302366
56	6.1	6.21365229824223	-0.113652298242231
57	6.3	6.35704497618846	-0.0570449761884631
58	6.2	6.3683076743703	-0.168307674370305
59	5.9	6.01083553986159	-0.110835539861590
60	5.7	5.72842063857376	-0.0284206385737645
61	5.4	5.61575641092097	-0.215756410920965
62	5.6	5.483038876468	0.116961123531995
63	6.2	6.06235175436499	0.137648245635012
64	6.3	6.31917293057065	-0.0191729305706478
65	6	5.93779813562253	0.062201864377468
66	5.6	5.58769357558717	0.0123064244128348
67	5.5	5.36766197838454	0.132338021615462
68	5.9	5.88152514775005	0.0184748522499469

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.070896565949497	0.141793131898994	0.929103434050503
22	0.292169664222265	0.584339328444531	0.707830335777735
23	0.409243589853337	0.818487179706674	0.590756410146663
24	0.394372056832244	0.788744113664489	0.605627943167756
25	0.499685849080791	0.999371698161582	0.500314150919209
26	0.489807497496349	0.979614994992698	0.510192502503651
27	0.385601876731586	0.771203753463172	0.614398123268414
28	0.332174642267692	0.664349284535383	0.667825357732308
29	0.241227059254966	0.482454118509932	0.758772940745034
30	0.173085522685968	0.346171045371937	0.826914477314032
31	0.118407934296864	0.236815868593728	0.881592065703136
32	0.0859997773113117	0.171999554622623	0.914000222688688
33	0.0730498029616252	0.146099605923250	0.926950197038375
34	0.0636659344001366	0.127331868800273	0.936334065599863
35	0.0613166261049286	0.122633252209857	0.938683373895071
36	0.247132878729067	0.494265757458135	0.752867121270933
37	0.202421598017989	0.404843196035978	0.797578401982011
38	0.145303151931329	0.290606303862658	0.854696848068671
39	0.344618348745716	0.689236697491433	0.655381651254284
40	0.253044446094798	0.506088892189596	0.746955553905202
41	0.189423599301285	0.37884719860257	0.810576400698715
42	0.518983110563405	0.96203377887319	0.481016889436595
43	0.540766152961248	0.918467694077503	0.459233847038752
44	0.455851458880042	0.911702917760085	0.544148541119958
45	0.840349888010117	0.319300223979765	0.159650111989883
46	0.73775051462401	0.52449897075198	0.26224948537599
47	0.679881261040032	0.640237477919936	0.320118738959968

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/t1258987601mrxhs1o41bmukft/10fvl51258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/10fvl51258987127.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/1urrj1258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/1urrj1258987127.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/26r601258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/26r601258987127.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/3f8yp1258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/3f8yp1258987127.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/489s51258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/489s51258987127.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/5k55q1258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/5k55q1258987127.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/616zp1258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/616zp1258987127.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/75z2g1258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/75z2g1258987127.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/817o81258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/817o81258987127.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/90zzt1258987127.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258987601mrxhs1o41bmukft/90zzt1258987127.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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