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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: Fri, 24 Dec 2010 15:17:47 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz.htm/, Retrieved Fri, 24 Dec 2010 16:15:36 +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/2010/Dec/24/t1293203736hf3b751s3tghvoz.htm/},
    year = {2010},
}
@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 = {2010},
    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 «

0 9 12 9 24 13 14 1 9 15 6 25 12 8 1 9 14 13 19 15 12 1 8 10 7 18 12 7 1 14 10 8 18 10 10 0 14 9 8 23 12 7 1 15 18 11 23 15 16 1 11 11 11 23 9 11 0 14 14 8 17 7 12 0 8 24 20 30 11 7 1 16 18 16 26 10 11 0 11 14 8 23 14 15 1 7 18 11 35 11 7 0 9 12 8 21 15 14 0 16 5 4 23 12 7 1 10 12 8 20 14 15 0 14 11 8 24 15 17 0 11 9 6 20 9 15 1 6 11 8 17 13 14 1 12 16 14 27 16 8 1 14 14 10 18 13 8 0 13 8 9 24 12 14 0 14 18 10 26 11 8 0 10 10 8 26 16 16 1 14 13 10 25 12 10 1 8 12 7 20 13 14 1 10 12 8 26 16 16 0 9 12 7 18 14 13 1 9 13 6 19 15 5 0 15 7 5 21 8 10 1 12 14 7 24 17 15 1 14 9 9 23 13 16 0 11 9 5 31 6 15 0 12 10 8 23 8 8 0 13 10 6 19 14 13 1 14 11 8 26 12 14 1 15 13 8 14 11 12 0 11 13 6 25 16 16 0 9 13 8 27 8 10 1 8 6 6 20 15 15 0 10 13 6 24 16 16 0 10 21 12 32 14 19 1 10 11 5 26 16 14 0 9 9 7 21 9 6 1 13 18 12 21 14 13 0 8 9 11 24 13 7 1 10 15 10 23 15 13 1 11 11 8 24 15 14 1 10 14 9 21 13 13 0 16 14 9 21 11 11 0 11 8 4 13 11 14 1 6 8 11 29 12 14 0 9 11 10 21 7 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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

DoubtsAboutActions[t] = + 13.7755021771417 -0.205953277107087Gen[t] + 0.0223981615071569ParentalExpectations[t] -0.000526927467739707ParentalCritism[t] -0.039162295223845PersonalStandards[t] -0.183120820978697Popularity[t] + 0.032976677326881KnowingPeople[t] + 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)	13.7755021771417	2.447909	5.6275	0	0
Gen	-0.205953277107087	0.696943	-0.2955	0.76848	0.38424
ParentalExpectations	0.0223981615071569	0.120446	0.186	0.853013	0.426507
ParentalCritism	-0.000526927467739707	0.161517	-0.0033	0.997406	0.498703
PersonalStandards	-0.039162295223845	0.08501	-0.4607	0.646457	0.323229
Popularity	-0.183120820978697	0.130754	-1.4005	0.16578	0.08289
KnowingPeople	0.032976677326881	0.121066	0.2724	0.786127	0.393064

Multiple Linear Regression - Regression Statistics
Multiple R	0.191864976857339
R-squared	0.0368121693444672
Adjusted R-squared	-0.0457467875688642
F-TEST (value)	0.445889467609333
F-TEST (DF numerator)	6
F-TEST (DF denominator)	70
p-value	0.845498890796329
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.92373858652028
Sum Squared Residuals	598.377312561533

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9	11.1807454924989	-2.1807454924989
2	9	10.9896659441101	-1.98966594411007
3	9	10.7810973080432	-1.78109730804324
4	8	11.1183075983466	-3.11830759834658
5	14	11.5829523448169	2.41704765518312
6	14	11.1055243103595	2.89447568964045
7	15	10.8470013374195	4.15299866258051
8	11	11.6240557461072	-0.624055746107171
9	14	12.5329763807663	1.46702361923371
10	8	11.3441583577658	-3.34415835776581
11	16	11.4776005326683	4.52239946733166
12	11	11.1150868945530	-0.115086894552985
13	7	10.8127469827062	-3.81274698270621
14	9	10.9325176636808	-1.93251766368078
15	16	11.0180393742019	4.98196062579812
16	10	10.9818241801031	-0.98182418010312
17	14	10.8915626484827	3.10843735151727
18	11	12.0372409325177	-1.03724093251770
19	6	11.2270570479193	-5.22705704791931
20	12	10.1970408115128	1.80295918848717
21	14	11.0561753183202	2.94382468167983
22	13	11.2742736674490	1.72572633255103
23	14	11.4046645216225	2.59533547837748
24	10	10.5747423982223	-0.574742398222309
25	14	11.0087152658786	2.99128473412144
26	8	11.1324952512227	-3.13249525122268
27	10	10.4135854441295	-0.413585444129536
28	9	11.2006756204719	-2.20067562047187
29	9	10.5315508975221	-1.53155089752209
30	15	11.9720466760916	3.02795332390843
31	12	10.3211357867537	1.6788642132463
32	14	11.0127133807480	2.98728661925205
33	11	12.1563450754592	-1.15634507545924
34	12	11.8933824331084	0.106617566891626
35	13	11.1172439297015	1.88275607029854
36	14	11.0577172118834	2.94228278811659
37	15	11.6896285439088	3.31037145609120
38	11	10.6821530329031	0.317846967096896
39	9	11.8698810913882	-2.86988109138823
40	8	10.6653682450170	-2.66536824501696
41	10	10.7213153281269	-0.721315328126949
42	10	11.0492123675250	-1.04921236752504
43	10	10.3268147103718	-0.326814710371836
44	9	11.7007616138842	-2.70076161388419
45	13	11.0089897893975	1.99101021060251
46	8	10.8816604117538	-2.88166041175379
47	10	10.6814037483851	-0.681403748385116
48	11	10.586679339395	0.413320660604996
49	10	11.1040987467508	-1.10409874675078
50	16	11.6103403111615	4.3896596888385
51	11	11.8908143732287	-0.890814373228665
52	6	10.8714550592872	-4.87145505928718
53	9	12.1431954737796	-3.14319547377956
54	20	11.4051856438499	8.59481435615008
55	12	11.1988680515686	0.801131948431438
56	9	10.8490657397215	-1.84906573972145
57	14	10.8042299051393	3.19577009486072
58	8	11.8838548081750	-3.88385480817496
59	7	11.4623285400194	-4.4623285400194
60	11	11.5390737238544	-0.539073723854375
61	14	11.9410981686288	2.05890183137123
62	14	11.9010285738741	2.09897142612588
63	9	10.5312349420945	-1.53123494209454
64	16	11.4700412025505	4.52995879744951
65	13	10.9696402192414	2.03035978075858
66	13	11.9558818695002	1.04411813049978
67	8	11.9264315100929	-3.92643151009285
68	9	11.7948590690610	-2.79485906906103
69	11	11.3679672958837	-0.367967295883691
70	8	10.8660373815593	-2.86603738155928
71	7	10.3247070005009	-3.32470700050088
72	11	11.3671496032191	-0.367149603219146
73	9	12.6719755695628	-3.67197556956278
74	16	11.4164141878630	4.58358581213701
75	13	10.8818609473565	2.11813905264355
76	12	11.9113965448716	0.088603455128375
77	9	10.1512325483833	-1.15123254838327

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.855109518745768	0.289780962508465	0.144890481254232
11	0.88498923679557	0.230021526408858	0.115010763204429
12	0.807760757776771	0.384478484446457	0.192239242223229
13	0.770930882224122	0.458138235551757	0.229069117775878
14	0.700212061688573	0.599575876622853	0.299787938311427
15	0.8375969437562	0.324806112487601	0.162403056243800
16	0.785102379339561	0.429795241320878	0.214897620660439
17	0.738086056753338	0.523827886493323	0.261913943246661
18	0.73890131199184	0.522197376016321	0.261098688008161
19	0.850140131079466	0.299719737841068	0.149859868920534
20	0.820767628743872	0.358464742512257	0.179232371256128
21	0.829164576141551	0.341670847716897	0.170835423858449
22	0.780102231070322	0.439795537859356	0.219897768929678
23	0.769262151996122	0.461475696007757	0.230737848003878
24	0.71067862316751	0.578642753664981	0.289321376832491
25	0.704879666811613	0.590240666376774	0.295120333188387
26	0.695080848806977	0.609838302386046	0.304919151193023
27	0.624945052432381	0.750109895135238	0.375054947567619
28	0.586554554444691	0.826890891110618	0.413445445555309
29	0.522326077459781	0.955347845080438	0.477673922540219
30	0.499634972563158	0.999269945126316	0.500365027436842
31	0.472567735376417	0.945135470752834	0.527432264623583
32	0.458999705234456	0.917999410468912	0.541000294765544
33	0.411955610412770	0.823911220825539	0.58804438958723
34	0.349567670370847	0.699135340741693	0.650432329629154
35	0.310894439193823	0.621788878387645	0.689105560806177
36	0.312672622895163	0.625345245790327	0.687327377104837
37	0.329677878099482	0.659355756198963	0.670322121900518
38	0.269875156391553	0.539750312783106	0.730124843608447
39	0.255105699031027	0.510211398062055	0.744894300968973
40	0.254862937565469	0.509725875130939	0.74513706243453
41	0.201729808766646	0.403459617533293	0.798270191233354
42	0.160355186587087	0.320710373174173	0.839644813412913
43	0.120425032224297	0.240850064448594	0.879574967775703
44	0.113164069217640	0.226328138435279	0.88683593078236
45	0.0948748936428449	0.189749787285690	0.905125106357155
46	0.0940279773557078	0.188055954711416	0.905972022644292
47	0.0679796263084104	0.135959252616821	0.93202037369159
48	0.0473807284057948	0.0947614568115897	0.952619271594205
49	0.0332710448866087	0.0665420897732174	0.966728955113391
50	0.049151938645239	0.098303877290478	0.950848061354761
51	0.0343392830483914	0.0686785660967829	0.965660716951609
52	0.0635592762675457	0.127118552535091	0.936440723732454
53	0.078039806019626	0.156079612039252	0.921960193980374
54	0.474779273053443	0.949558546106885	0.525220726946557
55	0.399744500030769	0.799489000061539	0.60025549996923
56	0.334438299798643	0.668876599597287	0.665561700201357
57	0.343650213685535	0.68730042737107	0.656349786314465
58	0.340332627680075	0.68066525536015	0.659667372319925
59	0.36905348279142	0.73810696558284	0.63094651720858
60	0.347744030047959	0.695488060095917	0.652255969952041
61	0.66435754843678	0.671284903126439	0.335642451563219
62	0.58186062574621	0.83627874850758	0.41813937425379
63	0.585996981089957	0.828006037820087	0.414003018910043
64	0.528863394332132	0.942273211335735	0.471136605667868
65	0.585822435046546	0.828355129906908	0.414177564953454
66	0.454087887384319	0.908175774768639	0.545912112615681
67	0.315652477428456	0.631304954856912	0.684347522571544

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	4	0.0689655172413793	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/10t3jl1293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/10t3jl1293203858.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/1424r1293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/1424r1293203858.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/2424r1293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/2424r1293203858.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/3fclc1293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/3fclc1293203858.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/4fclc1293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/4fclc1293203858.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/5fclc1293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/5fclc1293203858.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/68lkx1293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/68lkx1293203858.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/70cj01293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/70cj01293203858.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/80cj01293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/80cj01293203858.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/90cj01293203858.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203736hf3b751s3tghvoz/90cj01293203858.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No 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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