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paper 4

*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: Sat, 18 Dec 2010 12:03:00 +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/18/t129267366221b9j0bfadcnod9.htm/, Retrieved Sat, 18 Dec 2010 13:01:13 +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/18/t129267366221b9j0bfadcnod9.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 0 9 0 24 0 13 0 14 0 1 9 15 15 6 6 25 25 12 12 8 8 1 9 14 14 13 13 19 19 15 15 12 12 1 8 10 10 7 7 18 18 12 12 7 7 1 14 10 10 8 8 18 18 10 10 10 10 0 14 9 0 8 0 23 0 12 0 7 0 1 15 18 18 11 11 23 23 15 15 16 16 1 11 11 11 11 11 23 23 9 9 11 11 0 14 14 0 8 0 17 0 7 0 12 0 0 8 24 0 20 0 30 0 11 0 7 0 1 16 18 18 16 16 26 26 10 10 11 11 0 11 14 0 8 0 23 0 14 0 15 0 1 7 18 18 11 11 35 35 11 11 7 7 0 9 12 0 8 0 21 0 15 0 14 0 0 16 5 0 4 0 23 0 12 0 7 0 1 10 12 12 8 8 20 20 14 14 15 15 0 14 11 0 8 0 24 0 15 0 17 0 0 11 9 0 6 0 20 0 9 0 15 0 1 6 11 11 8 8 17 17 13 13 14 14 1 12 16 16 14 14 27 27 16 16 8 8 1 14 14 14 10 10 18 18 13 13 8 8 0 13 8 0 9 0 24 0 12 0 14 0 0 14 18 0 10 0 26 0 11 0 8 0 0 10 10 0 8 0 26 0 16 0 16 0 1 14 13 13 10 10 25 25 12 12 10 10 1 8 12 12 7 7 20 20 13 13 14 14 1 10 12 12 8 8 26 26 16 16 16 16 0 9 12 0 7 0 18 0 14 0 13 0 1 9 13 13 6 6 19 19 15 15 5 5 0 15 7 0 5 0 21 0 8 0 10 0 1 12 14 14 7 7 24 24 17 17 15 15 1 14 9 9 9 9 23 23 13 13 16 16 0 11 9 0 5 0 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	9 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

DoubtsAboutActions[t] = + 15.2586833489025 -5.71186637540504Gen[t] -0.0399576756832597ParentalExpectations[t] + 0.205268544988714Expect_gen[t] -0.269755212692944ParentalCritism[t] + 0.376649954168655Critism_gen[t] + 0.00542610174784382PersonalStandards[t] -0.0304886794035444PersStand_gen[t] -0.0841192000992633Popularity[t] -0.0928346463942527Popular_gen[t] -0.0288364530367456KnowingPeople[t] + 0.120074648121647Knowing_gen[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)	15.2586833489025	3.469091	4.3985	4.1e-05	2.1e-05
Gen	-5.71186637540504	5.142306	-1.1108	0.270764	0.135382
ParentalExpectations	-0.0399576756832597	0.172009	-0.2323	0.817034	0.408517
Expect_gen	0.205268544988714	0.252377	0.8133	0.418991	0.209496
ParentalCritism	-0.269755212692944	0.261671	-1.0309	0.306412	0.153206
Critism_gen	0.376649954168655	0.337174	1.1171	0.268073	0.134036
PersonalStandards	0.00542610174784382	0.134226	0.0404	0.967878	0.483939
PersStand_gen	-0.0304886794035444	0.174907	-0.1743	0.862161	0.43108
Popularity	-0.0841192000992633	0.206976	-0.4064	0.685768	0.342884
Popular_gen	-0.0928346463942527	0.269905	-0.344	0.731991	0.365996
KnowingPeople	-0.0288364530367456	0.180664	-0.1596	0.87368	0.43684
Knowing_gen	0.120074648121647	0.249418	0.4814	0.631835	0.315918

Multiple Linear Regression - Regression Statistics
Multiple R	0.330628707781600
R-squared	0.109315342409330
Adjusted R-squared	-0.041415907336783
F-TEST (value)	0.725233437614678
F-TEST (DF numerator)	11
F-TEST (DF denominator)	65
p-value	0.710389521294558
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.91767808551436
Sum Squared Residuals	553.3349516949

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9	10.9843608246103	-1.98436082461031
2	9	10.6477434232980	-1.64774342329802
3	9	11.2151624511158	-2.21516245111579
4	8	10.0122836667515	-2.01228366675145
5	14	10.7468006864689	3.2531993135311
6	14	11.6545373339616	2.34546266603840
7	15	11.927318915103	3.07268108489701
8	11	11.3756749335014	-0.375674933501403
9	14	11.6986060803708	2.30139391962917
10	8	7.94021155873154	0.0597884412684561
11	16	12.8151831465575	3.18481685344248
12	11	11.0558189310528	-0.0558189310528119
13	7	11.5132396134445	-4.51323961344454
14	9	11.0695993318611	-2.06959933186112
15	16	12.8933888874664	3.10661111253358
16	10	10.7756728592189	-0.775672859218851
17	14	11.0393259536777	2.96067404632232
18	11	12.1994354301078	-1.19943543010778
19	6	10.7712653742891	-4.77126537428911
20	12	10.9102716831237	1.08972831687631
21	14	10.9084957169918	3.09150428300821
22	13	11.2283107274425	1.77168927255746
23	14	10.8269688797324	3.17303112026758
24	10	11.0348530857941	-1.03485308579411
25	14	10.9271770407598	3.07282295924025
26	8	10.7544937691518	-2.75449376915176
27	10	10.3626278953825	-0.362627895382517
28	9	11.4360318924465	-2.43603189244655
29	9	9.66292102588606	-0.662921025886059
30	15	12.7828335611981	2.21716643880192
31	12	10.3682880062507	1.6317119937493
32	14	10.5796393013895	3.42036069861049
33	11	12.7812353623248	-1.7812353623248
34	12	11.9222200056387	0.077779994361349
35	13	11.7911285582539	1.20887144174614
36	14	10.7226560218813	3.27734397811868
37	15	11.3485061486844	3.65149385131565
38	11	11.4490643823824	-0.449064382382375
39	9	11.7663784795068	-2.76637847950676
40	8	9.39306431394118	-1.39306431394119
41	10	11.4436382806345	-1.44363828063453
42	10	9.63058345408183	0.369416545918172
43	10	9.69415641148013	0.305843588519872
44	9	12.1946343964934	-3.19463439649340
45	13	11.9875780731289	1.01242192687108
46	8	10.7665785975314	-2.76657859753135
47	10	11.0507769804562	-1.05077698045622
48	11	10.2419196377122	0.758080362287823
49	10	11.1826042179735	-1.18260421797349
50	16	11.1429149273090	4.85708507269105
51	11	12.6015188717802	-1.60151887178024
52	6	10.472219905425	-4.47221990542499
53	9	11.4448553542098	-2.44485535420982
54	20	11.7983635501627	8.2016364498373
55	12	11.0117708049917	0.988229195008255
56	9	10.3951598271970	-1.39515982719703
57	14	11.0859735608264	2.91402643917356
58	8	11.3444925794816	-3.34449257948161
59	7	11.9371245131594	-4.93712451315935
60	11	11.6167589961043	-0.616758996104273
61	14	14.0751805439643	-0.0751805439643221
62	14	11.913029947157	2.08697005284300
63	9	11.2299013659563	-2.2299013659563
64	16	10.7409509155000	5.25904908449995
65	13	11.4352697947752	1.56473020522476
66	13	11.1924780574597	1.80752194254028
67	8	11.8343779892329	-3.83437798923287
68	9	11.4366185295925	-2.43661852959245
69	11	12.8862909034053	-1.88629090340527
70	8	10.8575170252417	-2.85751702524167
71	7	10.1217353773830	-3.12173537738297
72	11	10.8241589584426	0.175841041557429
73	9	13.0950331364795	-4.09503313647951
74	16	11.4058140461976	4.59418595380238
75	13	10.2663944627851	2.73360553721488
76	12	12.3160322864701	-0.316032286470143
77	9	10.0327984600665	-1.03279846006646

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
15	0.095940395864396	0.191880791728792	0.904059604135604
16	0.0351689004798988	0.0703378009597976	0.964831099520101
17	0.220642977819309	0.441285955638617	0.779357022180691
18	0.240576218836068	0.481152437672136	0.759423781163932
19	0.519392963888404	0.961214072223192	0.480607036111596
20	0.642003763771067	0.715992472457866	0.357996236228933
21	0.564147982937138	0.871704034125725	0.435852017062862
22	0.482639314276423	0.965278628552847	0.517360685723577
23	0.421547656352764	0.843095312705528	0.578452343647236
24	0.339895428578835	0.67979085715767	0.660104571421165
25	0.443598380760956	0.887196761521913	0.556401619239044
26	0.383127540814516	0.766255081629032	0.616872459185484
27	0.366968498206375	0.733936996412749	0.633031501793625
28	0.308157699406414	0.616315398812829	0.691842300593586
29	0.250096885837798	0.500193771675596	0.749903114162202
30	0.210119392921976	0.420238785843952	0.789880607078024
31	0.196909316202087	0.393818632404174	0.803090683797913
32	0.253221944345662	0.506443888691324	0.746778055654338
33	0.258875023832403	0.517750047664807	0.741124976167597
34	0.215571048458639	0.431142096917279	0.78442895154136
35	0.16927503142144	0.33855006284288	0.83072496857856
36	0.187230072699170	0.374460145398339	0.81276992730083
37	0.226980619687358	0.453961239374715	0.773019380312643
38	0.171776083143599	0.343552166287198	0.828223916856401
39	0.169957451727388	0.339914903454775	0.830042548272612
40	0.128703513159424	0.257407026318848	0.871296486840576
41	0.0994995758701581	0.198999151740316	0.900500424129842
42	0.450594972415546	0.901189944831092	0.549405027584454
43	0.402730670544091	0.805461341088182	0.597269329455909
44	0.499121394499576	0.998242788999152	0.500878605500424
45	0.463264727270585	0.92652945454117	0.536735272729415
46	0.456208696563052	0.912417393126103	0.543791303436948
47	0.390474512980431	0.780949025960862	0.609525487019569
48	0.321542709497657	0.643085418995314	0.678457290502343
49	0.261187648522914	0.522375297045827	0.738812351477086
50	0.318917107639883	0.637834215279765	0.681082892360118
51	0.359959883420678	0.719919766841355	0.640040116579322
52	0.440482540370168	0.880965080740336	0.559517459629832
53	0.413418679326572	0.826837358653143	0.586581320673428
54	0.497394101280607	0.994788202561214	0.502605898719393
55	0.404258698216698	0.808517396433397	0.595741301783302
56	0.318583584508345	0.637167169016689	0.681416415491655
57	0.282665663811343	0.565331327622686	0.717334336188657
58	0.22500949091687	0.45001898183374	0.77499050908313
59	0.177370209639068	0.354740419278136	0.822629790360932
60	0.108041847192525	0.216083694385050	0.891958152807475
61	0.163288773663748	0.326577547327497	0.836711226336252
62	0.0883026119414018	0.176605223882804	0.911697388058598

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	1	0.0208333333333333	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/10xsrf1292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/10xsrf1292673769.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/1j0t61292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/1j0t61292673769.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/2j0t61292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/2j0t61292673769.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/3j0t61292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/3j0t61292673769.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/4urtr1292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/4urtr1292673769.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/5urtr1292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/5urtr1292673769.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/6urtr1292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/6urtr1292673769.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/7njac1292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/7njac1292673769.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/8xsrf1292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/8xsrf1292673769.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/9xsrf1292673769.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t129267366221b9j0bfadcnod9/9xsrf1292673769.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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