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Multiple Regression Analysis 4 Paper

*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: Sun, 20 Dec 2009 07:49:21 -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/Dec/20/t1261320803qwssciaryzxotcv.htm/, Retrieved Sun, 20 Dec 2009 15:53:35 +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/Dec/20/t1261320803qwssciaryzxotcv.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 «

-1,2 23,6 0,2 -1,9 -1,6 -4,2 -2,2 -2,4 25,7 -1,2 0,2 -1,9 -1,6 -4,2 0,8 32,5 -2,4 -1,2 0,2 -1,9 -1,6 -0,1 33,5 0,8 -2,4 -1,2 0,2 -1,9 -1,5 34,5 -0,1 0,8 -2,4 -1,2 0,2 -4,4 27,9 -1,5 -0,1 0,8 -2,4 -1,2 -4,2 45,3 -4,4 -1,5 -0,1 0,8 -2,4 3,5 40,8 -4,2 -4,4 -1,5 -0,1 0,8 10 58,5 3,5 -4,2 -4,4 -1,5 -0,1 8,6 32,5 10 3,5 -4,2 -4,4 -1,5 9,5 35,5 8,6 10 3,5 -4,2 -4,4 9,9 46,7 9,5 8,6 10 3,5 -4,2 10,4 53,2 9,9 9,5 8,6 10 3,5 16 36,1 10,4 9,9 9,5 8,6 10 12,7 54 16 10,4 9,9 9,5 8,6 10,2 58,1 12,7 16 10,4 9,9 9,5 8,9 41,8 10,2 12,7 16 10,4 9,9 12,6 43,1 8,9 10,2 12,7 16 10,4 13,6 76 12,6 8,9 10,2 12,7 16 14,8 42,8 13,6 12,6 8,9 10,2 12,7 9,5 41 14,8 13,6 12,6 8,9 10,2 13,7 61,4 9,5 14,8 13,6 12,6 8,9 17 34,2 13,7 9,5 14,8 13,6 12,6 14,7 53,8 17 13,7 9,5 14,8 13,6 17,4 80,7 14,7 17 13,7 9,5 14,8 9 79,5 17,4 14,7 17 13,7 9,5 9,1 96,5 9 17,4 14,7 17 13,7 12,2 108,3 9,1 9 17,4 14,7 17 15,9 100,1 12,2 9,1 9 17,4 14,7 12,9 108,5 15,9 12,2 9,1 9 17,4 10,9 127,4 12,9 15,9 12,2 9,1 9 10,6 86,5 10,9 1 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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = -0.322809223089442 + 0.0518061441391626X[t] + 0.975170502562991Y1[t] -0.116024070052944Y2[t] + 0.00885828143121708Y3[t] + 0.247106653868723Y4[t] -0.332969323286305Y5[t] -0.688673468306776M1[t] -1.09191559338719M2[t] -0.90814048713177M3[t] -0.371568623488736M4[t] -0.164833511373571M5[t] + 0.185268234324544M6[t] -1.15177125372001M7[t] + 1.4792396079914M8[t] + 0.75528855950583M9[t] + 0.176543249138199M10[t] + 0.611097713715677M11[t] -0.0455753246002475t + 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)	-0.322809223089442	1.850837	-0.1744	0.862193	0.431097
X	0.0518061441391626	0.019755	2.6225	0.01132	0.00566
Y1	0.975170502562991	0.12962	7.5233	0	0
Y2	-0.116024070052944	0.188871	-0.6143	0.541594	0.270797
Y3	0.00885828143121708	0.19186	0.0462	0.963345	0.481672
Y4	0.247106653868723	0.190309	1.2984	0.199651	0.099825
Y5	-0.332969323286305	0.132367	-2.5155	0.014893	0.007447
M1	-0.688673468306776	2.086495	-0.3301	0.74263	0.371315
M2	-1.09191559338719	2.193078	-0.4979	0.620582	0.310291
M3	-0.90814048713177	2.253233	-0.403	0.688511	0.344255
M4	-0.371568623488736	2.226363	-0.1669	0.868076	0.434038
M5	-0.164833511373571	2.203134	-0.0748	0.940636	0.470318
M6	0.185268234324544	2.19278	0.0845	0.932979	0.46649
M7	-1.15177125372001	2.232651	-0.5159	0.608047	0.304023
M8	1.4792396079914	2.182647	0.6777	0.500838	0.250419
M9	0.75528855950583	2.181834	0.3462	0.730559	0.36528
M10	0.176543249138199	2.183237	0.0809	0.93585	0.467925
M11	0.611097713715677	2.16532	0.2822	0.778853	0.389427
t	-0.0455753246002475	0.025781	-1.7678	0.082751	0.041376

Multiple Linear Regression - Regression Statistics
Multiple R	0.961390629342346
R-squared	0.924271942187273
Adjusted R-squared	0.899029256249697
F-TEST (value)	36.6154356344236
F-TEST (DF numerator)	18
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.71751652208532
Sum Squared Residuals	746.276170966775

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	-1.2	0.261558133992251	-1.46155813399225
2	-2.4	-0.381597201493626	-2.01840279850637
3	0.8	-1.82013639039304	2.62013639039304
4	-0.1	2.5888539611605	-2.6888539611605
5	-1.5	0.497074584303551	-1.99707458430355
6	-4.4	-0.603161017919336	-3.79683898208066
7	-4.2	-2.56757765486589	-1.63242234513411
8	3.5	-0.984065279716556	4.48406527971656
9	10	6.5770192135762	3.4229807864238
10	8.6	9.80228117090963	-1.20228117090963
11	9.5	9.31052471969642	0.189475280303575
12	9.9	12.1678738455548	-2.26787384555476
13	10.4	11.0859393943683	-0.685939394368294
14	16	7.69013503967916	8.30986496032084
15	12.7	14.8507039344068	-2.15070393440679
16	10.2	11.4899076249712	-1.28990762497121
17	8.9	8.79155241141967	0.108447588580332
18	12.6	9.3738456129976	3.2261543870024
19	13.6	10.752389221335	2.84761077866499
20	14.8	12.6332585827055	2.16674141729452
21	9.5	13.3686219826737	-3.86862198267375
22	13.7	9.8495271615151	3.85047283848491
23	17	12.5657729583791	4.43422704162089
24	14.7	15.571871679197	-0.871871679197
25	17.4	11.9334129051272	5.46658709487283
26	9	17.1540614889104	-8.15406148891044
27	9.1	9.0648752629318	0.0351247370681950
28	12.2	9.59607683063916	2.60392316936084
29	15.9	13.7024602321347	2.19753976786525
30	12.9	14.7167872690934	-1.81678726909335
31	10.9	13.8076216672225	-2.90762166722246
32	10.6	13.4374264500346	-2.83742645003463
33	13.2	11.6806391626175	1.51936083738247
34	9.6	12.5058892562393	-2.90588925623931
35	6.4	12.0402404147405	-5.64024041474054
36	5.8	5.9229666190873	-0.122966619087303
37	-1	6.56607528306986	-7.56607528306986
38	-0.2	-2.96358508383517	2.76358508383517
39	2.7	0.125484203052642	2.57451579694736
40	3.6	2.59748638922045	1.00251361077955
41	-0.9	1.02855817895485	-1.92855817895485
42	0.3	-1.45949197785129	1.75949197785129
43	-1.1	-0.468821161505506	-0.631178838494494
44	-2.5	-0.461044934714281	-2.03895506528572
45	-3.4	-2.40972974513665	-0.990270254863347
46	-3.5	-1.42799788986268	-2.07200211013732
47	-3.9	-3.10072368117839	-0.799276318821605
48	-4.6	-3.76459650892189	-0.835403491078113
49	-0.1	-4.72121951396673	4.62121951396673
50	4.3	1.21830094354682	3.08169905645318
51	10.2	6.80962438133781	3.39037561866219
52	8.7	11.8546745982653	-3.15467459826532
53	13.3	11.4649767166010	1.83502328339904
54	15	15.8011027877226	-0.801102787722553
55	20.7	17.3198171220391	3.38018287796087
56	20.7	23.3502375632766	-2.65023756327655
57	26.4	22.7468599702349	3.65314002976508
58	31.2	25.9101816537873	5.28981834621268
59	31.4	29.1665655348724	2.23343446512764
60	26.6	27.5024806662695	-0.902480666269482
61	26.6	24.3026897158864	2.29731028411364
62	19.2	23.1826848131924	-3.98268481319238
63	6.5	12.969448608664	-6.469448608664
64	3.1	-0.426999404256631	3.52699940425663
65	-0.2	0.0153778765862193	-0.215377876586219
66	-4	-5.42908267404288	1.42908267404288
67	-12.6	-11.5434291942252	-1.05657080577479
68	-13	-13.8758123815858	0.875812381585828
69	-17.6	-13.8634105839657	-3.73658941603426
70	-21.7	-18.7398813525887	-2.96011864741131
71	-23.2	-22.7823799465100	-0.417620053489954
72	-16.8	-21.8005963011867	5.00059630118666
73	-19.8	-17.1284559184772	-2.67154408152278

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.41281872233443	0.82563744466886	0.58718127766557
23	0.589003511730978	0.821992976538044	0.410996488269022
24	0.472771799639969	0.945543599279939	0.527228200360031
25	0.405686432115569	0.811372864231137	0.594313567884431
26	0.682656876175596	0.634686247648808	0.317343123824404
27	0.584665524406192	0.830668951187617	0.415334475593808
28	0.488619360190497	0.977238720380994	0.511380639809503
29	0.491698898654455	0.983397797308909	0.508301101345545
30	0.410900097767785	0.82180019553557	0.589099902232215
31	0.374409276003988	0.748818552007976	0.625590723996012
32	0.285408933027825	0.570817866055651	0.714591066972175
33	0.265155344813338	0.530310689626675	0.734844655186662
34	0.263174727617409	0.526349455234818	0.736825272382591
35	0.485764754635226	0.971529509270451	0.514235245364774
36	0.405791921564042	0.811583843128084	0.594208078435958
37	0.592617040845291	0.814765918309419	0.407382959154709
38	0.54787252006287	0.90425495987426	0.45212747993713
39	0.55562587711879	0.888748245762421	0.444374122881211
40	0.507197266568409	0.985605466863182	0.492802733431591
41	0.407772046516899	0.815544093033797	0.592227953483101
42	0.366590346051977	0.733180692103954	0.633409653948023
43	0.274526150371030	0.549052300742061	0.72547384962897
44	0.208007794480972	0.416015588961944	0.791992205519028
45	0.141002475930363	0.282004951860726	0.858997524069637
46	0.119122754566124	0.238245509132249	0.880877245433876
47	0.0922578946882801	0.184515789376560	0.90774210531172
48	0.124682750416208	0.249365500832416	0.875317249583792
49	0.144745458212401	0.289490916424802	0.855254541787599
50	0.107383262852390	0.214766525704780	0.89261673714761
51	0.23787297794845	0.4757459558969	0.76212702205155

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/Dec/20/t1261320803qwssciaryzxotcv/10329b1261320556.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/10329b1261320556.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/1odto1261320555.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/1odto1261320555.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/2t43g1261320555.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/2t43g1261320555.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/3a4c31261320555.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/3a4c31261320555.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/4a0qq1261320555.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/4a0qq1261320555.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/50edm1261320555.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/50edm1261320555.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/6u1i81261320555.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/6u1i81261320555.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/7rejt1261320555.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/7rejt1261320555.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/85yxx1261320555.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/85yxx1261320555.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/9pvuh1261320555.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261320803qwssciaryzxotcv/9pvuh1261320555.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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