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Multiple Linear Regression Investeringsgoederen

*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, 13 Dec 2008 05:42:32 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i.htm/, Retrieved Sat, 13 Dec 2008 13:43:31 +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/2008/Dec/13/t1229172201mmavnh26kjh6j8i.htm/},
    year = {2008},
}
@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 = {2008},
    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 «

97,7 0 101,5 0 119,6 0 108,1 0 117,8 0 125,5 0 89,2 0 92,3 0 104,6 0 122,8 0 96,0 0 94,6 0 93,3 0 101,1 0 114,2 0 104,7 0 113,3 0 118,2 0 83,6 0 73,9 0 99,5 0 97,7 0 103,0 0 106,3 0 92,2 0 101,8 0 122,8 0 111,8 0 106,3 0 121,5 0 81,9 0 85,4 0 110,9 0 117,3 0 106,3 0 105,5 0 101,3 0 105,9 0 126,3 0 111,9 0 108,9 0 127,2 0 94,2 0 85,7 0 116,2 0 107,2 0 110,6 0 112,0 0 104,5 0 112,0 0 132,8 0 110,8 0 128,7 0 136,8 0 94,9 0 88,8 0 123,2 0 125,3 0 122,7 0 125,7 0 116,3 0 118,7 0 142,0 0 127,9 0 131,9 0 152,3 0 110,8 1 99,1 1 135,0 1 133,2 1 131,0 1 133,9 1 119,9 1 136,9 1 148,9 1 145,1 1 142,4 1 159,6 1 120,7 1 109,0 1 142,0 1

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

Y[t] = + 109.771212121212 + 21.3954545454545X[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)	109.771212121212	1.916985	57.2624	0	0
X	21.3954545454545	4.45467	4.8029	7e-06	4e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.475401918013747
R-squared	0.226006983651150
Adjusted R-squared	0.216209603697367
F-TEST (value)	23.0681044031455
F-TEST (DF numerator)	1
F-TEST (DF denominator)	79
p-value	7.29512625641249e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	15.5736586420235
Sum Squared Residuals	19160.5686363636

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	97.7	109.771212121212	-12.0712121212122
2	101.5	109.771212121212	-8.27121212121214
3	119.6	109.771212121212	9.82878787878788
4	108.1	109.771212121212	-1.67121212121212
5	117.8	109.771212121212	8.02878787878788
6	125.5	109.771212121212	15.7287878787879
7	89.2	109.771212121212	-20.5712121212121
8	92.3	109.771212121212	-17.4712121212121
9	104.6	109.771212121212	-5.17121212121213
10	122.8	109.771212121212	13.0287878787879
11	96	109.771212121212	-13.7712121212121
12	94.6	109.771212121212	-15.1712121212121
13	93.3	109.771212121212	-16.4712121212121
14	101.1	109.771212121212	-8.67121212121213
15	114.2	109.771212121212	4.42878787878788
16	104.7	109.771212121212	-5.07121212121212
17	113.3	109.771212121212	3.52878787878788
18	118.2	109.771212121212	8.42878787878788
19	83.6	109.771212121212	-26.1712121212121
20	73.9	109.771212121212	-35.8712121212121
21	99.5	109.771212121212	-10.2712121212121
22	97.7	109.771212121212	-12.0712121212121
23	103	109.771212121212	-6.77121212121212
24	106.3	109.771212121212	-3.47121212121212
25	92.2	109.771212121212	-17.5712121212121
26	101.8	109.771212121212	-7.97121212121212
27	122.8	109.771212121212	13.0287878787879
28	111.8	109.771212121212	2.02878787878788
29	106.3	109.771212121212	-3.47121212121212
30	121.5	109.771212121212	11.7287878787879
31	81.9	109.771212121212	-27.8712121212121
32	85.4	109.771212121212	-24.3712121212121
33	110.9	109.771212121212	1.12878787878789
34	117.3	109.771212121212	7.52878787878788
35	106.3	109.771212121212	-3.47121212121212
36	105.5	109.771212121212	-4.27121212121212
37	101.3	109.771212121212	-8.47121212121212
38	105.9	109.771212121212	-3.87121212121211
39	126.3	109.771212121212	16.5287878787879
40	111.9	109.771212121212	2.12878787878789
41	108.9	109.771212121212	-0.871212121212114
42	127.2	109.771212121212	17.4287878787879
43	94.2	109.771212121212	-15.5712121212121
44	85.7	109.771212121212	-24.0712121212121
45	116.2	109.771212121212	6.42878787878788
46	107.2	109.771212121212	-2.57121212121212
47	110.6	109.771212121212	0.828787878787875
48	112	109.771212121212	2.22878787878788
49	104.5	109.771212121212	-5.27121212121212
50	112	109.771212121212	2.22878787878788
51	132.8	109.771212121212	23.0287878787879
52	110.8	109.771212121212	1.02878787878788
53	128.7	109.771212121212	18.9287878787879
54	136.8	109.771212121212	27.0287878787879
55	94.9	109.771212121212	-14.8712121212121
56	88.8	109.771212121212	-20.9712121212121
57	123.2	109.771212121212	13.4287878787879
58	125.3	109.771212121212	15.5287878787879
59	122.7	109.771212121212	12.9287878787879
60	125.7	109.771212121212	15.9287878787879
61	116.3	109.771212121212	6.52878787878788
62	118.7	109.771212121212	8.92878787878788
63	142	109.771212121212	32.2287878787879
64	127.9	109.771212121212	18.1287878787879
65	131.9	109.771212121212	22.1287878787879
66	152.3	109.771212121212	42.5287878787879
67	110.8	131.166666666667	-20.3666666666667
68	99.1	131.166666666667	-32.0666666666667
69	135	131.166666666667	3.83333333333334
70	133.2	131.166666666667	2.03333333333332
71	131	131.166666666667	-0.166666666666665
72	133.9	131.166666666667	2.73333333333334
73	119.9	131.166666666667	-11.2666666666667
74	136.9	131.166666666667	5.73333333333334
75	148.9	131.166666666667	17.7333333333333
76	145.1	131.166666666667	13.9333333333333
77	142.4	131.166666666667	11.2333333333333
78	159.6	131.166666666667	28.4333333333333
79	120.7	131.166666666667	-10.4666666666667
80	109	131.166666666667	-22.1666666666667
81	142	131.166666666667	10.8333333333333

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.310995495341577	0.621990990683154	0.689004504658423
6	0.33181800738519	0.66363601477038	0.66818199261481
7	0.463663371042777	0.927326742085554	0.536336628957223
8	0.458229526697044	0.916459053394088	0.541770473302956
9	0.340197688419933	0.680395376839867	0.659802311580067
10	0.34626050443714	0.69252100887428	0.65373949556286
11	0.305872504838024	0.611745009676049	0.694127495161976
12	0.275418207428769	0.550836414857538	0.724581792571231
13	0.253291043588394	0.506582087176788	0.746708956411606
14	0.188682734771476	0.377365469542952	0.811317265228524
15	0.151488907454204	0.302977814908409	0.848511092545796
16	0.104719686608084	0.209439373216169	0.895280313391916
17	0.0782986431644455	0.156597286328891	0.921701356835555
18	0.0690995436315011	0.138199087263002	0.930900456368499
19	0.126374997643752	0.252749995287504	0.873625002356248
20	0.342555345963605	0.68511069192721	0.657444654036395
21	0.28791541393267	0.57583082786534	0.71208458606733
22	0.245580114097890	0.491160228195781	0.75441988590211
23	0.195150775982427	0.390301551964853	0.804849224017573
24	0.151381055347812	0.302762110695623	0.848618944652188
25	0.148301375276132	0.296602750552264	0.851698624723868
26	0.115991445878884	0.231982891757767	0.884008554121116
27	0.135390867350290	0.270781734700580	0.86460913264971
28	0.108272752329014	0.216545504658028	0.891727247670986
29	0.0814574843589583	0.162914968717917	0.918542515641042
30	0.0851107185861191	0.170221437172238	0.914889281413881
31	0.156357546497077	0.312715092994155	0.843642453502923
32	0.220964426845074	0.441928853690148	0.779035573154926
33	0.184229705889722	0.368459411779445	0.815770294110278
34	0.166682601016347	0.333365202032694	0.833317398983653
35	0.134688153773804	0.269376307547608	0.865311846226196
36	0.10824988484498	0.21649976968996	0.89175011515502
37	0.091947392863928	0.183894785727856	0.908052607136072
38	0.0730414780213785	0.146082956042757	0.926958521978621
39	0.0889632687384045	0.177926537476809	0.911036731261595
40	0.0699788866049069	0.139957773209814	0.930021113395093
41	0.0539046010002787	0.107809202000557	0.946095398999721
42	0.064983398729596	0.129966797459192	0.935016601270404
43	0.074010360822218	0.148020721644436	0.925989639177782
44	0.14361332742458	0.28722665484916	0.85638667257542
45	0.121736597784241	0.243473195568483	0.878263402215758
46	0.103510251846557	0.207020503693114	0.896489748153443
47	0.0852468571550377	0.170493714310075	0.914753142844962
48	0.0695059256062989	0.139011851212598	0.930494074393701
49	0.0644672396809275	0.128934479361855	0.935532760319073
50	0.0533187224160984	0.106637444832197	0.946681277583902
51	0.0729276453255015	0.145855290651003	0.927072354674499
52	0.0610047577498175	0.122009515499635	0.938995242250182
53	0.0639378721380167	0.127875744276033	0.936062127861983
54	0.0942931368047851	0.188586273609570	0.905706863195215
55	0.141333850759121	0.282667701518242	0.858666149240879
56	0.343918830491739	0.687837660983479	0.65608116950826
57	0.314332255826699	0.628664511653397	0.685667744173301
58	0.286873990721513	0.573747981443026	0.713126009278487
59	0.25790623427269	0.51581246854538	0.74209376572731
60	0.231303615742542	0.462607231485084	0.768696384257458
61	0.230003428723485	0.460006857446971	0.769996571276515
62	0.240176279579666	0.480352559159332	0.759823720420334
63	0.260663362334823	0.521326724669647	0.739336637665177
64	0.247793198682974	0.495586397365948	0.752206801317026
65	0.257256685860556	0.514513371721111	0.742743314139444
66	0.290380764493589	0.580761528987178	0.70961923550641
67	0.316563274633229	0.633126549266457	0.683436725366771
68	0.610697777178778	0.778604445642444	0.389302222821222
69	0.545029178280098	0.909941643439803	0.454970821719901
70	0.457524841538138	0.915049683076277	0.542475158461862
71	0.36731508794422	0.73463017588844	0.63268491205578
72	0.275263587164985	0.550527174329969	0.724736412835015
73	0.269547460097858	0.539094920195715	0.730452539902142
74	0.181699301033182	0.363398602066364	0.818300698966818
75	0.141887084346821	0.283774168693643	0.858112915653179
76	0.0903261729513504	0.180652345902701	0.90967382704865

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://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/10mpeh1229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/10mpeh1229172147.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/1jw761229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/1jw761229172147.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/2lqbr1229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/2lqbr1229172147.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/3udir1229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/3udir1229172147.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/42sd81229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/42sd81229172147.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/5du211229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/5du211229172147.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/6lm6m1229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/6lm6m1229172147.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/7gmzn1229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/7gmzn1229172147.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/8u9fa1229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/8u9fa1229172147.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/9wame1229172147.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229172201mmavnh26kjh6j8i/9wame1229172147.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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