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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: Wed, 01 Dec 2010 21:03:34 +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/01/t1291237457o7xu03xd1i8lsyv.htm/, Retrieved Wed, 01 Dec 2010 22:04:27 +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/01/t1291237457o7xu03xd1i8lsyv.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 «

2502,66 10169,02 10433,44 24977 -7,9 -15 2,85 0,3 3,36 2466,92 9633,83 10238,83 24320 -8,8 -10 2,98 -0,1 3,37 2513,17 10066,24 9857,34 22680 -14,2 -12 3,06 -1 3,55 2443,27 10302,87 9634,97 22052 -17,8 -11 3,08 -1,2 3,53 2293,41 10430,35 9374,63 21467 -18,2 -11 3,3 -0,8 3,52 2070,83 9691,12 8679,75 21383 -22,8 -17 3,47 -1,7 3,54 2029,6 9810,31 8593 21777 -23,6 -18 3,72 -1,1 3,5 2052,02 9304,43 8398,37 21928 -27,6 -19 3,67 -0,4 3,44 1864,44 8767,96 7992,12 21814 -29,4 -22 3,82 0,6 3,38 1670,07 7764,58 7235,47 22937 -31,8 -24 3,85 0,6 3,35 1810,99 7694,78 7690,5 23595 -31,4 -24 3,9 1,9 3,68 1905,41 8331,49 8396,2 20830 -27,6 -20 3,99 2,3 3,92 1862,83 8460,94 8595,56 19650 -28,8 -25 4,35 2,6 4,05 2014,45 8531,45 8614,55 19195 -21,9 -22 4,98 3,1 4,14 2197,82 9117,03 9181,73 19644 -13,9 -17 5,46 4,7 4,53 2962,34 12123,53 11114,08 18483 -8 -9 5,19 5,5 4,54 3047,03 12989,35 11530,75 18079 -2,8 -11 5,03 5,4 4,9 3032,6 13168,91 11322,38 19178 -3,3 -13 5,38 5,9 4,92 3504,37 14084,6 12056,67 18391 -1,3 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

BEL_20[t] = -1880.76580017901 + 0.192813678210668Nikkei[t] + 0.286352347829325DJ_Indust[t] + 0.0145348419797004Goudprijs[t] -10.0672478500057Conjunct_Seizoenzuiver[t] -2.30528030345926Cons_vertrouw[t] -18.3563447065875Rend_oblig_EUR[t] + 35.9956460531887Alg_consumptie_index_BE[t] -234.559373355687Gem_rente_kasbon_5j[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)	-1880.76580017901	273.254826	-6.8828	0	0
Nikkei	0.192813678210668	0.015731	12.2568	0	0
DJ_Indust	0.286352347829325	0.034544	8.2895	0	0
Goudprijs	0.0145348419797004	0.008327	1.7454	0.085789	0.042894
Conjunct_Seizoenzuiver	-10.0672478500057	6.08984	-1.6531	0.103281	0.051641
Cons_vertrouw	-2.30528030345926	7.758898	-0.2971	0.767357	0.383678
Rend_oblig_EUR	-18.3563447065875	81.447108	-0.2254	0.822415	0.411208
Alg_consumptie_index_BE	35.9956460531887	19.585068	1.8379	0.070791	0.035396
Gem_rente_kasbon_5j	-234.559373355687	109.529101	-2.1415	0.036106	0.018053

Multiple Linear Regression - Regression Statistics
Multiple R	0.983718731063487
R-squared	0.967702541845157
Adjusted R-squared	0.963601277317558
F-TEST (value)	235.952237494821
F-TEST (DF numerator)	8
F-TEST (DF denominator)	63
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	161.334860280945
Sum Squared Residuals	1639823.03993793

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	2502.66	2715.11121737621	-212.451217376205
2	2466.92	2525.04678792393	-58.1267879239323
3	2513.17	2458.23207526742	54.937924732581
4	2443.27	2462.11526691126	-18.8452669112567
5	2293.41	2420.37565727693	-126.965657276927
6	2070.83	2097.57373016300	-26.7437301629974
7	2029.6	2138.19060900769	-108.590609007692
8	2052.02	2069.87463273278	-17.8546327327776
9	1864.44	1920.80090928068	-56.3609092806808
10	1670.07	1562.24769070221	107.822309297789
11	1810.99	1653.09716110496	157.892838895036
12	1905.41	1846.72884669622	58.6811533037796
13	1862.83	1908.92945799045	-46.0994579904478
14	2014.45	1830.29235960961	184.157640390387
15	2197.82	1977.37911028682	220.440889713182
16	2962.34	3047.09957305382	-84.7595730538206
17	3047.03	3194.64081645900	-147.610816459004
18	3032.6	3202.0950925811	-169.495092581102
19	3504.37	3659.56124244339	-155.19124244339
20	3801.06	3948.66912136436	-147.609121364356
21	3857.62	3830.83700857329	26.7829914267107
22	3674.4	3520.40647381457	153.993526185434
23	3720.98	3665.17605619967	55.803943800331
24	3844.49	3684.19213012148	160.297869878519
25	4116.68	4271.70370286783	-155.023702867825
26	4105.18	4188.01448667954	-82.8344866795445
27	4435.23	4574.11338074336	-138.883380743356
28	4296.49	4296.48693981688	0.00306018311966483
29	4202.52	4205.90718229782	-3.38718229781947
30	4562.84	4589.98746184695	-27.1474618469506
31	4621.4	4562.66660732138	58.7333926786242
32	4696.96	4566.91792302561	130.042076974394
33	4591.27	4377.72025694618	213.549743053825
34	4356.98	4195.95741884186	161.022581158144
35	4502.64	4406.82482368066	95.8151763193443
36	4443.91	4332.6346476118	111.275352388198
37	4290.89	4232.51989786714	58.3701021328598
38	4199.75	4043.98047642233	155.769523577667
39	4138.52	4018.02623632452	120.493763675484
40	3970.1	3811.40438107579	158.695618924211
41	3862.27	3696.03820748375	166.231792516251
42	3701.61	3505.66041887935	195.949581120654
43	3570.12	3503.17358467137	66.9464153286269
44	3801.06	3922.97674055146	-121.916740551459
45	3895.51	4073.91399629707	-178.403996297075
46	3917.96	3931.52329307046	-13.5632930704627
47	3813.06	3910.48894529271	-97.4289452927083
48	3667.03	3858.52945154261	-191.499451542606
49	3494.17	3791.34559426302	-297.175594263016
50	3363.99	3532.12246397146	-168.132463971461
51	3295.32	3273.35752520006	21.96247479994
52	3277.01	3309.00481489556	-31.9948148955643
53	3257.16	3174.07846882506	83.0815311749384
54	3161.69	3103.55342279670	58.1365772033041
55	3097.31	2991.46234121307	105.847658786932
56	3061.26	2850.41400538995	210.845994610050
57	3119.31	2858.82228946229	260.487710537713
58	3106.22	3019.84507318781	86.3749268121896
59	3080.58	2957.54148305236	123.038516947640
60	2981.85	2812.99259262929	168.857407370712
61	2921.44	2781.20598750142	140.234012498581
62	2849.27	2670.49488108333	178.775118916669
63	2756.76	2535.57989216804	221.180107831961
64	2645.64	2570.32042246488	75.319577535124
65	2497.84	2517.17044876234	-19.3304487623408
66	2448.05	2581.48762006692	-133.437620066922
67	2454.62	2726.19854089696	-271.578540896956
68	2407.6	2579.49381485465	-171.893814854649
69	2472.81	2869.19278930997	-396.382789309974
70	2408.64	2693.30659962385	-284.666599623851
71	2440.25	2590.90190935628	-150.651909356276
72	2350.44	2646.2535309255	-295.813530925502

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.0295705876302805	0.0591411752605609	0.97042941236972
13	0.00717844974521512	0.0143568994904302	0.992821550254785
14	0.0435359161531321	0.0870718323062642	0.956464083846868
15	0.0268057658008719	0.0536115316017438	0.973194234199128
16	0.0188968297021226	0.0377936594042452	0.981103170297877
17	0.00880785448045581	0.0176157089609116	0.991192145519544
18	0.00363552676402688	0.00727105352805377	0.996364473235973
19	0.0126834364079223	0.0253668728158446	0.987316563592078
20	0.0098312905236135	0.019662581047227	0.990168709476386
21	0.0085423274210427	0.0170846548420854	0.991457672578957
22	0.00964141881242596	0.0192828376248519	0.990358581187574
23	0.0101725478974864	0.0203450957949727	0.989827452102514
24	0.0486182409202005	0.0972364818404011	0.9513817590798
25	0.0358083590016406	0.0716167180032811	0.96419164099836
26	0.0571690072494385	0.114338014498877	0.942830992750561
27	0.066124279114496	0.132248558228992	0.933875720885504
28	0.115146615030104	0.230293230060208	0.884853384969896
29	0.11635616463503	0.23271232927006	0.88364383536497
30	0.116926248968341	0.233852497936681	0.88307375103166
31	0.101026985907348	0.202053971814697	0.898973014092652
32	0.0906935725684602	0.181387145136920	0.90930642743154
33	0.0811686440175722	0.162337288035144	0.918831355982428
34	0.066509819209235	0.13301963841847	0.933490180790765
35	0.04608417567395	0.0921683513479	0.95391582432605
36	0.0393803364857196	0.078760672971439	0.96061966351428
37	0.0257989522192675	0.051597904438535	0.974201047780733
38	0.0253574831125675	0.050714966225135	0.974642516887432
39	0.0255022671100953	0.0510045342201906	0.974497732889905
40	0.0306257244213581	0.0612514488427163	0.969374275578642
41	0.0296845535815120	0.0593691071630239	0.970315446418488
42	0.0217074982347763	0.0434149964695527	0.978292501765224
43	0.0262362903043584	0.0524725806087169	0.973763709695642
44	0.0509025446656283	0.101805089331257	0.949097455334372
45	0.0659967776606105	0.131993555321221	0.93400322233939
46	0.323763953998365	0.647527907996731	0.676236046001635
47	0.44756834105966	0.89513668211932	0.55243165894034
48	0.418157337423533	0.836314674847066	0.581842662576467
49	0.383416001052739	0.766832002105477	0.616583998947261
50	0.582636506925018	0.834726986149965	0.417363493074982
51	0.720832151855975	0.558335696288051	0.279167848144025
52	0.684592371163764	0.630815257672472	0.315407628836236
53	0.625380418285328	0.749239163429343	0.374619581714672
54	0.558957749436368	0.882084501127265	0.441042250563632
55	0.864147598744966	0.271704802510068	0.135852401255034
56	0.79014390877989	0.419712182440221	0.209856091220110
57	0.801865467746379	0.396269064507242	0.198134532253621
58	0.735939661979597	0.528120676040805	0.264060338020403
59	0.623104086239145	0.753791827521709	0.376895913760855
60	0.473003261752936	0.946006523505872	0.526996738247064

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.0204081632653061	NOK
5% type I error level	10	0.204081632653061	NOK
10% type I error level	23	0.469387755102041	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/10f2o21291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/10f2o21291237406.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/191r81291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/191r81291237406.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/2jb8t1291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/2jb8t1291237406.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/3jb8t1291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/3jb8t1291237406.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/4u2pw1291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/4u2pw1291237406.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/5u2pw1291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/5u2pw1291237406.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/6u2pw1291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/6u2pw1291237406.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/75t6z1291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/75t6z1291237406.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/85t6z1291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/85t6z1291237406.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/9f2o21291237406.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291237457o7xu03xd1i8lsyv/9f2o21291237406.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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