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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: Mon, 06 Dec 2010 18:03:06 +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/06/t12916584990tq0nwbkxfi5j2a.htm/, Retrieved Mon, 06 Dec 2010 19:01:42 +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/06/t12916584990tq0nwbkxfi5j2a.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 «

2350,44 10892,76 10540,05 10570 -4,9 -3 1,6 3,38 2440,25 10631,92 10601,61 10297 -4 -1 1,3 3,35 2408,64 11441,08 10323,73 10635 -3,1 -3 1,1 3,22 2472,81 11950,95 10418,4 10872 -1,3 -4 1,9 3,06 2407,6 11037,54 10092,96 10296 0 -6 2,6 3,17 2454,62 11527,72 10364,91 10383 -0,4 0 2,3 3,19 2448,05 11383,89 10152,09 10431 3 -4 2,4 3,35 2497,84 10989,34 10032,8 10574 0,4 -2 2,2 3,24 2645,64 11079,42 10204,59 10653 1,2 -2 2 3,23 2756,76 11028,93 10001,6 10805 0,6 -6 2,9 3,31 2849,27 10973 10411,75 10872 -1,3 -7 2,6 3,25 2921,44 11068,05 10673,38 10625 -3,2 -6 2,3 3,2 2981,85 11394,84 10539,51 10407 -1,8 -6 2,3 3,1 3080,58 11545,71 10723,78 10463 -3,6 -3 2,6 2,93 3106,22 11809,38 10682,06 10556 -4,2 -2 3,1 2,92 3119,31 11395,64 10283,19 10646 -6,9 -5 2,8 2,9 3061,26 11082,38 10377,18 10702 -8 -11 2,5 2,87 3097,31 11402,75 10486,64 11353 -7,5 -11 2,9 2,76 3161,69 11716,87 10545,38 11346 -8,2 -11 3,1 2,67 3257,16 12204,98 10554,27 11451 -7,6 -10 3,1 2,75 3277,01 12986,62 10532,54 11964 -3,7 -14 3,2 2 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	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

BEL_20[t] = -1886.04000678096 + 0.191792840371528Nikkei[t] + 0.288303751656477DJ_Indust[t] + 0.0147718309612608Goudprijs[t] -9.98349305638133Conjunct_Seizoenzuiver[t] -2.50766896022597Cons_vertrouw[t] + 33.9568643858045Alg_consumptie_index_BE[t] -255.69184028108Gem_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)	-1886.04000678096	270.224455	-6.9795	0	0
Nikkei	0.191792840371528	0.014953	12.8265	0	0
DJ_Indust	0.288303751656477	0.033193	8.6858	0	0
Goudprijs	0.0147718309612608	0.008199	1.8016	0.07632	0.03816
Conjunct_Seizoenzuiver	-9.98349305638133	6.033247	-1.6547	0.102872	0.051436
Cons_vertrouw	-2.50766896022597	7.649392	-0.3278	0.744113	0.372057
Alg_consumptie_index_BE	33.9568643858045	17.241466	1.9695	0.053229	0.026614
Gem_rente_kasbon_5j	-255.69184028108	56.189516	-4.5505	2.4e-05	1.2e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.9837054952436
R-squared	0.967676501372457
Adjusted R-squared	0.96414111871007
F-TEST (value)	273.711955332998
F-TEST (DF numerator)	7
F-TEST (DF denominator)	64
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	160.133985897204
Sum Squared Residuals	1641145.18011685

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	2350.44	2644.52226973597	-294.08226973597
2	2440.25	2591.6935085745	-151.443508574501
3	2408.64	2694.24239617322	-285.602396173219
4	2472.81	2875.44001921294	-402.630019212943
5	2407.6	2585.60087290272	-178.000872902716
6	2454.62	2732.94972929247	-278.329729292474
7	2448.05	2583.28819996326	-135.238199963261
8	2497.84	2517.61342566693	-19.7734256669314
9	2645.64	2573.36355195115	72.2764480488502
10	2756.76	2533.52907359763	223.230926402375
11	2849.27	2668.67035332043	180.599646679572
12	2921.44	2777.7390316414	143.700968358595
13	2981.85	2810.19182531178	171.658174688216
14	3080.58	2957.18251877454	123.397481225461
15	3106.22	3020.1160622249	86.1039377750954
16	3119.31	2856.50265543567	262.807344564329
17	3061.26	2847.85807442898	213.401925571025
18	3097.31	2987.19403896795	110.11596103205
19	3161.69	3101.06364918295	60.6263508170478
20	3257.16	3169.84060308332	87.3193969166838
21	3277.01	3303.22816215898	-26.2181621589782
22	3295.32	3271.32318186744	23.9968181325595
23	3363.99	3532.18819246	-168.198192460001
24	3494.17	3785.90185801967	-291.73185801967
25	3667.03	3851.58081047638	-184.550810476383
26	3813.06	3909.92717343976	-96.8671734397561
27	3917.96	3935.63905870863	-17.6790587086296
28	3895.51	4075.79262323747	-180.282623237466
29	3801.06	3923.64733261763	-122.587332617628
30	3570.12	3505.75287642029	64.3671235797117
31	3701.61	3507.86986753702	193.740132462985
32	3862.27	3695.22163010429	167.048369895706
33	3970.1	3812.15884936956	157.94115063044
34	4138.52	4018.99216934148	119.527830658519
35	4199.75	4045.36120653471	154.388793465295
36	4290.89	4236.48218094528	54.4078190547256
37	4443.91	4335.61262334952	108.297376650476
38	4502.64	4403.06192168819	99.5780783118134
39	4356.98	4190.67292615312	166.307073846883
40	4591.27	4377.34426916186	213.925730838143
41	4696.96	4571.73034500852	125.229654991476
42	4621.4	4565.31268828803	56.087311711967
43	4562.84	4589.48099026381	-26.6409902638104
44	4202.52	4205.08179278783	-2.56179278782505
45	4296.49	4296.5678631292	-0.0778631292036487
46	4435.23	4572.62116297434	-137.391162974342
47	4105.18	4188.10491509472	-82.924915094721
48	4116.68	4276.06118970188	-159.381189701878
49	3844.49	3682.14878243838	162.341217561616
50	3720.98	3662.70017341549	58.2798265845135
51	3674.4	3524.36380455471	150.036195445289
52	3857.62	3842.65385048212	14.9661495178809
53	3801.06	3956.29842765428	-155.238427654278
54	3504.37	3662.62139313115	-158.251393131149
55	3032.6	3195.12832183371	-162.528321833709
56	3047.03	3182.65793008691	-135.627930086909
57	2962.34	3044.78372345458	-82.4437234545783
58	2197.82	1982.56027771883	215.259722281166
59	2014.45	1837.89267623246	176.557323767537
60	1862.83	1908.05860030339	-45.2286003033879
61	1905.41	1841.71988517336	63.6901148266424
62	1810.99	1652.7428662126	158.247133787398
63	1670.07	1569.45106639554	100.618933604459
64	1864.44	1926.8019576247	-62.3619576247028
65	2052.02	2073.70878135401	-21.6887813540108
66	2029.6	2143.06199947823	-113.46199947823
67	2070.83	2098.29620424375	-27.466204243753
68	2293.41	2416.35750331553	-122.947503315532
69	2443.27	2455.47321046387	-12.2032104638691
70	2513.17	2451.72071577507	61.4492842249306
71	2466.92	2520.65788343882	-53.7378834388213
72	2502.66	2708.80824493619	-206.148244936194

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.505854090617104	0.988291818765792	0.494145909382896
12	0.700974909349596	0.598050181300807	0.299025090650404
13	0.918531286503864	0.162937426992273	0.0814687134961363
14	0.892759779021417	0.214480441957167	0.107240220978583
15	0.8660782380407	0.267843523918599	0.1339217619593
16	0.86648034865345	0.2670393026931	0.13351965134655
17	0.83754134043261	0.324917319134778	0.162458659567389
18	0.916663297805904	0.166673404388191	0.0833367021940955
19	0.905049303158132	0.189901393683736	0.0949506968418681
20	0.880231780292042	0.239536439415917	0.119768219707958
21	0.84449912144982	0.311001757100358	0.155500878550179
22	0.880063099614087	0.239873800771825	0.119936900385913
23	0.853738054341309	0.292523891317383	0.146261945658691
24	0.885071348926813	0.229857302146374	0.114928651073187
25	0.920476703590152	0.159046592819697	0.0795232964098484
26	0.927063590284131	0.145872819431738	0.0729364097158688
27	0.95499762198317	0.0900047560336614	0.0450023780168307
28	0.965258940966154	0.069482118067691	0.0347410590338455
29	0.987451343301985	0.0250973133960305	0.0125486566980152
30	0.990148104224632	0.019703791550737	0.00985189577536851
31	0.98665794603876	0.0266841079224821	0.013342053961241
32	0.988475076535893	0.0230498469282132	0.0115249234641066
33	0.991937459777008	0.0161250804459839	0.00806254022299196
34	0.993154172478057	0.0136916550438852	0.00684582752194259
35	0.993100247148823	0.0137995057023544	0.00689975285117722
36	0.99000114799435	0.0199977040113006	0.0099988520056503
37	0.98657991154674	0.0268401769065211	0.0134200884532605
38	0.979022041491126	0.041955917017747	0.0209779585088735
39	0.972226491202969	0.0555470175940622	0.0277735087970311
40	0.9614006004221	0.0771987991558008	0.0385993995779004
41	0.954783501077201	0.0904329978455974	0.0452164989227987
42	0.94250111327552	0.114997773448959	0.0574988867244794
43	0.934996691864158	0.130006616271683	0.0650033081358416
44	0.924985140420499	0.150029719159003	0.0750148595795013
45	0.956044888781052	0.0879102224378957	0.0439551112189479
46	0.971069026502952	0.0578619469940954	0.0289309734970477
47	0.97957590718105	0.0408481856379006	0.0204240928189503
48	0.97307423969702	0.0538515206059586	0.0269257603029793
49	0.996708005910833	0.00658398817833319	0.00329199408916659
50	0.997200308031106	0.00559938393778856	0.00279969196889428
51	0.995477680279964	0.00904463944007231	0.00452231972003615
52	0.996078075161607	0.0078438496767868	0.0039219248383934
53	0.996158286299355	0.00768342740128986	0.00384171370064493
54	0.99928657107326	0.00142685785347873	0.000713428926739364
55	0.997999780046884	0.00400043990623226	0.00200021995311613
56	0.994537825642462	0.0109243487150754	0.00546217435753769
57	0.992044725570226	0.0159105488595473	0.00795527442977364
58	0.981024562355698	0.0379508752886037	0.0189754376443019
59	0.996057916332778	0.00788416733444343	0.00394208366722172
60	0.985992727931948	0.0280145441361042	0.0140072720680521
61	0.95150346709066	0.096993065818681	0.0484965329093405

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.156862745098039	NOK
5% type I error level	23	0.450980392156863	NOK
10% type I error level	32	0.627450980392157	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/10m7ig1291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/10m7ig1291658575.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/1fo241291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/1fo241291658575.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/2fo241291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/2fo241291658575.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/3qx271291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/3qx271291658575.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/4qx271291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/4qx271291658575.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/5qx271291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/5qx271291658575.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/6161a1291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/6161a1291658575.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/7tg0d1291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/7tg0d1291658575.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/8tg0d1291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/8tg0d1291658575.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/9tg0d1291658575.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916584990tq0nwbkxfi5j2a/9tg0d1291658575.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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