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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 18:17:17 +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/t1291659334j6i2of8oxfg7g6c.htm/, Retrieved Mon, 06 Dec 2010 19:15:37 +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/t1291659334j6i2of8oxfg7g6c.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] = -2101.09115636334 + 0.192421203520586Nikkei[t] + 0.301255385502806DJ_Indust[t] + 0.011931737331997Goudprijs[t] -8.47433649695565Conjunct_Seizoenzuiver[t] -8.59705875060859Cons_vertrouw[t] + 27.7373411137287Alg_consumptie_index_BE[t] -259.760301077345Gem_rente_kasbon_5j[t] + 111.420983877046M1[t] + 147.196762063619M2[t] + 143.318245880678M3[t] + 78.5931152743392M4[t] + 68.8461761572716M5[t] + 46.0649034733879M6[t] + 77.6007840575792M7[t] + 97.8320949690298M8[t] + 117.535120245601M9[t] + 186.991868973947M10[t] + 126.132735266924M11[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)	-2101.09115636334	310.359456	-6.7699	0	0
Nikkei	0.192421203520586	0.01623	11.8557	0	0
DJ_Indust	0.301255385502806	0.036336	8.2908	0	0
Goudprijs	0.011931737331997	0.009028	1.3216	0.191979	0.09599
Conjunct_Seizoenzuiver	-8.47433649695565	7.250353	-1.1688	0.247708	0.123854
Cons_vertrouw	-8.59705875060859	9.855071	-0.8723	0.386953	0.193477
Alg_consumptie_index_BE	27.7373411137287	20.148195	1.3767	0.174403	0.087202
Gem_rente_kasbon_5j	-259.760301077345	64.403705	-4.0333	0.000177	8.9e-05
M1	111.420983877046	103.78436	1.0736	0.287874	0.143937
M2	147.196762063619	108.98029	1.3507	0.182539	0.09127
M3	143.318245880678	105.437294	1.3593	0.179815	0.089908
M4	78.5931152743392	103.698608	0.7579	0.451868	0.225934
M5	68.8461761572716	98.014898	0.7024	0.4855	0.24275
M6	46.0649034733879	100.363071	0.459	0.648124	0.324062
M7	77.6007840575792	100.811933	0.7698	0.444861	0.22243
M8	97.8320949690298	102.122201	0.958	0.342417	0.171209
M9	117.535120245601	100.054758	1.1747	0.245363	0.122681
M10	186.991868973947	101.093886	1.8497	0.069938	0.034969
M11	126.132735266924	97.967903	1.2875	0.203517	0.101759

Multiple Linear Regression - Regression Statistics
Multiple R	0.985174578486878
R-squared	0.970568950096798
Adjusted R-squared	0.960573499186276
F-TEST (value)	97.1010671539735
F-TEST (DF numerator)	18
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	167.91104430026
Sum Squared Residuals	1494288.29629421

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	2350.44	2641.39845917012	-290.958459170123
2	2440.25	2616.92159422002	-176.671594220024
3	2408.64	2726.85128534376	-318.211285343761
4	2472.81	2848.67839698914	-375.868396989144
5	2407.6	2555.27875871935	-147.678758719351
6	2454.62	2648.07394855203	-193.453948552026
7	2448.05	2555.18101653486	-107.131016534862
8	2497.84	2493.12734738574	4.71265261425865
9	2645.64	2573.12961019067	72.5103898093341
10	2756.76	2617.18842554128	139.571574458718
11	2849.27	2701.88921051192	147.380789488082
12	2921.44	2682.08741134108	239.352588658922
13	2981.85	2827.57150213298	154.278497867023
14	3080.58	2990.50145743186	90.078542568144
15	3106.22	3038.85373358509	67.3662664149108
16	3119.31	2820.97426345955	298.335736540447
17	3061.26	2840.30835845013	220.951641549868
18	3097.31	2955.34744355393	141.962556446073
19	3161.69	3099.79682263869	61.8931773613089
20	3257.16	3183.41935526259	73.7406447374149
21	3277.01	3365.00605759133	-87.9960575913262
22	3295.32	3369.21931312572	-73.8993131257238
23	3363.99	3579.75137323752	-215.761373237518
24	3494.17	3682.24303552564	-188.073035525642
25	3667.03	3835.91295714123	-168.882957141233
26	3813.06	3940.46332432876	-127.403324328763
27	3917.96	3988.71832487628	-70.7583248762794
28	3895.51	4056.70603718522	-161.196037185223
29	3801.06	3903.07056246709	-102.010562467086
30	3570.12	3441.34162636562	128.778373634377
31	3701.61	3476.62356237362	224.986437626383
32	3862.27	3686.12983968646	176.140160313544
33	3970.1	3829.7789676863	140.321032313697
34	4138.52	4090.23427658974	48.2857234102575
35	4199.75	4058.9205168308	140.829483169199
36	4290.89	4183.75209115137	107.137908848633
37	4443.91	4350.57395304606	93.3360469539419
38	4502.64	4437.45360683426	65.1863931657418
39	4356.98	4227.81174766188	129.168252338122
40	4591.27	4340.59763863395	250.672361366051
41	4696.96	4539.19373638599	157.766263614012
42	4621.4	4519.12703337337	102.272966626631
43	4562.84	4586.51564534514	-23.6756453451422
44	4202.52	4220.40753184759	-17.8875318475878
45	4296.49	4330.73253508512	-34.242535085116
46	4435.23	4665.27894660937	-230.048946609371
47	4105.18	4246.95871906507	-141.778719065071
48	4116.68	4182.39911961097	-65.7191196109737
49	3844.49	3694.88104502548	149.60895497452
50	3720.98	3688.87508233041	32.1049176695879
51	3674.4	3540.07672057885	134.323279421154
52	3857.62	3824.4788069052	33.1411930947961
53	3801.06	3939.72518592325	-138.665185923247
54	3504.37	3617.25030258715	-112.880302587152
55	3032.6	3175.59865467915	-142.998654679153
56	3047.03	3180.83366935079	-133.80366935079
57	2962.34	3036.39078338201	-74.0507833820095
58	2197.82	2058.23787204851	139.582127951488
59	2014.45	1876.18410773924	138.265892260758
60	1862.83	1829.96570874836	32.8642912516409
61	1905.41	1842.79208348413	62.6179165158713
62	1810.99	1694.28493485469	116.705065145313
63	1670.07	1611.95818795415	58.1118120458538
64	1864.44	1909.52485682693	-45.0848568269274
65	2052.02	2042.3833980542	9.63660194580358
66	2029.6	2096.2796455679	-66.679645567904
67	2070.83	2083.90429842853	-13.0742984285344
68	2293.41	2396.31225646684	-102.90225646684
69	2443.27	2459.81204606458	-16.5420460645794
70	2513.17	2536.66116608537	-23.491166085369
71	2466.92	2535.85607261545	-68.93607261545
72	2502.66	2628.22263362258	-125.56263362258

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.96903324338399	0.0619335132320174	0.0309667566160087
23	0.977472009625188	0.0450559807496248	0.0225279903748124
24	0.97212978765017	0.0557404246996613	0.0278702123498306
25	0.993755858668359	0.0124882826632827	0.00624414133164135
26	0.995601887574346	0.0087962248513077	0.00439811242565385
27	0.999543570232484	0.000912859535031758	0.000456429767515879
28	0.999835712681839	0.000328574636322558	0.000164287318161279
29	0.999975837638599	4.83247228021442e-05	2.41623614010721e-05
30	0.99999251223751	1.49755249784547e-05	7.48776248922733e-06
31	0.999981671375852	3.6657248295076e-05	1.8328624147538e-05
32	0.999991532452186	1.69350956282712e-05	8.4675478141356e-06
33	0.999989137272292	2.17254554164641e-05	1.0862727708232e-05
34	0.999978341505637	4.33169887260594e-05	2.16584943630297e-05
35	0.999949105766917	0.000101788466166304	5.08942330831522e-05
36	0.999883055373888	0.000233889252224994	0.000116944626112497
37	0.999684221328133	0.000631557343734217	0.000315778671867109
38	0.999656963770726	0.000686072458548511	0.000343036229274256
39	0.999817546903515	0.000364906192970489	0.000182453096485245
40	0.999492200189966	0.00101559962006827	0.000507799810034137
41	0.998753818991513	0.00249236201697309	0.00124618100848654
42	0.997517922594615	0.00496415481077025	0.00248207740538513
43	0.994985398202705	0.0100292035945891	0.00501460179729456
44	0.992926737308936	0.0141465253821284	0.0070732626910642
45	0.993619211445876	0.0127615771082479	0.00638078855412396
46	0.993976388268157	0.012047223463686	0.006023611731843
47	0.988080104211275	0.0238397915774499	0.011919895788725
48	0.970354503459332	0.0592909930813358	0.0296454965406679
49	0.979583972641073	0.0408320547178532	0.0204160273589266
50	0.96303688774757	0.0739262245048584	0.0369631122524292

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	17	0.586206896551724	NOK
5% type I error level	25	0.862068965517241	NOK
10% type I error level	29	1	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/10161a1291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/10161a1291659423.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/1u5my1291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/1u5my1291659423.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/25w3j1291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/25w3j1291659423.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/35w3j1291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/35w3j1291659423.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/45w3j1291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/45w3j1291659423.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/5fo241291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/5fo241291659423.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/6fo241291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/6fo241291659423.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/7qx271291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/7qx271291659423.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/8qx271291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/8qx271291659423.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/9161a1291659423.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659334j6i2of8oxfg7g6c/9161a1291659423.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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