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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 18:18:43 +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/t1291659422c8zb5nebz1koam1.htm/, Retrieved Mon, 06 Dec 2010 19:17:05 +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/t1291659422c8zb5nebz1koam1.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] = -204.722456427373 + 0.177400378019995Nikkei[t] + 0.220142962023709DJ_Indust[t] -0.0793121795843404Goudprijs[t] + 8.83568088071237Conjunct_Seizoenzuiver[t] -8.23572530490068Cons_vertrouw[t] + 18.5328439084293Alg_consumptie_index_BE[t] -280.1553727945Gem_rente_kasbon_5j[t] + 161.090413781246M1[t] + 247.445572641716M2[t] + 189.272612931734M3[t] + 111.726785113516M4[t] + 83.0601724822622M5[t] -3.16898498687954M6[t] + 12.8625451025933M7[t] + 4.30907126374565M8[t] + 35.700574039436M9[t] + 112.037528681405M10[t] + 84.5376550919719M11[t] + 22.318614447017t + 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)	-204.722456427373	567.980524	-0.3604	0.719979	0.359989
Nikkei	0.177400378019995	0.015	11.8267	0	0
DJ_Indust	0.220142962023709	0.038735	5.6833	1e-06	0
Goudprijs	-0.0793121795843404	0.025185	-3.1492	0.002713	0.001356
Conjunct_Seizoenzuiver	8.83568088071237	7.893963	1.1193	0.268158	0.134079
Cons_vertrouw	-8.23572530490068	8.790611	-0.9369	0.353153	0.176576
Alg_consumptie_index_BE	18.5328439084293	18.131434	1.0221	0.311447	0.155724
Gem_rente_kasbon_5j	-280.1553727945	57.69116	-4.8561	1.1e-05	6e-06
M1	161.090413781246	93.476107	1.7233	0.090771	0.045385
M2	247.445572641716	100.677285	2.4578	0.017348	0.008674
M3	189.272612931734	94.80828	1.9964	0.051142	0.025571
M4	111.726785113516	92.897672	1.2027	0.234545	0.117273
M5	83.0601724822622	87.502124	0.9492	0.346892	0.173446
M6	-3.16898498687954	90.438848	-0.035	0.972182	0.486091
M7	12.8625451025933	91.498011	0.1406	0.888747	0.444374
M8	4.30907126374565	94.313348	0.0457	0.963733	0.481867
M9	35.700574039436	91.773	0.389	0.698858	0.349429
M10	112.037528681405	92.275599	1.2142	0.230171	0.115086
M11	84.5376550919719	88.055742	0.96	0.341474	0.170737
t	22.318614447017	5.836967	3.8237	0.000354	0.000177

Multiple Linear Regression - Regression Statistics
Multiple R	0.988447189712945
R-squared	0.97702784685142
Adjusted R-squared	0.968634175508669
F-TEST (value)	116.400536422631
F-TEST (DF numerator)	19
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	149.766092731832
Sum Squared Residuals	1166353.89167231

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	2350.44	2457.19413196077	-106.754131960772
2	2440.25	2549.12448662837	-108.874486628374
3	2408.64	2625.97178134145	-217.331781341454
4	2472.81	2747.03073202865	-274.22073202865
5	2407.6	2562.79767991999	-155.197679919987
6	2454.62	2574.70138828411	-120.081388284106
7	2448.05	2556.89086760979	-108.840867609793
8	2497.84	2450.72649477586	47.1135052241398
9	2645.64	2558.13306496033	87.5069350396716
10	2756.76	2612.99804019027	143.761959809726
11	2849.27	2685.56989857376	163.700101426239
12	2921.44	2700.82327186014	220.616728139856
13	2981.85	2970.40997695728	11.4400230427194
14	3080.58	3154.54727189964	-73.9672718996395
15	3106.22	3147.43852908107	-41.2185290810652
16	3119.31	2924.76125570302	194.548744296976
17	3061.26	2921.63048091606	139.629519083944
18	3097.31	2929.66638572503	167.643614274971
19	3161.69	3059.96349556821	101.726504431785
20	3257.16	3128.60208016773	128.557919832274
21	3277.01	3353.1645764035	-76.1545764034988
22	3295.32	3384.93807770034	-89.6180777003413
23	3363.99	3577.88541387396	-213.895413873962
24	3494.17	3654.62023521827	-160.450235218271
25	3667.03	3812.5768870342	-145.546887034203
26	3813.06	3976.84952769536	-163.78952769536
27	3917.96	4003.99128282905	-86.031282829049
28	3895.51	3985.23019143339	-89.7201914333885
29	3801.06	3740.09181366709	60.9681863329113
30	3570.12	3477.08325846111	93.0367415388856
31	3701.61	3457.93546917912	243.674530820884
32	3862.27	3631.36036248836	230.90963751164
33	3970.1	3836.65793640225	133.44206359775
34	4138.52	4124.85245039267	13.6675496073332
35	4199.75	4050.88530692903	148.864693070973
36	4290.89	4261.58696403532	29.3030359646797
37	4443.91	4441.6502731501	2.25972684989876
38	4502.64	4485.98977698646	16.6502230135373
39	4356.98	4301.5762650501	55.4037349498997
40	4591.27	4395.30283486492	195.967165135077
41	4696.96	4585.94842480073	111.011575199273
42	4621.4	4555.7874283284	65.6125716715979
43	4562.84	4597.77025661072	-34.9302566107224
44	4202.52	4240.52368549951	-38.0036854995115
45	4296.49	4285.91149260098	10.5785073990201
46	4435.23	4509.52120170601	-74.2912017060101
47	4105.18	4158.55265483927	-53.3726548392649
48	4116.68	4097.4761433975	19.2038566024962
49	3844.49	3653.92798637897	190.562013621027
50	3720.98	3664.35861272375	56.6213872762538
51	3674.4	3524.29355076329	150.106449236707
52	3857.62	3842.89534489995	14.7246551000516
53	3801.06	3978.68360376993	-177.623603769927
54	3504.37	3631.39353313422	-127.023533134216
55	3032.6	3152.21281263012	-119.612812630116
56	3047.03	3251.44229105332	-204.412291053316
57	2962.34	3068.07875184762	-105.738751847617
58	2197.82	2117.63595509976	80.184044900241
59	2014.45	1969.42429064433	45.025709355671
60	1862.83	1834.11773222056	28.7122677794436
61	1905.41	1857.37074451867	48.0392554813297
62	1810.99	1537.63032406642	273.359675933582
63	1670.07	1530.99859093504	139.071409064962
64	1864.44	1905.73964107007	-41.2996410700661
65	2052.02	2030.80799692621	21.2120030737858
66	2029.6	2108.78800606713	-79.188006067132
67	2070.83	2152.84709840204	-82.0170984020375
68	2293.41	2457.57508601523	-164.165086015227
69	2443.27	2492.90417778533	-49.6341777853266
70	2513.17	2586.87427491095	-73.7042749109487
71	2466.92	2557.24243513966	-90.322435139655
72	2502.66	2640.04565326821	-137.385653268205

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
23	0.0199077415538393	0.0398154831076786	0.98009225844616
24	0.0287406105091539	0.0574812210183079	0.971259389490846
25	0.0261961990211743	0.0523923980423487	0.973803800978826
26	0.02105502897394	0.0421100579478801	0.97894497102606
27	0.013064701427931	0.026129402855862	0.98693529857207
28	0.098501067572858	0.197002135145716	0.901498932427142
29	0.377927279507537	0.755854559015074	0.622072720492463
30	0.89464735434311	0.21070529131378	0.10535264565689
31	0.86114189566001	0.277716208679979	0.13885810433999
32	0.811811311370217	0.376377377259565	0.188188688629783
33	0.741153883182425	0.517692233635149	0.258846116817574
34	0.756442761444181	0.487114477111637	0.243557238555819
35	0.709692932377164	0.580614135245672	0.290307067622836
36	0.627915847600644	0.744168304798711	0.372084152399356
37	0.592671158735817	0.814657682528366	0.407328841264183
38	0.633870455774966	0.732259088450069	0.366129544225034
39	0.697138685846532	0.605722628306937	0.302861314153468
40	0.608617306015579	0.782765387968842	0.391382693984421
41	0.5366390446716	0.9267219106568	0.4633609553284
42	0.513319342451182	0.973361315097635	0.486680657548818
43	0.535230888688171	0.929538222623657	0.464769111311829
44	0.86084784384436	0.27830431231128	0.13915215615564
45	0.90797837654516	0.184043246909679	0.0920216234548395
46	0.910362566296719	0.179274867406561	0.0896374337032807
47	0.945238893300655	0.109522213398691	0.0547611066993455
48	0.892001695304251	0.215996609391498	0.107998304695749
49	0.776095664231486	0.447808671537028	0.223904335768514

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	3	0.111111111111111	NOK
10% type I error level	5	0.185185185185185	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/10xx801291659513.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/10xx801291659513.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/1gmrt1291659512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/1gmrt1291659512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/2qwqx1291659512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/2qwqx1291659512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/3qwqx1291659512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/3qwqx1291659512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/4qwqx1291659512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/4qwqx1291659512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/5jnph1291659512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/5jnph1291659512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/6jnph1291659512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/6jnph1291659512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/7uwo21291659512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/7uwo21291659512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/8uwo21291659512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/8uwo21291659512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/9xx801291659513.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291659422c8zb5nebz1koam1/9xx801291659513.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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