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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: Fri, 03 Dec 2010 22:27:33 +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/03/t1291415258nv9iwoscvfxeva0.htm/, Retrieved Fri, 03 Dec 2010 23:27:38 +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/03/t1291415258nv9iwoscvfxeva0.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 «

12 1221.53 2617.2 10168.52 6957.61 23448.78 11 1180.55 2506.13 9937.04 6688.49 23007.99 10 1183.26 2679.07 9202.45 6601.37 23096.32 9 1141.2 2589.73 9369.35 6229.02 22358.17 8 1049.33 2457.46 8824.06 5925.22 20536.49 7 1101.6 2517.3 9537.3 6147.97 21029.81 6 1030.71 2386.53 9382.64 5965.52 20128.99 5 1089.41 2453.37 9768.7 5964.33 19765.19 4 1186.69 2529.66 11057.4 6135.7 21108.59 3 1169.43 2475.14 11089.94 6153.55 21239.35 2 1104.49 2525.93 10126.03 5598.46 20608.7 1 1073.87 2480.93 10198.04 5608.79 20121.99 12 1115.1 2229.85 10546.44 5957.43 21872.5 11 1095.63 2169.14 9345.55 5625.95 21821.5 10 1036.19 2030.98 10034.74 5414.96 21752.87 9 1057.08 2071.37 10133.23 5675.16 20955.25 8 1020.62 1953.35 10492.53 5458.04 19724.19 7 987.48 1748.74 10356.83 5332.14 20573.33 6 919.32 1696.58 9958.44 4808.64 18378.73 5 919.14 1900.09 9522.5 4940.82 18171 4 872.81 1908.64 8828.26 4769.45 15520.99 3 797.87 1881.46 8109.53 4084.76 13576.02 2 735.09 2100.18 7568.42 3843.74 12811.57 1 825.88 2672.2 79 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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation

Nikkei225[t] = + 1.76136822710579 -0.960255268334286month[t] -0.155257413606003`S&P`[t] -0.195263524204028Bel20[t] -0.186413495406639DAX[t] + 0.477200864515611HangSeng[t] + 164.108594105633t + 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)	1.76136822710579	1398.861404	0.0013	0.998999	0.4995
month	-0.960255268334286	0.117957	-8.1407	0	0
`S&P`	-0.155257413606003	0.131689	-1.179	0.242707	0.121353
Bel20	-0.195263524204028	0.141245	-1.3824	0.171568	0.085784
DAX	-0.186413495406639	0.128392	-1.4519	0.151337	0.075668
HangSeng	0.477200864515611	0.053238	8.9635	0	0
t	164.108594105633	14.162482	11.5876	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.893053076885623
R-squared	0.797543798134879
Adjusted R-squared	0.77885553334733
F-TEST (value)	42.6761824707350
F-TEST (DF numerator)	6
F-TEST (DF denominator)	65
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1906.20872451305
Sum Squared Residuals	236186060.591628

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	10168.52	9346.437303184	822.082696816002
2	9937.04	9379.37875181487	557.66124818513
3	9202.45	9568.64947580461	-366.19947580461
4	9369.35	9474.8585421197	-105.508542119701
5	8824.06	8867.34354546186	-43.2835454618575
6	9537.3	9206.50164491928	330.798355080720
7	9382.64	9012.25036286792	370.389637132077
8	9768.7	8981.77034565418	786.929654345822
9	11057.4	9725.96506025347	1331.43493974653
10	11089.94	9963.43072407693	1126.50927592307
11	10126.03	9931.19409745465	194.835902545351
12	10198.04	9875.61970324647	322.420296753529
13	10546.44	10842.1446761993	-295.704676199250
14	9345.55	11059.5459373377	-1713.99593733765
15	10034.74	11267.4019839445	-1232.66198394453
16	10133.23	10992.2110671459	-858.981067145916
17	10492.53	10638.9968047986	-146.466804798617
18	10356.83	11277.8435557134	-921.013555713358
19	9958.44	10514.0021434006	-555.562143400607
20	9522.5	10515.5917878896	-993.091787889585
21	8828.26	9453.5428278368	-625.282827836796
22	8109.53	8835.0480210873	-725.518021087306
23	7568.42	8647.2890726575	-1078.86907265750
24	7994.05	8817.04649278948	-822.996492789479
25	8859.56	9309.40218550052	-449.842185500521
26	8512.27	9291.2144136543	-778.944413654305
27	8576.98	9390.08391176119	-813.103911761187
28	11259.86	11184.9302988109	74.9297011891261
29	13072.87	12736.5552891172	336.314710882844
30	13376.81	13634.6638232082	-257.853823208185
31	13481.38	13501.6450861130	-20.2650861129508
32	14338.54	14688.4886219005	-349.948621900509
33	13849.99	15387.5579554255	-1537.56795542551
34	12525.54	14245.8807949503	-1720.34079495028
35	13603.02	15022.8775103913	-1419.85751039126
36	13592.47	14765.9497633315	-1173.47976333153
37	15307.78	16753.0874167666	-1445.30741676663
38	15680.67	17342.8244177144	-1662.15441771440
39	16737.63	18712.5231101385	-1974.89311013845
40	16785.69	16890.0214698121	-104.331469812120
41	16569.09	15613.9838828278	955.10611717218
42	17248.89	15438.5963041693	1810.29369583072
43	18138.36	14875.9018369313	3262.45816306867
44	17875.75	14491.6655091055	3384.08449089449
45	17400.41	14610.8597841871	2789.55021581294
46	17287.65	14678.4204874036	2609.22951259642
47	17604.12	14811.1482190489	2792.97178095107
48	17383.42	15189.060638939	2194.35936106100
49	17225.83	15346.0832759837	1879.7467240163
50	16274.33	15109.8065967231	1164.52340327686
51	16399.39	15000.4227720336	1398.96722796641
52	16127.58	14853.8861694359	1273.69383056407
53	16140.76	14933.0230883069	1207.73691169314
54	15456.81	14934.3093844185	522.500615581504
55	15505.18	14778.5757053962	726.604294603792
56	15467.33	14764.8337086467	702.496291353258
57	16906.23	15286.0513910921	1620.17860890790
58	17059.66	15082.6644263975	1976.99557360249
59	16205.43	15361.8773809537	843.552619046337
60	16649.82	15458.2555217915	1191.56447820850
61	16111.43	15268.4031780952	843.026821904754
62	14872.15	15501.5240272523	-629.374027252293
63	13606.5	15482.1238707915	-1875.6238707915
64	13574.3	16131.6184339976	-2557.31843399759
65	12413.6	16089.7957259637	-3676.19572596370
66	11899.6	16214.5688334325	-4314.96883343248
67	11584.01	16121.532501987	-4537.52250198701
68	4460.63	6186.34839206256	-1725.71839206256
69	13908.97	8250.84243521844	5658.12756478156
70	2	2347.91702144229	-2345.91702144229
71	1181.27	5179.37965986182	-3998.10965986182
72	2617.2	-174.500160151262	2791.70016015126

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.00139849166263179	0.00279698332526358	0.998601508337368
11	0.000127666623403460	0.000255333246806919	0.999872333376597
12	1.15113724470530e-05	2.30227448941059e-05	0.999988488627553
13	8.1252197446408e-07	1.62504394892816e-06	0.999999187478026
14	1.2562674907016e-05	2.5125349814032e-05	0.999987437325093
15	2.76349928548576e-06	5.52699857097151e-06	0.999997236500714
16	6.6470298007684e-07	1.32940596015368e-06	0.99999933529702
17	8.94268684915812e-08	1.78853736983162e-07	0.999999910573132
18	1.94912985266597e-08	3.89825970533194e-08	0.999999980508701
19	2.50249375321250e-09	5.00498750642499e-09	0.999999997497506
20	4.03657889385012e-10	8.07315778770024e-10	0.999999999596342
21	1.25937870742972e-10	2.51875741485945e-10	0.999999999874062
22	1.59839856798861e-11	3.19679713597723e-11	0.999999999984016
23	9.41731645539445e-12	1.88346329107889e-11	0.999999999990583
24	1.99098694482422e-12	3.98197388964845e-12	0.99999999999801
25	9.92005524288206e-12	1.98401104857641e-11	0.99999999999008
26	1.91818227289869e-12	3.83636454579739e-12	0.999999999998082
27	3.50124867618276e-12	7.00249735236552e-12	0.999999999996499
28	8.66688000622474e-13	1.73337600124495e-12	0.999999999999133
29	1.93782918965663e-13	3.87565837931325e-13	0.999999999999806
30	5.03343169456475e-14	1.00668633891295e-13	0.99999999999995
31	1.69020359059318e-14	3.38040718118636e-14	0.999999999999983
32	5.95224423951687e-14	1.19044884790337e-13	0.99999999999994
33	1.67748413412453e-13	3.35496826824906e-13	0.999999999999832
34	2.20352140219590e-11	4.40704280439181e-11	0.999999999977965
35	2.69136507523061e-10	5.38273015046121e-10	0.999999999730864
36	1.28985546040823e-06	2.57971092081647e-06	0.99999871014454
37	3.04618267732667e-06	6.09236535465333e-06	0.999996953817323
38	3.86589261617849e-06	7.73178523235698e-06	0.999996134107384
39	4.32381048732104e-06	8.64762097464208e-06	0.999995676189513
40	5.33674959548963e-06	1.06734991909793e-05	0.999994663250405
41	9.4176359374454e-06	1.88352718748908e-05	0.999990582364063
42	1.76451294800549e-05	3.52902589601097e-05	0.99998235487052
43	8.99725269701675e-06	1.79945053940335e-05	0.999991002747303
44	4.67870480103442e-06	9.35740960206885e-06	0.999995321295199
45	1.89409299079649e-06	3.78818598159298e-06	0.99999810590701
46	1.19104557909618e-06	2.38209115819236e-06	0.99999880895442
47	1.18879997784957e-06	2.37759995569913e-06	0.999998811200022
48	4.92673790875094e-07	9.85347581750187e-07	0.99999950732621
49	2.06842773584476e-07	4.13685547168953e-07	0.999999793157226
50	1.96864125208238e-07	3.93728250416475e-07	0.999999803135875
51	1.30065694753860e-07	2.60131389507720e-07	0.999999869934305
52	1.43917230846644e-07	2.87834461693289e-07	0.99999985608277
53	1.34407330355721e-07	2.68814660711442e-07	0.99999986559267
54	1.00693531302901e-06	2.01387062605802e-06	0.999998993064687
55	6.40937212841204e-05	0.000128187442568241	0.999935906278716
56	0.210296483333892	0.420592966667783	0.789703516666108
57	0.218034165646400	0.436068331292799	0.7819658343536
58	0.167983144801215	0.33596628960243	0.832016855198785
59	0.223537372056238	0.447074744112476	0.776462627943762
60	0.277057680850726	0.554115361701452	0.722942319149274
61	0.352693509872883	0.705387019745765	0.647306490127117
62	0.380298670332748	0.760597340665496	0.619701329667252

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	46	0.867924528301887	NOK
5% type I error level	46	0.867924528301887	NOK
10% type I error level	46	0.867924528301887	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/10ftaz1291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/10ftaz1291415244.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/1qac61291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/1qac61291415244.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/2qac61291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/2qac61291415244.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/31jc81291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/31jc81291415244.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/41jc81291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/41jc81291415244.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/51jc81291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/51jc81291415244.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/6uttb1291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/6uttb1291415244.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/7m2ae1291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/7m2ae1291415244.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/8m2ae1291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/8m2ae1291415244.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/9m2ae1291415244.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291415258nv9iwoscvfxeva0/9m2ae1291415244.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 4 ; par2 = Do not include Seasonal 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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