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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: Tue, 30 Nov 2010 09:39:53 +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/Nov/30/t1291109922onyg43xiplfgiq7.htm/, Retrieved Tue, 30 Nov 2010 10:38:55 +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/Nov/30/t1291109922onyg43xiplfgiq7.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 «

31.514 0 27.071 0 29.462 0 26.105 0 22.397 0 23.843 0 21.705 0 18.089 0 20.764 0 25.316 0 17.704 0 15.548 0 28.029 0 29.383 0 36.438 0 32.034 0 22.679 0 24.319 0 18.004 0 17.537 0 20.366 0 22.782 0 19.169 0 13.807 0 29.743 0 25.591 0 29.096 0 26.482 0 22.405 0 27.044 0 17.970 0 18.730 0 19.684 0 19.785 0 18.479 0 10.698 0 31.956 0 29.506 0 34.506 0 27.165 0 26.736 0 23.691 0 18.157 0 17.328 0 18.205 0 20.995 0 17.382 0 9.367 0 31.124 0 26.551 0 30.651 0 25.859 0 25.100 0 25.778 0 20.418 0 18.688 0 20.424 0 24.776 0 19.814 0 12.738 0 31.566 0 30.111 0 30.019 0 31.934 0 25.826 0 26.835 0 20.205 0 17.789 0 20.520 1 22.518 1 15.572 1 11.509 1 25.447 1 24.090 1 27.786 1 26.195 1 20.516 1 22.759 1 19.028 1 16.971 1 20.036 1 22.485 1 18.730 1 14.538 1

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	8 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 23.5437058823530 -2.99995588235294X[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)	23.5437058823530	0.681147	34.5648	0	0
X	-2.99995588235294	1.560704	-1.9222	0.058056	0.029028

Multiple Linear Regression - Regression Statistics
Multiple R	0.207642873485771
R-squared	0.0431155629094279
Adjusted R-squared	0.0314462405058843
F-TEST (value)	3.69477861853703
F-TEST (DF numerator)	1
F-TEST (DF denominator)	82
p-value	0.0580559022521918
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.61688408882437
Sum Squared Residuals	2587.04972311765

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	31.514	23.5437058823528	7.97029411764718
2	27.071	23.5437058823529	3.52729411764706
3	29.462	23.5437058823529	5.91829411764706
4	26.105	23.5437058823529	2.56129411764706
5	22.397	23.5437058823529	-1.14670588235294
6	23.843	23.5437058823529	0.299294117647057
7	21.705	23.5437058823529	-1.83870588235294
8	18.089	23.5437058823529	-5.45470588235294
9	20.764	23.5437058823529	-2.77970588235294
10	25.316	23.5437058823529	1.77229411764706
11	17.704	23.5437058823529	-5.83970588235294
12	15.548	23.5437058823529	-7.99570588235294
13	28.029	23.5437058823529	4.48529411764706
14	29.383	23.5437058823529	5.83929411764706
15	36.438	23.5437058823529	12.8942941176471
16	32.034	23.5437058823529	8.49029411764706
17	22.679	23.5437058823529	-0.864705882352944
18	24.319	23.5437058823529	0.775294117647056
19	18.004	23.5437058823529	-5.53970588235294
20	17.537	23.5437058823529	-6.00670588235294
21	20.366	23.5437058823529	-3.17770588235294
22	22.782	23.5437058823529	-0.761705882352943
23	19.169	23.5437058823529	-4.37470588235294
24	13.807	23.5437058823529	-9.73670588235294
25	29.743	23.5437058823529	6.19929411764706
26	25.591	23.5437058823529	2.04729411764706
27	29.096	23.5437058823529	5.55229411764706
28	26.482	23.5437058823529	2.93829411764706
29	22.405	23.5437058823529	-1.13870588235294
30	27.044	23.5437058823529	3.50029411764706
31	17.97	23.5437058823529	-5.57370588235294
32	18.73	23.5437058823529	-4.81370588235294
33	19.684	23.5437058823529	-3.85970588235294
34	19.785	23.5437058823529	-3.75870588235294
35	18.479	23.5437058823529	-5.06470588235294
36	10.698	23.5437058823529	-12.8457058823529
37	31.956	23.5437058823529	8.41229411764706
38	29.506	23.5437058823529	5.96229411764706
39	34.506	23.5437058823529	10.9622941176471
40	27.165	23.5437058823529	3.62129411764706
41	26.736	23.5437058823529	3.19229411764706
42	23.691	23.5437058823529	0.147294117647056
43	18.157	23.5437058823529	-5.38670588235294
44	17.328	23.5437058823529	-6.21570588235294
45	18.205	23.5437058823529	-5.33870588235294
46	20.995	23.5437058823529	-2.54870588235294
47	17.382	23.5437058823529	-6.16170588235294
48	9.367	23.5437058823529	-14.1767058823529
49	31.124	23.5437058823529	7.58029411764706
50	26.551	23.5437058823529	3.00729411764706
51	30.651	23.5437058823529	7.10729411764706
52	25.859	23.5437058823529	2.31529411764706
53	25.1	23.5437058823529	1.55629411764706
54	25.778	23.5437058823529	2.23429411764706
55	20.418	23.5437058823529	-3.12570588235294
56	18.688	23.5437058823529	-4.85570588235294
57	20.424	23.5437058823529	-3.11970588235294
58	24.776	23.5437058823529	1.23229411764706
59	19.814	23.5437058823529	-3.72970588235294
60	12.738	23.5437058823529	-10.8057058823529
61	31.566	23.5437058823529	8.02229411764706
62	30.111	23.5437058823529	6.56729411764706
63	30.019	23.5437058823529	6.47529411764706
64	31.934	23.5437058823529	8.39029411764706
65	25.826	23.5437058823529	2.28229411764706
66	26.835	23.5437058823529	3.29129411764706
67	20.205	23.5437058823529	-3.33870588235294
68	17.789	23.5437058823529	-5.75470588235294
69	20.52	20.54375	-0.0237500000000008
70	22.518	20.54375	1.97425
71	15.572	20.54375	-4.97175
72	11.509	20.54375	-9.03475
73	25.447	20.54375	4.90325
74	24.09	20.54375	3.54625
75	27.786	20.54375	7.24225
76	26.195	20.54375	5.65125
77	20.516	20.54375	-0.0277500000000022
78	22.759	20.54375	2.21525
79	19.028	20.54375	-1.51575000000000
80	16.971	20.54375	-3.57275
81	20.036	20.54375	-0.507749999999999
82	22.485	20.54375	1.94125
83	18.73	20.54375	-1.81375
84	14.538	20.54375	-6.00575

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.316465950319503	0.632931900639006	0.683534049680497
6	0.225980328083517	0.451960656167034	0.774019671916483
7	0.214001705868096	0.428003411736191	0.785998294131904
8	0.335630796905101	0.671261593810201	0.6643692030949
9	0.285103602556388	0.570207205112775	0.714896397443612
10	0.194550097437982	0.389100194875965	0.805449902562018
11	0.243522383725041	0.487044767450083	0.756477616274959
12	0.355435788736874	0.710871577473749	0.644564211263126
13	0.326343136114084	0.652686272228168	0.673656863885916
14	0.328382303932969	0.656764607865938	0.671617696067031
15	0.638262438659628	0.723475122680744	0.361737561340372
16	0.683558965297967	0.632882069404067	0.316441034702033
17	0.619348154540359	0.761303690919283	0.380651845459641
18	0.542452355573999	0.915095288852003	0.457547644426001
19	0.567739107167206	0.864521785665589	0.432260892832794
20	0.595407958806546	0.809184082386909	0.404592041193454
21	0.551686738664579	0.896626522670842	0.448313261335421
22	0.480633110458337	0.961266220916675	0.519366889541663
23	0.456923839561802	0.913847679123604	0.543076160438198
24	0.595179237269841	0.809641525460318	0.404820762730159
25	0.604146484175375	0.79170703164925	0.395853515824625
26	0.543160679905578	0.913678640188843	0.456839320094422
27	0.534548922471459	0.930902155057082	0.465451077528541
28	0.481584442783591	0.963168885567181	0.518415557216409
29	0.418471002795738	0.836942005591476	0.581528997204262
30	0.375203485698424	0.750406971396848	0.624796514301576
31	0.377994494174038	0.755988988348076	0.622005505825962
32	0.362658853174319	0.725317706348637	0.637341146825682
33	0.330710836636444	0.661421673272887	0.669289163363556
34	0.298187198010110	0.596374396020221	0.70181280198989
35	0.286500108371781	0.573000216743562	0.713499891628219
36	0.536933103971952	0.926133792056097	0.463066896028048
37	0.61059138240106	0.77881723519788	0.38940861759894
38	0.615333336174099	0.769333327651802	0.384666663825901
39	0.763718856052012	0.472562287895976	0.236281143947988
40	0.733962339782932	0.532075320434136	0.266037660217068
41	0.697983640084214	0.604032719831573	0.302016359915786
42	0.640626674612861	0.718746650774278	0.359373325387139
43	0.631863922824383	0.736272154351235	0.368136077175617
44	0.641215353359515	0.71756929328097	0.358784646640485
45	0.633465210532058	0.733069578935885	0.366534789467942
46	0.584675470054068	0.830649059891863	0.415324529945932
47	0.598440608006575	0.80311878398685	0.401559391993425
48	0.881060306085034	0.237879387829933	0.118939693914966
49	0.899828247236866	0.200343505526267	0.100171752763134
50	0.875106900814361	0.249786198371278	0.124893099185639
51	0.890950111264848	0.218099777470304	0.109049888735152
52	0.861658715488957	0.276682569022086	0.138341284511043
53	0.823718268571836	0.352563462856328	0.176281731428164
54	0.783625333052624	0.432749333894752	0.216374666947376
55	0.749270677954901	0.501458644090198	0.250729322045099
56	0.74248104057964	0.51503791884072	0.25751895942036
57	0.71139026011083	0.577219479778339	0.288609739889170
58	0.649781067045673	0.700437865908655	0.350218932954327
59	0.63080590136016	0.73838819727968	0.36919409863984
60	0.870415755835855	0.25916848832829	0.129584244164145
61	0.879141802160159	0.241716395679683	0.120858197839841
62	0.872704602450884	0.254590795098233	0.127295397549116
63	0.872577987360858	0.254844025278283	0.127422012639142
64	0.925055319169922	0.149889361660156	0.0749446808300779
65	0.907838073813788	0.184323852372424	0.092161926186212
66	0.921365892960567	0.157268214078867	0.0786341070394334
67	0.89064981244481	0.218700375110381	0.109350187555191
68	0.850750317956986	0.298499364086028	0.149249682043014
69	0.791776428147686	0.416447143704628	0.208223571852314
70	0.729555143830259	0.540889712339482	0.270444856169741
71	0.717546963783496	0.564906072433007	0.282453036216504
72	0.877846568969724	0.244306862060552	0.122153431030276
73	0.863923911611507	0.272152176776986	0.136076088388493
74	0.821946653179879	0.356106693640242	0.178053346820121
75	0.903598221386774	0.192803557226453	0.0964017786132263
76	0.954484754696879	0.0910304906062428	0.0455152453031214
77	0.90850512869406	0.182989742611881	0.0914948713059405
78	0.892801479827481	0.214397040345038	0.107198520172519
79	0.775151531215729	0.449696937568542	0.224848468784271

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	0	0	OK
10% type I error level	1	0.0133333333333333	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/10cg131291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/10cg131291109984.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/15w491291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/15w491291109984.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/2f6lc1291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/2f6lc1291109984.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/3f6lc1291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/3f6lc1291109984.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/4f6lc1291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/4f6lc1291109984.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/58fke1291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/58fke1291109984.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/68fke1291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/68fke1291109984.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/7j6kh1291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/7j6kh1291109984.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/8j6kh1291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/8j6kh1291109984.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/9cg131291109984.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291109922onyg43xiplfgiq7/9cg131291109984.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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