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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 10:08:34 +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/t12911117658z3ezht0l0q8mgh.htm/, Retrieved Tue, 30 Nov 2010 11:09:25 +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/t12911117658z3ezht0l0q8mgh.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 «

22.397 26.105 29.462 27.071 31.514 23.843 22.397 26.105 29.462 27.071 21.705 23.843 22.397 26.105 29.462 18.089 21.705 23.843 22.397 26.105 20.764 18.089 21.705 23.843 22.397 25.316 20.764 18.089 21.705 23.843 17.704 25.316 20.764 18.089 21.705 15.548 17.704 25.316 20.764 18.089 28.029 15.548 17.704 25.316 20.764 29.383 28.029 15.548 17.704 25.316 36.438 29.383 28.029 15.548 17.704 32.034 36.438 29.383 28.029 15.548 22.679 32.034 36.438 29.383 28.029 24.319 22.679 32.034 36.438 29.383 18.004 24.319 22.679 32.034 36.438 17.537 18.004 24.319 22.679 32.034 20.366 17.537 18.004 24.319 22.679 22.782 20.366 17.537 18.004 24.319 19.169 22.782 20.366 17.537 18.004 13.807 19.169 22.782 20.366 17.537 29.743 13.807 19.169 22.782 20.366 25.591 29.743 13.807 19.169 22.782 29.096 25.591 29.743 13.807 19.169 26.482 29.096 25.591 29.743 13.807 22.405 26.482 29.096 25.591 29.743 27.044 22.405 26.482 29.096 25.591 17.970 27.044 22.405 26.482 29.096 18.730 17.970 27.044 22.405 26.482 19.684 18.730 17 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Yt[t] = + 12.2406247261314 + 0.232373289440401Yt_1[t] + 0.334514566579160Yt_2[t] + 0.0190532461610643Yt_3[t] -0.0846729256870996Yt_4[t] -3.48048732019389M1[t] -0.467043484880352M2[t] -4.48331016033904M3[t] -5.27300518640634M4[t] -1.33980579687569M5[t] + 1.53664584963875M6[t] -4.77797385546277M7[t] -10.3001636157727M8[t] + 9.78306552323733M9[t] + 5.88075610040622M10[t] + 4.15159268968920M11[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)	12.2406247261314	4.418009	2.7706	0.007315	0.003658
Yt_1	0.232373289440401	0.125063	1.858	0.067763	0.033881
Yt_2	0.334514566579160	0.127477	2.6241	0.01085	0.005425
Yt_3	0.0190532461610643	0.128115	0.1487	0.882242	0.441121
Yt_4	-0.0846729256870996	0.122991	-0.6884	0.493658	0.246829
M1	-3.48048732019389	2.455267	-1.4176	0.16117	0.080585
M2	-0.467043484880352	2.331374	-0.2003	0.841858	0.420929
M3	-4.48331016033904	2.821579	-1.5889	0.117003	0.058502
M4	-5.27300518640634	2.787288	-1.8918	0.063043	0.031522
M5	-1.33980579687569	2.629014	-0.5096	0.612069	0.306034
M6	1.53664584963875	2.885375	0.5326	0.59618	0.29809
M7	-4.77797385546277	2.31827	-2.061	0.04337	0.021685
M8	-10.3001636157727	2.210753	-4.6591	1.7e-05	8e-06
M9	9.78306552323733	2.840305	3.4444	0.001015	0.000507
M10	5.88075610040622	3.148711	1.8677	0.066388	0.033194
M11	4.15159268968920	2.677194	1.5507	0.125899	0.062949

Multiple Linear Regression - Regression Statistics
Multiple R	0.95387231448526
R-squared	0.909872392341468
Adjusted R-squared	0.8887487342965
F-TEST (value)	43.0736187077311
F-TEST (DF numerator)	15
F-TEST (DF denominator)	64
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.89712768075047
Sum Squared Residuals	230.341979972458

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	22.397	22.5291181340573	-0.132118134057304
2	23.843	23.9797145325185	-0.136714532518468
3	21.705	18.7926649080345	2.91233509196549
4	18.089	18.2034614271835	-0.114461427183472
5	20.764	20.9227250611481	-0.158725061148055
6	25.316	23.0479976933294	2.26800230667058
7	17.704	18.7981018443605	-1.09410184436047
8	15.548	13.3869416446639	2.16105835533607
9	28.029	30.2830793911521	-2.25407939115208
10	29.383	28.0293431207762	1.35365687922375
11	36.438	31.393340961043	5.04465903895703
12	32.034	29.7544329446216	2.27956705537838
13	22.679	26.5795692347496	-3.90056923474957
14	24.319	25.9657323064195	-1.64673230641951
15	18.004	18.5198960684792	-0.515896068479212
16	17.537	17.0060240556748	0.530975944325163
17	20.366	19.5416081745964	0.824391825403613
18	22.782	22.6600407067113	0.121959293288710
19	19.169	18.3789882375070	0.790011762492963
20	13.807	12.9188648649897	0.888135135010294
21	29.743	30.3540002329261	-0.611000232926145
22	25.591	28.0877152777798	-2.49671527777984
23	29.096	30.9283218769036	-1.83232187690364
24	26.482	26.9599418446233	-0.477941844623321
25	22.405	22.6160474798818	-0.211047479881822
26	27.044	24.2260279523563	2.81797204764371
27	17.97	19.5773412886701	-1.60734128867007
28	18.73	18.3745590517287	0.355440948271281
29	19.684	19.8825764910623	-0.198576491062257
30	19.785	22.6692564683751	-2.88425646837505
31	18.479	17.4800359567907	0.998964043209319
32	10.698	11.6419780250115	-0.943978025011526
33	31.956	29.4013809816902	2.55461901830979
34	29.506	27.8025695982499	1.70343040175006
35	34.506	32.5775318173118	1.92846818268816
36	27.165	29.8321188283689	-2.66711882836893
37	26.736	24.4716945159379	2.26430548406211
38	23.691	25.2324936755626	-1.54149367556259
39	18.157	19.8019090761916	-1.64490907619155
40	17.328	17.3210735159934	0.00692648400657125
41	18.205	19.1887393876882	-0.983739387688245
42	20.995	22.1440582278097	-1.14905822780968
43	17.382	17.2239141048217	0.158085895178314
44	9.367	11.8823588427973	-2.51535884279729
45	31.124	28.8734153388538	2.25058466114619
46	26.551	27.0406404821986	-0.489640482198587
47	30.651	31.6800789564600	-1.02907895645996
48	25.859	28.0446766166183	-2.18567661661829
49	25.1	22.8928068775318	2.20719312246822
50	25.778	24.5922131815402	1.18578681845981
51	20.418	20.0411368893676	0.37686311063242
52	18.688	18.6240131540967	0.0639868459032659
53	20.424	20.4393935275249	-0.0153935275249063
54	24.776	22.9810013612868	1.79499863871322
55	19.814	18.6792722652355	1.13472773476449
56	12.738	13.6394142332491	-0.901414233249097
57	31.566	30.3544362251132	1.21156377488677
58	30.111	27.9971872427104	2.11381275728962
59	30.019	32.5134872428337	-2.4944872428337
60	31.934	28.8116776570257	3.12232234297426
61	25.826	24.1234655279839	1.70253447201612
62	26.835	26.4796149146225	0.355385085377546
63	20.205	20.6988747911053	-0.493874791105269
64	17.789	18.4275441734839	-0.638544173483901
65	20.52	20.1179050747800	0.402094925219965
66	22.518	22.6090229778348	-0.0910229778348198
67	15.572	18.1875932409432	-2.61559324094324
68	11.509	11.9763029199313	-0.467302919931338
69	25.447	28.5986878302645	-3.15168783026453
70	24.09	26.274544278285	-2.18454427828501
71	27.786	29.4032391454479	-1.61723914544789
72	26.195	26.2661521087421	-0.0711521087420993
73	20.516	22.4462982298578	-1.93029822985775
74	22.759	23.7932034369805	-1.03420343698049
75	19.028	18.0551769781518	0.972823021848202
76	16.971	17.1753246218389	-0.204324621838907
77	20.036	19.9060522832001	0.129947716799886
78	22.485	22.5456225646530	-0.0606225646529591
79	18.73	18.1020943503414	0.627905649658635
80	14.538	12.7591394693571	1.77886053064289

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.226322577501374	0.452645155002748	0.773677422498626
20	0.129298694921823	0.258597389843646	0.870701305078177
21	0.0667439956478228	0.133487991295646	0.933256004352177
22	0.231607612706371	0.463215225412743	0.768392387293629
23	0.936268425493576	0.127463149012848	0.0637315745064242
24	0.935894537775867	0.128210924448267	0.0641054622241335
25	0.900788131901932	0.198423736196136	0.0992118680980679
26	0.903938348609652	0.192123302780697	0.0960616513903484
27	0.925876396490294	0.148247207019413	0.0741236035097064
28	0.891971144562952	0.216057710874096	0.108028855437048
29	0.84322645553593	0.313547088928139	0.156773544464069
30	0.90679418920081	0.186411621598379	0.0932058107991896
31	0.872440447955312	0.255119104089376	0.127559552044688
32	0.8734749667193	0.253050066561399	0.126525033280700
33	0.880109598900876	0.239780802198248	0.119890401099124
34	0.855610948182793	0.288778103634415	0.144389051817207
35	0.858292855474146	0.283414289051707	0.141707144525854
36	0.905782705144926	0.188434589710148	0.094217294855074
37	0.931499473942995	0.137001052114010	0.0685005260570049
38	0.916189235301504	0.167621529396991	0.0838107646984957
39	0.906531774746914	0.186936450506171	0.0934682252530857
40	0.86698331842097	0.26603336315806	0.13301668157903
41	0.825549553295642	0.348900893408716	0.174450446704358
42	0.795876249412093	0.408247501175815	0.204123750587907
43	0.740681186733195	0.51863762653361	0.259318813266805
44	0.780934235694429	0.438131528611142	0.219065764305571
45	0.825622646773748	0.348754706452504	0.174377353226252
46	0.769374448567189	0.461251102865623	0.230625551432812
47	0.761062216002066	0.477875567995868	0.238937783997934
48	0.87149518536399	0.257009629272020	0.128504814636010
49	0.93115822400943	0.137683551981141	0.0688417759905703
50	0.896021615954446	0.207956768091107	0.103978384045554
51	0.870180832597439	0.259638334805123	0.129819167402561
52	0.83475805026639	0.330483899467222	0.165241949733611
53	0.766706200337978	0.466587599324043	0.233293799662022
54	0.764987182174378	0.470025635651244	0.235012817825622
55	0.688956826714916	0.622086346570167	0.311043173285084
56	0.644080708681889	0.711838582636222	0.355919291318111
57	0.784814738968301	0.430370522063398	0.215185261031699
58	0.694528819058535	0.610942361882931	0.305471180941465
59	0.634137134603605	0.73172573079279	0.365862865396395
60	0.846879610578524	0.306240778842952	0.153120389421476
61	0.721648454381738	0.556703091236524	0.278351545618262

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	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/10rqnb1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/10rqnb1291111704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/12pqz1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/12pqz1291111704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/22pqz1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/22pqz1291111704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/32pqz1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/32pqz1291111704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/4vy7k1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/4vy7k1291111704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/5vy7k1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/5vy7k1291111704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/6vy7k1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/6vy7k1291111704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/76ppn1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/76ppn1291111704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/8zyoq1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/8zyoq1291111704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/9zyoq1291111704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911117658z3ezht0l0q8mgh/9zyoq1291111704.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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