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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: Wed, 18 Nov 2009 05:33:37 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx.htm/, Retrieved Wed, 18 Nov 2009 13:34:40 +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/2009/Nov/18/t1258547664oqkmkmd8enhancx.htm/},
    year = {2009},
}
@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 = {2009},
    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 «

98,5 0 96,7 0 113,1 0 100 0 104,7 0 108,5 0 90,5 0 88,6 0 105,4 0 119,9 0 107,2 0 84,1 0 101,4 0 105,1 0 118,7 0 113,8 0 113,8 0 118,9 0 98,5 0 91 0 120,7 0 127,9 0 112,4 0 93,1 0 107,5 0 107,3 0 114,8 0 120,8 0 112,2 0 123,3 0 100,6 0 86,7 0 123,6 0 125,3 0 111,1 0 98,4 0 102,3 0 105 0 128,2 0 124,7 0 116,1 0 131,2 0 97,7 0 88,8 0 132,8 0 113,9 0 112,6 1 104,3 1 107,5 1 106 1 117,3 1 123,1 1 114,3 1 132 1 92,3 1 93,7 1 121,3 1 113,6 1 116,3 1 98,3 1 111,9 1 109,3 1 133,2 1 118 1 131,6 1 134,1 1 96,7 1 99,8 1 128,3 1 134,9 1 130,7 1 107,3 1 121,6 1 120,6 1 140,5 1 124,8 1 129,9 1 159,4 1 111 1 110,1 1 132,7 1 135 1 118,6 1 94 1 117,9 1 114,7 1 113,6 1 130,6 1 117,1 1 123,2 1 106,1 1 87,9 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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 86.4631868131869 -1.87596153846154X[t] + 12.5862637362637M1[t] + 11.8554258241759M2[t] + 25.9495879120879M3[t] + 22.7562500000000M4[t] + 20.5004120879121M5[t] + 31.6195741758242M6[t] + 1.72623626373626M7[t] -4.36710164835164M8[t] + 26.9334478021978M9[t] + 27.5043956043956M10[t] + 18.7290521978022M11[t] + 0.243337912087912t + 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)	86.4631868131869	3.03081	28.5281	0	0
X	-1.87596153846154	2.970788	-0.6315	0.52958	0.26479
M1	12.5862637362637	3.63192	3.4655	0.000864	0.000432
M2	11.8554258241759	3.630886	3.2652	0.001626	0.000813
M3	25.9495879120879	3.630714	7.1472	0	0
M4	22.7562500000000	3.631403	6.2665	0	0
M5	20.5004120879121	3.632953	5.6429	0	0
M6	31.6195741758242	3.635362	8.6978	0	0
M7	1.72623626373626	3.638629	0.4744	0.636526	0.318263
M8	-4.36710164835164	3.642752	-1.1988	0.234217	0.117109
M9	26.9334478021978	3.75943	7.1642	0	0
M10	27.5043956043956	3.762838	7.3095	0	0
M11	18.7290521978022	3.748601	4.9963	3e-06	2e-06
t	0.243337912087912	0.055919	4.3516	4.1e-05	2e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.89006531777351
R-squared	0.79221626990326
Adjusted R-squared	0.75758564822047
F-TEST (value)	22.8761781165760
F-TEST (DF numerator)	13
F-TEST (DF denominator)	78
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.0122100219102
Sum Squared Residuals	3835.34497252747

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	98.5	99.2927884615384	-0.792788461538376
2	96.7	98.8052884615385	-2.10528846153846
3	113.1	113.142788461538	-0.0427884615384562
4	100	110.192788461539	-10.1927884615386
5	104.7	108.180288461538	-3.48028846153841
6	108.5	119.542788461538	-11.0427884615384
7	90.5	89.8927884615384	0.607211538461551
8	88.6	84.0427884615385	4.55721153846151
9	105.4	115.586675824176	-10.1866758241758
10	119.9	116.400961538462	3.49903846153843
11	107.2	107.868956043956	-0.668956043956054
12	84.1	89.3832417582418	-5.28324175824176
13	101.4	102.212843406593	-0.812843406593408
14	105.1	101.725343406593	3.3746565934066
15	118.7	116.062843406593	2.6371565934066
16	113.8	113.112843406593	0.68715659340661
17	113.8	111.100343406593	2.69965659340658
18	118.9	122.462843406593	-3.56284340659341
19	98.5	92.8128434065934	5.68715659340659
20	91	86.9628434065934	4.0371565934066
21	120.7	118.506730769231	2.19326923076923
22	127.9	119.321016483516	8.57898351648353
23	112.4	110.789010989011	1.61098901098902
24	93.1	92.3032967032967	0.796703296703287
25	107.5	105.132898351648	2.36710164835163
26	107.3	104.645398351648	2.65460164835165
27	114.8	118.982898351648	-4.18289835164835
28	120.8	116.032898351648	4.76710164835167
29	112.2	114.020398351648	-1.82039835164835
30	123.3	125.382898351648	-2.08289835164836
31	100.6	95.7328983516484	4.86710164835164
32	86.7	89.8828983516484	-3.18289835164835
33	123.6	121.426785714286	2.17321428571427
34	125.3	122.241071428571	3.05892857142857
35	111.1	113.709065934066	-2.60906593406594
36	98.4	95.2233516483517	3.17664835164835
37	102.3	108.052953296703	-5.75295329670332
38	105	107.565453296703	-2.56545329670328
39	128.2	121.902953296703	6.2970467032967
40	124.7	118.952953296703	5.74704670329673
41	116.1	116.940453296703	-0.840453296703305
42	131.2	128.302953296703	2.89704670329669
43	97.7	98.6529532967033	-0.952953296703298
44	88.8	92.8029532967033	-4.0029532967033
45	132.8	124.346840659341	8.45315934065935
46	113.9	125.161126373626	-11.2611263736264
47	112.6	114.753159340659	-2.15315934065934
48	104.3	96.267445054945	8.03255494505494
49	107.5	109.097046703297	-1.59704670329672
50	106	108.609546703297	-2.60954670329669
51	117.3	122.947046703297	-5.64704670329671
52	123.1	119.997046703297	3.10295329670332
53	114.3	117.984546703297	-3.68454670329671
54	132	129.347046703297	2.65295329670329
55	92.3	99.6970467032967	-7.3970467032967
56	93.7	93.8470467032967	-0.147046703296698
57	121.3	125.390934065934	-4.09093406593408
58	113.6	126.205219780220	-12.6052197802198
59	116.3	117.673214285714	-1.37321428571428
60	98.3	99.1875	-0.887500000000004
61	111.9	112.017101648352	-0.117101648351656
62	109.3	111.529601648352	-2.22960164835164
63	133.2	125.867101648352	7.33289835164834
64	118	122.917101648352	-4.91710164835163
65	131.6	120.904601648352	10.6953983516483
66	134.1	132.267101648352	1.83289835164834
67	96.7	102.617101648352	-5.91710164835164
68	99.8	96.7671016483516	3.03289835164835
69	128.3	128.310989010989	-0.0109890109890100
70	134.9	129.125274725275	5.77472527472528
71	130.7	120.593269230769	10.1067307692308
72	107.3	102.107554945055	5.19244505494505
73	121.6	114.937156593407	6.66284340659339
74	120.6	114.449656593407	6.15034340659341
75	140.5	128.787156593407	11.7128434065934
76	124.8	125.837156593407	-1.03715659340657
77	129.9	123.824656593407	6.07534340659341
78	159.4	135.187156593407	24.2128434065934
79	111	105.537156593407	5.46284340659341
80	110.1	99.6871565934066	10.4128434065934
81	132.7	131.231043956044	1.46895604395602
82	135	132.045329670330	2.95467032967033
83	118.6	123.513324175824	-4.91332417582417
84	94	105.027609890110	-11.0276098901099
85	117.9	117.857211538462	0.0427884615384565
86	114.7	117.369711538462	-2.66971153846152
87	113.6	131.707211538462	-18.1072115384615
88	130.6	128.757211538462	1.84278846153848
89	117.1	126.744711538462	-9.64471153846155
90	123.2	138.107211538462	-14.9072115384615
91	106.1	108.457211538462	-2.35721153846154
92	87.9	102.607211538462	-14.7072115384615

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.109344165877025	0.218688331754050	0.890655834122975
18	0.0459035210234638	0.0918070420469275	0.954096478976536
19	0.0157682939157277	0.0315365878314554	0.984231706084272
20	0.0128281328579948	0.0256562657159896	0.987171867142005
21	0.0155345526424287	0.0310691052848574	0.984465447357571
22	0.00663777109210157	0.0132755421842031	0.993362228907898
23	0.00309679252787011	0.00619358505574023	0.99690320747213
24	0.00114618642890728	0.00229237285781456	0.998853813571093
25	0.000749552470185915	0.00149910494037183	0.999250447529814
26	0.000525737262486216	0.00105147452497243	0.999474262737514
27	0.00335868042649031	0.00671736085298062	0.99664131957351
28	0.00230061261854097	0.00460122523708194	0.99769938738146
29	0.00204445284471762	0.00408890568943524	0.997955547155282
30	0.00100420149626353	0.00200840299252706	0.998995798503737
31	0.000534956487216497	0.00106991297443299	0.999465043512783
32	0.00183611581966133	0.00367223163932266	0.998163884180339
33	0.000988978691751894	0.00197795738350379	0.999011021308248
34	0.000866225604587468	0.00173245120917494	0.999133774395413
35	0.000673435172869056	0.00134687034573811	0.99932656482713
36	0.000358395547659669	0.000716791095319337	0.99964160445234
37	0.000644560817606844	0.00128912163521369	0.999355439182393
38	0.00053266215406694	0.00106532430813388	0.999467337845933
39	0.000381777679517853	0.000763555359035706	0.999618222320482
40	0.000270985801497848	0.000541971602995695	0.999729014198502
41	0.000142598584968415	0.000285197169936829	0.999857401415032
42	0.000102686412090273	0.000205372824180547	0.99989731358791
43	0.000100332441809730	0.000200664883619461	0.99989966755819
44	0.000107277133395112	0.000214554266790223	0.999892722866605
45	0.000179579965351057	0.000359159930702115	0.99982042003465
46	0.00167885202291571	0.00335770404583143	0.998321147977084
47	0.000978422199282347	0.00195684439856469	0.999021577800718
48	0.000924897859103894	0.00184979571820779	0.999075102140896
49	0.000578964392203308	0.00115792878440662	0.999421035607797
50	0.000380454651211864	0.000760909302423728	0.999619545348788
51	0.000349495293386811	0.000698990586773622	0.999650504706613
52	0.000195184887720409	0.000390369775440817	0.99980481511228
53	0.000131290216141647	0.000262580432283295	0.999868709783858
54	9.31377158575366e-05	0.000186275431715073	0.999906862284142
55	0.000139904126573817	0.000279808253147633	0.999860095873426
56	7.35407161435812e-05	0.000147081432287162	0.999926459283856
57	5.1037452083681e-05	0.000102074904167362	0.999948962547916
58	0.000369838129085232	0.000739676258170463	0.999630161870915
59	0.000276555857722817	0.000553111715445634	0.999723444142277
60	0.000151750956539714	0.000303501913079428	0.99984824904346
61	0.000124017181903389	0.000248034363806778	0.999875982818097
62	0.000117445159711696	0.000234890319423392	0.999882554840288
63	8.96686079923569e-05	0.000179337215984714	0.999910331392008
64	0.000146610072941428	0.000293220145882857	0.999853389927059
65	0.000184467629809860	0.000368935259619721	0.99981553237019
66	0.000268201517703573	0.000536403035407147	0.999731798482296
67	0.00220013184260938	0.00440026368521876	0.99779986815739
68	0.00322751242899445	0.00645502485798889	0.996772487571006
69	0.00495620038475913	0.00991240076951826	0.99504379961524
70	0.00664914331672957	0.0132982866334591	0.99335085668327
71	0.00468965405659431	0.00937930811318862	0.995310345943406
72	0.00220277086334883	0.00440554172669766	0.997797229136651
73	0.00187039860031092	0.00374079720062184	0.99812960139969
74	0.00131196667864154	0.00262393335728307	0.998688033321358
75	0.00130947468126868	0.00261894936253735	0.998690525318731

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	52	0.88135593220339	NOK
5% type I error level	57	0.966101694915254	NOK
10% type I error level	58	0.983050847457627	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/105um81258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/105um81258547612.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/1s5a01258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/1s5a01258547612.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/26ldx1258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/26ldx1258547612.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/3hrln1258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/3hrln1258547612.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/4lggz1258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/4lggz1258547612.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/5j5481258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/5j5481258547612.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/6fo4x1258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/6fo4x1258547612.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/7j4q21258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/7j4q21258547612.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/8aqgv1258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/8aqgv1258547612.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/951rm1258547612.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258547664oqkmkmd8enhancx/951rm1258547612.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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