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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: Thu, 19 Nov 2009 08:42:20 -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/19/t1258645477hdgdfhps4ksqs8d.htm/, Retrieved Thu, 19 Nov 2009 16:44:49 +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/19/t1258645477hdgdfhps4ksqs8d.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 «

142773 0 142773 149657 133639 0 133639 142773 128332 0 128332 133639 120297 0 120297 128332 118632 0 118632 120297 155276 0 155276 118632 169316 0 169316 155276 167395 0 167395 169316 157939 0 157939 167395 149601 0 149601 157939 146310 0 146310 149601 141579 0 141579 146310 136473 0 136473 141579 129818 0 129818 136473 124226 0 124226 129818 116428 0 116428 124226 116440 0 116440 116428 147747 0 147747 116440 160069 0 160069 147747 163129 0 163129 160069 151108 0 151108 163129 141481 0 141481 151108 139174 0 139174 141481 134066 0 134066 139174 130104 0 130104 134066 123090 0 123090 130104 116598 0 116598 123090 109627 0 109627 116598 105428 0 105428 109627 137272 0 137272 105428 159836 0 159836 137272 155283 0 155283 159836 141514 0 141514 155283 131852 0 131852 141514 130691 0 130691 131852 128461 0 128461 130691 123066 0 123066 128461 117599 0 117599 123066 111599 0 111599 117599 105395 0 105395 111599 102334 0 102334 105395 131305 0 131305 102334 149033 0 149033 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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 4.92846343476301e-11 -2.98615544369428e-12X[t] + 1Y1[t] + 8.76083176041636e-18Y2[t] -3.59960750140816e-13M1[t] + 1.91039989183012e-13M2[t] + 9.58085589102287e-12M3[t] + 1.62581264265537e-12M4[t] + 2.27350604296845e-12M5[t] -4.41959246781001e-14M6[t] -1.80473205404753e-12M7[t] -1.53258490248065e-12M8[t] -7.35980514667547e-13M9[t] + 1.80890479746526e-13M10[t] + 1.87338371298928e-13M11[t] -5.19452604930502e-14t + 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)	4.92846343476301e-11	0	4.1656	7.9e-05	3.9e-05
X	-2.98615544369428e-12	0	-0.9624	0.338791	0.169395
Y1	1	0	3359192303481257	0	0
Y2	8.76083176041636e-18	0	0.0288	0.977115	0.488557
M1	-3.59960750140816e-13	0	-0.1008	0.919954	0.459977
M2	1.91039989183012e-13	0	0.0516	0.958966	0.479483
M3	9.58085589102287e-12	0	2.5791	0.011761	0.005881
M4	1.62581264265537e-12	0	0.4112	0.682011	0.341005
M5	2.27350604296845e-12	0	0.5708	0.56973	0.284865
M6	-4.41959246781001e-14	0	-0.0039	0.996871	0.498436
M7	-1.80473205404753e-12	0	-0.2813	0.779195	0.389597
M8	-1.53258490248065e-12	0	-0.3712	0.71148	0.35574
M9	-7.35980514667547e-13	0	-0.1582	0.874688	0.437344
M10	1.80890479746526e-13	0	0.0415	0.967013	0.483507
M11	1.87338371298928e-13	0	0.0529	0.957978	0.478989
t	-5.19452604930502e-14	0	-0.9922	0.324132	0.162066

Multiple Linear Regression - Regression Statistics
Multiple R	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	5.04145999650029e+31
F-TEST (DF numerator)	15
F-TEST (DF denominator)	79
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.77646115224013e-12
Sum Squared Residuals	3.62771363407775e-21

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	142773	142773	-6.97076020479089e-12
2	133639	133639	-4.62525687717883e-12
3	128332	128332	5.44183115870851e-11
4	120297	120297	-3.40053118173447e-12
5	118632	118632	-3.18394361020532e-12
6	155276	155276	-2.57676634316205e-12
7	169316	169316	-6.5738652310142e-14
8	167395	167395	-3.19090256683984e-12
9	157939	157939	-3.34301268480542e-12
10	149601	149601	-2.22858031249122e-12
11	146310	146310	-2.4625185072201e-12
12	141579	141579	-1.90520641912936e-12
13	136473	136473	-5.40804853090086e-13
14	129818	129818	-3.03322622107426e-13
15	124226	124226	-1.07290116431453e-11
16	116428	116428	-1.01714685867044e-12
17	116440	116440	-1.54992536523827e-12
18	147747	147747	-3.98203146897926e-13
19	160069	160069	-2.53182729282213e-12
20	163129	163129	-5.85571213715199e-13
21	151108	151108	-1.45731604890662e-12
22	141481	141481	-1.56500888007174e-12
23	139174	139174	-4.07146249988704e-13
24	134066	134066	8.21668768276585e-14
25	130104	130104	7.12718539250577e-13
26	123090	123090	-2.64089300562253e-13
27	116598	116598	-8.46659381828784e-12
28	109627	109627	-9.34354041977796e-13
29	105428	105428	4.02090450770148e-13
30	137272	137272	4.41916686385895e-13
31	159836	159836	-1.71288707538038e-12
32	155283	155283	-1.01901181872110e-13
33	141514	141514	7.81473043696383e-13
34	131852	131852	7.04690533827093e-13
35	130691	130691	4.42694807366088e-13
36	128461	128461	7.76367744610878e-13
37	123066	123066	8.83398016398544e-13
38	117599	117599	1.04877352990937e-12
39	111599	111599	-5.56753523644028e-12
40	105395	105395	1.61861052151793e-12
41	102334	102334	2.4412300609666e-12
42	131305	131305	1.47759031385716e-12
43	149033	149033	1.38689493736117e-13
44	144954	144954	1.58082312342448e-12
45	132404	132404	1.91684482289470e-12
46	122104	122104	1.20418801869533e-12
47	118755	118755	1.01328401782209e-12
48	116222	116222	-3.66583030304323e-13
49	110924	110924	6.09372201687507e-13
50	103753	103753	-2.85770229223607e-13
51	99983	99983	-7.88086321980542e-12
52	93302	93302	-5.01697637255762e-13
53	91496	91496	-2.34141071347797e-13
54	119321	119321	2.11389323339096e-13
55	139261	139261	6.25320839405668e-13
56	133739	133739	-9.46886526784736e-13
57	123913	123913	4.616692582237e-13
58	113438	113438	2.57877236088505e-13
59	109416	109416	1.27789733128369e-12
60	109406	109406	-2.62116264450192e-13
61	105645	105645	1.82582058110069e-12
62	101328	101328	1.46442816736043e-12
63	97686	97686	-6.21270645376455e-12
64	93093	93093	2.34901832418257e-13
65	91382	91382	4.41832730154226e-13
66	122257	122257	4.3691179031731e-13
67	139183	139183	1.25769982657461e-12
68	139887	139887	5.75501905279789e-13
69	131822	131822	-2.19450391589948e-13
70	116805	116805	2.21053179561928e-13
71	113706	113706	8.69565956885385e-13
72	113012	113012	1.44525348746748e-12
73	110452	110452	2.094509147549e-12
74	107005	107005	1.33491334573885e-12
75	102841	102841	-7.93622362508816e-12
76	98173	98173	2.18735745246233e-12
77	98181	98181	1.62893998675624e-12
78	137277	137277	1.12882204362521e-12
79	147579	147579	1.64608353994517e-12
80	146571	146571	1.78480351657972e-12
81	138920	138920	8.20366781548175e-13
82	130340	130340	2.07834327435405e-13
83	128140	128140	-5.10145421152348e-13
84	127059	127059	2.30117604977839e-13
85	122860	122860	1.38574657189466e-12
86	117702	117702	1.63032398606346e-12
87	113537	113537	-7.62537759055352e-12
88	108366	108366	1.81285991323993e-12
89	111078	111078	5.39168181441674e-14
90	150739	150739	-7.21660667464705e-13
91	159129	159129	6.42659320851088e-13
92	157928	157928	8.84132943927921e-13
93	147768	147768	1.03942521893901e-12
94	137507	137507	1.19794589695471e-12
95	136919	136919	-2.23631934996096e-13

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0	0	1
20	0.99998051905601	3.89618879815751e-05	1.94809439907876e-05
21	5.74677351011148e-11	1.14935470202230e-10	0.999999999942532
22	0.000241425322712505	0.000482850645425009	0.999758574677287
23	0.634960321146947	0.730079357706106	0.365039678853053
24	0.999999707964615	5.84070769135105e-07	2.92035384567552e-07
25	0.184083985183864	0.368167970367727	0.815916014816136
26	1	1.88675465857786e-18	9.43377329288929e-19
27	0.398091625361893	0.796183250723786	0.601908374638107
28	0.0203382305020757	0.0406764610041513	0.979661769497924
29	0.0593147742654999	0.118629548531000	0.9406852257345
30	0.00383863458627132	0.00767726917254264	0.996161365413729
31	0.166483997141679	0.332967994283357	0.833516002858321
32	1	1.42464844856028e-33	7.12324224280138e-34
33	0.000908685164452963	0.00181737032890593	0.999091314835547
34	0.998028682081328	0.00394263583734496	0.00197131791867248
35	0.141821516108999	0.283643032217997	0.858178483891001
36	0.999845051778694	0.000309896442612286	0.000154948221306143
37	9.170981533742e-13	1.8341963067484e-12	0.999999999999083
38	0.937976028630722	0.124047942738556	0.0620239713692782
39	2.09562493592928e-11	4.19124987185856e-11	0.999999999979044
40	0.00802944149110447	0.0160588829822089	0.991970558508896
41	0.00217995488652272	0.00435990977304545	0.997820045113477
42	0.999999995481257	9.03748692899215e-09	4.51874346449607e-09
43	0.824453291965989	0.351093416068022	0.175546708034011
44	0.962150405743702	0.0756991885125954	0.0378495942562977
45	2.28085882460876e-15	4.56171764921753e-15	0.999999999999998
46	0.999285828936187	0.00142834212762701	0.000714171063813504
47	0.103787733551610	0.207575467103221	0.89621226644839
48	0.99999975854879	4.82902421120387e-07	2.41451210560194e-07
49	1.57360404677747e-08	3.14720809355495e-08	0.99999998426396
50	2.93809074304749e-07	5.87618148609497e-07	0.999999706190926
51	0.997031199033838	0.00593760193232342	0.00296880096616171
52	0.999999692553607	6.14892785833258e-07	3.07446392916629e-07
53	0.999999999718613	5.62774842786012e-10	2.81387421393006e-10
54	0.313195948051021	0.626391896102042	0.686804051948979
55	0.199142779352543	0.398285558705086	0.800857220647457
56	0.999999999973853	5.2294254431622e-11	2.6147127215811e-11
57	0.98413535257619	0.0317292948476209	0.0158646474238105
58	0.99999999999999	2.14518213266390e-14	1.07259106633195e-14
59	0.000756823711712867	0.00151364742342573	0.999243176288287
60	1	4.50893324638215e-22	2.25446662319107e-22
61	0.852177202015498	0.295645595969003	0.147822797984502
62	0.999999999999986	2.69318134736742e-14	1.34659067368371e-14
63	0.239401048834182	0.478802097668365	0.760598951165818
64	0.999997848932304	4.30213539193941e-06	2.15106769596971e-06
65	0.965937989032791	0.068124021934417	0.0340620109672085
66	0.792126943980917	0.415746112038166	0.207873056019083
67	4.55520669046499e-08	9.11041338092998e-08	0.999999954447933
68	0.318325234438146	0.636650468876293	0.681674765561854
69	0.297920548272017	0.595841096544033	0.702079451727983
70	0.888523355220326	0.222953289559347	0.111476644779674
71	9.99011669378782e-06	1.99802333875756e-05	0.999990009883306
72	0.98938086351133	0.0212382729773395	0.0106191364886698
73	6.93940152158611e-11	1.38788030431722e-10	0.999999999930606
74	0.963666804141513	0.0726663917169746	0.0363331958584873
75	1.64297341712738e-07	3.28594683425475e-07	0.999999835702658
76	0.947680781315557	0.104638437368885	0.0523192186844426

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	33	0.568965517241379	NOK
5% type I error level	37	0.637931034482759	NOK
10% type I error level	40	0.689655172413793	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/105drq1258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/105drq1258645334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/1p0k91258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/1p0k91258645334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/2qhxc1258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/2qhxc1258645334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/3ji0c1258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/3ji0c1258645334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/4ucep1258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/4ucep1258645334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/516b71258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/516b71258645334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/6pucb1258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/6pucb1258645334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/7wcr61258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/7wcr61258645334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/8isik1258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/8isik1258645334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/9sw251258645334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645477hdgdfhps4ksqs8d/9sw251258645334.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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