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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: Sat, 27 Nov 2010 17:19:25 +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/27/t1290878430bbhdiezwcxlc4z1.htm/, Retrieved Sat, 27 Nov 2010 18:20:30 +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/27/t1290878430bbhdiezwcxlc4z1.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 «

5.81 0 5.76 0 5.99 0 6.12 0 6.03 0 6.25 0 5.80 0 5.67 0 5.89 0 5.91 0 5.86 0 6.07 0 6.27 0 6.68 0 6.77 0 6.71 0 6.62 0 6.50 0 5.89 0 6.05 0 6.43 0 6.47 0 6.62 0 6.77 0 6.70 0 6.95 0 6.73 0 7.07 0 7.28 0 7.32 0 6.76 0 6.93 0 6.99 0 7.16 0 7.28 0 7.08 0 7.34 0 7.87 0 6.28 1 6.30 1 6.36 1 6.28 1 5.89 1 6.04 1 5.96 1 6.10 1 6.26 1 6.02 1 6.25 1 6.41 1 6.22 1 6.57 1 6.18 1 6.26 1 6.10 1 6.02 1 6.06 1 6.35 1 6.21 1 6.48 1 6.74 1 6.53 1 6.80 1 6.75 1 6.56 1 6.66 1 6.18 1 6.40 1 6.43 1 6.54 1 6.44 1 6.64 1 6.82 1 6.97 1 7.00 1 6.91 1 6.74 1 6.98 1 6.37 1 6.56 1 6.63 1 6.87 1 6.68 1 6.75 1 6.84 1 7.15 1 7.09 1 6.97 1 7.15 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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 6.53684210526316 -0.0319401444788443X[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)	6.53684210526316	0.071895	90.9217	0	0
X	-0.0319401444788443	0.094975	-0.3363	0.737455	0.368727

Multiple Linear Regression - Regression Statistics
Multiple R	0.0360316991397718
R-squared	0.00129828334289903
Adjusted R-squared	-0.0101810467336194
F-TEST (value)	0.113097483411052
F-TEST (DF numerator)	1
F-TEST (DF denominator)	87
p-value	0.737454668526929
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.443192383986229
Sum Squared Residuals	17.0884955624355

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	5.81	6.53684210526316	-0.726842105263157
2	5.76	6.53684210526316	-0.776842105263158
3	5.99	6.53684210526316	-0.546842105263158
4	6.12	6.53684210526316	-0.416842105263158
5	6.03	6.53684210526316	-0.506842105263158
6	6.25	6.53684210526316	-0.286842105263158
7	5.8	6.53684210526316	-0.736842105263158
8	5.67	6.53684210526316	-0.866842105263158
9	5.89	6.53684210526316	-0.646842105263158
10	5.91	6.53684210526316	-0.626842105263158
11	5.86	6.53684210526316	-0.676842105263158
12	6.07	6.53684210526316	-0.466842105263158
13	6.27	6.53684210526316	-0.266842105263158
14	6.68	6.53684210526316	0.143157894736842
15	6.77	6.53684210526316	0.233157894736842
16	6.71	6.53684210526316	0.173157894736842
17	6.62	6.53684210526316	0.0831578947368422
18	6.5	6.53684210526316	-0.0368421052631579
19	5.89	6.53684210526316	-0.646842105263158
20	6.05	6.53684210526316	-0.486842105263158
21	6.43	6.53684210526316	-0.106842105263158
22	6.47	6.53684210526316	-0.0668421052631582
23	6.62	6.53684210526316	0.0831578947368422
24	6.77	6.53684210526316	0.233157894736842
25	6.7	6.53684210526316	0.163157894736842
26	6.95	6.53684210526316	0.413157894736842
27	6.73	6.53684210526316	0.193157894736843
28	7.07	6.53684210526316	0.533157894736842
29	7.28	6.53684210526316	0.743157894736842
30	7.32	6.53684210526316	0.783157894736842
31	6.76	6.53684210526316	0.223157894736842
32	6.93	6.53684210526316	0.393157894736842
33	6.99	6.53684210526316	0.453157894736842
34	7.16	6.53684210526316	0.623157894736842
35	7.28	6.53684210526316	0.743157894736842
36	7.08	6.53684210526316	0.543157894736842
37	7.34	6.53684210526316	0.803157894736842
38	7.87	6.53684210526316	1.33315789473684
39	6.28	6.50490196078431	-0.224901960784313
40	6.3	6.50490196078431	-0.204901960784314
41	6.36	6.50490196078431	-0.144901960784313
42	6.28	6.50490196078431	-0.224901960784313
43	5.89	6.50490196078431	-0.614901960784314
44	6.04	6.50490196078431	-0.464901960784314
45	5.96	6.50490196078431	-0.544901960784314
46	6.1	6.50490196078431	-0.404901960784314
47	6.26	6.50490196078431	-0.244901960784314
48	6.02	6.50490196078431	-0.484901960784314
49	6.25	6.50490196078431	-0.254901960784314
50	6.41	6.50490196078431	-0.0949019607843136
51	6.22	6.50490196078431	-0.284901960784314
52	6.57	6.50490196078431	0.0650980392156866
53	6.18	6.50490196078431	-0.324901960784314
54	6.26	6.50490196078431	-0.244901960784314
55	6.1	6.50490196078431	-0.404901960784314
56	6.02	6.50490196078431	-0.484901960784314
57	6.06	6.50490196078431	-0.444901960784314
58	6.35	6.50490196078431	-0.154901960784314
59	6.21	6.50490196078431	-0.294901960784314
60	6.48	6.50490196078431	-0.0249019607843133
61	6.74	6.50490196078431	0.235098039215687
62	6.53	6.50490196078431	0.0250980392156865
63	6.8	6.50490196078431	0.295098039215686
64	6.75	6.50490196078431	0.245098039215686
65	6.56	6.50490196078431	0.0550980392156859
66	6.66	6.50490196078431	0.155098039215686
67	6.18	6.50490196078431	-0.324901960784314
68	6.4	6.50490196078431	-0.104901960784313
69	6.43	6.50490196078431	-0.074901960784314
70	6.54	6.50490196078431	0.0350980392156863
71	6.44	6.50490196078431	-0.0649019607843133
72	6.64	6.50490196078431	0.135098039215686
73	6.82	6.50490196078431	0.315098039215687
74	6.97	6.50490196078431	0.465098039215686
75	7	6.50490196078431	0.495098039215686
76	6.91	6.50490196078431	0.405098039215686
77	6.74	6.50490196078431	0.235098039215687
78	6.98	6.50490196078431	0.475098039215687
79	6.37	6.50490196078431	-0.134901960784314
80	6.56	6.50490196078431	0.0550980392156859
81	6.63	6.50490196078431	0.125098039215686
82	6.87	6.50490196078431	0.365098039215686
83	6.68	6.50490196078431	0.175098039215686
84	6.75	6.50490196078431	0.245098039215686
85	6.84	6.50490196078431	0.335098039215686
86	7.15	6.50490196078431	0.645098039215687
87	7.09	6.50490196078431	0.585098039215686
88	6.97	6.50490196078431	0.465098039215686
89	7.15	6.50490196078431	0.645098039215687

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.082726646027585	0.16545329205517	0.917273353972415
6	0.0816815342224834	0.163363068444967	0.918318465777517
7	0.0520965014811969	0.104193002962394	0.947903498518803
8	0.0561938681166178	0.112387736233236	0.943806131883382
9	0.0302483361036129	0.0604966722072257	0.969751663896387
10	0.0161181359241503	0.0322362718483005	0.98388186407585
11	0.00948631065859355	0.0189726213171871	0.990513689341406
12	0.00662156860069499	0.0132431372013900	0.993378431399305
13	0.0117285083563026	0.0234570167126053	0.988271491643697
14	0.124368904805415	0.248737809610830	0.875631095194585
15	0.34215351498811	0.68430702997622	0.65784648501189
16	0.474885404335599	0.949770808671199	0.525114595664401
17	0.522060298835231	0.955879402329538	0.477939701164769
18	0.514904837432325	0.97019032513535	0.485095162567675
19	0.580421981382657	0.839156037234686	0.419578018617343
20	0.612019236592585	0.77596152681483	0.387980763407415
21	0.621725078018879	0.756549843962243	0.378274921981121
22	0.640532866525422	0.718934266949157	0.359467133474578
23	0.684360510672358	0.631278978655285	0.315639489327642
24	0.753363795706487	0.493272408587026	0.246636204293513
25	0.790932149946987	0.418135700106026	0.209067850053013
26	0.862575256368941	0.274849487262117	0.137424743631059
27	0.882517834376048	0.234964331247904	0.117482165623952
28	0.930318871572774	0.139362256854452	0.0696811284272261
29	0.971327877368074	0.0573442452638524	0.0286721226319262
30	0.98784058185563	0.0243188362887415	0.0121594181443707
31	0.988267061795046	0.0234658764099078	0.0117329382049539
32	0.989378455242145	0.0212430895157102	0.0106215447578551
33	0.990796279915993	0.0184074401680149	0.00920372008400743
34	0.992961220231037	0.0140775595379254	0.00703877976896269
35	0.995095594317465	0.00980881136507082	0.00490440568253541
36	0.996151972369746	0.00769605526050745	0.00384802763025372
37	0.99774428307579	0.00451143384842001	0.00225571692421000
38	0.999403559618137	0.00119288076372547	0.000596440381862735
39	0.999075382804122	0.00184923439175644	0.000924617195878222
40	0.998571451294942	0.00285709741011708	0.00142854870505854
41	0.997753150879026	0.00449369824194862	0.00224684912097431
42	0.996715937789212	0.00656812442157635	0.00328406221078817
43	0.997746089464362	0.00450782107127520	0.00225391053563760
44	0.997797998979804	0.00440400204039208	0.00220200102019604
45	0.998330452511058	0.00333909497788402	0.00166954748894201
46	0.99829430513212	0.00341138973576037	0.00170569486788018
47	0.997764690894823	0.00447061821035302	0.00223530910517651
48	0.99828845052382	0.00342309895235938	0.00171154947617969
49	0.997899299995702	0.00420140000859641	0.00210070000429821
50	0.996980807833463	0.00603838433307338	0.00301919216653669
51	0.99660945155136	0.00678109689728169	0.00339054844864085
52	0.995071233113774	0.00985753377245269	0.00492876688622634
53	0.995078151190482	0.00984369761903671	0.00492184880951836
54	0.994432093365206	0.0111358132695870	0.00556790663479351
55	0.99597589934871	0.00804820130257975	0.00402410065128987
56	0.998201440011136	0.00359711997772764	0.00179855998886382
57	0.999297357874725	0.00140528425055060	0.000702642125275302
58	0.999257799058834	0.00148440188233234	0.000742200941166172
59	0.999586817954112	0.00082636409177601	0.000413182045888005
60	0.999454037395028	0.00109192520994344	0.00054596260497172
61	0.99914632187857	0.00170735624286077	0.000853678121430383
62	0.998768305771746	0.00246338845650779	0.00123169422825389
63	0.998148972177748	0.00370205564450448	0.00185102782225224
64	0.997054648363902	0.00589070327219638	0.00294535163609819
65	0.995586096437981	0.00882780712403764	0.00441390356201882
66	0.992953848772144	0.0140923024557114	0.00704615122785568
67	0.997255043340204	0.00548991331959303	0.00274495665979651
68	0.99763852558248	0.00472294883503961	0.00236147441751981
69	0.997971745790503	0.00405650841899473	0.00202825420949737
70	0.997560337953901	0.00487932409219771	0.00243966204609885
71	0.998322058886483	0.00335588222703449	0.00167794111351724
72	0.99756278025953	0.00487443948093884	0.00243721974046942
73	0.995433282957616	0.0091334340847683	0.00456671704238415
74	0.992694441252595	0.0146111174948098	0.00730555874740492
75	0.989116091706797	0.0217678165864054	0.0108839082932027
76	0.980984388512728	0.0380312229745449	0.0190156114872724
77	0.966581693955926	0.0668366120881488	0.0334183060440744
78	0.948813710813888	0.102372578372223	0.0511862891861116
79	0.974003500591687	0.0519929988166252	0.0259964994083126
80	0.976087844659074	0.0478243106818517	0.0239121553409258
81	0.975561974706296	0.0488760505874086	0.0244380252937043
82	0.945726570279132	0.108546859441736	0.0542734297208681
83	0.944500812623313	0.110998374753375	0.0554991873766875
84	0.943948147254714	0.112103705490573	0.0560518527452864

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	37	0.4625	NOK
5% type I error level	53	0.6625	NOK
10% type I error level	57	0.7125	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/103r21290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/103r21290878356.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/10wdos1290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/10wdos1290878356.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/203r21290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/203r21290878356.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/303r21290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/303r21290878356.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/4td841290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/4td841290878356.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/5td841290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/5td841290878356.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/6td841290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/6td841290878356.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/74m7q1290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/74m7q1290878356.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/8wdos1290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/8wdos1290878356.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/9wdos1290878356.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290878430bbhdiezwcxlc4z1/9wdos1290878356.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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