Home » date » 2008 » Nov » 27 »

Gilliam Schoorel

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 02:17:07 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t1227777511adl6bw44529gdyx.htm/, Retrieved Thu, 27 Nov 2008 09:18:41 +0000

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/2008/Nov/27/t1227777511adl6bw44529gdyx.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

-3.3 0 -3.5 0 -3.5 0 -8.4 0 -15.7 0 -18.7 0 -22.8 0 -20.7 0 -14 0 -6.3 0 0.7 0 0.2 0 0.8 0 1.2 0 4.5 0 0.4 0 5.9 0 6.5 0 12.8 0 4.2 0 -3.3 0 -12.5 0 -16.3 0 -10.5 0 -11.8 0 -11.4 0 -17.7 0 -17.3 0 -18.6 0 -17.9 0 -21.4 0 -19.4 0 -15.5 0 -7.7 0 -0.7 0 -1.6 0 1.4 0 0.7 0 9.5 0 1.4 0 4.1 0 6.6 0 18.4 0 16.9 0 9.2 0 -4.3 0 -5.9 0 -7.7 0 -5.4 0 -2.3 0 -4.8 0 2.3 0 -5.2 0 -10 0 -17.1 0 -14.4 0 -3.9 0 3.7 0 6.5 0 0.9 0 -4.1 0 -7 0 -12.2 0 -2.5 0 4.4 0 13.7 0 12.3 0 13.4 0 2.2 0 1.7 0 -7.2 0 -4.8 0 -2.9 0 -2.4 0 -2.5 0 -5.3 0 -7.1 0 -8 0 -8.9 1 -7.7 1 -1.1 1 4 1 9.6 1 10.9 1 13 1 14.9 1 20.1 1 10.8 1 11 1 3.8 1 10.8 1 7.6 1 10.2 1 2.2 1 -0.1 1 -1.7 1 -4.8 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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Registraties[t] = -8.47257091447925 + 4.60894520382243Dummies[t] + 0.527825756913914M1[t] + 2.16321715298218M2[t] + 2.46075725273174M3[t] + 0.858297352481301M4[t] + 0.430837452230866M5[t] -0.0216224480195812M6[t] + 0.312299501252179M7[t] -0.315160398998258M8[t] + 0.0698797007513081M9[t] -0.407580199499129M10[t] + 0.214959900250429M11[t] + 0.102459900250438t + 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)	-8.47257091447925	3.991445	-2.1227	0.03676	0.01838
Dummies	4.60894520382243	3.409189	1.3519	0.180074	0.090037
M1	0.527825756913914	4.665385	0.1131	0.910195	0.455098
M2	2.16321715298218	4.810446	0.4497	0.654105	0.327053
M3	2.46075725273174	4.808803	0.5117	0.610206	0.305103
M4	0.858297352481301	4.807641	0.1785	0.858744	0.429372
M5	0.430837452230866	4.806958	0.0896	0.928799	0.464399
M6	-0.0216224480195812	4.806755	-0.0045	0.996422	0.498211
M7	0.312299501252179	4.802737	0.065	0.94831	0.474155
M8	-0.315160398998258	4.800573	-0.0657	0.947814	0.473907
M9	0.0698797007513081	4.79889	0.0146	0.988417	0.494208
M10	-0.407580199499129	4.797687	-0.085	0.932503	0.466251
M11	0.214959900250429	4.796965	0.0448	0.964365	0.482183
t	0.102459900250438	0.048048	2.1325	0.035924	0.017962

Multiple Linear Regression - Regression Statistics
Multiple R	0.439101265216227
R-squared	0.192809921114492
Adjusted R-squared	0.0663825593613397
F-TEST (value)	1.52506481540721
F-TEST (DF numerator)	13
F-TEST (DF denominator)	83
p-value	0.125646640794542
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.5934489512914
Sum Squared Residuals	7638.84381082584

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	-3.3	-7.84228525731489	4.54228525731489
2	-3.5	-6.10443396099618	2.60443396099618
3	-3.5	-5.70443396099621	2.20443396099621
4	-8.4	-7.2044339609962	-1.19556603900380
5	-15.7	-7.52943396099619	-8.17056603900381
6	-18.7	-7.8794339609962	-10.8205660390038
7	-22.8	-7.44305211147397	-15.3569478885260
8	-20.7	-7.96805211147401	-12.731947888526
9	-14	-7.48055211147401	-6.51944788852599
10	-6.3	-7.855552111474	1.55555211147400
11	0.7	-7.13055211147399	7.83055211147399
12	0.2	-7.24305211147399	7.44305211147399
13	0.8	-6.61276645430964	7.41276645430964
14	1.2	-4.87491515799094	6.07491515799094
15	4.5	-4.47491515799094	8.97491515799094
16	0.4	-5.97491515799094	6.37491515799094
17	5.9	-6.29991515799095	12.1999151579910
18	6.5	-6.64991515799094	13.1499151579909
19	12.8	-6.21353330846875	19.0135333084687
20	4.2	-6.73853330846875	10.9385333084687
21	-3.3	-6.25103330846875	2.95103330846875
22	-12.5	-6.62603330846875	-5.87396669153125
23	-16.3	-5.90103330846874	-10.3989666915313
24	-10.5	-6.01353330846874	-4.48646669153126
25	-11.8	-5.38324765130438	-6.41675234869562
26	-11.4	-3.64539635498569	-7.75460364501431
27	-17.7	-3.24539635498568	-14.4546036450143
28	-17.3	-4.74539635498569	-12.5546036450143
29	-18.6	-5.0703963549857	-13.5296036450143
30	-17.9	-5.42039635498569	-12.4796036450143
31	-21.4	-4.98401450546349	-16.4159854945365
32	-19.4	-5.5090145054635	-13.8909854945365
33	-15.5	-5.02151450546349	-10.4784854945365
34	-7.7	-5.39651450546349	-2.30348549453651
35	-0.7	-4.67151450546349	3.97151450546349
36	-1.6	-4.78401450546348	3.18401450546348
37	1.4	-4.15372884829913	5.55372884829913
38	0.7	-2.41587755198043	3.11587755198043
39	9.5	-2.01587755198043	11.5158775519804
40	1.4	-3.51587755198043	4.91587755198043
41	4.1	-3.84087755198044	7.94087755198044
42	6.6	-4.19087755198043	10.7908775519804
43	18.4	-3.75449570245823	22.1544957024582
44	16.9	-4.27949570245823	21.1794957024582
45	9.2	-3.79199570245824	12.9919957024582
46	-4.3	-4.16699570245823	-0.133004297541769
47	-5.9	-3.44199570245823	-2.45800429754177
48	-7.7	-3.55449570245823	-4.14550429754177
49	-5.4	-2.92421004529387	-2.47578995470613
50	-2.3	-1.18635874897518	-1.11364125102482
51	-4.8	-0.786358748975176	-4.01364125102482
52	2.3	-2.28635874897518	4.58635874897518
53	-5.2	-2.61135874897518	-2.58864125102482
54	-10	-2.96135874897518	-7.03864125102482
55	-17.1	-2.52497689945298	-14.5750231005470
56	-14.4	-3.04997689945298	-11.3500231005470
57	-3.9	-2.56247689945298	-1.33752310054702
58	3.7	-2.93747689945298	6.63747689945298
59	6.5	-2.21247689945298	8.71247689945298
60	0.9	-2.32497689945297	3.22497689945297
61	-4.1	-1.69469124228862	-2.40530875771138
62	-7	0.0431600540300785	-7.04316005403008
63	-12.2	0.443160054030082	-12.6431600540301
64	-2.5	-1.05683994596992	-1.44316005403008
65	4.4	-1.38183994596993	5.78183994596993
66	13.7	-1.73183994596992	15.4318399459699
67	12.3	-1.29545809644772	13.5954580964477
68	13.4	-1.82045809644772	15.2204580964477
69	2.2	-1.33295809644773	3.53295809644773
70	1.7	-1.70795809644772	3.40795809644772
71	-7.2	-0.982958096447725	-6.21704190355227
72	-4.8	-1.09545809644772	-3.70454190355228
73	-2.9	-0.46517243928336	-2.43482756071664
74	-2.4	1.27267885703533	-3.67267885703533
75	-2.5	1.67267885703533	-4.17267885703533
76	-5.3	0.172678857035332	-5.47267885703533
77	-7.1	-0.152321142964672	-6.94767885703533
78	-8	-0.502321142964665	-7.49767885703533
79	-8.9	4.54300591037996	-13.4430059103800
80	-7.7	4.01800591037996	-11.7180059103800
81	-1.1	4.50550591037996	-5.60550591037996
82	4	4.13050591037996	-0.13050591037996
83	9.6	4.85550591037996	4.74449408962004
84	10.9	4.74300591037997	6.15699408962003
85	13	5.37329156754432	7.62670843245568
86	14.9	7.11114286386301	7.78885713613699
87	20.1	7.51114286386302	12.588857136137
88	10.8	6.01114286386302	4.78885713613699
89	11	5.68614286386301	5.31385713613699
90	3.8	5.33614286386302	-1.53614286386302
91	10.8	5.77252471338521	5.02747528661479
92	7.6	5.24752471338521	2.35247528661479
93	10.2	5.73502471338521	4.46497528661479
94	2.2	5.36002471338522	-3.16002471338521
95	-0.1	6.08502471338521	-6.18502471338521
96	-1.7	5.97252471338522	-7.67252471338522
97	-4.8	6.60281037054957	-11.4028103705496

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.210430800999811	0.420861601999623	0.789569199000189
18	0.292639237705333	0.585278475410666	0.707360762294667
19	0.563971576165869	0.872056847668262	0.436028423834131
20	0.499551367112933	0.999102734225866	0.500448632887067
21	0.399685642649998	0.799371285299997	0.600314357350002
22	0.540900143242773	0.918199713514455	0.459099856757227
23	0.785148487555284	0.429703024889432	0.214851512444716
24	0.821012456602112	0.357975086795776	0.178987543397888
25	0.878257452566772	0.243485094866455	0.121742547433228
26	0.890442845646223	0.219114308707554	0.109557154353777
27	0.931074715843753	0.137850568312493	0.0689252841562465
28	0.934422293862173	0.131155412275654	0.0655777061378271
29	0.938515013206029	0.122969973587942	0.0614849867939712
30	0.937568245767423	0.124863508465154	0.062431754232577
31	0.95486357713845	0.0902728457231023	0.0451364228615512
32	0.9620262857524	0.0759474284952009	0.0379737142476005
33	0.963231990779037	0.073536018441926	0.036768009220963
34	0.953947145609415	0.0921057087811692	0.0460528543905846
35	0.943203206937695	0.113593586124610	0.0567967930623049
36	0.923977945544072	0.152044108911857	0.0760220544559283
37	0.906497998295164	0.187004003409672	0.0935020017048362
38	0.881816220386316	0.236367559227368	0.118183779613684
39	0.891485901488382	0.217028197023235	0.108514098511618
40	0.86940915349262	0.261181693014760	0.130590846507380
41	0.854765360586481	0.290469278827038	0.145234639413519
42	0.851865303468436	0.296269393063127	0.148134696531564
43	0.943422335040807	0.113155329918386	0.056577664959193
44	0.980682319955764	0.0386353600884716	0.0193176800442358
45	0.983516403279787	0.032967193440426	0.016483596720213
46	0.97514734068241	0.0497053186351786	0.0248526593175893
47	0.965273022977302	0.0694539540453964	0.0347269770226982
48	0.95499057626217	0.0900188474756603	0.0450094237378302
49	0.940793445062721	0.118413109874557	0.0592065549372786
50	0.919447322824902	0.161105354350196	0.0805526771750982
51	0.897852824686155	0.20429435062769	0.102147175313845
52	0.86915150508074	0.261696989838519	0.130848494919260
53	0.833575684119522	0.332848631760957	0.166424315880478
54	0.818857097832147	0.362285804335706	0.181142902167853
55	0.873079873358184	0.253840253283632	0.126920126641816
56	0.896959682766456	0.206080634467088	0.103040317233544
57	0.867267722598067	0.265464554803866	0.132732277401933
58	0.833255293743737	0.333489412512527	0.166744706256263
59	0.810417107869848	0.379165784260304	0.189582892130152
60	0.758938954475479	0.482122091049042	0.241061045524521
61	0.702768042466311	0.594463915067378	0.297231957533689
62	0.685645614639115	0.628708770721771	0.314354385360885
63	0.776853635372487	0.446292729255026	0.223146364627513
64	0.727212166891746	0.545575666216509	0.272787833108254
65	0.664542896590264	0.670914206819472	0.335457103409736
66	0.717386241636148	0.565227516727703	0.282613758363852
67	0.793576813277175	0.412846373445649	0.206423186722825
68	0.91633887840632	0.167322243187362	0.0836611215936808
69	0.89991572233694	0.200168555326121	0.100084277663060
70	0.896240684814807	0.207518630370385	0.103759315185193
71	0.852740614724348	0.294518770551303	0.147259385275652
72	0.803082499845988	0.393835000308025	0.196917500154012
73	0.772605329797579	0.454789340404843	0.227394670202421
74	0.686179140305426	0.627641719389148	0.313820859694574
75	0.603764774644179	0.792470450711643	0.396235225355821
76	0.493661355117639	0.987322710235278	0.506338644882361
77	0.389032610395924	0.778065220791848	0.610967389604076
78	0.278552113940037	0.557104227880074	0.721447886059963
79	0.358160842449209	0.716321684898417	0.641839157550791
80	0.464650944162313	0.929301888324626	0.535349055837687

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	3	0.046875	OK
10% type I error level	9	0.140625	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777511adl6bw44529gdyx/10k3u61227777421.png (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777511adl6bw44529gdyx/14odg1227777421.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777511adl6bw44529gdyx/351cn1227777421.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777511adl6bw44529gdyx/51rnc1227777421.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777511adl6bw44529gdyx/6ysin1227777421.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777511adl6bw44529gdyx/8hu2y1227777421.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777511adl6bw44529gdyx/99lif1227777421.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777511adl6bw44529gdyx/99lif1227777421.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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