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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: Sun, 28 Nov 2010 12:07:22 +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/28/t1290946104n6ebykqtvhcxapx.htm/, Retrieved Sun, 28 Nov 2010 13:08:34 +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/28/t1290946104n6ebykqtvhcxapx.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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 5.97918507128879 -1.18678734507501X[t] + 0.0967005152517632M1[t] + 0.264549164414625M2[t] + 0.206996231711863M3[t] + 0.246094880874725M4[t] + 0.160193530037586M5[t] + 0.218265247879974M6[t] -0.273350388671451M7[t] -0.202108882365732M8[t] -0.125153090345727M9[t] -0.00676872689715154M10[t] -0.039812934877147M11[t] + 0.0259013508371385t + 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)	5.97918507128879	0.100936	59.2374	0	0
X	-1.18678734507501	0.09706	-12.2273	0	0
M1	0.0967005152517632	0.11965	0.8082	0.421533	0.210766
M2	0.264549164414625	0.119611	2.2117	0.03003	0.015015
M3	0.206996231711863	0.120002	1.7249	0.088657	0.044328
M4	0.246094880874725	0.11986	2.0532	0.043541	0.021771
M5	0.160193530037586	0.119746	1.3378	0.185013	0.092506
M6	0.218265247879974	0.123977	1.7605	0.082394	0.041197
M7	-0.273350388671451	0.123822	-2.2076	0.03033	0.015165
M8	-0.202108882365732	0.123695	-1.6339	0.106466	0.053233
M9	-0.125153090345727	0.123597	-1.0126	0.314511	0.157255
M10	-0.00676872689715154	0.123526	-0.0548	0.956447	0.478223
M11	-0.039812934877147	0.123484	-0.3224	0.748036	0.374018
t	0.0259013508371385	0.001867	13.8755	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.87528582129607
R-squared	0.766125268961935
Adjusted R-squared	0.72558698224867
F-TEST (value)	18.8988073023632
F-TEST (DF numerator)	13
F-TEST (DF denominator)	75
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.230990987570176
Sum Squared Residuals	4.00176272539838

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	5.81	6.10178693737768	-0.291786937377685
2	5.76	6.2955369373777	-0.535536937377696
3	5.99	6.26388535551207	-0.273885355512067
4	6.12	6.32888535551207	-0.208885355512069
5	6.03	6.26888535551207	-0.238885355512068
6	6.25	6.3528584241916	-0.102858424191595
7	5.8	5.88714413847731	-0.0871441384773088
8	5.67	5.98428699562017	-0.314286995620166
9	5.89	6.0871441384773	-0.197144138477309
10	5.91	6.23142985276302	-0.321429852763023
11	5.86	6.22428699562017	-0.364286995620166
12	6.07	6.29000128133445	-0.220001281334451
13	6.27	6.41260314742335	-0.142603147423354
14	6.68	6.60635314742335	0.0736468525766483
15	6.77	6.57470156555773	0.195298434442270
16	6.71	6.63970156555773	0.0702984344422701
17	6.62	6.57970156555773	0.0402984344422701
18	6.5	6.66367463423726	-0.163674634237257
19	5.89	6.19796034852297	-0.307960348522971
20	6.05	6.29510320566583	-0.245103205665828
21	6.43	6.39796034852297	0.0320396514770291
22	6.47	6.54224606280868	-0.0722460628086852
23	6.62	6.53510320566583	0.0848967943341721
24	6.77	6.60081749138011	0.169182508619886
25	6.7	6.72341935746902	-0.0234193574690154
26	6.95	6.91716935746901	0.0328306425309854
27	6.73	6.88551777560339	-0.155517775603392
28	7.07	6.95051777560339	0.119482224396608
29	7.28	6.89051777560339	0.389482224396608
30	7.32	6.97449084428292	0.345509155717082
31	6.76	6.50877655856863	0.251223441431367
32	6.93	6.60591941571149	0.32408058428851
33	6.99	6.70877655856863	0.281223441431368
34	7.16	6.85306227285435	0.306937727145653
35	7.28	6.84591941571149	0.43408058428851
36	7.08	6.91163370142578	0.168366298574225
37	7.34	7.03423556751468	0.305764432485322
38	7.87	7.22798556751468	0.642014432485324
39	6.28	6.00954664057404	0.270453359425962
40	6.3	6.07454664057404	0.225453359425962
41	6.36	6.01454664057404	0.345453359425962
42	6.28	6.09851970925356	0.181480290746436
43	5.89	5.63280542353928	0.257194576460721
44	6.04	5.72994828068214	0.310051719317864
45	5.96	5.83280542353928	0.127194576460721
46	6.1	5.97709113782499	0.122908862175006
47	6.26	5.96994828068214	0.290051719317864
48	6.02	6.03566256639642	-0.0156625663964219
49	6.25	6.15826443248532	0.0917355675146763
50	6.41	6.35201443248532	0.0579855675146774
51	6.22	6.3203628506197	-0.100362850619701
52	6.57	6.3853628506197	0.184637149380300
53	6.18	6.3253628506197	-0.145362850619700
54	6.26	6.40933591929923	-0.149335919299227
55	6.1	5.94362163358494	0.156378366415059
56	6.02	6.0407644907278	-0.0207644907277982
57	6.06	6.14362163358494	-0.0836216335849412
58	6.35	6.28790734787065	0.0620926521293446
59	6.21	6.2807644907278	-0.0707644907277982
60	6.48	6.34647877644208	0.133521223557917
61	6.74	6.46908064253099	0.270919357469015
62	6.53	6.66283064253098	-0.132830642530985
63	6.8	6.63117906066536	0.168820939334638
64	6.75	6.69617906066536	0.0538209393346381
65	6.56	6.63617906066536	-0.0761790606653625
66	6.66	6.72015212934489	-0.0601521293448885
67	6.18	6.2544378436306	-0.0744378436306029
68	6.4	6.35158070077346	0.0484192992265406
69	6.43	6.4544378436306	-0.024437843630603
70	6.54	6.59872355791632	-0.058723557916317
71	6.44	6.59158070077346	-0.151580700773460
72	6.64	6.65729498648775	-0.0172949864877457
73	6.82	6.77989685257665	0.0401031474233526
74	6.97	6.97364685257665	-0.00364685257664711
75	7	6.94199527071102	0.0580047292889757
76	6.91	7.00699527071102	-0.0969952707110238
77	6.74	6.94699527071102	-0.206995270711024
78	6.98	7.03096833939055	-0.0509683393905502
79	6.37	6.56525405367626	-0.195254053676265
80	6.56	6.66239691081912	-0.102396910819122
81	6.63	6.76525405367626	-0.135254053676265
82	6.87	6.90953976796198	-0.0395397679619792
83	6.68	6.90239691081912	-0.222396910819123
84	6.75	6.96811119653341	-0.218111196533408
85	6.84	7.09071306262231	-0.25071306262231
86	7.15	7.28446306262231	-0.134463062622308
87	7.09	7.25281148075669	-0.162811480756686
88	6.97	7.31781148075669	-0.347811480756686
89	7.15	7.25781148075669	-0.107811480756686

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.385637449645481	0.771274899290962	0.614362550354519
18	0.583079031760847	0.833841936478307	0.416920968239153
19	0.833399403558263	0.333201192883473	0.166600596441736
20	0.8462849044067	0.307430191186599	0.153715095593300
21	0.785267971505576	0.429464056988848	0.214732028494424
22	0.76058443423127	0.47883113153746	0.23941556576873
23	0.76335674619542	0.473286507609159	0.236643253804580
24	0.715337428591277	0.569325142817446	0.284662571408723
25	0.75106556205209	0.497868875895819	0.248934437947910
26	0.754989963745908	0.490020072508184	0.245010036254092
27	0.969377040728405	0.0612459185431905	0.0306229592715952
28	0.970547350651175	0.058905298697649	0.0294526493488245
29	0.970583869588648	0.0588322608227033	0.0294161304113517
30	0.966302598196265	0.0673948036074694	0.0336974018037347
31	0.960996816020258	0.0780063679594832	0.0390031839797416
32	0.969070238553024	0.0618595228939527	0.0309297614469764
33	0.954638558723287	0.0907228825534261	0.0453614412767131
34	0.94783617414587	0.104327651708259	0.0521638258541297
35	0.945178317999338	0.109643364001325	0.0548216820006623
36	0.953418747731906	0.0931625045361884	0.0465812522680942
37	0.967716175171105	0.0645676496577908	0.0322838248288954
38	0.974126393324358	0.0517472133512831	0.0258736066756415
39	0.961152880028214	0.0776942399435727	0.0388471199717864
40	0.945612630260004	0.108774739479993	0.0543873697399964
41	0.953425806830863	0.0931483863382737	0.0465741931691368
42	0.941558088695997	0.116883822608006	0.0584419113040031
43	0.927203333080677	0.145593333838646	0.072796666919323
44	0.924921609504019	0.150156780991962	0.0750783904959808
45	0.912766481094107	0.174467037811787	0.0872335189058935
46	0.885240864919014	0.229518270161972	0.114759135080986
47	0.924108146871936	0.151783706256128	0.075891853128064
48	0.937275044007091	0.125449911985817	0.0627249559929086
49	0.93007359908734	0.139852801825319	0.0699264009126596
50	0.932509288981083	0.134981422037834	0.0674907110189170
51	0.98657196767186	0.0268560646562792	0.0134280323281396
52	0.986368278791666	0.0272634424166684	0.0136317212083342
53	0.997096261597598	0.00580747680480373	0.00290373840240187
54	0.99932202199446	0.00135595601108217	0.000677978005541086
55	0.999140906404668	0.00171818719066318	0.00085909359533159
56	0.99918808288195	0.00162383423609846	0.000811917118049232
57	0.999462107769959	0.00107578446008221	0.000537892230041104
58	0.999011731254067	0.00197653749186556	0.00098826874593278
59	0.99868969845242	0.00262060309516149	0.00131030154758075
60	0.997519350492559	0.00496129901488252	0.00248064950744126
61	0.998287169106806	0.00342566178638724	0.00171283089319362
62	0.999567056963	0.000865886074000416	0.000432943037000208
63	0.998969624488628	0.00206075102274397	0.00103037551137199
64	0.99857561136644	0.0028487772671201	0.00142438863356005
65	0.997895598670163	0.00420880265967331	0.00210440132983665
66	0.997092182919878	0.00581563416024404	0.00290781708012202
67	0.99341099218761	0.0131780156247784	0.00658900781238919
68	0.984007004747502	0.0319859905049966	0.0159929952524983
69	0.964202014869226	0.0715959702615476	0.0357979851307738
70	0.95092934967838	0.0981413006432389	0.0490706503216194
71	0.9080886523838	0.183822695232400	0.0919113476162002
72	0.803786511821017	0.392426976357965	0.196213488178983

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	14	0.25	NOK
5% type I error level	18	0.321428571428571	NOK
10% type I error level	32	0.571428571428571	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/10ep691290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/10ep691290946034.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/1porf1290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/1porf1290946034.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/2porf1290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/2porf1290946034.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/3if9i1290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/3if9i1290946034.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/4if9i1290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/4if9i1290946034.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/5if9i1290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/5if9i1290946034.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/6b6ql1290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/6b6ql1290946034.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/7mgpo1290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/7mgpo1290946034.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/8mgpo1290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/8mgpo1290946034.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/9mgpo1290946034.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290946104n6ebykqtvhcxapx/9mgpo1290946034.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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