Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2009 » Nov » 19 »

*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 01:54:58 -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/t1258622313rtnaskvoz02b12n.htm/, Retrieved Thu, 19 Nov 2009 10:18:45 +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/t1258622313rtnaskvoz02b12n.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 «

8.4 420 8.4 418 8.4 410 8.6 418 8.9 426 8.8 428 8.3 430 7.5 424 7.2 423 7.4 427 8.8 441 9.3 449 9.3 452 8.7 462 8.2 455 8.3 461 8.5 461 8.6 463 8.5 462 8.2 456 8.1 455 7.9 456 8.6 472 8.7 472 8.7 471 8.5 465 8.4 459 8.5 465 8.7 468 8.7 467 8.6 463 8.5 460 8.3 462 8,00 461 8.2 476 8.1 476 8.1 471 8,00 453 7.9 443 7.9 442 8,00 444 8,00 438 7.9 427 8,00 424 7.7 416 7.2 406 7.5 431 7.3 434 7,00 418 7,00 412 7,00 404 7.2 409 7.3 412 7.1 406 6.8 398 6.4 397 6.1 385 6.5 390 7.7 413 7.9 413 7.5 401 6.9 397 6.6 397 6.9 409 7.7 419 8,00 424 8,00 428 7.7 430 7.3 424 7.4 433 8.1 456 8.3 459 8.2 446

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

wgb[t] = -1.18542050343351 + 0.0208973933596284nwwz[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)	-1.18542050343351	0.972694	-1.2187	0.226993	0.113496
nwwz	0.0208973933596284	0.002226	9.3863	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.744142345521879
R-squared	0.553747830398803
Adjusted R-squared	0.547462588573434
F-TEST (value)	88.1028679220781
F-TEST (DF numerator)	1
F-TEST (DF denominator)	71
p-value	4.55191440096314e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.467505192541123
Sum Squared Residuals	15.5178384587568

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8.4	7.5914847076104	0.808515292389594
2	8.4	7.54968992089115	0.850310079108852
3	8.4	7.38251077401412	1.01748922598588
4	8.6	7.54968992089115	1.05031007910885
5	8.9	7.71686906776818	1.18313093223182
6	8.8	7.75866385448743	1.04133614551257
7	8.3	7.80045864120669	0.499541358793311
8	7.5	7.67507428104892	-0.175074281048919
9	7.2	7.65417688768929	-0.454176887689291
10	7.4	7.7377664611278	-0.337766461127804
11	8.8	8.0303299681626	0.769670031837399
12	9.3	8.19750911503963	1.10249088496037
13	9.3	8.26020129511851	1.03979870488149
14	8.7	8.4691752287148	0.230824771285202
15	8.2	8.3228934751974	-0.122893475197400
16	8.3	8.44827783535517	-0.148277835355169
17	8.5	8.44827783535517	0.0517221646448307
18	8.6	8.49007262207443	0.109927377925574
19	8.5	8.4691752287148	0.0308247712852023
20	8.2	8.34379086855703	-0.143790868557028
21	8.1	8.3228934751974	-0.222893475197399
22	7.9	8.34379086855703	-0.443790868557027
23	8.6	8.67814916231108	-0.0781491623110818
24	8.7	8.67814916231108	0.0218508376889179
25	8.7	8.65725176895145	0.0427482310485462
26	8.5	8.53186740879368	-0.0318674087936828
27	8.4	8.40648304863591	-0.00648304863591216
28	8.5	8.53186740879368	-0.0318674087936828
29	8.7	8.59455958887257	0.105440411127431
30	8.7	8.57366219551294	0.126337804487060
31	8.6	8.49007262207443	0.109927377925574
32	8.5	8.42738044199554	0.0726195580044591
33	8.3	8.4691752287148	-0.169175228714797
34	8	8.44827783535517	-0.448277835355169
35	8.2	8.7617387357496	-0.561738735749596
36	8.1	8.7617387357496	-0.661738735749595
37	8.1	8.65725176895145	-0.557251768951453
38	8	8.28109868847814	-0.281098688478142
39	7.9	8.07212475488186	-0.172124754881858
40	7.9	8.05122736152223	-0.151227361522230
41	8	8.09302214824149	-0.0930221482414869
42	8	7.96763778808372	0.0323622119162833
43	7.9	7.7377664611278	0.162233538872196
44	8	7.67507428104892	0.324925718951081
45	7.7	7.50789513417189	0.192104865828108
46	7.2	7.29892120057561	-0.0989212005756086
47	7.5	7.82135603456632	-0.321356034566318
48	7.3	7.8840482146452	-0.584048214645203
49	7	7.54968992089115	-0.549689920891149
50	7	7.42430556073338	-0.424305560733379
51	7	7.25712641385635	-0.257126413856352
52	7.2	7.36161338065449	-0.161613380654494
53	7.3	7.42430556073338	-0.124305560733379
54	7.1	7.29892120057561	-0.198921200575609
55	6.8	7.13174205369858	-0.331742053698582
56	6.4	7.11084466033895	-0.710844660338953
57	6.1	6.86007594002341	-0.760075940023413
58	6.5	6.96456290682155	-0.464562906821555
59	7.7	7.44520295409301	0.254797045906993
60	7.9	7.44520295409301	0.454797045906993
61	7.5	7.19443423377747	0.305565766222533
62	6.9	7.11084466033895	-0.210844660338953
63	6.6	7.11084466033895	-0.510844660338954
64	6.9	7.36161338065449	-0.461613380654494
65	7.7	7.57058731425078	0.129412685749223
66	8	7.67507428104892	0.324925718951081
67	8	7.75866385448743	0.241336145512567
68	7.7	7.80045864120669	-0.100458641206690
69	7.3	7.67507428104892	-0.375074281048920
70	7.4	7.86315082128557	-0.463150821285574
71	8.1	8.34379086855703	-0.243790868557028
72	8.3	8.40648304863591	-0.106483048635912
73	8.2	8.13481693496074	0.0651830650392556

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0968184860347128	0.193636972069426	0.903181513965287
6	0.0427718970120103	0.0855437940240205	0.95722810298799
7	0.171309887803864	0.342619775607729	0.828690112196136
8	0.834986540128599	0.330026919742803	0.165013459871402
9	0.985507843693706	0.0289843126125881	0.0144921563062940
10	0.993095196246727	0.0138096075065466	0.0069048037532733
11	0.996598052692674	0.00680389461465278	0.00340194730732639
12	0.999431699439248	0.00113660112150379	0.000568300560751897
13	0.999909689156265	0.000180621687469325	9.03108437346625e-05
14	0.999927112490073	0.000145775019854041	7.28875099270207e-05
15	0.999947783452897	0.000104433094205361	5.22165471026804e-05
16	0.999939177465678	0.000121645068643236	6.08225343216179e-05
17	0.999891192978769	0.000217614042462660	0.000108807021231330
18	0.999806499805139	0.000387000389722928	0.000193500194861464
19	0.99965826165532	0.000683476689359848	0.000341738344679924
20	0.999507975981552	0.000984048036895151	0.000492024018447575
21	0.999364176053686	0.00127164789262784	0.00063582394631392
22	0.999507804195266	0.000984391609468522	0.000492195804734261
23	0.99908390013399	0.00183219973201906	0.000916099866009532
24	0.998433180419851	0.00313363916029738	0.00156681958014869
25	0.997436717258778	0.00512656548244331	0.00256328274122165
26	0.995747788227375	0.00850442354525082	0.00425221177262541
27	0.993333468805535	0.0133330623889307	0.00666653119446535
28	0.989554520968785	0.0208909580624298	0.0104454790312149
29	0.98574704544522	0.0285059091095583	0.0142529545547792
30	0.981624880357753	0.0367502392844948	0.0183751196422474
31	0.976419545872063	0.0471609082558749	0.0235804541279375
32	0.96957417334001	0.0608516533199805	0.0304258266599903
33	0.958480205212431	0.0830395895751371	0.0415197947875686
34	0.957591484621636	0.0848170307567284	0.0424085153783642
35	0.956957850646827	0.0860842987063463	0.0430421493531731
36	0.968822567755763	0.0623548644884739	0.0311774322442369
37	0.977141167799987	0.0457176644000256	0.0228588322000128
38	0.974483968363997	0.0510320632720057	0.0255160316360029
39	0.96934268702246	0.0613146259550811	0.0306573129775405
40	0.962307695350653	0.0753846092986934	0.0376923046493467
41	0.950183258981895	0.0996334820362092	0.0498167410181046
42	0.933993999273074	0.132012001453851	0.0660060007269256
43	0.924011149949278	0.151977700101444	0.0759888500507219
44	0.929120999513979	0.141758000972042	0.0708790004860211
45	0.932138475357271	0.135723049285458	0.0678615246427292
46	0.936248549515459	0.127502900969083	0.0637514504845413
47	0.9323374339531	0.135325132093798	0.0676625660468992
48	0.959089587616418	0.0818208247671647	0.0409104123835823
49	0.972765691898009	0.0544686162039823	0.0272343081019912
50	0.973360011751966	0.0532799764960676	0.0266399882480338
51	0.965458846400301	0.0690823071993977	0.0345411535996988
52	0.951301855852258	0.097396288295484	0.048698144147742
53	0.93077706058034	0.13844587883932	0.06922293941966
54	0.905101606805026	0.189796786389947	0.0948983931949737
55	0.877962368422158	0.244075263155684	0.122037631577842
56	0.906809097179763	0.186381805640474	0.0931909028202371
57	0.942737331821574	0.114525336356851	0.0572626681784256
58	0.94155904005111	0.116881919897782	0.0584409599488909
59	0.927073787605636	0.145852424788729	0.0729262123943645
60	0.951510159391525	0.0969796812169507	0.0484898406084754
61	0.964924745065402	0.0701505098691968	0.0350752549345984
62	0.937728184279702	0.124543631440595	0.0622718157202977
63	0.926013732336915	0.147972535326170	0.0739862676630851
64	0.939466616061288	0.121066767877423	0.0605333839387117
65	0.892320143008665	0.215359713982669	0.107679856991335
66	0.907254289539447	0.185491420921106	0.0927457104605532
67	0.955698676105848	0.0886026477883049	0.0443013238941524
68	0.921971536094029	0.156056927811943	0.0780284639059714

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	16	0.25	NOK
5% type I error level	24	0.375	NOK
10% type I error level	42	0.65625	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/10dtc61258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/10dtc61258620892.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/165801258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/165801258620892.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/2f8yj1258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/2f8yj1258620892.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/35je01258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/35je01258620892.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/4kdle1258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/4kdle1258620892.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/5a88r1258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/5a88r1258620892.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/6lvmk1258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/6lvmk1258620892.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/7z1pe1258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/7z1pe1258620892.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/8zcqb1258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/8zcqb1258620892.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/9d7x81258620892.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258622313rtnaskvoz02b12n/9d7x81258620892.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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by