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multiple regression

*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: Tue, 16 Dec 2008 12:38:07 -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/Dec/16/t1229456387i4scdjv4tx4rvh6.htm/, Retrieved Tue, 16 Dec 2008 20:39:57 +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/2008/Dec/16/t1229456387i4scdjv4tx4rvh6.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},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

524 0 552 0 532 0 511 0 492 0 492 0 493 0 481 0 462 0 457 0 442 0 439 0 488 0 521 0 501 0 485 0 464 0 460 0 467 0 460 0 448 0 443 0 436 0 431 0 484 0 510 0 513 0 503 0 471 0 471 0 476 0 475 0 470 0 461 0 455 0 456 0 517 0 525 0 523 0 519 0 509 0 512 0 519 0 517 0 510 0 509 0 501 0 507 0 569 1 580 1 578 1 565 1 547 1 555 1 562 1 561 1 555 1 544 1 537 1 543 1 594 1 611 1 613 1 611 1 594 1 595 1 591 1 589 1 584 1 573 1 567 1 569 1 621 1 629 1 628 1 612 1 595 1 597 1 593 1 590 1 580 1 574 1 573 1 573 1 620 1 626 1 620 1 588 1 566 1 557 1 561 1 549 1 532 1 526 1 511 1 499 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	4 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Aantal_werklozen_(*1000)[t] = + 487.375 + 89.8541666666667dummyvariabele[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)	487.375	4.355789	111.8913	0	0
dummyvariabele	89.8541666666667	6.160016	14.5867	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.832816091460077
R-squared	0.693582642194839
Adjusted R-squared	0.690322883069252
F-TEST (value)	212.771132919209
F-TEST (DF numerator)	1
F-TEST (DF denominator)	94
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	30.1777939295420
Sum Squared Residuals	85605.7291666666

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	524	487.375000000001	36.6249999999989
2	552	487.375	64.625
3	532	487.375	44.625
4	511	487.375	23.6250000000000
5	492	487.375	4.62500000000003
6	492	487.375	4.62500000000003
7	493	487.375	5.62500000000003
8	481	487.375	-6.37499999999997
9	462	487.375	-25.3750000000000
10	457	487.375	-30.375
11	442	487.375	-45.375
12	439	487.375	-48.375
13	488	487.375	0.625000000000026
14	521	487.375	33.625
15	501	487.375	13.6250000000000
16	485	487.375	-2.37499999999997
17	464	487.375	-23.3750000000000
18	460	487.375	-27.3750000000000
19	467	487.375	-20.3750000000000
20	460	487.375	-27.3750000000000
21	448	487.375	-39.375
22	443	487.375	-44.375
23	436	487.375	-51.375
24	431	487.375	-56.375
25	484	487.375	-3.37499999999997
26	510	487.375	22.6250000000000
27	513	487.375	25.6250000000000
28	503	487.375	15.6250000000000
29	471	487.375	-16.3750000000000
30	471	487.375	-16.3750000000000
31	476	487.375	-11.3750000000000
32	475	487.375	-12.3750000000000
33	470	487.375	-17.3750000000000
34	461	487.375	-26.3750000000000
35	455	487.375	-32.375
36	456	487.375	-31.375
37	517	487.375	29.625
38	525	487.375	37.625
39	523	487.375	35.625
40	519	487.375	31.625
41	509	487.375	21.6250000000000
42	512	487.375	24.6250000000000
43	519	487.375	31.625
44	517	487.375	29.625
45	510	487.375	22.6250000000000
46	509	487.375	21.6250000000000
47	501	487.375	13.6250000000000
48	507	487.375	19.6250000000000
49	569	577.229166666667	-8.22916666666667
50	580	577.229166666667	2.77083333333333
51	578	577.229166666667	0.770833333333333
52	565	577.229166666667	-12.2291666666667
53	547	577.229166666667	-30.2291666666667
54	555	577.229166666667	-22.2291666666667
55	562	577.229166666667	-15.2291666666667
56	561	577.229166666667	-16.2291666666667
57	555	577.229166666667	-22.2291666666667
58	544	577.229166666667	-33.2291666666667
59	537	577.229166666667	-40.2291666666667
60	543	577.229166666667	-34.2291666666667
61	594	577.229166666667	16.7708333333333
62	611	577.229166666667	33.7708333333333
63	613	577.229166666667	35.7708333333333
64	611	577.229166666667	33.7708333333333
65	594	577.229166666667	16.7708333333333
66	595	577.229166666667	17.7708333333333
67	591	577.229166666667	13.7708333333333
68	589	577.229166666667	11.7708333333333
69	584	577.229166666667	6.77083333333333
70	573	577.229166666667	-4.22916666666667
71	567	577.229166666667	-10.2291666666667
72	569	577.229166666667	-8.22916666666667
73	621	577.229166666667	43.7708333333333
74	629	577.229166666667	51.7708333333333
75	628	577.229166666667	50.7708333333333
76	612	577.229166666667	34.7708333333333
77	595	577.229166666667	17.7708333333333
78	597	577.229166666667	19.7708333333333
79	593	577.229166666667	15.7708333333333
80	590	577.229166666667	12.7708333333333
81	580	577.229166666667	2.77083333333333
82	574	577.229166666667	-3.22916666666667
83	573	577.229166666667	-4.22916666666667
84	573	577.229166666667	-4.22916666666667
85	620	577.229166666667	42.7708333333333
86	626	577.229166666667	48.7708333333333
87	620	577.229166666667	42.7708333333333
88	588	577.229166666667	10.7708333333333
89	566	577.229166666667	-11.2291666666667
90	557	577.229166666667	-20.2291666666667
91	561	577.229166666667	-16.2291666666667
92	549	577.229166666667	-28.2291666666667
93	532	577.229166666667	-45.2291666666667
94	526	577.229166666667	-51.2291666666667
95	511	577.229166666667	-66.2291666666667
96	499	577.229166666667	-78.2291666666667

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.492938873975067	0.985877747950134	0.507061126024933
6	0.470602681883544	0.941205363767088	0.529397318116456
7	0.408775865233123	0.817551730466245	0.591224134766877
8	0.419554746861858	0.839109493723715	0.580445253138142
9	0.558891455344181	0.882217089311637	0.441108544655819
10	0.659828997976046	0.680342004047908	0.340171002023954
11	0.803478363204276	0.393043273591449	0.196521636795724
12	0.88359448534637	0.232811029307260	0.116405514653630
13	0.833517650143012	0.332964699713976	0.166482349856988
14	0.827547793648637	0.344904412702726	0.172452206351363
15	0.774606233421227	0.450787533157546	0.225393766578773
16	0.710744751117127	0.578510497765746	0.289255248882873
17	0.688389196816737	0.623221606366525	0.311610803183262
18	0.67677934516152	0.646441309676959	0.323220654838480
19	0.63703851168899	0.725922976622019	0.362961488311009
20	0.618840917381023	0.762318165237954	0.381159082618977
21	0.652464149353605	0.695071701292789	0.347535850646395
22	0.70529656612367	0.58940686775266	0.29470343387633
23	0.784474300331557	0.431051399336887	0.215525699668443
24	0.86794017028804	0.264119659423920	0.132059829711960
25	0.83141757907534	0.337164841849322	0.168582420924661
26	0.819892643488422	0.360214713023157	0.180107356511578
27	0.812818012591867	0.374363974816266	0.187181987408133
28	0.782471190262921	0.435057619474157	0.217528809737079
29	0.747234898680724	0.505530202638551	0.252765101319276
30	0.71091417318395	0.578171653632101	0.289085826816051
31	0.665914477516556	0.668171044966889	0.334085522483444
32	0.621926813642572	0.756146372714857	0.378073186357428
33	0.589508670953478	0.820982658093045	0.410491329046522
34	0.59014610944575	0.8197077811085	0.40985389055425
35	0.626637640003528	0.746724719992944	0.373362359996472
36	0.672741396153549	0.654517207692902	0.327258603846451
37	0.67419397659107	0.651612046817859	0.325806023408930
38	0.697511657540128	0.604976684919745	0.302488342459872
39	0.707196214291522	0.585607571416957	0.292803785708478
40	0.700506812284166	0.598986375431668	0.299493187715834
41	0.669034356028114	0.661931287943772	0.330965643971886
42	0.640294047946908	0.719411904106184	0.359705952053092
43	0.625782994670009	0.748434010659982	0.374217005329991
44	0.604041605243428	0.791916789513145	0.395958394756572
45	0.565319603742708	0.869360792514585	0.434680396257293
46	0.523516108787073	0.952967782425853	0.476483891212927
47	0.471303956862908	0.942607913725815	0.528696043137092
48	0.425133944614146	0.850267889228292	0.574866055385854
49	0.369844064895089	0.739688129790177	0.630155935104912
50	0.317160588704163	0.634321177408327	0.682839411295837
51	0.266368182388088	0.532736364776176	0.733631817611912
52	0.225440329339543	0.450880658679086	0.774559670660457
53	0.216217179452282	0.432434358904564	0.783782820547718
54	0.189593184716065	0.37918636943213	0.810406815283935
55	0.157514040379767	0.315028080759534	0.842485959620233
56	0.130126434061459	0.260252868122918	0.869873565938541
57	0.111542655045947	0.223085310091894	0.888457344954053
58	0.109564794439959	0.219129588879919	0.89043520556004
59	0.122545171823412	0.245090343646823	0.877454828176588
60	0.125105798115942	0.250211596231883	0.874894201884058
61	0.114595939473164	0.229191878946329	0.885404060526836
62	0.132629123547254	0.265258247094507	0.867370876452746
63	0.152105107055796	0.304210214111592	0.847894892944204
64	0.162984375687261	0.325968751374522	0.837015624312739
65	0.138301472402274	0.276602944804548	0.861698527597726
66	0.116622415462608	0.233244830925217	0.883377584537392
67	0.0935127205034258	0.187025441006852	0.906487279496574
68	0.0725412195293508	0.145082439058702	0.92745878047065
69	0.053583001871882	0.107166003743764	0.946416998128118
70	0.0383627504157966	0.0767255008315933	0.961637249584203
71	0.0277146422297487	0.0554292844594975	0.972285357770251
72	0.0193290598430783	0.0386581196861565	0.980670940156922
73	0.0262359284142363	0.0524718568284727	0.973764071585764
74	0.0465367730361711	0.0930735460723421	0.953463226963829
75	0.0793711424673844	0.158742284934769	0.920628857532616
76	0.088310882640648	0.176621765281296	0.911689117359352
77	0.0732326020498053	0.146465204099611	0.926767397950195
78	0.0628713731329439	0.125742746265888	0.937128626867056
79	0.0510732101637513	0.102146420327503	0.948926789836249
80	0.0397681194516217	0.0795362389032435	0.960231880548378
81	0.0272998323845766	0.0545996647691531	0.972700167615423
82	0.0174940429324651	0.0349880858649301	0.982505957067535
83	0.0107233045194147	0.0214466090388293	0.989276695480585
84	0.00628918044421369	0.0125783608884274	0.993710819555786
85	0.0152396171021161	0.0304792342042322	0.984760382897884
86	0.0702831574532312	0.140566314906462	0.929716842546769
87	0.319273579061651	0.638547158123302	0.680726420938349
88	0.489774458375883	0.979548916751766	0.510225541624117
89	0.510021675433428	0.979956649133143	0.489978324566572
90	0.48593689229327	0.97187378458654	0.51406310770673
91	0.585977569580432	0.828044860839136	0.414022430419568

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	5	0.0574712643678161	NOK
10% type I error level	11	0.126436781609195	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/10jddn1229456282.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/10jddn1229456282.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/13vjy1229456281.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/21w071229456281.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/21w071229456281.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/3yj041229456281.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/4erpz1229456281.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/4erpz1229456281.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/5d34v1229456281.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/5d34v1229456281.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/6ia861229456281.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/6ia861229456281.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/7t6x01229456281.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/8xnhq1229456282.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/8xnhq1229456282.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/9zr1s1229456282.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t1229456387i4scdjv4tx4rvh6/9zr1s1229456282.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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