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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: Tue, 21 Dec 2010 15:51:03 +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/Dec/21/t129294869888jduzolpeeiyw8.htm/, Retrieved Tue, 21 Dec 2010 17:25:01 +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/Dec/21/t129294869888jduzolpeeiyw8.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 «

1.4562 8.1000 7.9000 8.7000 104.5000 2443.2700 16.2000 16.3000 3.0000 -12.0000 65.0000 1.4268 8.3000 8.1000 8.9000 89.1000 2293.4100 12.5000 13.6000 6.0000 -11.0000 55.0000 1.4088 8.1000 8.3000 8.9000 82.6000 2070.8300 14.8000 14.3000 7.0000 -11.0000 57.0000 1.4016 7.4000 8.1000 8.1000 102.7000 2029.6000 15.4000 15.5000 -4.0000 -17.0000 57.0000 1.3650 7.3000 7.4000 8.0000 91.8000 2052.0200 13.6000 13.9000 -5.0000 -18.0000 57.0000 1.3190 7.7000 7.3000 8.3000 94.1000 1864.4400 14.2000 14.3000 -7.0000 -19.0000 65.0000 1.3050 8.0000 7.7000 8.5000 103.1000 1670.0700 15.0000 15.8000 -10.0000 -22.0000 69.0000 1.2785 8.0000 8.0000 8.7000 93.2000 1810.9900 14.1000 14.5000 -21.0000 -24.0000 70.0000 1.3239 7.7000 8.0000 8.6000 91.0000 1905.4100 13.7000 15.1000 -22.0000 -24.0000 71.0000 1.3449 6.9000 7.7000 8.3000 94.3000 1862.8300 14.4000 15.8000 -16.0000 -20.0000 71.0000 1.2732 6.6000 6.9000 7.9000 99.4000 2014.4500 15.6000 17.2000 -25.0000 -25.0000 73.0000 1.3322 6.9000 6.6000 7.9000 115.7000 2197.8200 19.7 etc...

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

WER[t] = -2.57593064449642 -0.0525602121412567WSK[t] + 0.754726043505015`WER(d-1)`[t] + 0.386600312376373`WER(d-12)`[t] + 0.00616360269856735INP[t] + 0.000325014145383515BE2[t] + 0.0104056299362115Uit[t] -0.0227331712258726INV[t] + 0.0171781802564919`CE-AES`[t] -0.0152629886526889`CE-CV`[t] + 0.00228401340448317`CE-WER`[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)	-2.57593064449642	0.768842	-3.3504	0.001163	0.000581
WSK	-0.0525602121412567	0.324036	-0.1622	0.871493	0.435746
`WER(d-1)`	0.754726043505015	0.05046	14.9571	0	0
`WER(d-12)`	0.386600312376373	0.074748	5.172	1e-06	1e-06
INP	0.00616360269856735	0.005369	1.148	0.253899	0.126949
BE2	0.000325014145383515	9e-05	3.6232	0.000472	0.000236
Uit	0.0104056299362115	0.050396	0.2065	0.836865	0.418433
INV	-0.0227331712258726	0.05169	-0.4398	0.66109	0.330545
`CE-AES`	0.0171781802564919	0.006063	2.8334	0.005636	0.002818
`CE-CV`	-0.0152629886526889	0.011471	-1.3305	0.186567	0.093284
`CE-WER`	0.00228401340448317	0.003439	0.6641	0.508224	0.254112

Multiple Linear Regression - Regression Statistics
Multiple R	0.929644126482349
R-squared	0.864238201903129
Adjusted R-squared	0.849795457424738
F-TEST (value)	59.8389179560861
F-TEST (DF numerator)	10
F-TEST (DF denominator)	94
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.297710738998776
Sum Squared Residuals	8.33137830682857

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8.1	8.29265521881706	-0.192655218817059
2	8.3	8.415149808463	-0.115149808463007
3	8.1	8.48440197102304	-0.384401971023039
4	7.4	8.0166245485608	-0.616624548560796
5	7.3	7.40741128683155	-0.107411286831546
6	7.7	7.39987552361507	0.300124476384933
7	8	7.74973649722792	0.250263502772079
8	8	7.90368660856757	0.0963133914324344
9	7.7	7.84707193180696	-0.147071931806962
10	6.9	7.54345889520785	-0.643458895207855
11	6.6	6.77645918512078	-0.17645918512078
12	6.9	6.67124732998323	0.228752670016769
13	7.5	7.12839903207102	0.371600967928981
14	7.9	7.51500092027702	0.384999079722978
15	7.7	7.66306267357985	0.0369373264201524
16	6.5	7.31643228740354	-0.816432287403536
17	6.1	6.46590361258185	-0.365903612581846
18	6.4	6.40435916830324	-0.00435916830323796
19	6.8	6.71244985651625	0.0875501434837498
20	7.1	7.1066703343962	-0.00667033439620104
21	7.3	7.16828674381323	0.131713256186766
22	7.2	7.01916937364359	0.180830626356414
23	7	6.90566847039909	0.0943315296009105
24	7	7.00328990836448	-0.00328990836448503
25	7	7.27321957647593	-0.273219576475927
26	7.3	7.36830191971521	-0.0683019197152102
27	7.5	7.54299276653779	-0.0429927665377928
28	7.2	7.56508085729152	-0.365080857291522
29	7.7	7.1791118711444	0.520888128855604
30	8	7.61998689486496	0.380013105135042
31	7.9	8.00481740743841	-0.104817407438413
32	8	7.96319531848592	0.0368046815140762
33	8	8.0973650306042	-0.0973650306041957
34	7.9	8.02979728047357	-0.12979728047357
35	7.9	7.90516660227695	-0.00516660227694742
36	8	7.94103694430597	0.0589630556940319
37	8.1	8.00756376256173	0.0924362374382648
38	8.1	7.97146485015701	0.128535149842987
39	8.2	7.89345934758696	0.306540652413034
40	8	8.05394496528462	-0.0539449652846225
41	8.3	8.09163873249065	0.208361267509347
42	8.5	8.43579512324347	0.0642048767565263
43	8.6	8.56722223893005	0.0327777610699528
44	8.7	8.61785223060782	0.0821477693921784
45	8.7	8.62470229959837	0.0752977004016266
46	8.5	8.54627348499163	-0.0462734849916292
47	8.4	8.40543692931311	-0.00543692931311358
48	8.5	8.3605990837627	0.139400916237309
49	8.7	8.5017756372469	0.198224362753104
50	8.7	8.41803357081566	0.281966429184342
51	8.6	8.4670313838749	0.132968616125111
52	7.9	8.39026060830668	-0.490260608306676
53	8.1	7.92468811086888	0.175311889131122
54	8.2	8.22751789428266	-0.0275178942826648
55	8.5	8.4108728065162	0.089127193483804
56	8.6	8.71263616317767	-0.112636163177673
57	8.5	8.67626750388815	-0.176267503888146
58	8.3	8.54268309060236	-0.242683090602365
59	8.2	8.36611858008647	-0.166118580086469
60	8.7	8.32412553155678	0.37587446844322
61	9.3	8.73511389934706	0.564886100652942
62	9.3	9.10524018079751	0.194759819202487
63	8.8	9.02875417026727	-0.228754170267271
64	7.4	8.42803208949464	-1.02803208949464
65	7.2	7.48090964152376	-0.28090964152376
66	7.5	7.36815662946784	0.131843370532158
67	8.3	7.7614891586653	0.538510841334707
68	8.8	8.39956799834042	0.400432001659585
69	8.9	8.70421364948551	0.195786350514487
70	8.6	8.62229276904944	-0.0222927690494447
71	8.4	8.35198169025646	0.0480183097435403
72	8.4	8.45057160394317	-0.0505716039431707
73	8.4	8.60057761136297	-0.20057761136297
74	8.4	8.5286008257302	-0.128600825730197
75	8.3	8.27416509393097	0.0258349060690335
76	7.6	7.69473932431204	-0.0947393243120431
77	7.6	7.05742612407264	0.542573875927363
78	7.9	7.26208016924409	0.637919830755915
79	8	7.63174788652705	0.368252113472954
80	8.2	7.71398095046635	0.486019049533648
81	8.3	8.04060770237596	0.25939229762404
82	8.2	7.98740745780507	0.212592542194925
83	8.1	8.03718062789166	0.0628193721083405
84	8	8.03268557668898	-0.032685576688981
85	7.8	7.9406108315492	-0.140610831549197
86	7.6	7.69770267845844	-0.0977026784584374
87	7.5	7.48489275817161	0.0151072418283927
88	6.8	7.37556351200715	-0.575563512007151
89	6.9	6.98099484623923	-0.080994846239232
90	7.1	7.1778906988426	-0.0778906988425973
91	7.3	7.30607468092066	-0.00607468092066037
92	7.4	7.51995053896731	-0.11995053896731
93	7.6	7.59043938233448	0.00956061766551647
94	7.6	7.64177745034414	-0.0417774503441385
95	7.5	7.61176252596301	-0.111762525963013
96	7.5	7.40666464942669	0.0933353505733095
97	6.8	7.24633541148015	-0.446335411480155
98	6.4	6.80407907912955	-0.404079079129547
99	6.2	6.41950399451627	-0.219503994516271
100	6	6.08312999138659	-0.0831299913865905
101	6.3	5.93734415055105	0.362655849448949
102	6.3	6.2252975634943	0.0747024365056934
103	6.1	6.31660085754083	-0.216600857540832
104	6.1	6.21828691635673	-0.118286916356734
105	6.3	6.27306706533321	0.0269329346667916

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
14	0.575447851414081	0.849104297171839	0.424552148585919
15	0.405239871391364	0.810479742782728	0.594760128608636
16	0.77296956126167	0.454060877476658	0.227030438738329
17	0.911505928912672	0.176988142174655	0.0884940710873275
18	0.900545679320772	0.198908641358457	0.0994543206792284
19	0.856591233854794	0.286817532290413	0.143408766145206
20	0.794384435551402	0.411231128897195	0.205615564448598
21	0.866628496157418	0.266743007685164	0.133371503842582
22	0.905269379886933	0.189461240226134	0.094730620113067
23	0.880959806839757	0.238080386320486	0.119040193160243
24	0.848980768263947	0.302038463472106	0.151019231736053
25	0.902828550990133	0.194342898019735	0.0971714490098674
26	0.880339797318404	0.239320405363193	0.119660202681597
27	0.86100587989141	0.277988240217181	0.138994120108591
28	0.863331555536306	0.273336888927387	0.136668444463694
29	0.883926820821561	0.232146358356877	0.116073179178439
30	0.893326888755227	0.213346222489545	0.106673111244773
31	0.865976492587713	0.268047014824574	0.134023507412287
32	0.827040359478318	0.345919281043364	0.172959640521682
33	0.788032722081325	0.423934555837349	0.211967277918675
34	0.747399340183005	0.505201319633991	0.252600659816995
35	0.720888595168949	0.558222809662102	0.279111404831051
36	0.667174709779132	0.665650580441735	0.332825290220868
37	0.634218438500468	0.731563122999063	0.365781561499532
38	0.58023626330824	0.83952747338352	0.41976373669176
39	0.593696687603427	0.812606624793146	0.406303312396573
40	0.529213055011792	0.941573889976416	0.470786944988208
41	0.518419369772674	0.963161260454652	0.481580630227326
42	0.46010778211486	0.92021556422972	0.53989221788514
43	0.399529083819942	0.799058167639884	0.600470916180058
44	0.347351417860022	0.694702835720043	0.652648582139978
45	0.300836959141643	0.601673918283287	0.699163040858356
46	0.295493033918937	0.590986067837874	0.704506966081063
47	0.243333498884499	0.486666997768999	0.756666501115501
48	0.262275983647605	0.52455196729521	0.737724016352395
49	0.304956097127471	0.609912194254942	0.695043902872529
50	0.338032416089841	0.676064832179682	0.661967583910159
51	0.313519340647997	0.627038681295994	0.686480659352003
52	0.348475140377585	0.696950280755171	0.651524859622415
53	0.301015605096222	0.602031210192445	0.698984394903778
54	0.248789598773885	0.497579197547769	0.751210401226115
55	0.203825800122504	0.407651600245008	0.796174199877496
56	0.171542316147811	0.343084632295622	0.828457683852189
57	0.137018193567351	0.274036387134703	0.862981806432649
58	0.126110122667567	0.252220245335133	0.873889877332433
59	0.110554134023613	0.221108268047227	0.889445865976387
60	0.140596323897308	0.281192647794616	0.859403676102692
61	0.51480872733346	0.97038254533308	0.48519127266654
62	0.702555472784368	0.594889054431264	0.297444527215632
63	0.740141545914891	0.519716908170219	0.259858454085109
64	0.990146673897263	0.0197066522054734	0.00985332610273668
65	0.99501626104045	0.00996747791910093	0.00498373895955046
66	0.99614377210929	0.00771245578142067	0.00385622789071033
67	0.99622951372966	0.00754097254067951	0.00377048627033976
68	0.997697380184136	0.00460523963172759	0.0023026198158638
69	0.99958446201174	0.000831075976518373	0.000415537988259186
70	0.999222256789359	0.00155548642128257	0.000777743210641286
71	0.99904836736598	0.00190326526804157	0.000951632634020785
72	0.99889668202475	0.00220663595050047	0.00110331797525023
73	0.998588467451837	0.00282306509632491	0.00141153254816246
74	0.997863740983417	0.00427251803316553	0.00213625901658276
75	0.996185497820013	0.0076290043599745	0.00381450217998725
76	0.994304411777803	0.011391176444394	0.00569558822219701
77	0.995753956107442	0.00849208778511684	0.00424604389255842
78	0.99946069942467	0.00107860115065827	0.000539300575329135
79	0.999151269932753	0.00169746013449443	0.000848730067247217
80	0.998602542177643	0.00279491564471429	0.00139745782235714
81	0.997382275865272	0.00523544826945633	0.00261772413472816
82	0.994971197796549	0.0100576044069025	0.00502880220345125
83	0.995869661339567	0.00826067732086675	0.00413033866043338
84	0.991664452775398	0.0166710944492036	0.00833554722460181
85	0.984233065428594	0.0315338691428124	0.0157669345714062
86	0.998505045514764	0.00298990897047159	0.00149495448523579
87	0.997112444325238	0.00577511134952467	0.00288755567476233
88	0.994957375337502	0.0100852493249957	0.00504262466249784
89	0.98834520199	0.0233095960200005	0.0116547980100002
90	0.965521385955628	0.068957228088745	0.0344786140443725
91	0.966510372928656	0.0669792541426881	0.033489627071344

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	19	0.243589743589744	NOK
5% type I error level	26	0.333333333333333	NOK
10% type I error level	28	0.358974358974359	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294869888jduzolpeeiyw8/10jcwa1292946652.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294869888jduzolpeeiyw8/10jcwa1292946652.ps (open in new window)

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http://www.freestatistics.org/blog/date/2010/Dec/21/t129294869888jduzolpeeiyw8/883fp1292946652.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294869888jduzolpeeiyw8/983fp1292946652.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294869888jduzolpeeiyw8/983fp1292946652.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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