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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 12:43:21 -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/t12294581065rhydqdqc6froyw.htm/, Retrieved Tue, 16 Dec 2008 21:08:36 +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/t12294581065rhydqdqc6froyw.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] = + 444.020833333333 + 68.7708333333334Xt[t] + 54.8315972222221M1[t] + 71.517361111111M2[t] + 65.3281250000001M3[t] + 50.638888888889M4[t] + 30.6996527777778M5[t] + 30.3854166666667M6[t] + 32.8211805555556M7[t] + 27.3819444444445M8[t] + 16.8177083333334M9[t] + 9.62847222222226M10[t] + 1.06423611111115M11[t] + 0.439236111111113t + 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)	444.020833333333	9.344531	47.5167	0	0
Xt	68.7708333333334	9.027675	7.6178	0	0
M1	54.8315972222221	10.940204	5.0119	3e-06	2e-06
M2	71.517361111111	10.914307	6.5526	0	0
M3	65.3281250000001	10.890823	5.9985	0	0
M4	50.638888888889	10.869768	4.6587	1.2e-05	6e-06
M5	30.6996527777778	10.851157	2.8292	0.005866	0.002933
M6	30.3854166666667	10.835001	2.8044	0.006292	0.003146
M7	32.8211805555556	10.821312	3.033	0.003242	0.001621
M8	27.3819444444445	10.810099	2.533	0.013215	0.006607
M9	16.8177083333334	10.801369	1.557	0.123322	0.061661
M10	9.62847222222226	10.79513	0.8919	0.375042	0.187521
M11	1.06423611111115	10.791384	0.0986	0.921681	0.460841
t	0.439236111111113	0.164167	2.6755	0.009005	0.004503

Multiple Linear Regression - Regression Statistics
Multiple R	0.929144528606628
R-squared	0.863309555039633
Adjusted R-squared	0.841639118643477
F-TEST (value)	39.8381250500696
F-TEST (DF numerator)	13
F-TEST (DF denominator)	82
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	21.5802706022953
Sum Squared Residuals	38188.0624999999

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	524	499.291666666668	24.7083333333323
2	552	516.416666666667	35.583333333333
3	532	510.666666666667	21.3333333333334
4	511	496.416666666666	14.5833333333338
5	492	476.916666666667	15.0833333333332
6	492	477.041666666667	14.9583333333334
7	493	479.916666666667	13.0833333333334
8	481	474.916666666667	6.08333333333330
9	462	464.791666666667	-2.79166666666656
10	457	458.041666666667	-1.04166666666663
11	442	449.916666666667	-7.91666666666664
12	439	449.291666666667	-10.2916666666666
13	488	504.5625	-16.5624999999998
14	521	521.6875	-0.68749999999995
15	501	515.9375	-14.9375
16	485	501.6875	-16.6875000000000
17	464	482.1875	-18.1875000000000
18	460	482.3125	-22.3125
19	467	485.1875	-18.1875
20	460	480.1875	-20.1875000000000
21	448	470.0625	-22.0625000000000
22	443	463.3125	-20.3125
23	436	455.1875	-19.1875
24	431	454.5625	-23.5624999999999
25	484	509.833333333333	-25.8333333333332
26	510	526.958333333333	-16.9583333333333
27	513	521.208333333333	-8.20833333333334
28	503	506.958333333333	-3.95833333333339
29	471	487.458333333333	-16.4583333333333
30	471	487.583333333333	-16.5833333333333
31	476	490.458333333333	-14.4583333333333
32	475	485.458333333333	-10.4583333333333
33	470	475.333333333333	-5.33333333333335
34	461	468.583333333333	-7.58333333333333
35	455	460.458333333333	-5.45833333333333
36	456	459.833333333333	-3.83333333333329
37	517	515.104166666667	1.89583333333348
38	525	532.229166666667	-7.22916666666664
39	523	526.479166666667	-3.47916666666669
40	519	512.229166666667	6.77083333333326
41	509	492.729166666667	16.2708333333333
42	512	492.854166666667	19.1458333333333
43	519	495.729166666667	23.2708333333333
44	517	490.729166666667	26.2708333333333
45	510	480.604166666667	29.3958333333333
46	509	473.854166666667	35.1458333333333
47	501	465.729166666667	35.2708333333333
48	507	465.104166666667	41.8958333333333
49	569	589.145833333333	-20.1458333333332
50	580	606.270833333333	-26.2708333333333
51	578	600.520833333333	-22.5208333333333
52	565	586.270833333333	-21.2708333333334
53	547	566.770833333333	-19.7708333333333
54	555	566.895833333333	-11.8958333333333
55	562	569.770833333333	-7.77083333333333
56	561	564.770833333333	-3.77083333333331
57	555	554.645833333333	0.354166666666665
58	544	547.895833333333	-3.89583333333332
59	537	539.770833333333	-2.77083333333333
60	543	539.145833333333	3.85416666666671
61	594	594.416666666667	-0.416666666666513
62	611	611.541666666667	-0.541666666666616
63	613	605.791666666667	7.20833333333332
64	611	591.541666666667	19.4583333333333
65	594	572.041666666667	21.9583333333334
66	595	572.166666666667	22.8333333333333
67	591	575.041666666667	15.9583333333333
68	589	570.041666666667	18.9583333333333
69	584	559.916666666667	24.0833333333333
70	573	553.166666666667	19.8333333333333
71	567	545.041666666667	21.9583333333333
72	569	544.416666666667	24.5833333333334
73	621	599.6875	21.3125000000001
74	629	616.8125	12.1875000000000
75	628	611.0625	16.9375000000000
76	612	596.8125	15.1874999999999
77	595	577.3125	17.6875
78	597	577.4375	19.5625000000000
79	593	580.3125	12.6875000000000
80	590	575.3125	14.6875
81	580	565.1875	14.8125000000000
82	574	558.4375	15.5625000000000
83	573	550.3125	22.6875
84	573	549.6875	23.3125
85	620	604.958333333333	15.0416666666668
86	626	622.083333333333	3.91666666666669
87	620	616.333333333333	3.66666666666662
88	588	602.083333333333	-14.0833333333334
89	566	582.583333333333	-16.5833333333333
90	557	582.708333333333	-25.7083333333334
91	561	585.583333333333	-24.5833333333334
92	549	580.583333333333	-31.5833333333334
93	532	570.458333333333	-38.4583333333334
94	526	563.708333333333	-37.7083333333334
95	511	555.583333333333	-44.5833333333334
96	499	554.958333333333	-55.9583333333334

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.00356027760287468	0.00712055520574937	0.996439722397125
18	0.000430629633161459	0.000861259266322918	0.999569370366838
19	8.73666465551226e-05	0.000174733293110245	0.999912633353445
20	6.49367418071446e-05	0.000129873483614289	0.999935063258193
21	0.000164633503018172	0.000329267006036345	0.999835366496982
22	0.000142527125914436	0.000285054251828872	0.999857472874086
23	0.000328393683813822	0.000656787367627644	0.999671606316186
24	0.000310802090510721	0.000621604181021443	0.99968919790949
25	0.000209522557368961	0.000419045114737923	0.99979047744263
26	7.73190721997105e-05	0.000154638144399421	0.9999226809278
27	0.000427610298193983	0.000855220596387966	0.999572389701806
28	0.00197605331406046	0.00395210662812092	0.99802394668594
29	0.00162658773868952	0.00325317547737903	0.99837341226131
30	0.00140937614794502	0.00281875229589004	0.998590623852055
31	0.00117599613576818	0.00235199227153636	0.998824003864232
32	0.00154285926929756	0.00308571853859513	0.998457140730702
33	0.00356294002188531	0.00712588004377062	0.996437059978115
34	0.00470796070407145	0.0094159214081429	0.995292039295929
35	0.00718594330273684	0.0143718866054737	0.992814056697263
36	0.0122935659063764	0.0245871318127527	0.987706434093624
37	0.017048692011129	0.034097384022258	0.98295130798887
38	0.0124289217442285	0.0248578434884570	0.987571078255772
39	0.0111804907440320	0.0223609814880641	0.988819509255968
40	0.0128028657561299	0.0256057315122598	0.98719713424387
41	0.0228212196467642	0.0456424392935284	0.977178780353236
42	0.0376475966088389	0.0752951932176779	0.96235240339116
43	0.0539810744869927	0.107962148973985	0.946018925513007
44	0.0756882923025018	0.151376584605004	0.924311707697498
45	0.104393283739676	0.208786567479352	0.895606716260324
46	0.140424708215835	0.28084941643167	0.859575291784165
47	0.171520320296232	0.343040640592465	0.828479679703768
48	0.220012343303368	0.440024686606735	0.779987656696633
49	0.217976129963317	0.435952259926633	0.782023870036683
50	0.226692743568165	0.45338548713633	0.773307256431835
51	0.245235656648221	0.490471313296441	0.754764343351779
52	0.266684352176948	0.533368704353896	0.733315647823052
53	0.306066469339241	0.612132938678483	0.693933530660759
54	0.326990398202059	0.653980796404119	0.673009601797941
55	0.328726849673796	0.657453699347592	0.671273150326204
56	0.327389464504999	0.654778929009998	0.672610535495001
57	0.32340278010137	0.64680556020274	0.67659721989863
58	0.337957454516433	0.675914909032866	0.662042545483567
59	0.376123996438467	0.752247992876934	0.623876003561533
60	0.396065695872585	0.79213139174517	0.603934304127415
61	0.546792057469771	0.906415885060458	0.453207942530229
62	0.658383029714668	0.683233940570664	0.341616970285332
63	0.768415860421348	0.463168279157303	0.231584139578652
64	0.772732602331879	0.454534795336243	0.227267397668121
65	0.77469331761684	0.45061336476632	0.22530668238316
66	0.760453243417669	0.479093513164662	0.239546756582331
67	0.75692747380941	0.486145052381179	0.243072526190589
68	0.739175298531381	0.521649402937238	0.260824701468619
69	0.696503618397665	0.606992763204671	0.303496381602335
70	0.67564158037686	0.648716839246279	0.324358419623140
71	0.663927507658329	0.672144984683342	0.336072492341671
72	0.62462130299678	0.75075739400644	0.37537869700322
73	0.697349001893388	0.605301996213223	0.302650998106612
74	0.78970443090033	0.420591138199339	0.210295569099669
75	0.88193534158671	0.236129316826582	0.118064658413291
76	0.881405676280911	0.237188647438178	0.118594323719089
77	0.867875579752464	0.264248840495073	0.132124420247536
78	0.786690736149793	0.426618527700414	0.213309263850207
79	0.780340952664293	0.439318094671415	0.219659047335707

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	18	0.285714285714286	NOK
5% type I error level	25	0.396825396825397	NOK
10% type I error level	26	0.412698412698413	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/10tke31229456596.png (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/26jle1229456596.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/26jle1229456596.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/399np1229456596.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/45kyj1229456596.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/45kyj1229456596.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/5uaxl1229456596.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/6ocae1229456596.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/75qnt1229456596.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/8qyto1229456596.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/95d821229456596.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/16/t12294581065rhydqdqc6froyw/95d821229456596.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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