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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: Mon, 22 Nov 2010 10:15:57 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu.htm/, Retrieved Mon, 22 Nov 2010 11:14:35 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu.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 «

9 15 6 25 68 14 10 8 23 48 8 10 7 17 44 8 12 9 19 67 14 9 8 29 46 15 18 11 23 54 9 14 9 23 61 11 11 11 21 52 14 11 12 26 46 14 9 6 24 55 6 17 8 25 52 10 21 12 26 76 9 16 9 23 49 11 21 7 29 30 14 14 8 24 75 8 24 20 20 51 11 7 8 23 50 10 9 6 29 38 16 18 16 24 47 8 14 6 22 52 11 13 6 22 66 11 13 6 22 66 7 18 11 17 33 13 14 12 24 48 10 12 8 21 57 9 12 8 24 64 9 9 7 23 58 15 11 9 21 59 13 8 9 24 42 16 5 4 24 39 11 9 6 19 59 6 11 8 26 37 14 11 8 24 49 4 15 4 28 80 12 16 14 22 62 10 12 8 23 44 14 14 10 24 53 9 13 6 23 58 10 10 8 23 69 14 18 10 30 63 14 17 11 20 36 10 12 8 23 38 9 13 8 21 46 14 13 10 27 56 8 11 8 12 37 9 13 10 15 51 8 12 7 22 44 10 12 8 27 58 9 12 8 21 37 9 12 7 21 65 9 13 6 21 48 9 17 9 21 53 11 18 5 18 51 15 7 5 24 39 8 17 7 24 64 12 14 7 28 47 8 12 7 25 47 14 9 9 14 64 11 9 5 30 59 10 13 8 19 54 12 10 8 29 55 9 12 9 25 72 13 10 6 25 58 14 11 8 25 59 15 13 8 16 36 8 6 6 25 62 7 7 4 28 63 10 13 6 24 50 10 11 5 24 70 11 9 6 22 59 8 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	10 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Intrinsic[t] = + 52.995589547492 -0.587791444894091Doubts[t] + 0.0631178119834426PerantalExpectations[t] -0.393267962263190ParentalCriticism[t] + 0.379101730424418Organization[t] + 0.0160581050867852t + 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)	52.995589547492	10.766167	4.9224	5e-06	2e-06
Doubts	-0.587791444894091	0.454726	-1.2926	0.19986	0.09993
PerantalExpectations	0.0631178119834426	0.399024	0.1582	0.874713	0.437357
ParentalCriticism	-0.393267962263190	0.55794	-0.7049	0.482946	0.241473
Organization	0.379101730424418	0.328127	1.1554	0.251386	0.125693
t	0.0160581050867852	0.04975	0.3228	0.747705	0.373853

Multiple Linear Regression - Regression Statistics
Multiple R	0.222694688895234
R-squared	0.049592924462145
Adjusted R-squared	-0.00980751775897093
F-TEST (value)	0.834891502617728
F-TEST (DF numerator)	5
F-TEST (DF denominator)	80
p-value	0.52878801247544
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	11.074329671832
Sum Squared Residuals	9811.2622144335

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	68	55.7862273153151	12.2137726846849
2	48	51.0029997506388	-3.00299975063881
3	44	52.6644641048068	-8.66446410480682
4	67	52.778425370183	14.2215746298170
5	46	53.2626666364622	-7.26266663646223
6	54	49.8045793351698	4.19542066483017
7	61	53.8814507862138	7.11854921378623
8	52	50.9878331801868	1.01216681981317
9	46	50.7427576404502	-4.74275764045024
10	55	52.2339844343004	2.76601556569956
11	52	57.0498824003055	-5.04988240030553
12	76	53.7732758551214	22.2267241448786
13	49	54.1040350407014	-5.10403504070136
14	30	56.32124562299	-26.3212456229901
15	75	51.8433280951252	23.1566719048748
16	51	49.781690520555	1.21830947944499
17	50	52.8178922256725	-2.81789222567253
18	38	56.6091237066932	-18.6091237066932
19	47	47.8383051755124	-0.838305175512415
20	52	55.4786997536012	-3.47869975360122
21	66	53.6682657120223	12.3317342879777
22	66	53.6843238171091	12.3156761828909
23	33	52.5052882982514	-19.5052882982514
24	48	51.0025706367476	-3.0025706367476
25	57	53.0915341103293	3.90846588967072
26	64	54.8326888515834	9.1673111484166
27	58	54.6735597525586	3.32644024744136
28	59	49.7443654268725	9.25563457312745
29	42	51.8839581770704	-9.88395817707044
30	39	51.9136283228406	-12.9136283228406
31	59	52.4390703236831	6.56092967631689
32	37	57.3874974656518	-20.3874974656518
33	49	51.943020550737	-2.94302055073701
34	80	61.1789431234489	18.8210568765511
35	62	50.348497476188	11.651502523812
36	44	54.0263767271327	-10.0263767271327
37	53	51.4100704825081	1.58992951749191
38	58	55.4959381187102	2.50406188128977
39	69	53.9483154184262	15.0516845815738
40	63	53.9853264282487	9.01467357175127
41	36	49.7539814548447	-13.7539814548447
42	38	54.1227253576535	-16.1227253576535
43	46	54.0314892587689	-8.03148925876895
44	56	52.5966645974054	3.4033354025946
45	37	51.11324571605	-14.1132457160500
46	51	51.0185172669564	-0.0185172669564133
47	44	55.3927650047143	-11.3927650047143
48	58	55.7354809098718	2.26451909012816
49	37	54.0647200773062	-17.0647200773062
50	65	54.4740461446562	10.5259538553438
51	48	54.9464900239896	-6.9464900239896
52	53	54.0352154902206	-1.03521549022059
53	51	53.3745751752821	-2.37457517528214
54	39	52.6197819515212	-13.6197819515212
55	64	56.5950223661747	7.40497763382533
56	47	55.5869681774324	-8.58696817743243
57	47	56.6906512468554	-9.69065124685545
58	64	48.0339522874324	15.9660477125676
59	59	57.4520842630449	1.54791573695512
60	54	52.9584821395014	1.04151786049864
61	55	55.4006212230938	-0.400621223093818
62	72	55.3966144028689	16.6033855971311
63	58	54.115074991202	3.88492500879800
64	59	52.8199235388518	6.18007646114824
65	36	48.9625102491916	-12.9625102491916
66	62	56.849735282999	5.15026471700095
67	63	59.440543760763	3.55945623923700
68	50	55.7689915568441	-5.76899155684411
69	70	56.0520820002272	13.9479179997728
70	59	54.202641613341	4.79735838665901
71	73	53.2575307809453	19.7424692190547
72	62	55.859013140339	6.14098685966104
73	41	53.2651406659189	-12.2651406659189
74	56	55.1229806548263	0.877019345173695
75	52	55.5229156784943	-3.52291567849432
76	54	51.6331233837921	2.36687661620786
77	73	51.6265508876717	21.3734491123283
78	40	51.188234703252	-11.1882347032519
79	41	55.6357658907175	-14.6357658907175
80	54	54.8402275549226	-0.840227554922598
81	42	50.4143590988877	-8.41435909888767
82	70	54.2845523202021	15.7154476797979
83	51	52.3805954479669	-1.38059544796685
84	60	56.8172323509159	3.18276764908414
85	49	55.4400216665866	-6.4400216665866
86	52	56.1800520753717	-4.18005207537175

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0781693826225086	0.156338765245017	0.921830617377492
10	0.244678028156819	0.489356056313637	0.755321971843181
11	0.366569642051724	0.733139284103449	0.633430357948276
12	0.429193045866603	0.858386091733205	0.570806954133398
13	0.387008290473238	0.774016580946475	0.612991709526763
14	0.718190273529607	0.563619452940786	0.281809726470393
15	0.941978756699374	0.116042486601251	0.0580212433006255
16	0.932397523446713	0.135204953106574	0.067602476553287
17	0.89763047945135	0.204739041097301	0.102369520548651
18	0.890029461114749	0.219941077770502	0.109970538885251
19	0.852118596910498	0.295762806179005	0.147881403089502
20	0.799637348125833	0.400725303748334	0.200362651874167
21	0.800310557689443	0.399378884621115	0.199689442310557
22	0.784624318665998	0.430751362668003	0.215375681334001
23	0.903159893815807	0.193680212368386	0.0968401061841928
24	0.866622175641624	0.266755648716751	0.133377824358376
25	0.833208321109806	0.333583357780388	0.166791678890194
26	0.84413577915448	0.311728441691042	0.155864220845521
27	0.807983701331301	0.384032597337397	0.192016298668699
28	0.780435268809778	0.439129462380444	0.219564731190222
29	0.75478998924955	0.4904200215009	0.24521001075045
30	0.772113067703257	0.455773864593486	0.227886932296743
31	0.73245644857926	0.53508710284148	0.26754355142074
32	0.771627984673227	0.456744030653547	0.228372015326773
33	0.718022010179869	0.563955979640262	0.281977989820131
34	0.872351606212316	0.255296787575369	0.127648393787684
35	0.885406782393092	0.229186435213817	0.114593217606908
36	0.873043488632507	0.253913022734985	0.126956511367493
37	0.837850942050737	0.324298115898527	0.162149057949263
38	0.797827470639806	0.404345058720388	0.202172529360194
39	0.8482172449667	0.303565510066601	0.151782755033301
40	0.863059270150507	0.273881459698986	0.136940729849493
41	0.879303180477862	0.241393639044276	0.120696819522138
42	0.89597200460411	0.208055990791780	0.104027995395890
43	0.872545348499134	0.254909303001732	0.127454651500866
44	0.85062325187506	0.29875349624988	0.14937674812494
45	0.867816410147935	0.264367179704129	0.132183589852065
46	0.830290530958916	0.339418938082167	0.169709469041084
47	0.824964379849386	0.350071240301228	0.175035620150614
48	0.787790848822428	0.424418302355144	0.212209151177572
49	0.846182889826891	0.307634220346218	0.153817110173109
50	0.844090640931993	0.311818718136014	0.155909359068007
51	0.823636979257051	0.352726041485898	0.176363020742949
52	0.775312601801968	0.449374796396063	0.224687398198032
53	0.723523929784379	0.552952140431242	0.276476070215621
54	0.788548466304752	0.422903067390495	0.211451533695248
55	0.781894109364961	0.436211781270078	0.218105890635039
56	0.745424624386434	0.509150751227131	0.254575375613566
57	0.753044003939207	0.493911992121587	0.246955996060793
58	0.785821824851079	0.428356350297842	0.214178175148921
59	0.747871579227324	0.504256841545352	0.252128420772676
60	0.686255957056376	0.627488085887247	0.313744042943624
61	0.627888960375847	0.744222079248305	0.372111039624153
62	0.720969488501447	0.558061022997107	0.279030511498553
63	0.653740043636562	0.692519912726876	0.346259956363438
64	0.603127055989159	0.793745888021682	0.396872944010841
65	0.627449171867947	0.745101656264107	0.372550828132053
66	0.560961242497231	0.878077515005537	0.439038757502769
67	0.527138055462916	0.945723889074167	0.472861944537084
68	0.552732157533403	0.894535684933194	0.447267842466597
69	0.483988741330954	0.967977482661908	0.516011258669046
70	0.452978733469416	0.905957466938832	0.547021266530584
71	0.73014024288009	0.539719514239820	0.269859757119910
72	0.695747138894148	0.608505722211704	0.304252861105852
73	0.602256016111605	0.79548796777679	0.397743983888395
74	0.485437547397661	0.970875094795321	0.514562452602339
75	0.360418313158485	0.720836626316971	0.639581686841515
76	0.335925126221554	0.671850252443107	0.664074873778446
77	0.360289203229878	0.720578406459757	0.639710796770122

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	0	0	OK
10% type I error level	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/10t3p11290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/10t3p11290420945.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/142a81290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/142a81290420945.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/2etra1290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/2etra1290420945.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/3etra1290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/3etra1290420945.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/4etra1290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/4etra1290420945.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/5plqd1290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/5plqd1290420945.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/6plqd1290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/6plqd1290420945.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/7icqg1290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/7icqg1290420945.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/8icqg1290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/8icqg1290420945.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/9icqg1290420945.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290420863jyg9suy3u54a6bu/9icqg1290420945.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal 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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