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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: Wed, 26 Nov 2008 13:48:10 -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/Nov/26/t1227732605rjwlvsgra16s2w1.htm/, Retrieved Wed, 26 Nov 2008 20:50:05 +0000

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/Nov/26/t1227732605rjwlvsgra16s2w1.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},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

hout

Dataseries X:

» Textbox « » Textfile « » CSV «

98.5 0 96.7 0 113.1 0 100 0 104.7 0 108.5 0 90.5 0 88.6 0 105.4 0 119.9 0 107.2 0 84.1 0 101.4 0 105.1 0 118.7 0 113.8 0 113.8 0 118.9 0 98.5 0 91 0 120.7 0 127.9 0 112.4 0 93.1 0 107.5 0 107.3 0 114.8 0 120.8 0 112.2 0 123.3 0 100.6 0 86.7 0 123.6 0 125.3 0 111.1 0 98.4 0 102.3 0 105 0 128.2 0 124.7 0 116.1 0 131.2 0 97.7 0 88.8 0 132.8 0 113.9 0 112.6 1 104.3 1 107.5 1 106 1 117.3 1 123.1 1 114.3 1 132 1 92.3 1 93.7 1 121.3 1 113.6 1 116.3 1 98.3 1 111.9 1 109.3 1 133.2 1 118 1 131.6 1 134.1 1 96.7 1 99.8 1 128.3 1 134.9 1 130.7 1 107.3 1 121.6 1 120.6 1 140.5 1 124.8 1 129.9 1 159.4 1 111 1 110.1 1 132.7 1 135 1 118.6 1 94 1 117.9 1 114.7 1 113.6 1 130.6 1 117.1 1 123.2 1 106.1 1 87.9 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

y[t] = + 108.8 + 8.54130434782609x[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)	108.8	2.013266	54.0415	0	0
x	8.54130434782609	2.847188	2.9999	0.003493	0.001746

Multiple Linear Regression - Regression Statistics
Multiple R	0.301503020875576
R-squared	0.0909040715970979
Adjusted R-squared	0.0808030057259546
F-TEST (value)	8.99945339994187
F-TEST (DF numerator)	1
F-TEST (DF denominator)	90
p-value	0.00349286262717896
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	13.6546335325490
Sum Squared Residuals	16780.4115217391

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	98.5	108.8	-10.3000000000001
2	96.7	108.8	-12.1
3	113.1	108.8	4.3
4	100	108.8	-8.8
5	104.7	108.8	-4.1
6	108.5	108.8	-0.299999999999998
7	90.5	108.8	-18.3
8	88.6	108.8	-20.2
9	105.4	108.8	-3.39999999999999
10	119.9	108.8	11.1
11	107.2	108.8	-1.60000000000000
12	84.1	108.8	-24.7
13	101.4	108.8	-7.4
14	105.1	108.8	-3.70000000000000
15	118.7	108.8	9.9
16	113.8	108.8	5
17	113.8	108.8	5
18	118.9	108.8	10.1
19	98.5	108.8	-10.3
20	91	108.8	-17.8
21	120.7	108.8	11.9
22	127.9	108.8	19.1
23	112.4	108.8	3.60000000000001
24	93.1	108.8	-15.7
25	107.5	108.8	-1.30000000000000
26	107.3	108.8	-1.5
27	114.8	108.8	6
28	120.8	108.8	12
29	112.2	108.8	3.40000000000000
30	123.3	108.8	14.5
31	100.6	108.8	-8.2
32	86.7	108.8	-22.1
33	123.6	108.8	14.8
34	125.3	108.8	16.5
35	111.1	108.8	2.30000000000000
36	98.4	108.8	-10.4
37	102.3	108.8	-6.5
38	105	108.8	-3.8
39	128.2	108.8	19.4
40	124.7	108.8	15.9
41	116.1	108.8	7.3
42	131.2	108.8	22.4
43	97.7	108.8	-11.1
44	88.8	108.8	-20
45	132.8	108.8	24
46	113.9	108.8	5.10000000000001
47	112.6	117.341304347826	-4.74130434782609
48	104.3	117.341304347826	-13.0413043478261
49	107.5	117.341304347826	-9.84130434782609
50	106	117.341304347826	-11.3413043478261
51	117.3	117.341304347826	-0.0413043478260883
52	123.1	117.341304347826	5.75869565217391
53	114.3	117.341304347826	-3.04130434782609
54	132	117.341304347826	14.6586956521739
55	92.3	117.341304347826	-25.0413043478261
56	93.7	117.341304347826	-23.6413043478261
57	121.3	117.341304347826	3.95869565217391
58	113.6	117.341304347826	-3.74130434782609
59	116.3	117.341304347826	-1.04130434782609
60	98.3	117.341304347826	-19.0413043478261
61	111.9	117.341304347826	-5.44130434782608
62	109.3	117.341304347826	-8.04130434782609
63	133.2	117.341304347826	15.8586956521739
64	118	117.341304347826	0.658695652173915
65	131.6	117.341304347826	14.2586956521739
66	134.1	117.341304347826	16.7586956521739
67	96.7	117.341304347826	-20.6413043478261
68	99.8	117.341304347826	-17.5413043478261
69	128.3	117.341304347826	10.9586956521739
70	134.9	117.341304347826	17.5586956521739
71	130.7	117.341304347826	13.3586956521739
72	107.3	117.341304347826	-10.0413043478261
73	121.6	117.341304347826	4.25869565217391
74	120.6	117.341304347826	3.25869565217391
75	140.5	117.341304347826	23.1586956521739
76	124.8	117.341304347826	7.45869565217391
77	129.9	117.341304347826	12.5586956521739
78	159.4	117.341304347826	42.0586956521739
79	111	117.341304347826	-6.34130434782609
80	110.1	117.341304347826	-7.24130434782609
81	132.7	117.341304347826	15.3586956521739
82	135	117.341304347826	17.6586956521739
83	118.6	117.341304347826	1.25869565217391
84	94	117.341304347826	-23.3413043478261
85	117.9	117.341304347826	0.55869565217392
86	114.7	117.341304347826	-2.64130434782608
87	113.6	117.341304347826	-3.74130434782609
88	130.6	117.341304347826	13.2586956521739
89	117.1	117.341304347826	-0.241304347826091
90	123.2	117.341304347826	5.85869565217392
91	106.1	117.341304347826	-11.2413043478261
92	87.9	117.341304347826	-29.4413043478261

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.174046928943483	0.348093857886967	0.825953071056517
6	0.0965317563265598	0.193063512653120	0.90346824367344
7	0.126543559925766	0.253087119851532	0.873456440074234
8	0.149255059555177	0.298510119110353	0.850744940444823
9	0.0949767943814801	0.189953588762960	0.90502320561852
10	0.189167416171238	0.378334832342475	0.810832583828762
11	0.129092672296764	0.258185344593528	0.870907327703236
12	0.224207500969855	0.448415001939711	0.775792499030145
13	0.158796185210058	0.317592370420115	0.841203814789942
14	0.111144255015512	0.222288510031023	0.888855744984488
15	0.146531577759446	0.293063155518892	0.853468422240554
16	0.130241460271387	0.260482920542774	0.869758539728613
17	0.112114954323516	0.224229908647033	0.887885045676484
18	0.121741804000638	0.243483608001275	0.878258195999362
19	0.0966019732424488	0.193203946484898	0.903398026757551
20	0.109928433247529	0.219856866495057	0.890071566752471
21	0.130698859488026	0.261397718976051	0.869301140511974
22	0.219602524167524	0.439205048335048	0.780397475832476
23	0.178127302481759	0.356254604963519	0.82187269751824
24	0.185694285161363	0.371388570322725	0.814305714838637
25	0.143137196863420	0.286274393726841	0.85686280313658
26	0.108111490697215	0.216222981394430	0.891888509302785
27	0.0896389415427184	0.179277883085437	0.910361058457282
28	0.0924915682138425	0.184983136427685	0.907508431786157
29	0.0704381539703816	0.140876307940763	0.929561846029618
30	0.0802381770074184	0.160476354014837	0.919761822992582
31	0.0656144193359843	0.131228838671969	0.934385580664016
32	0.113191046249812	0.226382092499625	0.886808953750188
33	0.124983571953895	0.249967143907791	0.875016428046105
34	0.145007643319159	0.290015286638318	0.854992356680841
35	0.113105176550178	0.226210353100355	0.886894823449822
36	0.104058960824141	0.208117921648283	0.895941039175859
37	0.0868331942959814	0.173666388591963	0.913166805704019
38	0.0691084329183081	0.138216865836616	0.930891567081692
39	0.0915156243163284	0.183031248632657	0.908484375683672
40	0.0980931620086888	0.196186324017378	0.901906837991311
41	0.0792563493148252	0.158512698629650	0.920743650685175
42	0.122462223918922	0.244924447837844	0.877537776081078
43	0.113567813094490	0.227135626188981	0.88643218690551
44	0.180933627503705	0.361867255007409	0.819066372496295
45	0.234526871550696	0.469053743101391	0.765473128449304
46	0.19365887328175	0.3873177465635	0.80634112671825
47	0.156511101647059	0.313022203294118	0.843488898352941
48	0.139237113720456	0.278474227440912	0.860762886279544
49	0.115550076092980	0.231100152185961	0.88444992390702
50	0.0976223034348211	0.195244606869642	0.902377696565179
51	0.0787214194832382	0.157442838966476	0.921278580516762
52	0.0680725779679144	0.136145155935829	0.931927422032086
53	0.0510061359261339	0.102012271852268	0.948993864073866
54	0.0581911535888248	0.116382307177650	0.941808846411175
55	0.0998443502346994	0.199688700469399	0.9001556497653
56	0.146638558680978	0.293277117361955	0.853361441319022
57	0.12239307661563	0.24478615323126	0.87760692338437
58	0.0963370160166428	0.192674032033286	0.903662983983357
59	0.0739782438676871	0.147956487735374	0.926021756132313
60	0.0912659004595284	0.182531800919057	0.908734099540472
61	0.0724053762287668	0.144810752457534	0.927594623771233
62	0.0598428025374333	0.119685605074867	0.940157197462567
63	0.0698382874778517	0.139676574955703	0.930161712522148
64	0.0520769929016617	0.104153985803323	0.947923007098338
65	0.0534675377327801	0.106935075465560	0.94653246226722
66	0.0606328654334125	0.121265730866825	0.939367134566587
67	0.0890235179858293	0.178047035971659	0.91097648201417
68	0.111464122424079	0.222928244848157	0.888535877575921
69	0.0968828132251677	0.193765626450335	0.903117186774832
70	0.107170371737347	0.214340743474695	0.892829628262653
71	0.0986574651467335	0.197314930293467	0.901342534853266
72	0.0876204388533758	0.175240877706752	0.912379561146624
73	0.0633378015415309	0.126675603083062	0.93666219845847
74	0.0439812231262445	0.087962446252489	0.956018776873755
75	0.0680884963892333	0.136176992778467	0.931911503610767
76	0.0496482758621515	0.099296551724303	0.950351724137849
77	0.0421017756181979	0.0842035512363958	0.957898224381802
78	0.425687426253381	0.851374852506763	0.574312573746619
79	0.348955580110783	0.697911160221566	0.651044419889217
80	0.280227486884304	0.560454973768609	0.719772513115696
81	0.312795022824413	0.625590045648825	0.687204977175587
82	0.428033049457881	0.856066098915763	0.571966950542119
83	0.342689298506392	0.685378597012783	0.657310701493608
84	0.435420094368515	0.87084018873703	0.564579905631485
85	0.322694088210608	0.645388176421216	0.677305911789392
86	0.210342135893194	0.420684271786388	0.789657864106806
87	0.117591153565425	0.235182307130851	0.882408846434575

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	3	0.036144578313253	OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/10vi6q1227732479.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/10vi6q1227732479.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/1d8fm1227732478.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/2d9cu1227732478.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/2d9cu1227732478.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/3biah1227732478.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/3biah1227732478.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/4ngms1227732478.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/4ngms1227732478.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/504ie1227732478.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/504ie1227732478.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/6ppyp1227732478.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/6ppyp1227732478.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/71u9p1227732479.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/8y3s71227732479.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/9078v1227732479.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227732605rjwlvsgra16s2w1/9078v1227732479.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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