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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, 23 Nov 2010 16:33:32 +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/23/t1290529917xz3l9imb6ef68ko.htm/, Retrieved Tue, 23 Nov 2010 17:32:08 +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/23/t1290529917xz3l9imb6ef68ko.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 «

10 14 11 24 26 14 11 7 25 23 18 6 17 30 25 15 12 10 19 23 18 8 12 22 19 11 10 12 22 29 17 10 11 25 25 19 11 11 23 21 7 16 12 17 22 12 11 13 21 25 13 13 14 19 24 15 12 16 19 18 14 8 11 15 22 14 12 10 16 15 16 11 11 23 22 16 4 15 27 28 12 9 9 22 20 12 8 11 14 12 13 8 17 22 24 16 14 17 23 20 9 15 11 23 21 11 11 11 20 28 14 8 15 23 24 11 9 13 19 24 17 9 13 22 23 14 8 12 32 25 15 9 17 25 21 11 16 9 29 26 15 11 9 28 22 14 16 12 17 22 11 12 18 28 22 12 12 12 29 23 9 10 15 14 17 16 9 16 25 23 13 10 10 26 23 15 12 11 20 25 10 14 9 32 24 13 14 17 25 21 16 10 12 20 28 15 6 6 15 16 13 13 12 24 29 16 11 11 23 22 15 7 7 22 28 16 15 13 14 16 15 9 12 24 25 13 10 13 24 24 11 10 12 22 29 17 10 11 19 23 10 11 9 31 30 17 8 11 22 24 14 13 10 19 25 15 11 11 25 25 16 9 15 27 26 12 12 14 22 24 11 12 13 19 22 16 8 16 25 24 9 14 8 19 27 15 11 16 20 24 15 10 12 17 21 13 11 9 17 23 15 10 15 22 20 15 12 16 19 18 18 8 15 21 22 16 14 11 20 29 12 14 11 17 15 15 8 16 18 24 13 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	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Perceived_happiness[t] = + 17.1715157230984 -0.416465963958886Doubts_about_actions[t] + 0.113485164740974Parental_expectations[t] + 0.0461100852218752Personal_standards[t] -0.0662166842626288Organization[t] + 0.00538992359532798t + 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)	17.1715157230984	2.483115	6.9153	0	0
Doubts_about_actions	-0.416465963958886	0.099705	-4.177	8e-05	4e-05
Parental_expectations	0.113485164740974	0.092264	1.23	0.222591	0.111296
Personal_standards	0.0461100852218752	0.064902	0.7105	0.479652	0.239826
Organization	-0.0662166842626288	0.076932	-0.8607	0.392172	0.196086
t	0.00538992359532798	0.011059	0.4874	0.627446	0.313723

Multiple Linear Regression - Regression Statistics
Multiple R	0.468551377754906
R-squared	0.219540393596021
Adjusted R-squared	0.166806636406563
F-TEST (value)	4.16318512650771
F-TEST (DF numerator)	5
F-TEST (DF denominator)	74
p-value	0.00218216744827227
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.26281152301808
Sum Squared Residuals	378.903383164052

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	10	11.9797272179168	-1.97972721791676
2	14	13.0253345124346	0.974665487565415
3	18	16.3460229608182	1.65397703918180
4	15	12.6834433785580	2.31655662144197
5	18	14.984864480187	3.01513551981301
6	11	13.4951556332383	-2.49515563323826
7	17	13.7902573848088	3.20974261519124
8	19	13.5518279110520	5.44817208894804
9	7	11.2454959840000	-4.24549598399995
10	12	13.4324911802303	-1.4324911802303
11	13	12.6924308544677	0.307569145532292
12	15	13.7385571770796	1.26144282292036
13	14	14.3930780548676	-0.393078054867631
14	14	13.1287458329467	0.871254167053282
15	16	13.5233406919566	2.47665930804337
16	16	16.6850732575398	-0.685073257539783
17	12	14.2264054208865	-2.22640542088649
18	12	15.0360844302487	-3.03608443024868
19	13	15.2966658129133	-2.29666581291331
20	16	13.1142367750277	2.88576322497229
21	9	11.9560330619557	-2.95603306195568
22	11	13.0254397958825	-2.02543979588253
23	14	15.1373652630345	-1.13736526303455
24	11	14.3148785523015	-3.31487855230154
25	17	14.5248154158251	2.47518458417488
26	14	15.1618536223319	-1.16185362233186
27	15	15.2602995461706	-0.260299546170558
28	11	11.2959033237003	-0.295903323700252
29	15	13.6023797189186	1.39762028108135
30	14	11.3586843795018	2.64131562049816
31	11	14.2180600848192	-3.21806008481918
32	12	13.5224324209279	-1.52243242092792
33	9	14.4068585939116	-5.40685859391158
34	16	15.0521104780716	0.947889521928377
35	13	14.0062335344841	-1.00623353448410
36	15	12.8830828150461	2.11691718495388
37	10	12.4481081881669	-2.44810818816686
38	13	13.2372588859247	-0.237258885924736
39	16	13.6470196257030	2.35298037429704
40	15	15.2014122017302	-0.201412201730163
41	13	12.5266252376418	0.473374762358168
42	16	13.6688686290305	2.33113137096951
43	15	14.4427715586998	0.557228441300185
44	16	12.2230642884464	3.77693571155356
45	15	14.4789155249092	0.521084475090796
46	13	14.2475413335492	-1.24754133354925
47	11	13.7161425006467	-2.71614250064671
48	17	13.8670171094112	3.13298289058879
49	10	13.3187749723898	-3.31877497238980
50	17	14.7828424559226	2.21715754407737
51	14	12.3878704550543	1.61212954494570
52	15	13.6163379826396	1.38366201736037
53	16	14.9346039792977	1.06539602070226
54	12	13.4789937886913	-1.47899378869132
55	11	13.3650016604053	-2.36500166040531
56	16	15.5209380768651	0.479061923134904
57	9	11.6443403346602	-2.64434033466018
58	15	14.0517696060697	0.948230393930283
59	15	14.0800046317823	0.919995368217702
60	13	13.1960397286706	-0.196039728670561
61	15	14.7280070835679	0.271992916432120
62	15	14.0080533568460	0.99194664315396
63	18	15.3931754049292	2.60682459507083
64	16	11.9362020107470	4.06379798925298
65	12	12.7302952583535	-0.73029525835352
66	15	15.2520667162652	-0.252066716265249
67	13	15.7559348715538	-2.75593487155382
68	13	15.0607213248986	-2.06072132489861
69	13	12.8403958794444	0.159604120555556
70	14	13.5041760864014	0.495823913598601
71	15	14.0593011722805	0.940698827719508
72	11	13.2124941624682	-2.21249416246822
73	14	14.3584303942251	-0.358430394225115
74	17	14.7987154574695	2.20128454253047
75	13	14.3846832594306	-1.38468325943065
76	12	14.5777864832943	-2.57778648329434
77	13	13.5554758963067	-0.555475896306692
78	16	14.1770410847986	1.82295891520145
79	13	14.8112319451956	-1.81123194519565
80	19	15.6778798371541	3.32212016284588

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.857396735787115	0.28520652842577	0.142603264212885
10	0.788207381060675	0.423585237878650	0.211792618939325
11	0.73375289793857	0.53249420412286	0.26624710206143
12	0.630099341264958	0.739801317470084	0.369900658735042
13	0.588621077856228	0.822757844287544	0.411378922143772
14	0.609699389021423	0.780601221957154	0.390300610978577
15	0.537566223831791	0.924867552336417	0.462433776168209
16	0.536157436869718	0.927685126260565	0.463842563130282
17	0.712562981888567	0.574874036222866	0.287437018111433
18	0.829295614219036	0.341408771561928	0.170704385780964
19	0.785023003734198	0.429953992531603	0.214976996265802
20	0.802439784855335	0.395120430289331	0.197560215144665
21	0.820832071339266	0.358335857321468	0.179167928660734
22	0.804173981494126	0.391652037011749	0.195826018505874
23	0.746807456992467	0.506385086015065	0.253192543007533
24	0.734669975599321	0.530660048801357	0.265330024400679
25	0.811222225213158	0.377555549573684	0.188777774786842
26	0.786456790895471	0.427086418209057	0.213543209104529
27	0.728954277925585	0.542091444148831	0.271045722074415
28	0.670625106808368	0.658749786383265	0.329374893191632
29	0.643975038019117	0.712049923961765	0.356024961980883
30	0.773255194369316	0.453489611261368	0.226744805630684
31	0.815795972546519	0.368408054906962	0.184204027453481
32	0.777830477089314	0.444339045821373	0.222169522910687
33	0.91736572426761	0.165268551464779	0.0826342757323897
34	0.907922378142143	0.184155243715713	0.0920776218578567
35	0.879484895874094	0.241030208251812	0.120515104125906
36	0.909244244216089	0.181511511567823	0.0907557557839113
37	0.907613727371596	0.184772545256808	0.0923862726284042
38	0.885117749535182	0.229764500929635	0.114882250464818
39	0.906703775670305	0.18659244865939	0.093296224329695
40	0.87779785648793	0.244404287024139	0.122202143512070
41	0.844902891991057	0.310194216017886	0.155097108008943
42	0.845253193041498	0.309493613917003	0.154746806958502
43	0.803955462444183	0.392089075111634	0.196044537555817
44	0.879202287307154	0.241595425385691	0.120797712692846
45	0.843133051966385	0.313733896067229	0.156866948033615
46	0.809077314387473	0.381845371225054	0.190922685612527
47	0.8373617271729	0.325276545654201	0.162638272827101
48	0.879670746212441	0.240658507575117	0.120329253787559
49	0.926793414853729	0.146413170292542	0.0732065851462712
50	0.927567816614352	0.144864366771296	0.0724321833856482
51	0.920020604564565	0.159958790870869	0.0799793954354347
52	0.906475596817385	0.187048806365229	0.0935244031826147
53	0.876674494769132	0.246651010461737	0.123325505230868
54	0.851093648651238	0.297812702697524	0.148906351348762
55	0.84896559327006	0.302068813459881	0.151034406729940
56	0.798939676309967	0.402120647380067	0.201060323690034
57	0.849727389479749	0.300545221040501	0.150272610520251
58	0.799224020136398	0.401551959727205	0.200775979863602
59	0.745356752886664	0.509286494226672	0.254643247113336
60	0.673326510131194	0.653346979737613	0.326673489868806
61	0.590667624271953	0.818664751456095	0.409332375728047
62	0.529950760937303	0.940098478125393	0.470049239062697
63	0.677821772995044	0.644356454009913	0.322178227004956
64	0.733803147434432	0.532393705131135	0.266196852565568
65	0.698293362643771	0.603413274712459	0.301706637356229
66	0.641689149771271	0.716621700457457	0.358310850228729
67	0.649396321113407	0.701207357773185	0.350603678886593
68	0.708884209824166	0.582231580351668	0.291115790175834
69	0.708092290978423	0.583815418043155	0.291907709021577
70	0.568896638305785	0.86220672338843	0.431103361694215
71	0.579367048663399	0.841265902673202	0.420632951336601

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/23/t1290529917xz3l9imb6ef68ko/10oypq1290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/10oypq1290530003.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/1ifsw1290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/1ifsw1290530003.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/2ifsw1290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/2ifsw1290530003.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/3s6rz1290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/3s6rz1290530003.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/4s6rz1290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/4s6rz1290530003.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/5s6rz1290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/5s6rz1290530003.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/63y8k1290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/63y8k1290530003.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/7w7751290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/7w7751290530003.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/8w7751290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/8w7751290530003.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/9w7751290530003.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290529917xz3l9imb6ef68ko/9w7751290530003.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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