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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 16:04:09 +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/t1290528255aerphb6upb323gm.htm/, Retrieved Tue, 23 Nov 2010 17:04:31 +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/t1290528255aerphb6upb323gm.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 24 14 11 12 24 26 14 25 11 7 8 25 23 18 17 6 17 8 30 25 15 18 12 10 8 19 23 18 18 8 12 9 22 19 11 16 10 12 7 22 29 17 20 10 11 4 25 25 19 16 11 11 11 23 21 7 18 16 12 7 17 22 12 17 11 13 7 21 25 13 23 13 14 12 19 24 15 30 12 16 10 19 18 14 23 8 11 10 15 22 14 18 12 10 8 16 15 16 15 11 11 8 23 22 16 12 4 15 4 27 28 12 21 9 9 9 22 20 12 15 8 11 8 14 12 13 20 8 17 7 22 24 16 31 14 17 11 23 20 9 27 15 11 9 23 21 11 19 11 11 8 20 28 14 16 8 15 9 23 24 11 21 9 13 9 19 24 17 17 9 13 6 22 23 14 25 8 12 7 32 25 15 17 9 17 9 25 21 11 32 16 9 6 29 26 15 33 11 9 6 28 22 14 13 16 12 5 17 22 11 32 12 18 12 28 22 12 25 12 12 7 29 23 9 18 10 15 8 14 17 16 17 9 16 5 25 23 13 20 10 10 8 26 23 15 15 12 11 8 20 25 10 33 14 9 6 32 24 13 23 14 17 7 25 21 16 20 10 12 8 20 28 15 11 6 6 4 15 16 13 26 13 12 8 24 29 16 15 11 11 8 23 22 15 12 7 7 4 22 28 16 14 15 13 8 14 16 15 17 9 12 9 24 25 13 21 10 13 6 24 24 11 16 10 12 7 22 29 17 10 10 11 5 19 23 10 29 11 9 5 31 30 17 31 8 11 8 22 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	14 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Perceived_happiness[t] = + 16.7653226652419 -0.0589756016812406Concern_over_mistakes[t] -0.366095504550008Doubts_about_actions[t] + 0.119754749652656Parental_expectations[t] + 0.0379382299071181Parental_criticism[t] + 0.0817201066571233Personal_standards[t] -0.0714427177666822Organization[t] + 0.00639294723885901t + 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)	16.7653226652419	2.59331	6.4648	0	0
Concern_over_mistakes	-0.0589756016812406	0.055125	-1.0699	0.288258	0.144129
Doubts_about_actions	-0.366095504550008	0.1125	-3.2542	0.001734	0.000867
Parental_expectations	0.119754749652656	0.101328	1.1819	0.241152	0.120576
Parental_criticism	0.0379382299071181	0.132266	0.2868	0.775064	0.387532
Personal_standards	0.0817201066571233	0.073945	1.1051	0.272778	0.136389
Organization	-0.0714427177666822	0.079607	-0.8974	0.372474	0.186237
t	0.00639294723885901	0.011177	0.572	0.569114	0.284557

Multiple Linear Regression - Regression Statistics
Multiple R	0.481497231972275
R-squared	0.231839584396963
Adjusted R-squared	0.15715732176889
F-TEST (value)	3.10434601521854
F-TEST (DF numerator)	7
F-TEST (DF denominator)	72
p-value	0.00642993924968027
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.27587675343079
Sum Squared Residuals	372.932279770080

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	10	12.1072970113327	-2.10729701133274
2	14	12.8182772122584	1.18172278774156
3	18	16.5902150899761	1.40978491002391
4	15	12.7467404229701	2.25325957702994
5	18	15.0258943086595	2.97410569134051
6	11	13.6277438126798	-2.62774381267975
7	17	13.6955961048577	3.30440389514226
8	19	13.9596942213739	5.04030577862614
9	7	11.4238969148150	-4.42389691481496
10	12	13.5520500094662	-1.5520500094662
11	13	12.6898467411583	0.310153258841718
12	15	13.2417953272696	1.75820467273036
13	14	13.9139744585187	0.0860255414812899
14	14	13.1370513175207	0.862948682479257
15	16	13.8781630462391	2.12183695376092
16	16	16.8496415293823	-0.84964152938227
17	12	14.1288803992074	-2.12888039920742
18	12	14.9745746193584	-2.97457461935839
19	13	15.1631280662567	-2.16312806625667
20	16	12.8434602650542	3.15653973494585
21	9	11.8538124389711	-2.85381243897111
22	11	13.0131946436147	-2.01319464361466
23	14	15.3426893291031	-1.34268932910311
24	11	14.1217188374520	-3.12171883745195
25	17	14.5668025394325	2.43319746056753
26	14	15.0599852890637	-1.05998528906375
27	15	15.5604678777469	-0.560467877746906
28	11	11.0173724187696	-0.0173724187695717
29	15	12.9993180514868	2.00068194851316
30	14	11.7771503554230	2.22284964457704
31	11	14.0104061694124	-3.01040616941241
32	12	13.5316860698589	-1.53168606985886
33	9	14.2831564235748	-5.28315642357475
34	16	15.1908254036044	0.809174596395574
35	13	14.1312023397121	-1.13120233971209
36	15	13.1868309604337	1.81316903956631
37	10	12.1361701068428	-2.13617010684281
38	13	13.3705827047226	-0.370582704722631
39	16	13.5487493991967	2.45125060080331
40	15	15.1287354421369	-0.12873544213691
41	13	12.3648328787988	0.635167121201249
42	16	14.0507726216883	1.94922737831173
43	15	14.5573260606746	0.44267393932543
44	16	12.5908369456385	3.40916305436151
45	15	14.7092762020592	0.290723797940766
46	13	14.1910540157211	-1.19105401572111
47	11	13.8898546494730	-2.88985464947297
48	17	14.2379659839611	2.76203401603890
49	10	12.9987597509198	-2.99875975091978
50	17	14.0319875441588	2.96801245584117
51	14	12.99313195843	1.00686804157001
52	15	13.7689361456849	1.23106385431514
53	16	14.5267188092766	1.47328119072336
54	12	13.5801358105917	-1.58013581059172
55	11	13.3265608938328	-2.32656089383280
56	16	15.6388873741345	0.361112625865480
57	9	11.4194025490300	-2.41940254902998
58	15	13.971995930274	1.028004069726
59	15	13.9525844201434	1.0474155798566
60	13	13.1917825235704	-0.191782523570402
61	15	14.5898127796805	0.410187220319455
62	15	13.5614426892126	1.43855731078741
63	18	15.4978394674212	2.50216053257877
64	16	11.9519432493193	4.04805675068069
65	12	12.4057315724223	-0.405731572422276
66	15	14.7348477462876	0.265152253712442
67	13	16.5683557984367	-3.56835579843668
68	13	15.0756693226037	-2.07566932260374
69	13	12.8276913530023	0.172308646997713
70	14	13.0185051225712	0.981494877428774
71	15	14.3152958796993	0.684704120300686
72	11	13.5822926187289	-2.58229261872895
73	14	14.4377215445217	-0.437721544521706
74	17	14.8575202355102	2.14247976448983
75	13	14.5697258355525	-1.56972583555245
76	12	14.4361380697922	-2.43613806979218
77	13	13.8573303710775	-0.857330371077534
78	16	14.0754052908862	1.92459470911382
79	13	14.6988431298621	-1.69884312986214
80	19	15.7463071764372	3.25369282356277

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.8424468290657	0.315106341868601	0.157553170934300
12	0.742465171043833	0.515069657912335	0.257534828956167
13	0.706464672725847	0.587070654548307	0.293535327274153
14	0.735777045845958	0.528445908308084	0.264222954154042
15	0.662702672318301	0.674594655363398	0.337297327681699
16	0.632661152967825	0.73467769406435	0.367338847032175
17	0.793715041400866	0.412569917198268	0.206284958599134
18	0.890056351672361	0.219887296655278	0.109943648327639
19	0.85186707372725	0.296265852545499	0.148132926272750
20	0.8641877126267	0.271624574746601	0.135812287373300
21	0.871665424269246	0.256669151461508	0.128334575730754
22	0.848151602004423	0.303696795991154	0.151848397995577
23	0.797559776988695	0.404880446022611	0.202440223011305
24	0.779704353697678	0.440591292604644	0.220295646302322
25	0.852987204581466	0.294025590837068	0.147012795418534
26	0.826290877434244	0.347418245131512	0.173709122565756
27	0.77411516089113	0.451769678217740	0.225884839108870
28	0.722496627180673	0.555006745638653	0.277503372819327
29	0.713416814493363	0.573166371013274	0.286583185506637
30	0.788297976531109	0.423404046937783	0.211702023468891
31	0.801476605386341	0.397046789227318	0.198523394613659
32	0.764073900690905	0.471852198618191	0.235926099309096
33	0.907227298671316	0.185545402657368	0.0927727013286838
34	0.885770523604786	0.228458952790427	0.114229476395214
35	0.853118939705676	0.293762120588649	0.146881060294324
36	0.877548581469129	0.244902837061742	0.122451418530871
37	0.866208706041958	0.267582587916084	0.133791293958042
38	0.82968316616274	0.340633667674521	0.170316833837260
39	0.87499903960511	0.250001920789779	0.125000960394889
40	0.836929712547122	0.326140574905756	0.163070287452878
41	0.801879191161078	0.396241617677844	0.198120808838922
42	0.780772450291723	0.438455099416555	0.219227549708277
43	0.727615080673267	0.544769838653467	0.272384919326733
44	0.794486716459848	0.411026567080304	0.205513283540152
45	0.741383161197596	0.517233677604809	0.258616838802404
46	0.691572226900252	0.616855546199496	0.308427773099748
47	0.729574906159835	0.54085018768033	0.270425093840165
48	0.781340202413624	0.437319595172751	0.218659797586376
49	0.850855534812199	0.298288930375602	0.149144465187801
50	0.873766115272288	0.252467769455425	0.126233884727712
51	0.876091968685934	0.247816062628133	0.123908031314066
52	0.859992705870454	0.280014588259092	0.140007294129546
53	0.822318934231846	0.355362131536307	0.177681065768154
54	0.784466974643488	0.431066050713025	0.215533025356512
55	0.773665126646148	0.452669746707704	0.226334873353852
56	0.706486370013119	0.587027259973761	0.293513629986881
57	0.77006764707458	0.459864705850838	0.229932352925419
58	0.707002065119564	0.585995869760872	0.292997934880436
59	0.640916094048006	0.718167811903987	0.359083905951994
60	0.551924186674918	0.896151626650164	0.448075813325082
61	0.461825400047161	0.923650800094322	0.538174599952839
62	0.383961373999046	0.767922747998091	0.616038626000954
63	0.526240262105409	0.947519475789183	0.473759737894591
64	0.697163177643091	0.605673644713818	0.302836822356909
65	0.590405565115348	0.819188869769304	0.409594434884652
66	0.542362206091998	0.915275587816004	0.457637793908002
67	0.459089311485635	0.91817862297127	0.540910688514365
68	0.457010761835037	0.914021523670074	0.542989238164963
69	0.650726425437646	0.698547149124708	0.349273574562354

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/t1290528255aerphb6upb323gm/10jmez1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/10jmez1290528234.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/1u30n1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/1u30n1290528234.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/2nczq1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/2nczq1290528234.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/3nczq1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/3nczq1290528234.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/4nczq1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/4nczq1290528234.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/5x3gb1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/5x3gb1290528234.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/6x3gb1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/6x3gb1290528234.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/78cfw1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/78cfw1290528234.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/88cfw1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/88cfw1290528234.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/9jmez1290528234.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528255aerphb6upb323gm/9jmez1290528234.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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