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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: Wed, 15 Dec 2010 10:03:46 +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/Dec/15/t1292407307lex8uac1av6qlpv.htm/, Retrieved Wed, 15 Dec 2010 11:01:50 +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/Dec/15/t1292407307lex8uac1av6qlpv.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 «

998 1.2 613 -1906 -2.3 -0.6 499 2.3 998 -706 1.2 -1.1 59 1.3 499 326 2.3 -0.6 175 1.4 59 146 1.3 -2 -413 -1.5 175 625 1.4 0 -223 1.4 -413 104 -1.5 -1.1 110 -0.9 -223 65 1.4 3.4 13 -0.6 110 25 -0.9 0.8 74 1.8 13 3 -0.6 -3.2 643 -3.9 74 -393 1.8 3.1 44 2.4 643 -358 -3.9 -1.7 216 1.1 44 613 2.4 -2.3 -1189 -2.3 216 998 1.1 1.2 -47 -4.3 -1189 499 -2.3 2.3 279 1 -47 59 -4.3 1.3 374 0.8 279 175 1 1.4 13 0.3 374 -413 0.8 -1.5 152 2.2 13 -223 0.3 1.4 -27 1.7 152 110 2.2 -0.9 334 1.8 -27 13 1.7 -0.6 411 0.6 334 74 1.8 1.8 33 -2.6 411 643 0.6 -3.9 313 -0.3 33 44 -2.6 2.4 751 0.1 313 216 -0.3 1.1 446 0.9 751 -1189 0.1 -2.3 -329 2.2 446 -47 0.9 -4.3 -560 -2.2 -329 279 2.2 1 -783 0.4 -560 374 -2.2 0.8 -371 -1.1 -783 13 0.4 0.3 -308 -3 -371 152 -1.1 2.2 -264 -2.1 -308 -27 -3 1.7 -787 -1.5 -264 334 -2.1 1.8 -486 0.5 -787 411 -1.5 0.6 -243 3.8 -486 33 0.5 -2.6 -416 -1.9 -243 313 3.8 -0.3 -992 -1.6 -416 751 -1.9 0.1 -316 1.5 -992 446 -1.6 0.9 825 -2.6 -316 -329 1.5 2.2 1513 0.6 825 -560 -2 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	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

N12S[t] = -52.4100540892267 + 42.7705862096527N12T[t] + 0.256586786394052N12S1[t] -0.418733841178069N12S12[t] -41.6357254273286N12T1[t] + 45.5257486013084N12T12[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)	-52.4100540892267	49.770193	-1.053	0.296162	0.148081
N12T	42.7705862096527	26.456369	1.6166	0.110725	0.055362
N12S1	0.256586786394052	0.109176	2.3502	0.021761	0.010881
N12S12	-0.418733841178069	0.108382	-3.8635	0.000257	0.000129
N12T1	-41.6357254273286	22.549626	-1.8464	0.069318	0.034659
N12T12	45.5257486013084	29.687202	1.5335	0.129929	0.064965

Multiple Linear Regression - Regression Statistics
Multiple R	0.639027994821959
R-squared	0.408356778166173
Adjusted R-squared	0.363535321966641
F-TEST (value)	9.11074321968223
F-TEST (DF numerator)	5
F-TEST (DF denominator)	66
p-value	1.25979543208476e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	417.328344799402
Sum Squared Residuals	11494754.5266186

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	998	1022.75577002938	-24.755770029381
2	499	497.620804911722	1.3791950882784
3	59	-128.356335473738	187.356335473738
4	175	-183.705694068606	358.705694068606
5	-413	-391.6619121193	-21.3380878807001
6	-223	-129.674630979422	-93.3253690205783
7	110	-78.8426050741738	188.842605074174
8	13	13.5765464245182	-0.576546424518219
9	74	-94.044532480053	168.044532480053
10	643	30.5199963641334	612.480003635867
11	44	450.116928151422	-406.116928151422
12	216	-455.391398108025	671.391398108025
13	-1189	-504.424429654517	-684.575570345483
14	-47	-549.882060295252	502.882060295252
15	279	191.812749049614	87.1872509503864
16	374	2.21602869077702	371.783971309223
17	13	127.724453047762	-114.724453047762
18	152	189.643840791474	-37.6438407914745
19	-27	-119.331358211809	92.3313582118093
20	334	-85.8905644670497	419.89056446705
21	411	34.9680217581648	376.031978241835
22	33	-529.934123705366	562.934123705366
23	313	142.316527741241	170.683472258759
24	751	4.30120006825166	746.698799931748
25	446	567.781892916376	-121.781892916376
26	-329	-57.429439031096	-271.570560968904
27	-560	-393.821985501602	-166.178014498398
28	-783	-207.577681765463	-575.422318234537
29	-371	-308.805258192241	-62.1947418077589
30	-308	-193.607109436505	-114.392890563495
31	-264	-7.6502527228472	-256.349747277153
32	-787	-154.780577085465	-632.219422914535
33	-486	-315.289133298927	-170.710866701073
34	-243	-167.586030515998	-75.4139694840021
35	-416	-498.961930474298	82.9619304742983
36	-992	-458.391756717271	-533.608243282729
37	-316	-321.953225618162	5.95322561816243
38	825	-69.228510205374	894.228510205374
39	1513	427.523586659553	1085.47641334045
40	138	639.794981067667	-501.794981067667
41	363	130.5874959455	232.4125040545
42	180	93.6834638136426	86.3165361863574
43	-493	-31.7836351750391	-461.216364824961
44	-325	-49.1063861439158	-275.893613856084
45	-225	212.118253811015	-437.118253811015
46	-115	233.948913624658	-348.948913624658
47	-145	-106.980095825436	-38.0199041745636
48	-68	291.080703924177	-359.080703924177
49	-335	-22.542632735145	-312.457367264855
50	-832	-481.558875205522	-350.441124794478
51	-931	-983.43612313404	52.4361231340408
52	-149	-314.750283784912	165.750283784912
53	-251	-122.819935882812	-128.180064117188
54	-43	-177.213098235085	134.213098235085
55	1484	426.898668389656	1057.10133161034
56	195	155.939533169133	39.0604668308667
57	170	226.065000294765	-56.0650002947649
58	-277	77.958640828154	-354.958640828154
59	-57	47.7222745562737	-104.722274556274
60	-665	-33.5039199316133	-631.496080068387
61	-220	-84.0291929230422	-135.970807076958
62	534	198.445981997234	335.554018002766
63	-449	313.865262069252	-762.865262069252
64	158	-100.834334833842	258.834334833842
65	-261	-61.4169414750066	-199.583058524993
66	-300	-154.999571697726	-145.000428302274
67	-1276	-797.76521185113	-478.23478814887
68	-108	-204.250031791221	96.2500317912209
69	-29	-290.745506123803	261.745506123803
70	305	77.1961697126347	227.803830287365
71	805	246.137398853682	558.862601146318
72	-88	560.637223290022	-648.637223290022

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0368060913061975	0.073612182612395	0.963193908693803
10	0.0166236661474357	0.0332473322948713	0.983376333852564
11	0.0120387299128878	0.0240774598257756	0.987961270087112
12	0.0170998079218576	0.0341996158437151	0.982900192078142
13	0.0925127476134246	0.185025495226849	0.907487252386575
14	0.0828973000403048	0.16579460008061	0.917102699959695
15	0.143076118154278	0.286152236308556	0.856923881845722
16	0.117519659785338	0.235039319570676	0.882480340214662
17	0.0935560365379067	0.187112073075813	0.906443963462093
18	0.0909241692398646	0.181848338479729	0.909075830760135
19	0.0642324384317765	0.128464876863553	0.935767561568223
20	0.0439129095119445	0.0878258190238891	0.956087090488055
21	0.0355483801923312	0.0710967603846624	0.964451619807669
22	0.0629364469481638	0.125872893896328	0.937063553051836
23	0.0524751659777396	0.104950331955479	0.94752483402226
24	0.162145588074049	0.324291176148099	0.83785441192595
25	0.128994699197508	0.257989398395017	0.871005300802492
26	0.117992102788698	0.235984205577395	0.882007897211302
27	0.143068966248688	0.286137932497375	0.856931033751312
28	0.218157367765802	0.436314735531604	0.781842632234198
29	0.180615128221117	0.361230256442234	0.819384871778883
30	0.145599168782722	0.291198337565445	0.854400831217278
31	0.118756109165604	0.237512218331208	0.881243890834396
32	0.167163409845188	0.334326819690377	0.832836590154812
33	0.128934238842578	0.257868477685157	0.871065761157422
34	0.0948634861141825	0.189726972228365	0.905136513885818
35	0.0783404494948754	0.156680898989751	0.921659550505125
36	0.0843026467664894	0.168605293532979	0.91569735323351
37	0.0604263078870434	0.120852615774087	0.939573692112957
38	0.173798422053479	0.347596844106958	0.82620157794652
39	0.586098093458952	0.827803813082096	0.413901906541048
40	0.633418476699691	0.733163046600617	0.366581523300309
41	0.590115583374642	0.819768833250716	0.409884416625358
42	0.529820977715068	0.940358044569863	0.470179022284932
43	0.545095096659774	0.909809806680452	0.454904903340226
44	0.504877377188965	0.99024524562207	0.495122622811035
45	0.513068442712172	0.973863114575656	0.486931557287828
46	0.519904969999124	0.960190060001753	0.480095030000876
47	0.458899369301341	0.917798738602681	0.54110063069866
48	0.423975288976844	0.847950577953687	0.576024711023156
49	0.38459179584113	0.76918359168226	0.61540820415887
50	0.333669819499547	0.667339638999094	0.666330180500453
51	0.276687587230466	0.553375174460931	0.723312412769535
52	0.221140567242747	0.442281134485494	0.778859432757253
53	0.166526248450281	0.333052496900561	0.83347375154972
54	0.123619959699412	0.247239919398824	0.876380040300588
55	0.428481486297102	0.856962972594204	0.571518513702898
56	0.596343817925423	0.807312364149153	0.403656182074577
57	0.534344171887812	0.931311656224376	0.465655828112188
58	0.475820062993867	0.951640125987735	0.524179937006133
59	0.369549565706284	0.739099131412569	0.630450434293716
60	0.381980587467331	0.763961174934662	0.618019412532669
61	0.267816410565717	0.535632821131435	0.732183589434283
62	0.169274076898437	0.338548153796874	0.830725923101563
63	0.148302191554575	0.296604383109149	0.851697808445425

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	3	0.0545454545454545	NOK
10% type I error level	6	0.109090909090909	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/10j6uw1292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/10j6uw1292407417.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/1u5fk1292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/1u5fk1292407417.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/2u5fk1292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/2u5fk1292407417.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/35ew51292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/35ew51292407417.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/45ew51292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/45ew51292407417.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/55ew51292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/55ew51292407417.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/6ynv81292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/6ynv81292407417.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/7qxdt1292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/7qxdt1292407417.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/8qxdt1292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/8qxdt1292407417.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/9qxdt1292407417.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292407307lex8uac1av6qlpv/9qxdt1292407417.ps (open in new window)

Parameters (Session):

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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