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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: Sat, 29 Nov 2008 09:33:12 -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/29/t1227976467wzdwuex8znmtd6l.htm/, Retrieved Sat, 29 Nov 2008 16:34:27 +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/29/t1227976467wzdwuex8znmtd6l.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)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply] 
test

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1.1608 0 1.1208 0 1.0883 0 1.0704 0 1.0628 0 1.0378 0 1.0353 0 1.0604 0 1.0501 0 1.0706 0 1.0338 0 1.011 0 1.0137 0 0.9834 0 0.9643 0 0.947 0 0.906 0 0.9492 0 0.9397 0 0.9041 0 0.8721 0 0.8552 0 0.8564 0 0.8973 0 0.9383 0 0.9217 0 0.9095 0 0.892 0 0.8742 0 0.8532 0 0.8607 0 0.9005 0 0.9111 0 0.9059 0 0.8883 0 0.8924 0 0.8833 0 0.87 0 0.8758 0 0.8858 0 0.917 0 0.9554 0 0.9922 0 0.9778 0 0.9808 0 0.9811 0 1.0014 0 1.0183 0 1.0622 0 1.0773 0 1.0807 0 1.0848 0 1.1582 0 1.1663 0 1.1372 0 1.1139 0 1.1222 0 1.1692 0 1.1702 0 1.2286 0 1.2613 0 1.2646 0 1.2262 0 1.1985 0 1.2007 0 1.2138 0 1.2266 0 1.2176 0 1.2218 0 1.249 0 1.2991 0 1.3408 0 1.3119 0 1.3014 0 1.3201 0 1.2938 0 1.2694 0 1.2165 0 1.2037 0 1.2292 0 1.2256 0 1.2015 0 1.1786 0 1.1856 0 1.2103 0 1.1938 0 1.202 0 1.2271 0 1.277 0 1.265 0 1.2684 0 1.2811 0 1.2727 0 1.2611 0 1.2881 0 1.3213 0 1.2999 0 1.3074 0 1.3242 0 1.3516 0 1.3511 0 1.3419 1 1.3716 1 1.3622 1 1.3896 1 1.4227 1 1.4684 1 1.457 1 1.4718 1 1.4748 1 1.5527 1 1.5751 1 1.5557 1 1.5553 1 1.577 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	5 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Multiple Linear Regression - Estimated Regression Equation

y[t] = + 1.10883781640744 + 0.372759652333028x[t] + 0.0152362183592561M1[t] + 0.00540621835925587M2[t] + 0.00826621835925592M3[t] + 0.0064962183592559M4[t] + 0.0110962183592559M5[t] -0.0279497468740469M6[t] -0.0221497468740469M7[t] -0.0339444444444444M8[t] -0.0340333333333333M9[t] -0.0262222222222222M10[t] -0.0186666666666666M11[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)	1.10883781640744	0.052067	21.2964	0	0
x	0.372759652333028	0.044619	8.3544	0	0
M1	0.0152362183592561	0.071445	0.2133	0.831551	0.415775
M2	0.00540621835925587	0.071445	0.0757	0.93983	0.469915
M3	0.00826621835925592	0.071445	0.1157	0.908117	0.454059
M4	0.0064962183592559	0.071445	0.0909	0.927729	0.463865
M5	0.0110962183592559	0.071445	0.1553	0.876882	0.438441
M6	-0.0279497468740469	0.071553	-0.3906	0.696896	0.348448
M7	-0.0221497468740469	0.071553	-0.3096	0.757529	0.378765
M8	-0.0339444444444444	0.073299	-0.4631	0.644282	0.322141
M9	-0.0340333333333333	0.073299	-0.4643	0.643416	0.321708
M10	-0.0262222222222222	0.073299	-0.3577	0.721275	0.360637
M11	-0.0186666666666666	0.073299	-0.2547	0.799495	0.399747

Multiple Linear Regression - Regression Statistics
Multiple R	0.640775064532684
R-squared	0.410592683326865
Adjusted R-squared	0.341250646071202
F-TEST (value)	5.92126651562048
F-TEST (DF numerator)	12
F-TEST (DF denominator)	102
p-value	1.00597258190227e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.155490707653993
Sum Squared Residuals	2.46609073700742

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.1608	1.12407403476670	0.0367259652333049
2	1.1208	1.11424403476670	0.00655596523330277
3	1.0883	1.11710403476670	-0.0288040347666970
4	1.0704	1.11533403476670	-0.044934034766697
5	1.0628	1.11993403476670	-0.0571340347666971
6	1.0378	1.08088806953339	-0.0430880695333942
7	1.0353	1.08668806953339	-0.0513880695333941
8	1.0604	1.07489337196300	-0.0144933719629967
9	1.0501	1.07480448307411	-0.0247044830741079
10	1.0706	1.08261559418522	-0.0120155941852191
11	1.0338	1.09017114974077	-0.0563711497407745
12	1.011	1.10883781640744	-0.0978378164074413
13	1.0137	1.12407403476670	-0.110374034766697
14	0.9834	1.11424403476670	-0.130844034766697
15	0.9643	1.11710403476670	-0.152804034766697
16	0.947	1.11533403476670	-0.168334034766697
17	0.906	1.11993403476670	-0.213934034766697
18	0.9492	1.08088806953339	-0.131688069533394
19	0.9397	1.08668806953339	-0.146988069533394
20	0.9041	1.07489337196300	-0.170793371962997
21	0.8721	1.07480448307411	-0.202704483074108
22	0.8552	1.08261559418522	-0.227415594185219
23	0.8564	1.09017114974077	-0.233771149740775
24	0.8973	1.10883781640744	-0.211537816407441
25	0.9383	1.12407403476670	-0.185774034766697
26	0.9217	1.11424403476670	-0.192544034766697
27	0.9095	1.11710403476670	-0.207604034766697
28	0.892	1.11533403476670	-0.223334034766697
29	0.8742	1.11993403476670	-0.245734034766697
30	0.8532	1.08088806953339	-0.227688069533394
31	0.8607	1.08668806953339	-0.225988069533394
32	0.9005	1.07489337196300	-0.174393371962997
33	0.9111	1.07480448307411	-0.163704483074108
34	0.9059	1.08261559418522	-0.176715594185219
35	0.8883	1.09017114974077	-0.201871149740775
36	0.8924	1.10883781640744	-0.216437816407441
37	0.8833	1.12407403476670	-0.240774034766698
38	0.87	1.11424403476670	-0.244244034766697
39	0.8758	1.11710403476670	-0.241304034766697
40	0.8858	1.11533403476670	-0.229534034766697
41	0.917	1.11993403476670	-0.202934034766697
42	0.9554	1.08088806953339	-0.125488069533394
43	0.9922	1.08668806953339	-0.0944880695333943
44	0.9778	1.07489337196300	-0.0970933719629968
45	0.9808	1.07480448307411	-0.094004483074108
46	0.9811	1.08261559418522	-0.101515594185219
47	1.0014	1.09017114974077	-0.0887711497407746
48	1.0183	1.10883781640744	-0.0905378164074413
49	1.0622	1.12407403476670	-0.0618740347666973
50	1.0773	1.11424403476670	-0.0369440347666972
51	1.0807	1.11710403476670	-0.0364040347666972
52	1.0848	1.11533403476670	-0.0305340347666971
53	1.1582	1.11993403476670	0.0382659652333027
54	1.1663	1.08088806953339	0.0854119304666056
55	1.1372	1.08668806953339	0.0505119304666057
56	1.1139	1.07489337196300	0.0390066280370031
57	1.1222	1.07480448307411	0.0473955169258922
58	1.1692	1.08261559418522	0.086584405814781
59	1.1702	1.09017114974077	0.0800288502592253
60	1.2286	1.10883781640744	0.119762183592559
61	1.2613	1.12407403476670	0.137225965233303
62	1.2646	1.11424403476670	0.150355965233303
63	1.2262	1.11710403476670	0.109095965233303
64	1.1985	1.11533403476670	0.0831659652333027
65	1.2007	1.11993403476670	0.080765965233303
66	1.2138	1.08088806953339	0.132911930466606
67	1.2266	1.08668806953339	0.139911930466606
68	1.2176	1.07489337196300	0.142706628037003
69	1.2218	1.07480448307411	0.146995516925892
70	1.249	1.08261559418522	0.166384405814781
71	1.2991	1.09017114974077	0.208928850259225
72	1.3408	1.10883781640744	0.231962183592559
73	1.3119	1.12407403476670	0.187825965233303
74	1.3014	1.11424403476670	0.187155965233303
75	1.3201	1.11710403476670	0.202995965233303
76	1.2938	1.11533403476670	0.178465965233303
77	1.2694	1.11993403476670	0.149465965233303
78	1.2165	1.08088806953339	0.135611930466606
79	1.2037	1.08668806953339	0.117011930466606
80	1.2292	1.07489337196300	0.154306628037003
81	1.2256	1.07480448307411	0.150795516925892
82	1.2015	1.08261559418522	0.118884405814781
83	1.1786	1.09017114974077	0.0884288502592255
84	1.1856	1.10883781640744	0.0767621835925588
85	1.2103	1.12407403476670	0.0862259652333026
86	1.1938	1.11424403476670	0.0795559652333028
87	1.202	1.11710403476670	0.0848959652333028
88	1.2271	1.11533403476670	0.111765965233303
89	1.277	1.11993403476670	0.157065965233303
90	1.265	1.08088806953339	0.184111930466606
91	1.2684	1.08668806953339	0.181711930466606
92	1.2811	1.07489337196300	0.206206628037003
93	1.2727	1.07480448307411	0.197895516925892
94	1.2611	1.08261559418522	0.178484405814781
95	1.2881	1.09017114974077	0.197928850259225
96	1.3213	1.10883781640744	0.212462183592559
97	1.2999	1.12407403476670	0.175825965233303
98	1.3074	1.11424403476670	0.193155965233303
99	1.3242	1.11710403476670	0.207095965233303
100	1.3516	1.11533403476670	0.236265965233303
101	1.3511	1.11993403476670	0.231165965233303
102	1.3419	1.45364772186642	-0.111747721866423
103	1.3716	1.45944772186642	-0.0878477218664227
104	1.3622	1.44765302429603	-0.0854530242960251
105	1.3896	1.44756413540714	-0.0579641354071364
106	1.4227	1.45537524651825	-0.0326752465182474
107	1.4684	1.46293080207380	0.00546919792619696
108	1.457	1.48159746874047	-0.0245974687404695
109	1.4718	1.49683368709973	-0.0250336870997258
110	1.4748	1.48700368709973	-0.0122036870997254
111	1.5527	1.48986368709973	0.0628363129002744
112	1.5751	1.48809368709973	0.0870063129002745
113	1.5557	1.49269368709973	0.0630063129002745
114	1.5553	1.45364772186642	0.101652278133577
115	1.577	1.45944772186642	0.117552278133577

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/1h0m21227976381.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/1h0m21227976381.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/2nigs1227976381.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/2nigs1227976381.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/3cjzm1227976381.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/3cjzm1227976381.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/4z9g41227976381.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/4z9g41227976381.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/5zgc91227976381.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/5zgc91227976381.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/6ypon1227976381.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/6ypon1227976381.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/733391227976381.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/733391227976381.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/806wq1227976381.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/806wq1227976381.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/92ta91227976381.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976467wzdwuex8znmtd6l/92ta91227976381.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)

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))

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')

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()

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')

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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