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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 09:38:14 -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/t1227976742qkps6jygvi277wi.htm/, Retrieved Sat, 29 Nov 2008 16:39:11 +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/t1227976742qkps6jygvi277wi.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	3 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

y[t] = + 0.883858800039933 + 0.130031266846361x[t] + 0.0335349727130548M1[t] + 0.0195058254301020M2[t] + 0.0181666781471496M3[t] + 0.0121975308641974M4[t] + 0.0125983835812451M5[t] -0.00637389038634339M6[t] -0.00477303766929572M7[t] -0.0171478553126354M8[t] -0.0214358914844766M9[t] -0.0178239276563178M10[t] -0.0144675193837145M11[t] + 0.00419914728295231t + 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)	0.883858800039933	0.037074	23.8403	0	0
x	0.130031266846361	0.033839	3.8426	0.000213	0.000106
M1	0.0335349727130548	0.044668	0.7508	0.454544	0.227272
M2	0.0195058254301020	0.044659	0.4368	0.663206	0.331603
M3	0.0181666781471496	0.044652	0.4069	0.684976	0.342488
M4	0.0121975308641974	0.044647	0.2732	0.785258	0.392629
M5	0.0125983835812451	0.044645	0.2822	0.778375	0.389187
M6	-0.00637389038634339	0.044745	-0.1424	0.887009	0.443504
M7	-0.00477303766929572	0.044733	-0.1067	0.915239	0.45762
M8	-0.0171478553126354	0.045823	-0.3742	0.709023	0.354512
M9	-0.0214358914844766	0.045814	-0.4679	0.640873	0.320437
M10	-0.0178239276563178	0.045808	-0.3891	0.698022	0.349011
M11	-0.0144675193837145	0.045805	-0.3159	0.752766	0.376383
t	0.00419914728295231	0.000332	12.6577	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.87869455699998
R-squared	0.77210412450139
Adjusted R-squared	0.74277099201147
F-TEST (value)	26.3219117414996
F-TEST (DF numerator)	13
F-TEST (DF denominator)	101
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.097163759502998
Sum Squared Residuals	0.9535204122364

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.1608	0.921592920035934	0.239207079964066
2	1.1208	0.91176292003594	0.209037079964061
3	1.0883	0.914622920035939	0.173677079964061
4	1.0704	0.91285292003594	0.157547079964061
5	1.0628	0.917452920035939	0.145347079964061
6	1.0378	0.902679793351303	0.135120206648697
7	1.0353	0.908479793351303	0.126820206648697
8	1.0604	0.900304122990916	0.160095877009084
9	1.0501	0.900215234102027	0.149884765897973
10	1.0706	0.908026345213138	0.162573654786862
11	1.0338	0.915581900768693	0.118218099231307
12	1.011	0.93424856743536	0.0767514325646397
13	1.0137	0.971982687431367	0.0417173125686328
14	0.9834	0.962152687431367	0.0212473125686328
15	0.9643	0.965012687431367	-0.000712687431366661
16	0.947	0.963242687431367	-0.0162426874313669
17	0.906	0.967842687431367	-0.0618426874313668
18	0.9492	0.95306956074673	-0.00386956074673059
19	0.9397	0.95886956074673	-0.0191695607467307
20	0.9041	0.950693890386343	-0.0465938903863432
21	0.8721	0.950605001497454	-0.0785050014974544
22	0.8552	0.958416112608566	-0.103216112608566
23	0.8564	0.965971668164121	-0.109571668164121
24	0.8973	0.984638334830788	-0.087338334830788
25	0.9383	1.02237245482680	-0.0840724548267951
26	0.9217	1.01254245482679	-0.0908424548267944
27	0.9095	1.01540245482679	-0.105902454826795
28	0.892	1.01363245482679	-0.121632454826795
29	0.8742	1.01823245482679	-0.144032454826795
30	0.8532	1.00345932814216	-0.150259328142158
31	0.8607	1.00925932814216	-0.148559328142158
32	0.9005	1.00108365778177	-0.100583657781771
33	0.9111	1.00099476889288	-0.0898947688928821
34	0.9059	1.00880588000399	-0.102905880003993
35	0.8883	1.01636143555955	-0.128061435559549
36	0.8924	1.03502810222622	-0.142628102226216
37	0.8833	1.07276222222222	-0.189462222222223
38	0.87	1.06293222222222	-0.192932222222222
39	0.8758	1.06579222222222	-0.189992222222222
40	0.8858	1.06402222222222	-0.178222222222222
41	0.917	1.06862222222222	-0.151622222222222
42	0.9554	1.05384909553759	-0.098449095537586
43	0.9922	1.05964909553759	-0.0674490955375861
44	0.9778	1.05147342517720	-0.0736734251771987
45	0.9808	1.05138453628831	-0.0705845362883099
46	0.9811	1.05919564739942	-0.078095647399421
47	1.0014	1.06675120295498	-0.0653512029549765
48	1.0183	1.08541786962164	-0.0671178696216434
49	1.0622	1.12315198961765	-0.0609519896176506
50	1.0773	1.11332198961765	-0.03602198961765
51	1.0807	1.11618198961765	-0.0354819896176499
52	1.0848	1.11441198961765	-0.02961198961765
53	1.1582	1.11901198961765	0.0391880103823499
54	1.1663	1.10423886293301	0.062061137066986
55	1.1372	1.11003886293301	0.0271611370669861
56	1.1139	1.10186319257263	0.0120368074273734
57	1.1222	1.10177430368374	0.0204256963162624
58	1.1692	1.10958541479485	0.0596145852051513
59	1.1702	1.11714097035040	0.0530590296495956
60	1.2286	1.13580763701707	0.0927923629829288
61	1.2613	1.17354175701308	0.0877582429869218
62	1.2646	1.16371175701308	0.100888242986922
63	1.2262	1.16657175701308	0.0596282429869223
64	1.1985	1.16480175701308	0.0336982429869222
65	1.2007	1.16940175701308	0.0312982429869224
66	1.2138	1.15462863032844	0.0591713696715584
67	1.2266	1.16042863032844	0.0661713696715583
68	1.2176	1.15225295996805	0.0653470400319458
69	1.2218	1.15216407107917	0.0696359289208346
70	1.249	1.15997518219028	0.0890248178097236
71	1.2991	1.16753073774583	0.131569262254168
72	1.3408	1.1861974044125	0.154602595587501
73	1.3119	1.22393152440851	0.087968475591494
74	1.3014	1.21410152440851	0.0872984755914945
75	1.3201	1.21696152440851	0.103138475591495
76	1.2938	1.21519152440851	0.0786084755914946
77	1.2694	1.21979152440851	0.0496084755914946
78	1.2165	1.20501839772387	0.0114816022761307
79	1.2037	1.21081839772387	-0.00711839772386933
80	1.2292	1.20264272736348	0.0265572726365181
81	1.2256	1.20255383847459	0.0230461615254069
82	1.2015	1.21036494958570	-0.00886494958570423
83	1.1786	1.21792050514126	-0.0393205051412597
84	1.1856	1.23658717180793	-0.0509871718079266
85	1.2103	1.27432129180393	-0.0640212918039339
86	1.1938	1.26449129180393	-0.0706912918039331
87	1.202	1.26735129180393	-0.0653512918039331
88	1.2271	1.26558129180393	-0.0384812918039332
89	1.277	1.27018129180393	0.00681870819606661
90	1.265	1.25540816511930	0.00959183488070291
91	1.2684	1.26120816511930	0.00719183488070291
92	1.2811	1.25303249475891	0.0280675052410902
93	1.2727	1.25294360587002	0.0197563941299791
94	1.2611	1.26075471698113	0.000345283018868117
95	1.2881	1.26831027253669	0.0197897274633126
96	1.3213	1.28697693920335	0.0343230607966455
97	1.2999	1.32471105919936	-0.0248110591993615
98	1.3074	1.31488105919936	-0.00748105919936094
99	1.3242	1.31774105919936	0.00645894080063922
100	1.3516	1.31597105919936	0.0356289408006389
101	1.3511	1.32057105919936	0.0305289408006389
102	1.3419	1.43582919936109	-0.093929199361086
103	1.3716	1.44162919936109	-0.0700291993610862
104	1.3622	1.4334535290007	-0.0712535290006987
105	1.3896	1.43336464011181	-0.0437646401118099
106	1.4227	1.44117575122292	-0.0184757512229210
107	1.4684	1.44873130677848	0.0196686932215233
108	1.457	1.46739797344514	-0.0103979734451434
109	1.4718	1.50513209344115	-0.0333320934411506
110	1.4748	1.49530209344115	-0.0205020934411498
111	1.5527	1.49816209344115	0.05453790655885
112	1.5751	1.49639209344115	0.0787079065588499
113	1.5557	1.50099209344115	0.0547079065588501
114	1.5553	1.48621896675651	0.0690810332434861
115	1.577	1.49201896675651	0.084981033243486

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/1vo0z1227976690.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/1vo0z1227976690.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/2ek4q1227976690.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/2ek4q1227976690.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/3ppr71227976690.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/3ppr71227976690.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/442sx1227976690.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/442sx1227976690.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/5puv41227976690.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/5puv41227976690.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/6p7361227976690.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/6p7361227976690.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/7y2ky1227976690.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/7y2ky1227976690.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/8b1px1227976690.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/8b1px1227976690.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/94gao1227976690.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227976742qkps6jygvi277wi/94gao1227976690.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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