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brood drie dummies zonder trend

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Tue, 18 Dec 2007 06:47:20 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Dec/18/t1197984650d4danp2cyc35ac2.htm/, Retrieved Tue, 18 Dec 2007 14:31:01 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,43 0 0 0 1,44 0 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,48 1 0 0 1,57 1 1 0 1,58 1 1 0 1,58 1 1 0 1,58 1 1 0 1,58 1 1 0 1,59 1 1 0 1,6 1 1 1 1,6 1 1 2 1,61 1 1 3 1,61 1 1 4 1,61 1 1 5 1,62 1 1 6 1,63 1 1 7 1,63 1 1 8 1,64 1 1 9 1,64 1 1 10 1,64 1 1 11 1,64 1 1 12 1,64 1 1 13 1,65 1 1 14 1,65 1 1 15 1,65 1 1 16 1,65 1 1 17

Text written by user:

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 compuational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 1.43052631578947 + 0.0494736842105260x1[t] + 0.107514450867052x2[t] + 0.00442593222260001x3[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)	1.43052631578947	0.001096	1304.8902	0	0
x1	0.0494736842105260	0.001401	35.3114	0	0
x2	0.107514450867052	0.001752	61.3513	0	0
x3	0.00442593222260001	0.000173	25.6546	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.998178686668432
R-squared	0.996360690519116
Adjusted R-squared	0.9962001327479
F-TEST (value)	6205.62108564577
F-TEST (DF numerator)	3
F-TEST (DF denominator)	68
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00477857810284735
Sum Squared Residuals	0.00155276699058083

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.43	1.43052631578947	-0.000526315789470217
2	1.43	1.43052631578947	-0.000526315789473545
3	1.43	1.43052631578947	-0.000526315789474022
4	1.43	1.43052631578947	-0.000526315789473954
5	1.43	1.43052631578947	-0.000526315789473884
6	1.43	1.43052631578947	-0.000526315789473884
7	1.43	1.43052631578947	-0.000526315789473884
8	1.43	1.43052631578947	-0.000526315789473884
9	1.43	1.43052631578947	-0.000526315789473884
10	1.43	1.43052631578947	-0.000526315789473884
11	1.43	1.43052631578947	-0.000526315789473884
12	1.43	1.43052631578947	-0.000526315789473884
13	1.43	1.43052631578947	-0.000526315789473884
14	1.43	1.43052631578947	-0.000526315789473884
15	1.43	1.43052631578947	-0.000526315789473884
16	1.43	1.43052631578947	-0.000526315789473884
17	1.43	1.43052631578947	-0.000526315789473884
18	1.43	1.43052631578947	-0.000526315789473884
19	1.44	1.43052631578947	0.00947368421052612
20	1.48	1.48	1.42301535138722e-19
21	1.48	1.48	1.42301535138722e-19
22	1.48	1.48	1.42301535138722e-19
23	1.48	1.48	1.42301535138722e-19
24	1.48	1.48	1.42301535138722e-19
25	1.48	1.48	1.42301535138722e-19
26	1.48	1.48	1.42301535138722e-19
27	1.48	1.48	1.42301535138722e-19
28	1.48	1.48	1.42301535138722e-19
29	1.48	1.48	1.42301535138722e-19
30	1.48	1.48	1.42301535138722e-19
31	1.48	1.48	1.42301535138722e-19
32	1.48	1.48	1.42301535138722e-19
33	1.48	1.48	1.42301535138722e-19
34	1.48	1.48	1.42301535138722e-19
35	1.48	1.48	1.42301535138722e-19
36	1.48	1.48	1.42301535138722e-19
37	1.48	1.48	1.42301535138722e-19
38	1.48	1.48	1.42301535138722e-19
39	1.48	1.48	1.42301535138722e-19
40	1.48	1.48	1.42301535138722e-19
41	1.48	1.48	1.42301535138722e-19
42	1.48	1.48	1.42301535138722e-19
43	1.48	1.48	1.42301535138722e-19
44	1.48	1.48	1.42301535138722e-19
45	1.48	1.48	1.42301535138722e-19
46	1.48	1.48	1.42301535138722e-19
47	1.48	1.48	1.42301535138722e-19
48	1.48	1.48	1.42301535138722e-19
49	1.48	1.48	1.42301535138722e-19
50	1.57	1.58751445086705	-0.0175144508670520
51	1.58	1.58751445086705	-0.00751445086705203
52	1.58	1.58751445086705	-0.00751445086705203
53	1.58	1.58751445086705	-0.00751445086705203
54	1.58	1.58751445086705	-0.00751445086705203
55	1.59	1.58751445086705	0.00248554913294797
56	1.6	1.59194038308965	0.00805961691034797
57	1.6	1.59636631531225	0.00363368468774796
58	1.61	1.60079224753485	0.00920775246514796
59	1.61	1.60521817975745	0.00478182024254795
60	1.61	1.60964411198005	0.000355888019947941
61	1.62	1.61407004420265	0.00592995579734794
62	1.63	1.61849597642525	0.0115040235747477
63	1.63	1.62292190864785	0.00707809135214771
64	1.64	1.62734784087045	0.0126521591295477
65	1.64	1.63177377309305	0.0082262269069477
66	1.64	1.63619970531565	0.00380029468434769
67	1.64	1.64062563753825	-0.000625637538252325
68	1.64	1.64505156976085	-0.00505156976085233
69	1.65	1.64947750198345	0.000522498016547664
70	1.65	1.65390343420605	-0.00390343420605234
71	1.65	1.65832936642865	-0.00832936642865235
72	1.65	1.66275529865125	-0.0127552986512524

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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)

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')

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Software written by Ed van Stee & Patrick Wessa

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