Home » date » 2008 » Nov » 23 »

Q3 invloed rookverbod op tabakproductie

*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: Sun, 23 Nov 2008 11:38:26 -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/23/t1227465618hif52obqysw27ns.htm/, Retrieved Sun, 23 Nov 2008 18:40:27 +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/23/t1227465618hif52obqysw27ns.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)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

41.1 0 58 0 63 0 53.8 0 54.7 0 55.5 0 56.1 0 69.6 0 69.4 0 57.2 0 68 0 53.3 0 47.9 0 60.8 0 61.7 0 57.8 0 51.4 0 50.5 0 48.1 0 58.7 0 54 0 56.1 0 60.4 0 51.2 0 50.7 0 56.4 0 53.3 0 52.6 0 47.7 0 49.5 0 48.5 0 55.3 0 49.8 0 57.4 0 64.6 0 53 0 41.5 0 55.9 0 58.4 0 53.5 0 50.6 0 58.5 1 49.1 1 61.1 1 52.3 1 58.4 1 65.5 1 61.7 1 45.1 1 52.1 1 59.3 1 57.9 1 45 1 64.9 1 63.8 1 69.4 1 71.1 1 62.9 1 73.5 1 62.6 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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Tabakproductie[t] = + 55.0487804878049 + 4.64595635430039rookverbod[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)	55.0487804878049	1.089145	50.5431	0	0
rookverbod	4.64595635430039	1.935462	2.4004	0.019605	0.009802

Multiple Linear Regression - Regression Statistics
Multiple R	0.300614014507371
R-squared	0.0903687857182379
Adjusted R-squared	0.0746854889202766
F-TEST (value)	5.76210390470864
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0.0196047568983011
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.97393164813844
Sum Squared Residuals	2820.87191270860

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	41.1	55.048780487805	-13.9487804878050
2	58	55.0487804878049	2.95121951219512
3	63	55.0487804878049	7.95121951219512
4	53.8	55.0487804878049	-1.24878048780488
5	54.7	55.0487804878049	-0.348780487804872
6	55.5	55.0487804878049	0.451219512195125
7	56.1	55.0487804878049	1.05121951219513
8	69.6	55.0487804878049	14.5512195121951
9	69.4	55.0487804878049	14.3512195121951
10	57.2	55.0487804878049	2.15121951219513
11	68	55.0487804878049	12.9512195121951
12	53.3	55.0487804878049	-1.74878048780488
13	47.9	55.0487804878049	-7.14878048780488
14	60.8	55.0487804878049	5.75121951219512
15	61.7	55.0487804878049	6.65121951219513
16	57.8	55.0487804878049	2.75121951219512
17	51.4	55.0487804878049	-3.64878048780488
18	50.5	55.0487804878049	-4.54878048780487
19	48.1	55.0487804878049	-6.94878048780487
20	58.7	55.0487804878049	3.65121951219513
21	54	55.0487804878049	-1.04878048780487
22	56.1	55.0487804878049	1.05121951219513
23	60.4	55.0487804878049	5.35121951219512
24	51.2	55.0487804878049	-3.84878048780487
25	50.7	55.0487804878049	-4.34878048780487
26	56.4	55.0487804878049	1.35121951219512
27	53.3	55.0487804878049	-1.74878048780488
28	52.6	55.0487804878049	-2.44878048780487
29	47.7	55.0487804878049	-7.34878048780487
30	49.5	55.0487804878049	-5.54878048780487
31	48.5	55.0487804878049	-6.54878048780488
32	55.3	55.0487804878049	0.251219512195122
33	49.8	55.0487804878049	-5.24878048780488
34	57.4	55.0487804878049	2.35121951219512
35	64.6	55.0487804878049	9.55121951219512
36	53	55.0487804878049	-2.04878048780487
37	41.5	55.0487804878049	-13.5487804878049
38	55.9	55.0487804878049	0.851219512195124
39	58.4	55.0487804878049	3.35121951219512
40	53.5	55.0487804878049	-1.54878048780487
41	50.6	55.0487804878049	-4.44878048780487
42	58.5	59.6947368421053	-1.19473684210526
43	49.1	59.6947368421053	-10.5947368421053
44	61.1	59.6947368421053	1.40526315789474
45	52.3	59.6947368421053	-7.39473684210527
46	58.4	59.6947368421053	-1.29473684210526
47	65.5	59.6947368421053	5.80526315789474
48	61.7	59.6947368421053	2.00526315789474
49	45.1	59.6947368421053	-14.5947368421053
50	52.1	59.6947368421053	-7.59473684210526
51	59.3	59.6947368421053	-0.394736842105266
52	57.9	59.6947368421053	-1.79473684210526
53	45	59.6947368421053	-14.6947368421053
54	64.9	59.6947368421053	5.20526315789474
55	63.8	59.6947368421053	4.10526315789473
56	69.4	59.6947368421053	9.70526315789474
57	71.1	59.6947368421053	11.4052631578947
58	62.9	59.6947368421053	3.20526315789474
59	73.5	59.6947368421053	13.8052631578947
60	62.6	59.6947368421053	2.90526315789474

Charts produced by software:

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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')

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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