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Beta Regression Beta Regression

Beta Regression - PowerPoint Presentation

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Beta Regression - PPT Presentation

Proportion of Prize Money for Ford in NASCAR Winston Cup Races 19942000 Methodology SLP Ferrari and F CribariNeto 2004 Beta Regression for Modelling Rates and Proportions Journal of Applied Statistics ID: 464471

mod1 beta ford distribution beta mod1 distribution ford link logit track year winner type data races cup winston proportion

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Slide1

Beta Regression

Proportion of Prize Money for Ford in NASCAR Winston Cup Races – 1994-2000

Methodology: S.L.P. Ferrari and F.

Cribari-Neto

(2004). “Beta Regression for Modelling Rates and Proportions,”

Journal of Applied Statistics

, Vol. 31, #7, pp. 799-815.

Data:

L. Winner (2006). “NASCAR Winston Cup Race Results for 1975-2003,”

Journal of Statistics Education,

Vol.14,#3, www.amstat.org/publications/jse/v14n3/datasets.winner.htmlSlide2

Data Description

Units: 267 Winston Cup Races for Years 1992-2000

Response: Proportion of Prize Money Won by Ford Cars

Predictor Variables:

Proportion of all Cars that are Fords for the race

Track LENGTH (Miles)

Track Turn BANK (Degrees)

Number of LAPS

Year Dummy Variables (Year1993-Year2000)

Distribution: Beta (Scaled for responses between 0 and 1)

Link Function: Logit: log(

m

/(1-

m

))=

b

0

+

b

1

X

1

+…+

b

P

X

pSlide3
Slide4
Slide5

Beta Distribution – Likelihood FunctionSlide6

Beta Distribution – Logit LinkSlide7

Beta Distribution – Logit LinkSlide8

Beta Distribution – Logit LinkSlide9

Beta Distribution – Logit LinkSlide10

Variance-Covariance Matrix & Starting Values Slide11

First 6 Races & Preliminary OLS RegressionSlide12

Iterative Results for

qSlide13

Diagnostic MeasuresSlide14

Influence MeasuresSlide15
Slide16
Slide17
Slide18
Slide19

R Program

### Fisher Scoring Method

ford <- read.csv("http

://www.stat.ufl.edu/~winner/data/nas_ford_1992_2000a.csv

",header=T)

attach(ford); names(ford)

library(

betareg

)

Year <- factor(Year)

Track_id

<- factor(

Track_id

)

beta.mod1 <-

betareg

(

FPrzp

~

FDrvp

+

TrkLng

+ Bank + Laps + Year)

summary(beta.mod1)

resid

(beta.mod1,type="

pearson

")

resid

(beta.mod1,type="deviance")

cooks.distance

(beta.mod1)

gleverage

(beta.mod1)

hatvalues

(beta.mod1)

par(

mfrow

=c(2,2))

plot(beta.mod1,which=1:4,type="

pearson

")