This analyze is to analysis how Haemophilus influenza which also been called H.influenza cause
Otitis media. Otitis media is a medical term for middle ear infection, it is very common occur at any age
in childhood. An i
This course concerns analysis of discrete random variables. An important special case is a
categorical variable, a variable that takes values in a set of categories. However, we will
also consider variables that are inherently numerical, yet discrete.
Now well consider generalizing logistic regression to the case in which the response Y has
more than 2 ordered categories. In particular, suppose Y takes values in cfw_1, 2, ., J. In
P [Y j|x] = 1(x) + 2(x) + + j (x)
for j = 1, 2, ., J.
February 13, 2016
Analysis of PIMA data In this data report, we will analyze the pima dataset from the faraway package.
In order to answer some specific questions, we are interested in checking for the association between r
This exam will involve analysis of three datasets. One is a Poisson regression problem, one involves a binary response, and the other an ordered categorical variable. All
problems are based on exercises from Julian Faraways Ext
1. The number of boys among the first 7 children was recorded on a sample of 1,334 very
large families in Sweden, and the results are tabulatd below.
Number of boys 0 1
a. Find t
(Use front and back of each sheet if you need to, but show sufficient work to justify your
1. The relationship between coffee drinking and myocardial infarction was studied in
young women, aged 30-49. This retrospectiv
log() = 1.6094 + 0.5878x
= 0.5878 > 0, which indicates that the average number of imperfection is higher for
treatment B than for A.
log(A ) =
log(B ) = + 1 = +
log(B ) log(A ) =
Do Wald test or lik
lilllljijl cfw_if boys among the rst 7 (,xliildmiiii \9215 1'<.L(>1(l(3l (iii a tizri'lllpll ml 1,.5513-1, Vii-1:
lailgt" i'eliiiilies in Sweden. and the results, we Lalmlziial below.
Number of boy: U l _, :5 1: ,3 (j: 7
Frequency 0 57 206 2363 '3
a. Binomial distribution with n = 100 and = 0.25.
b. = n = 25 and sd = n(1 ) = 4.33. P(correct responses 50) = 6.638502e-08, so it is surprising
if the student made at least 50 correct responses.
c. Multinomial distribution with n = 100 and
# Example 3.1:
Seat Belt Data (Confidence Intervals)
seatbelt <- data.frame(Use=c("No","No","Yes","Yes"),
# children under 18 in auto accidents in Florida in 2008
sb.tab <- xtabs(F