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Introduction to Categorical Data Analysis

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PROBLEMS 319<br />

and yi = number of those for which mother’s age was under 18. Let πi be the<br />

probability that a pregnancy in county i is <strong>to</strong> a mother of age under 18. The<br />

logistic-normal model, logit(πi) = ui + α, has ˆα =−3.24 and ˆσ = 0.33.<br />

a. Find ˆπi for a county estimated <strong>to</strong> be (i) at the mean, (ii) two standard<br />

deviations below the mean, (iii) two standard deviations above the mean<br />

on the random effects distribution.<br />

b. For estimating {πi}, what advantage does this model have over the fixed<br />

effects model, logit(πi) = βi?<br />

10.4 Table 10.9 shows the free-throw shooting, by game, of Shaq O’Neal of the<br />

Los Angeles Lakers during the 2000 NBA (basketball) playoffs. In game i,<br />

let yi = number made out of Ti attempts.<br />

a. Fit the model, logit(πi) = ui + α, where {ui} are independent N(0,σ),<br />

and given {ui}, {yi} are independent binomial variates for {Ti} trials with<br />

success probabilities {πi}. Report ˆα and ˆσ .<br />

b. Use ˆα <strong>to</strong> summarize O’Neal’s free-throw shooting in an average game<br />

(for which ui = 0).<br />

c. Use ˆα and ˆσ <strong>to</strong> estimate how O’Neal’s free-throw shooting varies among<br />

games.<br />

Table 10.9. Shaq O’Neal Basketball <strong>Data</strong> for Problem 10.4<br />

No. No. No. No. No. No.<br />

Game Made Attempts Game Made Attempts Game Made Attempts<br />

1 4 5 9 4 12 17 8 12<br />

2 5 11 10 1 4 18 1 6<br />

3 5 14 11 13 27 19 18 39<br />

4 5 12 12 5 17 20 3 13<br />

5 2 7 13 6 12 21 10 17<br />

6 7 10 14 9 9 22 1 6<br />

7 6 14 15 7 12 23 3 12<br />

8 9 15 16 3 10<br />

Source: www.nba.com.<br />

10.5 For 10 coins, let πi denote the probability of a head for coin i. You<br />

flip each coin five times. The sample numbers of heads are {2, 4, 1, 3, 3,<br />

5, 4, 2, 3, 1}.<br />

a. Report the sample proportion estimates of πi. Formulate a model for which<br />

these are the ML estimates.<br />

b. Formulate a random effects model for the data. Using software, find the<br />

ML estimates of the parameters. Interpret.<br />

c. Using software, for the model in (b) obtain predicted values {ˆπi}.

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