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Introduction to Categorical Data Analysis

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132 LOGISTIC REGRESSION<br />

b. In Table 4.18, explain what the coefficients of R and S represent, for the<br />

coding as given above. What hypotheses do the P -values refer <strong>to</strong> for these<br />

variables?<br />

c. Suppose the model also contained an A × R interaction term, with coefficient<br />

0.04. In the prediction equation, show that this represents the<br />

difference between the effect of A for blacks and for whites.<br />

4.24 Table 4.19 shows results of a study about Y = whether a patient having surgery<br />

with general anesthesia experienced a sore throat on waking (1 = yes) as a<br />

function of D = duration of the surgery (in minutes) and T = type of device<br />

used <strong>to</strong> secure the airway (0 = laryngeal mask airway, 1 = tracheal tube).<br />

a. Fit a main effects model using these predic<strong>to</strong>rs. Interpret parameter<br />

estimates.<br />

b. Conduct inference about the D effect in (a).<br />

c. Fit a model permitting interaction. Report the prediction equation for the<br />

effect of D when (i) T = 1, (ii) T = 0. Interpret.<br />

d. Conduct inference about whether you need the interaction term in (c).<br />

Table 4.19. <strong>Data</strong> for Problem 4.24 on Sore Throat after Surgery<br />

Patient D T Y Patient D T Y Patient D T Y<br />

1 45 0 0 13 50 1 0 25 20 1 0<br />

2 15 0 0 14 75 1 1 26 45 0 1<br />

3 40 0 1 15 30 0 0 27 15 1 0<br />

4 83 1 1 16 25 0 1 28 25 0 1<br />

5 90 1 1 17 20 1 0 29 15 1 0<br />

6 25 1 1 18 60 1 1 30 30 0 1<br />

7 35 0 1 19 70 1 1 31 40 0 1<br />

8 65 0 1 20 30 0 1 32 15 1 0<br />

9 95 0 1 21 60 0 1 33 135 1 1<br />

10 35 0 1 22 61 0 0 34 20 1 0<br />

11 75 0 1 23 65 0 1 35 40 1 0<br />

12 45 1 1 24 15 1 0<br />

Source: <strong>Data</strong> from D. Collett, in Encyclopedia of Biostatistics, Wiley, New York, 1998, pp. 350–358.<br />

Predic<strong>to</strong>rs are D = duration of surgery, T = type of device.<br />

4.25 For model (4.11) for the horseshoe crabs with color and width predic<strong>to</strong>rs, add<br />

three terms <strong>to</strong> permit interaction between color and width.<br />

a. Report the prediction equations relating width <strong>to</strong> the probability of a<br />

satellite, for each color. Plot or sketch them, and interpret.<br />

b. Test whether the interaction model gives a better fit than the simpler model<br />

lacking the interaction terms. Interpret.

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