Consider the snoring and heart disease data of Table 3.1 : With scores {0, 2, 4, 5} for snoring levels, the logistic regression ML fit is logit() = −3.866 + 0.397x. a. Interpret the sign of the estimated effect of x b. Estimate the probabilities of heart disease at snoring levels 0 and 5 c. Describe the estimated effect of snoring on the odds of heart disease.
Consider the snoring and heart disease data of Table 3.1 : With scores {0, 2, 4, 5} for snoring levels, the logistic regression ML fit is logit() = −3.866 + 0.397x. a. Interpret the sign of the estimated effect of x b. Estimate the probabilities of heart disease at snoring levels 0 and 5 c. Describe the estimated effect of snoring on the odds of heart disease.
MATLAB: An Introduction with Applications
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Question
Consider the snoring and heart disease data of Table 3.1 :
With scores {0, 2, 4, 5} for snoring levels, the logistic regression ML fit is
logit() = −3.866 + 0.397x.
a. Interpret the sign of the estimated effect of x
b. Estimate the probabilities of heart disease at snoring levels 0 and 5
c. Describe the estimated effect of snoring on the odds of heart disease.
Transcribed Image Text:Table 3.1. Relationship Between Snoring and Heart Disease
Heart Disease
Snoring
Never
Occasional
Nearly every night
Every night
70
Predicted Probability
0.20
Yes
0.15-
0.10
0.05
24
35
21
30
No
0.0 +
0
1355
603
192
224
1
Proportion
Yes
Note: Model fits refer to proportion of "yes" responses.
Source: P. G. Norton and E. V. Dunn, Br. Med. J., 291: 630-632, 1985, published by BMJ
Publishing Group.
0.017
0.055
0.099
0.118
2
probit
Linear
Fit
3
0.017
0.057
0.096
0.116
logistic
Logit
Fit
4
0.021
0.044
0.093
0.132
GENERALIZED LINEAR MODELS
Snoring
Figure 3.1. Fit of models for snoring and heart disease data.
linear
Probit
Fit
0.020
0.046
0.095
0.131
5
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