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1. When discussing logistic regression, the “logit” refers to which of the following? a. The natural...

1. When discussing logistic regression, the “logit” refers to which of the following?

a. The natural logarithm of the odds ratio.
b. The probability p that an observation is in category 1
c. The logistic function 1/(1+EXP(-x))
d. The odds ratio.

2. Which of the following is an advantage of using the logistic function in logistic regression?

a. Although it is a nonlinear function, the usual least-squares multiple regression method can still be used on it.
b. It transforms a linear function of the explanatory variables, which could have any value, into a value between 0 and 1, which is suitable for a log odds ratio.
c. It transforms a linear function of the explanatory variables, which could have any value, into a value between 0 and 1, which is suitable for a probability.
d. It transforms a linear function of the explanatory variables, which could have any value, into a value between 0 and 1, which is suitable for an odds ratio.

3. In a logistic regression output, the last column, Exp(Coef), indicates the effect of a change in each explanatory variable. Suppose this value is 1.25 for explanatory variable X. Which of the following does this imply?

a. If X increases by 1 unit, the log odds increases by 1.25 units.
b. If X increases by 1 unit, the probability of being in category 1 by 25%.
c. If X increases by 1 unit, the probability of being in category 1 increases by 0.25.
d. If X increases by 1 unit, the log odds increases by 25%.
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Answer #1

1)

The natural logarithm of the odds ratio

2)

It transforms a linear function of the explanatory variables, which could have any value, into a value between 0 and 1, which is suitable for a probability

3)

If X increases by 1 unit, the log odds increases by 25%

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