# Multiple Choice Quizzes

Take the quiz test your understanding of the key concepts covered in the chapter. Try testing yourself before you read the chapter to see where your strengths and weaknesses are, then test yourself again once you’ve read the chapter to see how well you’ve understood.

1. Who is considered to be the ‘inventor’ of logistic regression?

1. Thomas Malthus
2. Alphonse Quetelet
3. Pierre-François Verhulst
4. Karl Pearson

c. Pierre-François Verhulst

2. The logistic model is estimated by way of?

1. Ordinary least squares
2. Maximum likelihood estimation
3. Poisson distribution
4. Negative binomial distribution

b. Maximum likelihood estimation

3. In logistic regression, what do we estimate for one each unit’s change in X?

1. The change in Y multiplied with Y
2. The change in Y from its mean
3. How much Y changes
4. How much the natural logarithm of the odds for Y = 1 changes

d. How much the natural logarithm of the odds for Y = 1 changes

4. A total predicted logit of 0 can be transformed to a probability of?

1. 0
2. 0.5
3. 1
4. 0.75

b. 0.5

5. The log likelihood is parallel to?

1. The t-test in OLS regression
2. The F-test in OLS regression
3. The standardized coefficient in OLS regression
4. The Wald test

b. The F-test in OLS regression

6. In categorical variables, when all, or close to all with a given X-value has the same value on Y, we call this a problem of?

Discrimination

Multicollienarity

Autocorrelation

Prediction

a. Discrimination

7. What makes the interpretation of conditional effects extra challenging in logistic regression?

1. It is not possible to model interaction effects in logistic regression
2. The results has to be raised by its natural logarithm
3. The conditional effect is dependent on the values of all X-variables
4. The maximum likelihood estimation makes the results unstable

c. The conditional effect is dependent on the values of all X-variables

8. Which variant of logistic regression is recommended when you have a categorical dependent variable with more than two values?

1. Logistic regression
2. Multinomial logistic regression
3. Ordered logit regression
4. Poisson regression