## Multiple choice questions

Logistic regression is used when you want to:

1. Predict a dichotomous variable from continuous or dichotomous variables.
2. Predict a continuous variable from dichotomous variables.
3. Predict any categorical variable from several other categorical variables.
4. Predict a continuous variable from dichotomous or continuous variables.

#### Answer: Predict a dichotomous variable from continuous or dichotomous variables

The odds ratio is:

1. The ratio of the probability of an event not happening to the probability of the event happening.
2. The probability of an event occurring.
3. The ratio of the odds after a unit change in the predictor to the original odds.
4. The ratio of the probability of an event happening to the probability of the event not happening.

#### Answer: The ratio of the odds after a unit change in the predictor to the original odds

Large values of the log-likelihood statistic indicate:

1. That there are a greater number of explained vs. unexplained observations.
2. That the statistical model fits the data well.
3. That as the predictor variable increases, the likelihood of the outcome occurring decreases.
4. That the statistical model is a poor fit of the data.

#### Answer: That the statistical model is a poor fit of the data

Logistic regression assumes a:

1. Linear relationship between continuous predictor variables and the outcome variable.
2. Linear relationship between continuous predictor variables and the logit of the outcome variable.
3. Linear relationship between continuous predictor variables.
4. Linear relationship between observations.

#### Answer: Linear relationship between continuous predictor variables and the logit of the outcome variable

In binary logistic regression: