Chartered Financial Analyst (CFA) Practice Exam Level 2

What do Logit Models primarily analyze?

1. Continuous outcome variables

2. Binary (two) outcome variables

3. Multivariate data sets

4. Time series data

The correct answer is: Binary (two) outcome variables

Logit models are primarily used to analyze binary outcome variables, which refer to situations where the outcome can take on only two possible values, such as "yes" or "no," "success" or "failure," "1" or "0." This makes logit models particularly useful in fields like finance, medicine, and social sciences where researchers are interested in understanding the factors that influence a binary event occurring. The logit function specifically transforms the probability of the event occurring into a log-odds format, allowing for various predictors (independent variables) to be used in the analysis. This model estimates the relationship between one or more predictor variables and a binary response variable, enabling analysts to derive meaningful insights about the likelihood of an event based on different influences. Other options denote different data types that are not the focus of logit analysis. Continuous outcome variables involve measures that can take on an infinite number of values within a range, such as height or weight. Multivariate data sets consist of multiple different variables and may require other analytical methods that can handle complexity beyond the binary outcome framework. Time series data pertains to observations collected over time, illustrating trends or changes, which would typically call for models specifically designed for these types of data, such as AR