Empirical Logits
- Method for modeling the probability of a binary outcome (e.g., success/failure) using predictor variables.
- Fit a logistic regression model to estimate how predictors relate to the outcome; coefficients indicate direction and relative strength.
- Once fitted, the model predicts outcome probabilities for specific predictor values.
Definition
Section titled “Definition”Empirical logits, also known as binary logistic regression, is a statistical technique used to analyze the relationship between a binary outcome variable (for example, success/failure or yes/no) and a set of predictor variables. It models the probability of the outcome occurring based on the values of those predictor variables.
Explanation
Section titled “Explanation”To perform an empirical logits analysis, a researcher collects data on the binary outcome and the chosen predictor variables. That data is used to fit a logistic regression model which estimates the probability of the outcome as a function of the predictors. The model’s coefficients represent the relative strength and direction of each predictor’s association with the outcome. After fitting, the model can generate predicted probabilities for the outcome given specific predictor values.
Examples
Section titled “Examples”Election data
Section titled “Election data”A researcher interested in predicting the likelihood of a candidate winning an election could use empirical logits with predictor variables such as the candidate’s party affiliation, the amount of money spent on their campaign, and the number of endorsements they receive. The fitted model would estimate the probability of winning based on those predictors.
Medical research
Section titled “Medical research”Empirical logits can be applied to predict the likelihood of a patient developing a disease using predictors like age, gender, and medical history. For example, a researcher might model the probability of a patient developing diabetes based on their age, gender, and BMI.
Use cases
Section titled “Use cases”- Researchers who want to understand the relationship between a binary outcome and predictor variables.
- Situations requiring modeling of probabilities and making predictions of binary outcomes from specific predictor values.
Related terms
Section titled “Related terms”- Binary logistic regression