Logistic Regression can be categorized into three primary types: Binary Logistic Regression, Ordinal Logistic Regression, and Multinomial Logistic Regression.
Binary Logistic Regression: This is the most common type and is used when the dependent variable is binary, with only two possible outcomes. For instance, it’s applied in deciding whether to offer a loan to a bank customer (yes or no), evaluating the risk of cancer (high or low), or predicting a team’s win in a football match (yes or no).
Ordinal Logistic Regression: In this type, the dependent variable is ordinal, meaning it has ordered categories, but the intervals between the values are not necessarily equal. It’s useful for scenarios like predicting whether a student will choose to join a college, vocational/trade school, or enter the corporate industry, or estimating the type of food consumed by pets (wet food, dry food, or junk food).
Multinomial Logistic Regression: This type is employed when the dependent variable is nominal and includes more than two levels with no specific order or priority. For example, it can be used to predict formal shirt size (XS/S/M/L/XL), analyze survey answers (agree/disagree/unsure), or evaluate scores on a math test (poor/average/good).
The effective application of Logistic Regression involves several key practices:
- Carefully identifying dependent variables to ensure model consistency.
- Understanding the technical requirements of the chosen model.
- Properly estimating the model and assessing the goodness of fit.
- Interpreting the results in a meaningful way.
- Validating the observed results to ensure the model’s accuracy and reliability.