The choice of the best logistic regression method for fatal-police-shooting analysis depends on several factors, including the nature of the data, the distribution of the outcome variable, and the goals of the analysis. Here are two common logistic regression methods:

Binary Logistic Regression:

Use Case: Appropriate when the dependent variable is binary (e.g., armed/unarmed, manner of death).

Advantages: Simple to interpret and widely used for binary outcomes.

Considerations: Assumes a linear relationship between the independent variables and the log-odds of the binary outcome.

Multinomial Logistic Regression:

Use Case: Suitable when the dependent variable has more than two categories (e.g., different threat levels, flee types).

Advantages: Handles multiple categories, allowing for the analysis of outcomes with more than two possible levels.

Considerations: Requires the assumption of independence of irrelevant alternatives (IIA).

Choosing the Best Method:

If the outcome variable is binary (e.g., armed/unarmed), binary logistic regression is appropriate.

If the outcome variable has multiple categories (e.g., threat levels, flee types), multinomial logistic regression may be more suitable.

In practice, it’s essential to assess model performance, check assumptions, and consider the research question when selecting the appropriate logistic regression method. Model evaluation metrics, such as accuracy, precision, recall, and the area under the ROC curve, can help determine the effectiveness of the chosen logistic regression approach for fatal-police-shooting analysis