Different between LPM and logit model write in points

Answer 1

1. LPM: LPM refers to Linear Probability Model, which is a regression model used to estimate the relationship between a binary dependent variable and one or more independent variables. It assumes a linear relationship between the independent variables and the probability of the dependent variable.

Logit Model: The logit model, also known as the logistic regression model, is another type of regression model used to estimate the relationship between a binary dependent variable and independent variables. It uses the logistic function to transform the linear relationship between the independent variables and the log-odds of the dependent variable.

2. Linear vs. Non-linear: The LPM assumes a linear relationship between the independent variables and the probability of the dependent variable, while the logit model assumes a non-linear relationship between the independent variables and the log-odds of the dependent variable.

3. Interpretability of coefficients: In LPM, the coefficients represent the change in the probability of the dependent variable for a unit change in the independent variable. In logit model, the coefficients represent the change in the log-odds of the dependent variable for a unit change in the independent variable. To interpret them in terms of probabilities, they need to be exponentiated using the inverse of the logistic function.

4. Assumptions: LPM assumes that the relationship between independent and dependent variables is additive and that the residuals are normally distributed. Logit model assumes that the log-odds of the dependent variable is a linear combination of the independent variables and that the errors are logistically distributed.

5. Predicted values: In LPM, the predicted values range from 0 to 1, representing probabilities. In logit model, the predicted values are in the log-odds scale and need to be transformed using the logistic function to obtain probabilities.

6. Handling outliers: LPM is sensitive to outliers, as it may result in predicted probabilities greater than 1 or less than 0. In logit model, the predicted probabilities are bounded between 0 and 1, making it less susceptible to outliers.

Overall, while both LPM and logit model are used to estimate the relationship between binary dependent and independent variables, they differ in terms of linearity assumption, interpretability of coefficients, assumptions, predicted values, and handling of outliers.