1. Binary response variable: The logit model assumes that the dependent variable is binary, meaning it has only two outcomes.
2. Independent observations: Each observation in the sample is independent and not correlated with the others.
3. Linearity of the logit: The relationship between the independent variables and the log-odds of the dependent variable is linear.
4. No multicollinearity: The independent variables are not highly correlated with each other.
5. No outlier observations: The sample does not contain any extreme observations that may affect the accuracy of the regression coefficients.
6. No perfect separation of the data: The observed outcomes should not perfectly separate the groups defined by the independent variables.
7. Adequate sample size: The sample size is sufficiently large to ensure reliable estimation of the regression coefficients and standard errors.
Assumptions of the logit model
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