Logistic regression is basically an extension of multiple regression in situations where the DV is not a continuous or quantitative variable.
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Q2: Logistic regression is also sometimes used as
Q3: The value that is being predicted in
Q4: Like both discriminant analysis and multiple regression,
Q5: Logistic regression is unable to produce nonlinear
Q6: The chi-square goodness-of-fit test compares the actual
Q7: Smaller values on the -2 Log Likelihood
Q8: Cox & Snell R Square and Nagelkerke
Q9: The classification table compares the predicted values
Q10: The significance of each predictor is tested
Q11: The odds ratio represents the increase (or
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