The classification table compares the predicted values for the IVs, based on the logistic regression model, with the actual observed values from the data.
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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
Q10: The significance of each predictor is tested
Q11: The odds ratio represents the increase (or
Q12: Even though logistic regression does not require
Q13: Logistic regression may produce extremely large parameter
Q14: Logistic regression is not sensitive to high
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