Logistic regression may produce extremely large parameter estimates and standard errors, especially in situations where combinations of discrete variables result in too many cells with no cases.
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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
Q12: Even though logistic regression does not require
Q14: Logistic regression is not sensitive to high
Q15: Logistic regression is also not sensitive to
Q16: Probabilities are simply the number of outcomes
Q17: In a logistic regression application, odds are
Q18: Probabilities will always have values that range
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