Imagine we were trying to predict whether someone will pass (score = 1) , or fail (score = 0) their driving test based on: (1) the number of hours of lessons they'd had; (2) whether or not they owned a car; (3) how many previous tests they had taken; and (4) their score on a test of spatial awareness. What analysis could we use on these data?
A) Multinomial logistic regression
B) Bimodal logistic regression
C) Binary logistic regression
D) Multiple regression
Correct Answer:
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Q1: A researcher was interested in predicting whether
Q2: The statistical implication of using a parsimony
Q3: The assumption of linearity in logistic regression:
A)Assumes
Q5: Which of the following statements about the
Q6: The _ the value of the log-likelihood
Q7: In the following table, how many
Q8: The interpretation of the odds ratio, Exp(B),
Q9: It is useful to compare a logistic
Q10: Multinomial logistic regression can be used on:
A)Ordinal
Q11: Complete separation is:
A)When the observed variance is
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