QUESTION: 29

Screening for non-linearity in binary logistic regression can be achieved by visualizing:

A scatter plot of binary response versus a predictor variable.
A trend plot of empirical logit versus a predictor variable.
A logistic regression plot of predicted probability values versus a predictor variable.
A box plot of the odds ratio values versus a predictor variable.

Answer(s): B

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QUESTION: 30

Given the following SAS data set TEST:

Which SAS program is NOT a correct way to create dummy variables?

Option A
Option B
Option C
Option D

Answer(s): D

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An analyst fits a logistic regression model to predict whether or not a client will default on a loan. One of the predictors in the model is agent, and each agent serves 15-20 clients each. The model fails to converge. The analyst prints the summarized data, showing the number of defaulted loans per agent. See the partial output below:

What is the most likely reason that the model fails to converge?

There is quasi-complete separation in the data.
There is collinearity among the predictors.
There are missing values in the data.
There are too many observations in the data.

Answer(s): A

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QUESTION: 32

An analyst knows that the categorical predictor, storeId, is an important predictor of the target.

However, store_Id has too many levels to be a feasible predictor in the model. The analyst wants to combine stores and treat them as members of the same class level.

What are the two most effective ways to address the problem? (Choose two.)

Eliminate store_id as a predictor in the model because it has too many levels to be feasible.
Cluster by using Greenacre's method to combine stores that are similar.
Use subject matter expertise to combine stores that are similar.
Randomly combine the stores into five groups to keep the stochastic variation among the observations intact.

Answer(s): B,C

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Free A00-240 Exam Braindumps (page: 8)

QUESTION: 29

QUESTION: 30

QUESTION: 31

QUESTION: 32