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Question ID 9012

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?

Option A

There is quasi-complete separation in the data.

Option B

There is collinearity among the predictors.

Option C

There are missing values in the data.

Option D

There are too many observations in the data.

Correct Answer A
Explanation


Question ID 20190

Refer to the ROC curve:

As you move along the curve, what changes?

Option A

The priors in the population

Option B

The true negative rate in the population

Option C

The proportion of events in the training data

Option D

The probability cutoff for scoring

Correct Answer D
Explanation

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