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An analyst fits a logistic regression model to predict whether or not a client will...

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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?

A.

There is quasi-complete separation in the data.

B.

There is collinearity among the predictors.

C.

There are missing values in the data.

D.

There are too many observations in the data.

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  • Update on: Jan 2, 2026
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TESTED 02 Jan 2026