A.40 or more

B.20-39

C.0

D.1-19

A.Each unique value has the potential of being the optimal split point.

B.The variable goes through a binning process, the bins are weighted based on the proportion of events in each bin, and then finally tested as an optimal split point.

C.The variable goes through a non-linear transformation, and the transformed variable is used for testing.

D.Each unique value has the potential of being the optimal split point, except for the extreme observation.

A.Sample Properties, Sample Statistics, sample data for the selected variable, and a pie chart of the selected variable

B.Sample Properties, Sample Statistics, sample data with all variables and values, and a histogram of the data for the selected variable

C.Sample Properties, Sample Statistics for all variables, and sample data for all variables

D.Sample Properties, Sample Statistics, sample data for the selected variable, and a histogram of the data for the selected variable

A.none of the above

B.Maximum Correlation

C.Optimal

D.Maximum Normal

A.It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.

B.It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.

C.It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.

D.It looks for a set of colinear inputs that correlate with the target.