Steps in the data mining process include the following (in sequence) a. (identify opportunity), (collect data), (explore, understand and prepare data), (identify tasks and tools, (partition data), (build and evaluate models), (deploy models) b. (identify opportunity), (collect data), (explore, understand and prepare data), (identify tasks and tools, (build and evaluate models), (deploy models) c. (collect data), (explore, understand and prepare data), (identify tasks and tools, (partition data), (build and evaluate models), (deploy models) d. (identify opportunity), (collect data), (identify tasks and tools, (partition data), (build and evaluate models), (deploy models)

question 2

The goal of discriminant analysis is

a. to develop a model to predict new dependent values.

b. the develop a rule for predicting to what group a new observation is most likely to belong.

c. to develop a rule for predicting how independent variable values predict dependent values.

d. none of these.

QUESTION 3 The k-nearest neighbor classification technique

a. identifies the k observations in the training data that are most similar to a new observation we want to classify.

b. sorts the data in increasing order.

c. sorts the data in decreasing order.

d. works very much like the ranking algorithm.

question 4

Simulation is used to

a. find possible worst case values for the dependent variable(s).

b. find worst case and best case values for the dependent variable(s).

c. find distribution information for the dependent variable(s).

d. find median values for the dependent variable(s).

question 5

A way to detecting and avoiding overfitting is to

a. use the validation sample to calibrate the model.

b. use repeated runs of the model and averaging the results.

c. use computer simulation.

d. use rigorous statistical tools.