Question 1

What are the two main goals of estimating the function f in the following equation, and when are they used?

Y=f(X)+

a.The main goals are inference and description. The inference is useful for determining when effects exist, and description describes the effects.

b.The main goals are inference and prediction. Inference allows testing whether effects are important and describes their form or shape, while prediction is used to accurately predict the outcome of interest (Y)

c. The main goals are desciption and prediction. Description describes the effects of the covariates and prediction aims to accurately predict the outcome

Question 2

What are the benefits of using a parametric estimation approach? Pick the most complete answer.

a.The model is often a simplification of reality and is easy to interpret.

b.The model is often a simplification of reality. It is usually easier to interpret than a nonparametric method because it uses a very specific form for the f(X) function. The estimation problem is reduced to estimating a fixed number of parameters. These models also tend to make inference easier because of their fixed form.

c.The model is good at inference due to having a very specific, fixed form.

Question 3

In the classification setting, what quantity is equivalent to the irreducible error in regression: Var()

a.The Bayes' error rate.

b. The difference between 1 and the max of the probabilities of a group membership.

c.There is no analog to the irreducible error rate in classification problems.