Exam 16: Predictive Analytics Ii: Logistic Regression, Discriminate Analysis,

Which type of predictive analytic would be used to determine whether players on a football team, either historical or future, would perform successfully or unsuccessfully in a certain position on the field?

A ________ consists of three layers: input, hidden, and output.

In a neural network model, the ________ is a nonlinear function of the linear predictor values.

Define linear discriminate analysis.

Maximum likelihood estimation is an advanced statistical procedure which provides point estimates, and it is typically

To avoid overfitting in the neural network model, the parameter estimates that are used minimize the least squares criterion.

In logistic regression,

Two tests are performed. The first has a p-value of .001, the second has a p-value of .15. What does this information mean? Describe.

Below gives the data concerning (1) the dependent variable Default which equals 1 if a customer defaults on their loan and 0 if they do not; (2) the independent variable Price of Home, which is the price of the home (in tens) and (3) the independent variable First Purchase which equals 1 if the customer has owned a home before and 0 if this is their first home. Estimate the probability that a buyer who spent $435,000 on their first home purchase will default.

Below gives the data concerning (1) the dependent variable Default which equals 1 if a customer defaults on their loan and 0 if they do not; (2) the independent variable Price of Home, which is the price of the home (in tens) and (3) the independent variable First Purchase which equals 1 if the customer has owned a home before and 0 if this is their first home. Estimate the probability that a buyer who spent $135,000 on their first home purchase will default.

Linear discriminate analysis does not assume that for each particular category being considered, the joint probability distribution of the predictor variables is a multivariate normal distribution.

Consider the following model for logistic regression: P (y = 1|x, h) = g(h₀ + h₁x) where g(z) is the logistic function. In the preceding equation the P (y = 1|x; h) , viewed as a function of x, that we can get by changing the parameters h. What would be the range of P in such case?

Below gives the data concerning (1) the dependent variable Default which equals 1 if a customer defaults on their loan and 0 if they do not; (2) the independent variable Price of Home, which is the price of the home (in tens) and (3) the independent variable First Purchase which equals 0 if the customer has owned a home before and 1 if this is their first home. Estimate the probability that a buyer who spent $678,700 on their first home will default.

The penalty weight used in the neural network models controls the trade-off between overfitting and underfitting.

The logistic regression equation involves the exponential function and it estimates the probability that an observation described by a specified set of predictor variable values will fall into a particular class.

A major drawback of neural network modeling is that its parameters are usually uninterpretable.

What are used to avoid overfitting in the neural network model, and are used to minimize a penalized least squares criterion?

Squared distances can be used in two different ways in linear discriminate analysis.

One of the very good methods to analyze the performance of logistic regression is AIC, which is similar to R-squared in Linear Regression. Which of the following is true about AIC?

A single-layer perceptron neural network model consists of only one layer called the hidden layer.