Question 1

A confusion matrix helps:

Accepted Answer

A)  To give misclassification rates of the model 
B)  To assign p-values for independent variables in the model 
C)  Both "To give misclassification rates of the model" and "To assign p-values for independent variables in the model" 
D)  None of the choices are correct. 
A)  To give misclassification rates of the model 
B)  To assign p-values for independent variables in the model 
C)  Both "To give misclassification rates of the model" and "To assign p-values for independent variables in the model" 
D)  None of the choices are correct.

Question 2

The chart provided shows the probability that a customer will use a coupon offered in a local paper by Lucky Shirts Store. Using this information, compute the odds that a customer will use the coupon, who spends $4,000 and does NOT bank at BankOne. Annual Spending at Lucky Shirts Store

Accepted Answer

A)  .4594 
B)  .3148 
C)  1.4845 
D)  .7742 
A)  .4594 
B)  .3148 
C)  1.4845 
D)  .7742

Question 3

Define linear discriminant analysis. Give an example of where this type of analysis is useful.

Accepted Answer

This process uses various data items and

Question 4

If the outcome variable is quantitative and all explanatory variables take values 0 or 1, a logistic regression model is appropriate.

Accepted Answer

A) True 
 B)False

Question 5

What is a method that can be used to minimize the penalized least squares criterion when using neural network modeling?

Accepted Answer

A)  Fit the model 10-50 times using differing sets of randomly selected starter values to each fit. 
B)  Fit the model once with a predetermined starter value. 
C)  Use the set of parameters that yields the largest value of the penalized least squares. 
D)  Fit the model hundreds of times using differing sets of randomly selected starter values to each fit, to mimic using the entire population of data. 
A)  Fit the model 10-50 times using differing sets of randomly selected starter values to each fit. 
B)  Fit the model once with a predetermined starter value. 
C)  Use the set of parameters that yields the largest value of the penalized least squares. 
D)  Fit the model hundreds of times using differing sets of randomly selected starter values to each fit, to mimic using the entire population of data.

Question 6

In linear discriminate analysis we calculate the means of the predictor variable values for the observations belonging to both categories that are in the data set.

Accepted Answer

A) True 
 B)False

Question 7

The dependent variable in logistic regression can take on values of 0 and 1.

Accepted Answer

A) True 
 B)False

Question 8

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 $535,600 on their fourth home purchase will default.

Accepted Answer

A)  .5842 
B)  .5674 
C)  .9834 
D)  .9465 
A)  .5842 
B)  .5674 
C)  .9834 
D)  .9465

Question 9

What is a confusion matrix, and what is the purpose in preparing it?

Accepted Answer

A confusion matrix in predictive analyti

Question 10

Which method will we use to best fit the data in logistic regression?

Accepted Answer

A)  Maximum likelihood 
B)  Least effect likelihood 
C)  Least square error 
D)  Jaccard distance 
A)  Maximum likelihood 
B)  Least effect likelihood 
C)  Least square error 
D)  Jaccard distance

Question 11

What is the natural logarithm of the odds called?

Accepted Answer

A)  The logit 
B)  The odds logarithm 
C)  The odds logit 
D)  The natural odds 
A)  The logit 
B)  The odds logarithm 
C)  The odds logit 
D)  The natural odds

Question 12

Neural network modeling represents the response variable as a linear function of the predictor variables.

Accepted Answer

A) True 
 B)False

Question 13

Neural networks have developed due to:

Accepted Answer

A)  large amounts of data with huge numbers of observations. 
B)  increasingly unavailable data sets at reasonable costs for researchers. 
C)  work more effectively with data sets having fewer than 1000 observations. 
D)  have a clear and simple equation to use with very large data sets. 
A)  large amounts of data with huge numbers of observations. 
B)  increasingly unavailable data sets at reasonable costs for researchers. 
C)  work more effectively with data sets having fewer than 1000 observations. 
D)  have a clear and simple equation to use with very large data sets.

Question 14

Since the neural network model employs many parameters, there is a danger that we will overfit the model.

Accepted Answer

A) True 
 B)False

Question 15

Review the JMP data in Figure 16.8 of the text. This information relates to the churn data for cell phone customers. Describe the data, the meaning of the data, and how you could use this data in decision making.

Accepted Answer

will vary, but this should include infor

Question 16

The chart provided shows the probability that a customer will use a coupon offered in a local paper by Lucky Shirts Store. Define the two dependent variables in this chart. How would you determine the odds of a BankOne customer, using the coupon if they annually spend $2,000 at the Store?
Annual Spending at Lucky Shirts Store

Accepted Answer

Using odds formula, and the ch

Question 17

What is a major drawback of the neural network modeling?

Accepted Answer

A)  The parameters are typically uninterpretable. 
B)  The model is not easily utilized with existing data sets. 
C)  The parameters minimize the least squares criterion. 
D)  This model only uses a single-layer perceptron. 
A)  The parameters are typically uninterpretable. 
B)  The model is not easily utilized with existing data sets. 
C)  The parameters minimize the least squares criterion. 
D)  This model only uses a single-layer perceptron.

Question 18

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. Interpret the confusion matrix.

Accepted Answer

A)  The misclassification rate for defaulters is .08 and the misclassification for non-defaulters is .1290. 
B)  The misclassification rate for defaulters is .08 and the misclassification for non-defaulters is .1482. 
C)  The misclassification rate for defaulters is .1290 and the misclassification for non-defaulters is .1111. 
D)  The misclassification rate for defaulters is .1482 and the misclassification for non-defaulters is .0870. 
A)  The misclassification rate for defaulters is .08 and the misclassification for non-defaulters is .1290. 
B)  The misclassification rate for defaulters is .08 and the misclassification for non-defaulters is .1482. 
C)  The misclassification rate for defaulters is .1290 and the misclassification for non-defaulters is .1111. 
D)  The misclassification rate for defaulters is .1482 and the misclassification for non-defaulters is .0870.

Question 19

Define confidence interval. Is a smaller or larger confidence interval more desirable?

Accepted Answer

The confidence interval is how sure you

Question 20

Define overfitting of data and give an example of when it may happen.

Accepted Answer

The data will be too closely aligned wit

A confusion matrix helps:

The chart provided shows the probability that a customer will use a coupon offered in a local paper by Lucky Shirts Store. Using this information, compute the odds that a customer will use the coupon, who spends $4,000 and does NOT bank at BankOne. Annual Spending at Lucky Shirts Store

Define linear discriminant analysis. Give an example of where this type of analysis is useful.

If the outcome variable is quantitative and all explanatory variables take values 0 or 1, a logistic regression model is appropriate.

What is a method that can be used to minimize the penalized least squares criterion when using neural network modeling?

In linear discriminate analysis we calculate the means of the predictor variable values for the observations belonging to both categories that are in the data set.

The dependent variable in logistic regression can take on values of 0 and 1.

What is a confusion matrix, and what is the purpose in preparing it?

Which method will we use to best fit the data in logistic regression?

What is the natural logarithm of the odds called?

Neural network modeling represents the response variable as a linear function of the predictor variables.

Neural networks have developed due to:

Since the neural network model employs many parameters, there is a danger that we will overfit the model.

Review the JMP data in Figure 16.8 of the text. This information relates to the churn data for cell phone customers. Describe the data, the meaning of the data, and how you could use this data in decision making.

What is a major drawback of the neural network modeling?

Define confidence interval. Is a smaller or larger confidence interval more desirable?

Define overfitting of data and give an example of when it may happen.

An Introduction to Business Statistics and Analytics

Descriptive Statistics and Analytics: Tabular and Graphical Methods

Descriptive Statistics and Analytics: Numerical Methods

Probability and Probability Models

Predictive Analytics I: Trees, K-Nearest Neighbors, Naive Bayes,

Discrete Random Variables

Continuous Random Variables

Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Chi-Square Tests

Simple Linear Regression Analysis

Multiple Regression and Model Building

Time Series Forecasting and Index Numbers

Nonparametric Methods

Decision Theory

Filters

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

A confusion matrix helps:

The chart provided shows the probability that a customer will use a coupon offered in a local paper by Lucky Shirts Store. Using this information, compute the odds that a customer will use the coupon, who spends $4,000 and does NOT bank at BankOne. Annual Spending at Lucky Shirts Store

Define linear discriminant analysis. Give an example of where this type of analysis is useful.

If the outcome variable is quantitative and all explanatory variables take values 0 or 1, a logistic regression model is appropriate.

What is a method that can be used to minimize the penalized least squares criterion when using neural network modeling?

In linear discriminate analysis we calculate the means of the predictor variable values for the observations belonging to both categories that are in the data set.

The dependent variable in logistic regression can take on values of 0 and 1.

What is a confusion matrix, and what is the purpose in preparing it?

Which method will we use to best fit the data in logistic regression?

What is the natural logarithm of the odds called?

Neural network modeling represents the response variable as a linear function of the predictor variables.

Neural networks have developed due to:

Since the neural network model employs many parameters, there is a danger that we will overfit the model.

Review the JMP data in Figure 16.8 of the text. This information relates to the churn data for cell phone customers. Describe the data, the meaning of the data, and how you could use this data in decision making.

What is a major drawback of the neural network modeling?

Define confidence interval. Is a smaller or larger confidence interval more desirable?

Define overfitting of data and give an example of when it may happen.

An Introduction to Business Statistics and Analytics

Descriptive Statistics and Analytics: Tabular and Graphical Methods

Descriptive Statistics and Analytics: Numerical Methods

Probability and Probability Models

Predictive Analytics I: Trees, K-Nearest Neighbors, Naive Bayes,

Discrete Random Variables

Continuous Random Variables

Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Chi-Square Tests

Simple Linear Regression Analysis

Multiple Regression and Model Building

Time Series Forecasting and Index Numbers

Nonparametric Methods

Decision Theory

Filters