Question 1

When considering the logistic regression model, define the logistic curve.

Accepted Answer

The logistic curve is theoretical. It is

Question 2

In logistic regression, ________ is used as a goodness-of-fit test.

Accepted Answer

A)  deviance 
B)  Pearson 
C)  Hosmer-Lemeshow 
D)  All of the choices are correct. 
A)  deviance 
B)  Pearson 
C)  Hosmer-Lemeshow 
D)  All of the choices are correct.

Question 3

The hidden node function in neural networks is usually linear.

Accepted Answer

A) True 
 B)False

Question 4

Which analysis method is best used when a response variable is divided into more than two categories? Why?

Accepted Answer

Linear discriminate analysis i

Question 5

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 hundred 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. Identify the independent variables in this equation.

Accepted Answer

A)  Price of Home and Default 
B)  Default 
C)  Price of Home, First Purchase, Default 
D)  Price of Home and First Purchase 
A)  Price of Home and Default 
B)  Default 
C)  Price of Home, First Purchase, Default 
D)  Price of Home and First Purchase

Question 6

Why is neural network modeling a good choice when dealing with large data sets containing many predictor variables?

Accepted Answer

It does not require the use of p-values

Question 7

What is a positive attribute of linear discriminate analysis?

Accepted Answer

A)  The model is interpretable and prediction is easy. 
B)  It can be used with a constant p-value less than .05, so significance is implied. 
C)  It can be used in place of logistic and simple regression techniques in most cases. 
D)  It used the cubed distance between predictor variables, creating validation of the data. 
A)  The model is interpretable and prediction is easy. 
B)  It can be used with a constant p-value less than .05, so significance is implied. 
C)  It can be used in place of logistic and simple regression techniques in most cases. 
D)  It used the cubed distance between predictor variables, creating validation of the data.

Question 8

In logistic regression, the goal is to find

Accepted Answer

A)  the actual value of the dependent variable. 
B)  the odds of the dependent variable. 
C)  the log of the dependent variable. 
D)  None of the choices are correct. 
A)  the actual value of the dependent variable. 
B)  the odds of the dependent variable. 
C)  the log of the dependent variable. 
D)  None of the choices are correct.

Question 9

Which of the following methods do we use to best fit the data in logistic regression?

Accepted Answer

A)  Least square error 
B)  Maximum likelihood 
C)  Exponential decay 
D)  Both least square error and exponential decay 
A)  Least square error 
B)  Maximum likelihood 
C)  Exponential decay 
D)  Both least square error and exponential decay

Question 10

Linear regression requires the assumptions of independence, linearity, normality, homogeneity of variance and non-multicollinearity.

Accepted Answer

A) True 
 B)False

Question 11

The ________ regression method is used when the response variable is a qualitative or a categorical variable.

Accepted Answer

A)  quadratic 
B)  logistic 
C)  multiple 
D)  simple 
A)  quadratic 
B)  logistic 
C)  multiple 
D)  simple

Question 12

What are some general rules of when logistic regression is used? Give examples.

Accepted Answer

a question such as whether a s

Question 13

The number of dummy variables required if a categorical variable has k levels is:

Accepted Answer

A)  k − 1 
B)  b.k 
C)  k + 1 
D)    ! 
A)  k − 1 
B)  b.k 
C)  k + 1 
D)    !

Question 14

Logistic regression analysis can answer what three major questions about a dataset?

Accepted Answer

A)  Causal analysis, forecasting of an outcome, and trend forecasting 
B)  Causal analysis, odds of success, and trend forecasting 
C)  Odds of success, trend forecasting, and the confusion matrix 
D)  Maximum likelihood, causal analysis, and odds of success 
A)  Causal analysis, forecasting of an outcome, and trend forecasting 
B)  Causal analysis, odds of success, and trend forecasting 
C)  Odds of success, trend forecasting, and the confusion matrix 
D)  Maximum likelihood, causal analysis, and odds of success

Question 15

The odds of an event occurring is the probability that the event will not occur divided by the probability that the event will occur.

Accepted Answer

A) True 
 B)False

Question 16

Even if a residual may be unusually large, the standardized residual rule might not identify the observation as being an outlier. How can this difficulty be circumvented?

Accepted Answer

A)  by using studentized deleted residuals 
B)  by performing a logistic regression 
C)  through linear discriminate analysis 
D)  It cannot be circumvented and should be considered a component of the validation process. 
A)  by using studentized deleted residuals 
B)  by performing a logistic regression 
C)  through linear discriminate analysis 
D)  It cannot be circumvented and should be considered a component of the validation process.

Question 17

A regression technique for analyzing large data sets is neural network modeling.

Accepted Answer

A) True 
 B)False

Question 18

What is churn data? How is this information useful to a company?

Accepted Answer

Churn data, also called customer churn,

Question 19

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 hundred 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. Evaluate the significance of each independent variable in the model.

Accepted Answer

A)  Intercept = −3.4592 significant, price of home = .70935 significant 
B)  Intercept = −3.492 not significant, first purchase of home = .70935 not significant 
C)  Price of home is significant p-value = .0201, first purchase is significant p-value = .0136 
D)  Price of home is significant p-value = .0136, first purchase is significant p-value = .201 
A)  Intercept = −3.4592 significant, price of home = .70935 significant 
B)  Intercept = −3.492 not significant, first purchase of home = .70935 not significant 
C)  Price of home is significant p-value = .0201, first purchase is significant p-value = .0136 
D)  Price of home is significant p-value = .0136, first purchase is significant p-value = .201

Question 20

The chart provided shows the probability that a customer will use a coupon offered in a local paper by Lucky Shirts Store. Compute the odds ratio for the BankOne variable x2 = 1, holding annual spending constant at x1 = 4000. Annual Spending at Lucky Shirts Store

Accepted Answer

A)  1.3787 
B)  .4594 
C)  1.4845 
D)  .3148 
A)  1.3787 
B)  .4594 
C)  1.4845 
D)  .3148

When considering the logistic regression model, define the logistic curve.

In logistic regression, ________ is used as a goodness-of-fit test.

The hidden node function in neural networks is usually linear.

Which analysis method is best used when a response variable is divided into more than two categories? Why?

Why is neural network modeling a good choice when dealing with large data sets containing many predictor variables?

What is a positive attribute of linear discriminate analysis?

In logistic regression, the goal is to find

Which of the following methods do we use to best fit the data in logistic regression?

Linear regression requires the assumptions of independence, linearity, normality, homogeneity of variance and non-multicollinearity.

The ________ regression method is used when the response variable is a qualitative or a categorical variable.

What are some general rules of when logistic regression is used? Give examples.

The number of dummy variables required if a categorical variable has k levels is:

Logistic regression analysis can answer what three major questions about a dataset?

The odds of an event occurring is the probability that the event will not occur divided by the probability that the event will occur.

Even if a residual may be unusually large, the standardized residual rule might not identify the observation as being an outlier. How can this difficulty be circumvented?

A regression technique for analyzing large data sets is neural network modeling.

What is churn data? How is this information useful to a company?

The chart provided shows the probability that a customer will use a coupon offered in a local paper by Lucky Shirts Store. Compute the odds ratio for the BankOne variable x₂ = 1, holding annual spending constant at x₁ = 4000. Annual Spending at Lucky Shirts Store

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,

When considering the logistic regression model, define the logistic curve.

In logistic regression, ________ is used as a goodness-of-fit test.

The hidden node function in neural networks is usually linear.

Which analysis method is best used when a response variable is divided into more than two categories? Why?

Why is neural network modeling a good choice when dealing with large data sets containing many predictor variables?

What is a positive attribute of linear discriminate analysis?

In logistic regression, the goal is to find

Which of the following methods do we use to best fit the data in logistic regression?

Linear regression requires the assumptions of independence, linearity, normality, homogeneity of variance and non-multicollinearity.

The ________ regression method is used when the response variable is a qualitative or a categorical variable.

What are some general rules of when logistic regression is used? Give examples.

The number of dummy variables required if a categorical variable has k levels is:

Logistic regression analysis can answer what three major questions about a dataset?

The odds of an event occurring is the probability that the event will not occur divided by the probability that the event will occur.

Even if a residual may be unusually large, the standardized residual rule might not identify the observation as being an outlier. How can this difficulty be circumvented?

A regression technique for analyzing large data sets is neural network modeling.

What is churn data? How is this information useful to a company?

The chart provided shows the probability that a customer will use a coupon offered in a local paper by Lucky Shirts Store. Compute the odds ratio for the BankOne variable x2 = 1, holding annual spending constant at x1 = 4000. Annual Spending at Lucky Shirts Store

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

The chart provided shows the probability that a customer will use a coupon offered in a local paper by Lucky Shirts Store. Compute the odds ratio for the BankOne variable x₂ = 1, holding annual spending constant at x₁ = 4000. Annual Spending at Lucky Shirts Store