Question 1

Which of the following is not a methodology useful for data mining?

Accepted Answer

A) Classification analysis 
B) Prediction 
C) Cluster analysis 
D) Stock market analysis 
A) Classification analysis 
B) Prediction 
C) Cluster analysis 
D) Stock market analysis

Question 2

Once a dissimilarity measure is developed,a clustering algorithm attempts to find

Accepted Answer

A) clusters of rows where rows within a cluster are dissimilar and rows in different clusters are dissimilar. 
B) clusters of rows where rows within a cluster are similar and rows in different clusters are similar. 
C) clusters of rows where rows within a cluster are dissimilar and rows in different clusters are similar. 
D) clusters of rows where rows within a cluster are similar and rows in different clusters are dissimilar. 
A) clusters of rows where rows within a cluster are dissimilar and rows in different clusters are dissimilar. 
B) clusters of rows where rows within a cluster are similar and rows in different clusters are similar. 
C) clusters of rows where rows within a cluster are dissimilar and rows in different clusters are similar. 
D) clusters of rows where rows within a cluster are similar and rows in different clusters are dissimilar.

Question 3

Which of the following statements about logistic regression is false?

Accepted Answer

A) Logistic regression estimates the probability that an individual is in a particular category. 
B) Logistic regression uses a nonlinear function of the explanatory variables for classification. 
C) Logistic regression is essentially regression with a binary dependent variable. 
D) Logistic regression requires that the error terms are uniformly distributed. 
A) Logistic regression estimates the probability that an individual is in a particular category. 
B) Logistic regression uses a nonlinear function of the explanatory variables for classification. 
C) Logistic regression is essentially regression with a binary dependent variable. 
D) Logistic regression requires that the error terms are uniformly distributed.

Question 4

Data mining is used to examine known,expected patterns and relationships among variables.

Accepted Answer

A) True 
 B)False

Question 5

The logarithm of the odds ratio is called the

Accepted Answer

A) logit. 
B) logos. 
C) lods. 
D) logodra. 
A) logit. 
B) logos. 
C) lods. 
D) logodra.

Question 6

Segmentation is also known as clustering,and involves trying to group entities into similar clusters.

Accepted Answer

A) True 
 B)False

Question 7

The higher the "score" for a particular member in logistic regression,the

Accepted Answer

A) higher the likelihood that member is in category 1. 
B) lower the likelihood that member is in category 1. 
C) higher the likelihood that member is in category 0. 
D) higher the likelihood that member is not in a category. 
A) higher the likelihood that member is in category 1. 
B) lower the likelihood that member is in category 1. 
C) higher the likelihood that member is in category 0. 
D) higher the likelihood that member is not in a category.

Question 8

Clustering is considered a supervised data mining technique.

Accepted Answer

A) True 
 B)False

Question 9

The K in K-Means refers to the number of clusters.

Accepted Answer

A) True 
 B)False

Question 10

Melody is a department store manager and wants to examine whether or not female shoppers are more likely than male shoppers to use a department credit card."Female = 1" indicates the individual is a female."Credit Card = 1" indicates the individual used a credit card to make the purchase."Amount spent" is in dollars.Melody runs a logistic regression.If the estimate on the female variable is positive,what does this indicate about credit card usage?

Accepted Answer

A positive estimate

Which of the following is not a methodology useful for data mining?

Once a dissimilarity measure is developed,a clustering algorithm attempts to find

Which of the following statements about logistic regression is false?

Data mining is used to examine known,expected patterns and relationships among variables.

The logarithm of the odds ratio is called the

Segmentation is also known as clustering,and involves trying to group entities into similar clusters.

The higher the "score" for a particular member in logistic regression,the

Clustering is considered a supervised data mining technique.

The K in K-Means refers to the number of clusters.

Introduction to Business Analytics

Describing the Distribution of a Variable

Finding Relationships Among Variables

Business Intelligence Bifor Data Analysis

Probability and Probability Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Analysis of Variance and Experimental Design

Statistical Process Control

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Exam 17: Data Mining

Which of the following is not a methodology useful for data mining?

Once a dissimilarity measure is developed,a clustering algorithm attempts to find

Which of the following statements about logistic regression is false?

Data mining is used to examine known,expected patterns and relationships among variables.

The logarithm of the odds ratio is called the

Segmentation is also known as clustering,and involves trying to group entities into similar clusters.

The higher the "score" for a particular member in logistic regression,the

Clustering is considered a supervised data mining technique.

The K in K-Means refers to the number of clusters.

Introduction to Business Analytics

Describing the Distribution of a Variable

Finding Relationships Among Variables

Business Intelligence Bifor Data Analysis

Probability and Probability Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Analysis of Variance and Experimental Design

Statistical Process Control

Filters