Question 1

Discriminant analysis (DA) differs from most other predictive statistical methods because the dependent variable is

Accepted Answer

A)  continuous 
B)  random 
C)  stochastic 
D)  discrete 
A)  continuous 
B)  random 
C)  stochastic 
D)  discrete

Question 2

Data mining tasks fall in the following categories

Accepted Answer

A)  classification, prediction, association 
B)  categorization, segmentation 
C)  prediction, association, mining 
D)  observation, categorization, association 
A)  classification, prediction, association 
B)  categorization, segmentation 
C)  prediction, association, mining 
D)  observation, categorization, association

Question 3

Exhibit 10.6
The information below is used for the following questions.
An investor wants to classify companies as being either a good investment, Group 1, or a poor investment, Group 2. He has gathered Liquidity, Profitability and Activity data on 18 companies he has invested in and run a regression analysis. Discriminant Analysis output has also been generated. The data for the problem and the relevant output are shown below.   ​   ​   ​   ​   ​   ​  
-Refer to Exhibit 10.6. Based on the 20 observations, what percentage of the observations are correctly classified?

Accepted Answer

A)  80.00% 
B)  88.89% 
C)  75.25% 
D)  95.14% 
A)  80.00% 
B)  88.89% 
C)  75.25% 
D)  95.14%

Question 4

A classification tree is a graphical representation of a set of rules for classifying observations into one group.

Accepted Answer

A) True 
 B)False

Question 5

The data might be normalized so that each variable is expressed on a common scale.

Accepted Answer

A) True 
 B)False

Question 6

Suppose that all observations in partition j belong to the same group. The Gini index for this situation is equal to

Accepted Answer

A)  0 
B)  0.25 
C)  0.5 
D)  1 
A)  0 
B)  0.25 
C)  0.5 
D)  1

Question 7

Suppose that the correlation coefficient between X1 and X2 is equal to -1. This means that

Accepted Answer

A)  X1 and X2 are perfectly positively correlated 
B)  X1 and X2 are perfectly negatively correlated 
C)  X1 and X2 are weakly and positively correlated 
D)  X1 and X2 are weakly and negatively correlated 
A)  X1 and X2 are perfectly positively correlated 
B)  X1 and X2 are perfectly negatively correlated 
C)  X1 and X2 are weakly and positively correlated 
D)  X1 and X2 are weakly and negatively correlated

Question 8

Exhibit 10.1
The following questions are based on the problem description and the output below.
A college admissions officer wants to evaluate graduate school applicants based on their GMAT scores, verbal and quantitative. Students are classified as either successful or not-successful in their graduate studies. The officer has data on 20 current students, ten of whom are doing very well (Group 1) and ten who are not (Group 2).   ​   ​   ​   ​  
-Refer to Exhibit 10.1. What is the verbal test score value of the group centroid for group 1?

Accepted Answer

A)  683.8 
B)  654.2 
C)  610.7 
D)  605.7 
A)  683.8 
B)  654.2 
C)  610.7 
D)  605.7

Question 9

Exhibit 10.1
The following questions are based on the problem description and the output below.
A college admissions officer wants to evaluate graduate school applicants based on their GMAT scores, verbal and quantitative. Students are classified as either successful or not-successful in their graduate studies. The officer has data on 20 current students, ten of whom are doing very well (Group 1) and ten who are not (Group 2).   ​   ​   ​   ​  
-Refer to Exhibit 10.1. What number of observations is classified incorrectly?

Accepted Answer

A)  3 
B)  9 
C)  17 
D)  20 
A)  3 
B)  9 
C)  17 
D)  20

Question 10

Logistic regression in XLMiner add-in can be used for ______ groups

Accepted Answer

A)  2 
B)  3 
C)  4 
D)  5 
A)  2 
B)  3 
C)  4 
D)  5

Question 11

Before effectively applying the k nearest neighbor classification technique, the variables need to be

Accepted Answer

A)  standardized 
B)  normalized 
C)  randomized 
D)  trimmed 
A)  standardized 
B)  normalized 
C)  randomized 
D)  trimmed

Question 12

A ___________ algorithm is used during the training process to adjust weights in a neural network

Accepted Answer

A)  backpropagation 
B)  branch-and-bound 
C)  Simplex 
D)  cutting plane 
A)  backpropagation 
B)  branch-and-bound 
C)  Simplex 
D)  cutting plane

Question 13

Technique(s) used in association step of data mining include

Accepted Answer

A)  affinity analysis 
B)  binary analysis 
C)  integer networks 
D)  all of the above 
A)  affinity analysis 
B)  binary analysis 
C)  integer networks 
D)  all of the above

Question 14

Steps in the data mining process include the following (in sequence)

Accepted Answer

A)  (identify opportunity), (collect data), (explore, understand and prepare data), (identify tasks and tools, (partition data), (build and evaluate models), (deploy models) 
B)  (identify opportunity), (collect data), (explore, understand and prepare data), (identify tasks and tools, (build and evaluate models), (deploy models) 
C)  (collect data), (explore, understand and prepare data), (identify tasks and tools, (partition data), (build and evaluate models), (deploy models) 
D)  (identify opportunity), (collect data), (identify tasks and tools, (partition data), (build and evaluate models), (deploy models) 
A)  (identify opportunity), (collect data), (explore, understand and prepare data), (identify tasks and tools, (partition data), (build and evaluate models), (deploy models) 
B)  (identify opportunity), (collect data), (explore, understand and prepare data), (identify tasks and tools, (build and evaluate models), (deploy models) 
C)  (collect data), (explore, understand and prepare data), (identify tasks and tools, (partition data), (build and evaluate models), (deploy models) 
D)  (identify opportunity), (collect data), (identify tasks and tools, (partition data), (build and evaluate models), (deploy models)

Question 15

Exhibit 10.2
The following questions are based on the problem description and the output below.
A college admissions officer wants to evaluate graduate school applicants based on their GMAT scores, verbal and quantitative. Students are classified as either successful (Group 1), marginally successful (Group 2) or not-successful (Group 3) in their graduate studies. The officer has data on 20 current students, 7 successful (Group 1), 6 marginally successful (Group 2) and 7 not successful (Group 3).   ​   ​   ​   ​  
-Refer to Exhibit 10.2. What percentage of observations is classified incorrectly?

Accepted Answer

A)  5% 
B)  15% 
C)  95% 
D)  90% 
A)  5% 
B)  15% 
C)  95% 
D)  90%

Question 16

Exhibit 10.4
The information below is used for the following questions.
A manager wants to classify people as belonging to one of two groups based on two scores. The manager has collected data on four current employees and has performed a regression analysis on the data. The data for the problem and the relevant output are shown below.              
-Refer to Exhibit 10.4. Compute the discriminant score and predicted group for someone with Score 1 of 242 and Score 2 of 142.

Accepted Answer

DISCRIMINA

Question 17

In the k nearest neighbor technique, a small value of k produces classifications that are

Accepted Answer

A)  very sensitive to the sample-specific characteristics of the training data 
B)  not sensitive to the sample-specific characteristics of the training data 
C)  robust 
D)  reliable 
A)  very sensitive to the sample-specific characteristics of the training data 
B)  not sensitive to the sample-specific characteristics of the training data 
C)  robust 
D)  reliable

Question 18

A test sample is often used to perform ___________ of how well the model will work with new data

Accepted Answer

A)  an honest assessment 
B)  an estimate 
C)  a guess 
D)  a belief 
A)  an honest assessment 
B)  an estimate 
C)  a guess 
D)  a belief

Question 19

Classification refers to a type of data mining problem that uses the information available in a set of independent variables to predict the value of a discrete, or categorical, dependent variable.

Accepted Answer

A) True 
 B)False

Question 20

In classification techniques the dependent variable is

Accepted Answer

A)  discrete 
B)  continuous 
C)  bounded from above 
D)  bounded from below 
A)  discrete 
B)  continuous 
C)  bounded from above 
D)  bounded from below

Discriminant analysis (DA) differs from most other predictive statistical methods because the dependent variable is

Data mining tasks fall in the following categories

A classification tree is a graphical representation of a set of rules for classifying observations into one group.

The data might be normalized so that each variable is expressed on a common scale.

Suppose that all observations in partition j belong to the same group. The Gini index for this situation is equal to

Suppose that the correlation coefficient between X₁ and X₂ is equal to -1. This means that

Logistic regression in XLMiner add-in can be used for ______ groups

Before effectively applying the k nearest neighbor classification technique, the variables need to be

A ___________ algorithm is used during the training process to adjust weights in a neural network

Technique(s) used in association step of data mining include

Steps in the data mining process include the following (in sequence)

In the k nearest neighbor technique, a small value of k produces classifications that are

A test sample is often used to perform ___________ of how well the model will work with new data

Classification refers to a type of data mining problem that uses the information available in a set of independent variables to predict the value of a discrete, or categorical, dependent variable.

In classification techniques the dependent variable is

Introduction to Modeling and Decision Analysis

Introduction to Optimization and Linear Programming

Modeling and Solving Lp Problems in a Spreadsheet

Sensitivity Analysis and the Simplex Method

Network Modeling

Integer Linear Programming

Goal Programming and Multiple Objective Optimization

Nonlinear Programming Evolutionary Optimization

Regression Analysis

Time Series Forecasting

Introduction to Simulation Using Analytic Solver Platform

Queuing Theory

Decision Analysis

Project Management Online

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Exam 10: Data Mining

Discriminant analysis (DA) differs from most other predictive statistical methods because the dependent variable is

Data mining tasks fall in the following categories

A classification tree is a graphical representation of a set of rules for classifying observations into one group.

The data might be normalized so that each variable is expressed on a common scale.

Suppose that all observations in partition j belong to the same group. The Gini index for this situation is equal to

Suppose that the correlation coefficient between X1 and X2 is equal to -1. This means that

Logistic regression in XLMiner add-in can be used for ______ groups

Before effectively applying the k nearest neighbor classification technique, the variables need to be

A ___________ algorithm is used during the training process to adjust weights in a neural network

Technique(s) used in association step of data mining include

Steps in the data mining process include the following (in sequence)

In the k nearest neighbor technique, a small value of k produces classifications that are

A test sample is often used to perform ___________ of how well the model will work with new data

Classification refers to a type of data mining problem that uses the information available in a set of independent variables to predict the value of a discrete, or categorical, dependent variable.

In classification techniques the dependent variable is

Introduction to Modeling and Decision Analysis

Introduction to Optimization and Linear Programming

Modeling and Solving Lp Problems in a Spreadsheet

Sensitivity Analysis and the Simplex Method

Network Modeling

Integer Linear Programming

Goal Programming and Multiple Objective Optimization

Nonlinear Programming Evolutionary Optimization

Regression Analysis

Time Series Forecasting

Introduction to Simulation Using Analytic Solver Platform

Queuing Theory

Decision Analysis

Project Management Online

Filters

Suppose that the correlation coefficient between X₁ and X₂ is equal to -1. This means that