Question 1

When purity is perfect, the Gini index 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 2

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 quantitative 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 3

The Fisher linear discriminant function

Accepted Answer

A)  identifies a linear function for each of the groups in the classification problem 
B)  fits a nonlinear function for each of the groups in the classification problem 
C)  defines a hyperplane 
D)  defines a half-space 
A)  identifies a linear function for each of the groups in the classification problem 
B)  fits a nonlinear function for each of the groups in the classification problem 
C)  defines a hyperplane 
D)  defines a half-space

Question 4

The objective function in k-means clustering attempts to

Accepted Answer

A)  minimize the sum of within-cluster dispersions for all clusters 
B)  minimize the sum of between-cluster dispersions for all clusters 
C)  maximize the sum of within-cluster dispersions for all clusters 
D)  maximize the sum of between-cluster dispersions for all clusters 
A)  minimize the sum of within-cluster dispersions for all clusters 
B)  minimize the sum of between-cluster dispersions for all clusters 
C)  maximize the sum of within-cluster dispersions for all clusters 
D)  maximize the sum of between-cluster dispersions for all clusters

Question 5

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. How many observations are classified correctly?

Accepted Answer

A)  11 
B)  9 
C)  17 
D)  20 
A)  11 
B)  9 
C)  17 
D)  20

Question 6

Suppose that an analyst classified a new record using the following sequential steps (i) find identical records in the training sample, (ii) determine a group, to which majority of these records belong, (iii) assign the new record to the group in step (ii). This technique is called

Accepted Answer

A)  naive Bayes 
B)  Bayes 
C)  conditional probability estimation 
D)  posterior probability estimation 
A)  naive Bayes 
B)  Bayes 
C)  conditional probability estimation 
D)  posterior probability estimation

Question 7

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 formula is entered in cell E4 and copied to cells E5:E25 of the spreadsheet?

Accepted Answer

A)  = 7.452402 + 0.00694*C4 + 0.00232*D4 
B)  = 7.452402 − 0.00694*C4 − 0.00232*D4 
C)  = 1.157926 + 0.001545*C4 + 0.01297*D4 
D)  = 7.452402 − 0.00694*D4 − 0.00232*C4 
A)  = 7.452402 + 0.00694*C4 + 0.00232*D4 
B)  = 7.452402 − 0.00694*C4 − 0.00232*D4 
C)  = 1.157926 + 0.001545*C4 + 0.01297*D4 
D)  = 7.452402 − 0.00694*D4 − 0.00232*C4

Question 8

Exhibit 10.5
The information below is used for the following questions.
A counselor wants to classify people as belonging to one of three groups based on two scores. The counselor has collected data on twenty people who are known to be in one of the three groups. The data for the problem are in the following spreadsheet. Relevant output is also included.   ​   ​   ​   ​   ​
-Refer to Exhibit 10.5. Based on the 20 observations in the model complete the following confusion/classification matrix.

Accepted Answer

The answer of Exhibit 10.5
The information below is used for...

Question 9

Overfitting refers to

Accepted Answer

A)  placing too much emphasis on the sample-specific noise 
B)  fitting the model too tightly 
C)  fitting the model too loosely 
D)  underestimating model parameters 
A)  placing too much emphasis on the sample-specific noise 
B)  fitting the model too tightly 
C)  fitting the model too loosely 
D)  underestimating model parameters

Question 10

The k-nearest neighbor classification technique

Accepted Answer

A)  identifies the k observations in the training data that are most similar to a new observation we want to classify 
B)  sorts the data in an increasing order 
C)  sorts the data in a decreasing order 
D)  works very much like the ranking algorithm 
A)  identifies the k observations in the training data that are most similar to a new observation we want to classify 
B)  sorts the data in an increasing order 
C)  sorts the data in a decreasing order 
D)  works very much like the ranking algorithm

Question 11

In a two-group discriminant analysis problem using regression, why is the midpoint cut-off value used to determine group classification?

Accepted Answer

A)  Because the value minimizes the absolute misclassification error. 
B)  Because the value minimizes the probability of misclassification error. 
C)  Because the value represents an equal division between the groups. 
D)  Because the value incorporates problem specific knowledge. 
A)  Because the value minimizes the absolute misclassification error. 
B)  Because the value minimizes the probability of misclassification error. 
C)  Because the value represents an equal division between the groups. 
D)  Because the value incorporates problem specific knowledge.

Question 12

Normalization of data involves

Accepted Answer

A)  expressing each variable on a common, standardized scale 
B)  subtracting the grand mean from each observation 
C)  dividing each observation by total variance 
D)  dividing each observation by average range 
A)  expressing each variable on a common, standardized scale 
B)  subtracting the grand mean from each observation 
C)  dividing each observation by total variance 
D)  dividing each observation by average range

Question 13

Neural networks classification methodology

Accepted Answer

A)  is one of the options available in XLMiner Excel add-in 
B)  is superior to other classification schemes 
C)  is inferior to other classification schemes 
D)  produces results that are identical to the outcomes of the DA technique 
A)  is one of the options available in XLMiner Excel add-in 
B)  is superior to other classification schemes 
C)  is inferior to other classification schemes 
D)  produces results that are identical to the outcomes of the DA technique

Question 14

​Data mining is the process of finding and extracting useful information and insights from large data sets.

Accepted Answer

A) True 
 B)False

Question 15

Exhibit 10.7
The information below is used for the following questions.
An investor wants to classify companies as being a High Risk Investment, Group 1, a Medium Risk Investment, Group 2, or a Low Risk Investment, Group 3. He has gathered Liquidity, Profitability data on 18 companies he has invested in and produced the following spreadsheet. The following Discriminant Analysis output using Analytic Solver Platform has also been generated.          
-Refer to Exhibit 10.7. Based on the 18 observations in the model complete the following confusion/classification matrix.

Accepted Answer

The answer of Exhibit 10.7
The information below is used for...

Question 16

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. Based on the regression output, what is the discriminant score for a student with a quantitative score of 635 and a verbal score of 570?

Accepted Answer

A)  1.72 ≤ discriminant score ≤ 1.73 
B)  2.02 ≤ discriminant score ≤ 2.03 
C)  3.04 ≤ discriminant score ≤ 3.05 
D)  6.12 ≤ discriminant score ≤ 6.14 
A)  1.72 ≤ discriminant score ≤ 1.73 
B)  2.02 ≤ discriminant score ≤ 2.03 
C)  3.04 ≤ discriminant score ≤ 3.05 
D)  6.12 ≤ discriminant score ≤ 6.14

Question 17

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. Compute the discriminant score and predicted group for a company with Liquidity = 0.80, Profitability = 0.27 and Activity = 1.55.

Accepted Answer

DISCRIMINA

Question 18

In using neural networks, an analyst must decide __________ and ___________

Accepted Answer

A)  how many hidden layers to use and how many nodes to use in each of the hidden layers 
B)  how accurate the prediction should be and how many iterations to use 
C)  how many nodes to use and how much time to allow for simulation 
D)  how many iterations to use and what is the limit on the budget constraint 
A)  how many hidden layers to use and how many nodes to use in each of the hidden layers 
B)  how accurate the prediction should be and how many iterations to use 
C)  how many nodes to use and how much time to allow for simulation 
D)  how many iterations to use and what is the limit on the budget constraint

Question 19

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 percentage of the observations is classified incorrectly?

Accepted Answer

A)  90% 
B)  80% 
C)  85% 
D)  15% 
A)  90% 
B)  80% 
C)  85% 
D)  15%

Question 20

Suppose that the observations are partitioned into m groups in equal proportion. The entropy measure for this situation is equal to

Accepted Answer

A)  log2(m) 
B)  0.25 
C)  0.5 
D)  1 
A)  log2(m) 
B)  0.25 
C)  0.5 
D)  1

When purity is perfect, the Gini index is equal to

The Fisher linear discriminant function

The objective function in k-means clustering attempts to

Suppose that an analyst classified a new record using the following sequential steps (i) find identical records in the training sample, (ii) determine a group, to which majority of these records belong, (iii) assign the new record to the group in step (ii). This technique is called

Overfitting refers to

The k-nearest neighbor classification technique

In a two-group discriminant analysis problem using regression, why is the midpoint cut-off value used to determine group classification?

Normalization of data involves

Neural networks classification methodology

Data mining is the process of finding and extracting useful information and insights from large data sets.

In using neural networks, an analyst must decide and _

Suppose that the observations are partitioned into m groups in equal proportion. The entropy measure for this situation is equal to

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

Exam 10: Data Mining

When purity is perfect, the Gini index is equal to

The Fisher linear discriminant function

The objective function in k-means clustering attempts to

Suppose that an analyst classified a new record using the following sequential steps (i) find identical records in the training sample, (ii) determine a group, to which majority of these records belong, (iii) assign the new record to the group in step (ii). This technique is called

Overfitting refers to

The k-nearest neighbor classification technique

In a two-group discriminant analysis problem using regression, why is the midpoint cut-off value used to determine group classification?

Normalization of data involves

Neural networks classification methodology

​Data mining is the process of finding and extracting useful information and insights from large data sets.

In using neural networks, an analyst must decide __________ and ___________

Suppose that the observations are partitioned into m groups in equal proportion. The entropy measure for this situation is equal to

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

Data mining is the process of finding and extracting useful information and insights from large data sets.

In using neural networks, an analyst must decide and _