Question 1

Standardization of a variable

Accepted Answer

A)  removes the scale factor from consideration 
B)  requires subtracting the mean and dividing the difference by the standard deviation 
C)  results in all variables having the mean of 0 and standard deviation of 1 
D)  all of the above 
A)  removes the scale factor from consideration 
B)  requires subtracting the mean and dividing the difference by the standard deviation 
C)  results in all variables having the mean of 0 and standard deviation of 1 
D)  all of the above

Question 2

Neural networks technique attempts to learn

Accepted Answer

A)  what relationship exists between a set of input and output variables 
B)  whether a linear relationship exists between a set of input and output variables 
C)  whether a nonlinear relationship exists between a set of input and output variables 
D)  whether multicollinearity exists between a set of input variables 
A)  what relationship exists between a set of input and output variables 
B)  whether a linear relationship exists between a set of input and output variables 
C)  whether a nonlinear relationship exists between a set of input and output variables 
D)  whether multicollinearity exists between a set of input variables

Question 3

Mahalanobis distance is a measure of

Accepted Answer

A)  a likelihood of correctly classifying an observation to a group 
B)  quantifying covariance between independent variables in the model 
C)  quantifying covariance between dependent variables in the model 
D)  quantifying correlations between independent variables in the model 
A)  a likelihood of correctly classifying an observation to a group 
B)  quantifying covariance between independent variables in the model 
C)  quantifying covariance between dependent variables in the model 
D)  quantifying correlations between independent variables in the model

Question 4

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. Suppose that for a given observation, the difference between Mahalanobis distances between group 1 and 2 (G1-G2) is big and negative. This means that

Accepted Answer

A)  The observation is likely to be classified correctly to group 2 
B)  The observation is likely to be classified correctly to group 1 
C)  The observation is likely to be classified incorrectly to group 2 
D)  The observation is likely to be classified incorrectly to group 1 
A)  The observation is likely to be classified correctly to group 2 
B)  The observation is likely to be classified correctly to group 1 
C)  The observation is likely to be classified incorrectly to group 2 
D)  The observation is likely to be classified incorrectly to group 1

Question 5

To create the training and validation data set for the model use the __________ option in the XLMiner tab

Accepted Answer

A)  partition with oversampling 
B)  partition without oversampling 
C)  partition 
D)  sample 
A)  partition with oversampling 
B)  partition without oversampling 
C)  partition 
D)  sample

Question 6

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

Accepted Answer

A)  discriminant analysis 
B)  logistic regression 
C)  neural networks 
D)  all of the above 
A)  discriminant analysis 
B)  logistic regression 
C)  neural networks 
D)  all of the above

Question 7

The Mahalanobis distance measure accounts for differences in the covariances between all possible pairings of the independent variables.

Accepted Answer

A) True 
 B)False

Question 8

In the ________ step of data mining, a researcher attempts to predict the value of a continuous response variable based on the data set

Accepted Answer

A)  classification 
B)  prediction 
C)  categorization 
D)  association/segmentation 
A)  classification 
B)  prediction 
C)  categorization 
D)  association/segmentation

Question 9

One element in cleaning the data set in the mining process involves

Accepted Answer

A)  removing unimportant variables 
B)  adding more variables to the data set 
C)  calculating the adjusted R2 
D)  calculating the coefficient of multiple correlation 
A)  removing unimportant variables 
B)  adding more variables to the data set 
C)  calculating the adjusted R2 
D)  calculating the coefficient of multiple correlation

Question 10

A way to detecting and avoiding overfitting is to

Accepted Answer

A)  use the validation sample to calibrate the model 
B)  use repeated runs of the model and averaging the results 
C)  use computer simulation 
D)  use rigorous statistical tools 
A)  use the validation sample to calibrate the model 
B)  use repeated runs of the model and averaging the results 
C)  use computer simulation 
D)  use rigorous statistical tools

Question 11

If using the regression tool for two-group discriminant analysis, in the regression dialog box, the Input Y-Range entry corresponds to

Accepted Answer

A)  the Group values. 
B)  the independent variable values. 
C)  the predictor variable values. 
D)  (b) and (c) are both correct 
A)  the Group values. 
B)  the independent variable values. 
C)  the predictor variable values. 
D)  (b) and (c) are both correct

Question 12

Suppose that all observations belong to the same class. The entropy measure 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 13

A graphical representation of a set of rules for classifying observations into 2 or more groups is called

Accepted Answer

A)  a classification tree 
B)  a binary tree 
C)  a Pareto diagram 
D)  a branch-and-bound tree 
A)  a classification tree 
B)  a binary tree 
C)  a Pareto diagram 
D)  a branch-and-bound tree

Question 14

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 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. Based on the analysis presented in the spreadsheet, what percentage of the observations were correctly classified?

Accepted Answer

A)  80% 
B)  85% 
C)  95% 
D)  100% 
A)  80% 
B)  85% 
C)  95% 
D)  100%

Question 16

Cluster analysis is a data mining technique used for

Accepted Answer

A)  grouping together similar data 
B)  segmentation of records within a data set 
C)  designing effective marketing strategies 
D)  all of the above 
A)  grouping together similar data 
B)  segmentation of records within a data set 
C)  designing effective marketing strategies 
D)  all of the above

Question 17

Which of the following is not true regarding discriminant analysis?

Accepted Answer

A)  The classification rule translates discriminant scores into group membership. 
B)  Discriminant analysis is based on discrete or categorical dependent variables. 
C)  The classification rule selected perfectly classifies the data used to derive the classification rule. 
D)  The confusion matrix summarizes classification results. 
A)  The classification rule translates discriminant scores into group membership. 
B)  Discriminant analysis is based on discrete or categorical dependent variables. 
C)  The classification rule selected perfectly classifies the data used to derive the classification rule. 
D)  The confusion matrix summarizes classification results.

Question 18

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 number of observations is classified correctly?

Accepted Answer

A)  19 
B)  20 
C)  7 
D)  8 
A)  19 
B)  20 
C)  7 
D)  8

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. The university has received applications from several new students and would like to predict which group they would fall into. What is the discriminant score for a student with a Quantitative score of 686 and a Verbal score of 601. Use five (5) significant figures in your coefficients.

Accepted Answer

A)  1.29 ≤ discriminant score ≤ 1.30 
B)  1.69 ≤ discriminant score ≤ 1.70 
C)  2.69 ≤ discriminant score ≤ 2.70 
D)  6.05 ≤ discriminant score ≤ 6.06 
A)  1.29 ≤ discriminant score ≤ 1.30 
B)  1.69 ≤ discriminant score ≤ 1.70 
C)  2.69 ≤ discriminant score ≤ 2.70 
D)  6.05 ≤ discriminant score ≤ 6.06

Question 20

Given the following confusion matrix   what is the correct classification rate?

Accepted Answer

A)  9/13 = 69% 
B)  10/14 = 86% 
C)  19/25 = 76% 
D)  6/19 = 32% 
A)  9/13 = 69% 
B)  10/14 = 86% 
C)  19/25 = 76% 
D)  6/19 = 32%

Standardization of a variable

Neural networks technique attempts to learn

Mahalanobis distance is a measure of

To create the training and validation data set for the model use the __________ option in the XLMiner tab

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

The Mahalanobis distance measure accounts for differences in the covariances between all possible pairings of the independent variables.

In the ________ step of data mining, a researcher attempts to predict the value of a continuous response variable based on the data set

One element in cleaning the data set in the mining process involves

A way to detecting and avoiding overfitting is to

If using the regression tool for two-group discriminant analysis, in the regression dialog box, the Input Y-Range entry corresponds to

Suppose that all observations belong to the same class. The entropy measure for this situation is equal to

A graphical representation of a set of rules for classifying observations into 2 or more groups is called

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

Cluster analysis is a data mining technique used for

Which of the following is not true regarding discriminant analysis?

Given the following confusion matrix what is the correct classification rate?

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

Standardization of a variable

Neural networks technique attempts to learn

Mahalanobis distance is a measure of

To create the training and validation data set for the model use the __________ option in the XLMiner tab

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

The Mahalanobis distance measure accounts for differences in the covariances between all possible pairings of the independent variables.

In the ________ step of data mining, a researcher attempts to predict the value of a continuous response variable based on the data set

One element in cleaning the data set in the mining process involves

A way to detecting and avoiding overfitting is to

If using the regression tool for two-group discriminant analysis, in the regression dialog box, the Input Y-Range entry corresponds to

Suppose that all observations belong to the same class. The entropy measure for this situation is equal to

A graphical representation of a set of rules for classifying observations into 2 or more groups is called

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

Cluster analysis is a data mining technique used for

Which of the following is not true regarding discriminant analysis?

Given the following confusion matrix what is the correct classification rate?

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