Question 1

The objective of classification tree algorithms is to

Accepted Answer

A) minimize the average weighted impurity of the resulting partitions 
B) maximize the average weighted impurity of the resulting partitions 
C) maximize the average weighted purity of the resulting partitions 
D) maximize the likelihood estimator of the resulting partitions 
A) minimize the average weighted impurity of the resulting partitions 
B) maximize the average weighted impurity of the resulting partitions 
C) maximize the average weighted purity of the resulting partitions 
D) maximize the likelihood estimator of the resulting partitions

Question 2

_ is a classification technique that estimates the probability of an observation belonging to a particular group

Accepted Answer

A) logistic regression 
B) binary regression 
C) multivariate analysis 
D) ANCOVA 
A) logistic regression 
B) binary regression 
C) multivariate analysis 
D) ANCOVA

Question 3

Given the following confusion matrix
Predicted Group
$\begin{array}{llrrr}
&&1 & 2 & \text { Total }\
\hline\text { Actual } & 1 & 9 & 4 & 13 \
\text { Group } & 2 & 2 & 10 & 12 \
\text { Tntal } & & 11 & 14 &
\end{array}$
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%

Question 4

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 5

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

Question 6

Neural networks are

Accepted Answer

A) a pattern recognition technique 
B) a physical model representation of interrelationships 
C) a non-directed graph 
D) a binary network 
A) a pattern recognition technique 
B) a physical model representation of interrelationships 
C) a non-directed graph 
D) a binary network

Question 7

_ and must be chosen each time a partition is subdivided

Accepted Answer

A) an independent variable and splitting value 
B) a dependent variable and cutoff value 
C) a significance level and an upper bound 
D) a significance level and a lower bound 
A) an independent variable and splitting value 
B) a dependent variable and cutoff value 
C) a significance level and an upper bound 
D) a significance level and a lower bound

Question 8

Suppose that there are 3 variables in a data set.Approximately how many data records are required using a rule of thumb discussed in the textbook?

Accepted Answer

A) 30 to 45 
B) 20 to 30 
C) 45 to 60 
D) 50 to 100 
A) 30 to 45 
B) 20 to 30 
C) 45 to 60 
D) 50 to 100

Question 9

Normalization of data involves

Accepted Answer

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

Question 10

Exhibit 10.1
The following questions are based on the problem description,regression results,and the Analytic Solver Platform Discriminant Analysis report 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).
     Unpooled Estimates of within-group Covariance matrices are used,assuming they are different.
Group Centroids
Group Quantitative Verbal
1 683.8 654.2
2 610.7 605.7
 $\text { Classitication Matrix }$
$\begin{array}{cccc} 
\text { Actual / Predicted }
& \text { Group1 } & \text { Group2 } & \text { Total } & \% \text { correct } \
\hline \text { Group1 } & 9 & 1 & 1 & 90.00 \% \
&& & 0 & \
\text { Group2 }&  2 &8 & 1 & 80.00 \% \
&  &  & 0 & \\
\text { Total } & 1 & 9& 2 & 85.00 \%\
&1&&0 
\end{array}$  
-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 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

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 13

The regression approach can be used in the two-group discriminant analysis problem because

Accepted Answer

A) the data are not normally distributed. 
B) the R2 statistic is not very meaningful. 
C) the regression equation can generate a discriminant score. 
D) it scales to the k -group problem easily. 
A) the data are not normally distributed. 
B) the R2 statistic is not very meaningful. 
C) the regression equation can generate a discriminant score. 
D) it scales to the k -group problem easily.

Question 14

Techniques)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 15

Which of the following goodness-of-fit measures is used for discriminant analysis problems?

Accepted Answer

A) R2 
B) multiple R2 
C) adjusted R2 
D) none of these 
A) R2 
B) multiple R2 
C) adjusted R2 
D) none of these

Question 16

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 17

The parameters of the logistic regression model

Accepted Answer

A) are derived through a nonlinear maximum likelihood estimation procedure 
B) are negative 
C) are positive 
D) are negative fractions 
A) are derived through a nonlinear maximum likelihood estimation procedure 
B) are negative 
C) are positive 
D) are negative fractions

Question 18

Exhibit 10.1
The following questions are based on the problem description,regression results,and the Analytic Solver Platform Discriminant Analysis report 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).
     Unpooled Estimates of within-group Covariance matrices are used,assuming they are different.
Group Centroids
Group Quantitative Verbal
1 683.8 654.2
2 610.7 605.7
 $\text { Classitication Matrix }$
$\begin{array}{cccc} 
\text { Actual / Predicted }
& \text { Group1 } & \text { Group2 } & \text { Total } & \% \text { correct } \
\hline \text { Group1 } & 9 & 1 & 1 & 90.00 \% \
&& & 0 & \
\text { Group2 }&  2 &8 & 1 & 80.00 \% \
&  &  & 0 & \\
\text { Total } & 1 & 9& 2 & 85.00 \%\
&1&&0 
\end{array}$  
-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 19

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 20

A major challenge in affinity analysis is to

Accepted Answer

A) identify the most meaningful rules used to determine what goes with what 
B) the conditional probabilities 
C) the posterior probabilities 
D) the probabilities of states of nature 
A) identify the most meaningful rules used to determine what goes with what 
B) the conditional probabilities 
C) the posterior probabilities 
D) the probabilities of states of nature

The objective of classification tree algorithms is to

_ is a classification technique that estimates the probability of an observation belonging to a particular group

Given the following confusion matrix Predicted Group 1 2 Total Actual 1 9 4 13 Group 2 2 10 12 Tntal 11 14 What is the correct classification rate?

Overfitting refers to

In classification techniques the dependent variable is

Neural networks are

_ and must be chosen each time a partition is subdivided

Suppose that there are 3 variables in a data set.Approximately how many data records are required using a rule of thumb discussed in the textbook?

Normalization of data involves

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

The objective function in k-means clustering attempts to

The regression approach can be used in the two-group discriminant analysis problem because

Techniques)used in classification step of data mining include

Which of the following goodness-of-fit measures is used for discriminant analysis problems?

Suppose that the correlation coefficient between X₁and X₂is equal to 1.This means that

The parameters of the logistic regression model

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

A major challenge in affinity analysis is 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 and 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

The objective of classification tree algorithms is to

_ is a classification technique that estimates the probability of an observation belonging to a particular group

Given the following confusion matrix Predicted Group 1 2 Total Actual 1 9 4 13 Group 2 2 10 12 Tntal 11 14 What is the correct classification rate?

Overfitting refers to

In classification techniques the dependent variable is

Neural networks are

_ and must be chosen each time a partition is subdivided

Suppose that there are 3 variables in a data set.Approximately how many data records are required using a rule of thumb discussed in the textbook?

Normalization of data involves

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

The objective function in k-means clustering attempts to

The regression approach can be used in the two-group discriminant analysis problem because

Techniques)used in classification step of data mining include

Which of the following goodness-of-fit measures is used for discriminant analysis problems?

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

The parameters of the logistic regression model

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

A major challenge in affinity analysis is 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 and 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 X₁and X₂is equal to 1.This means that