Question 1

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 2

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 3

Prediction step in data mining is an option available in

Accepted Answer

A) XLMiner Excel add-in 
B) Excel 
C) data mining software 
D) nonlinear multivariate regression 
A) XLMiner Excel add-in 
B) Excel 
C) data mining software 
D) nonlinear multivariate regression

Question 4

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 5

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 6

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

Accepted Answer

A) the Group values. 
B) the independent variable values. 
C) the predicted variable values. 
D) the fitted variable values. 
A) the Group values. 
B) the independent variable values. 
C) the predicted variable values. 
D) the fitted variable values.

Question 7

The goal of discriminant analysis is

Accepted Answer

A) to develop a model to predict new dependent values. 
B) the develop a rule for predicting to what group a new observation is most likely to belong. 
C) to develop a rule for predicting how independent variable values predict dependent values. 
D) none of these. 
A) to develop a model to predict new dependent values. 
B) the develop a rule for predicting to what group a new observation is most likely to belong. 
C) to develop a rule for predicting how independent variable values predict dependent values. 
D) none of these.

Question 8

Suppose the Fisher classification scores for an observation have been converted to the following probabilities: i)0.6 for Group 1 and ii)0.4 for Group 2.The observation will be classified to

Accepted Answer

A) Group 1 
B) Group 2 
C) Group 1 or Group 2 with equal probabilities 
D) neither Group 1 nor Group 2 
A) Group 1 
B) Group 2 
C) Group 1 or Group 2 with equal probabilities 
D) neither Group 1 nor Group 2

Question 9

Suppose that a data set contains a variable EDUCATION,which has 7 discrete levels.EDUCATION is an example of

Accepted Answer

A) a categorical variable 
B) a classification variable 
C) a continuous variable 
D) an exponential variable 
A) a categorical variable 
B) a classification variable 
C) a continuous variable 
D) an exponential variable

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.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 11

A correlation coefficient

Accepted Answer

A) measures the strength of a linear association between two variables 
B) measures the strength of statistical association between two variables 
C) measures the strength of physical association between two variables 
D) measures the strength of logical association between two variables 
A) measures the strength of a linear association between two variables 
B) measures the strength of statistical association between two variables 
C) measures the strength of physical association between two variables 
D) measures the strength of logical association between two variables

Question 12

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

Accepted Answer

A) The observation is likely to be classified incorrectly 
B) The observation is likely to be classified correctly 
C) The observation is unlikely to be classified 
D) The observation should be deleted from the data set 
A) The observation is likely to be classified incorrectly 
B) The observation is likely to be classified correctly 
C) The observation is unlikely to be classified 
D) The observation should be deleted from the data set

Question 13

Techniques)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

Suppose that a data set contains a variable EDUCATION,which has 7 discrete levels.EDUCATION can be represented by binary variables

Accepted Answer

A) 6 
B) 7 
C) 8 
D) 9 
A) 6 
B) 7 
C) 8 
D) 9

Question 15

In discriminant analysis the averages for the independent variables for a group define the

Accepted Answer

A) centroid. 
B) median. 
C) mode. 
D) central tendency. 
A) centroid. 
B) median. 
C) mode. 
D) central tendency.

Question 16

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 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 17

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 is the quantitative test score value of the group centroid for group 2?

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 18

Exhibit 10.2
The following questions are based on the problem description,spreadsheet,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 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).
   Discriminant Analysis Report
October 1,2013 4:22:38 PM
Unpooled Estimates of within-group Covariance matrices are used,assuming they are different.
 $\begin{array}{lll}
\text {Group}\
\text { Centroids}\
\text { Group } & \text { Quantitative } & \text { Verbal } \
\hline 1 & 697.7142857 & 650.4285714 \
2 & 647.8571429 & 630.7142857 \
3 & 5876666667 & 6051666667
\end{array}$
 $\text {Group}$
$\text {Frenuencies}$
$\begin{array}{cr}
\text { Group } & \begin{array}{r}
\text { Relative } \
\text { Frequency }
\end{array} \
\hline 1 & 35.00 \% \
2 & 35.00 \% \
3 & 30.00\%
\end{array}$
 Training Sample Classification
Mahalanobis Distances
   $\text {Classification}$
$\text {Matrix}$
$\begin{array}{llllll}
\text { Actual / Predicted } & \text { Group1 } & \text { Group2 } & \text { Group3 } & \text { Total } & \% \text { correct } \
\hline \text { Group1 } & 6 & 1 & 0 & 7 & 85.71 \% \
\text { Group2 } & 0 & 7 & 0 & 7 & 100.00 \% \
\text { Group3 } & 0 & 0 & 6 & 6 & 100.00 \% \
\text { Total } & 6 & 8 & 6 & 2 & 95.00 \%
\end{array}$

-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 19

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 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 20

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)

A way to detecting and avoiding overfitting is to

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

Prediction step in data mining is an option available in

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

Data mining tasks fall in the following categories

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

The goal of discriminant analysis is

Suppose the Fisher classification scores for an observation have been converted to the following probabilities: i)0.6 for Group 1 and ii)0.4 for Group 2.The observation will be classified to

Suppose that a data set contains a variable EDUCATION,which has 7 discrete levels.EDUCATION is an example of

A correlation coefficient

Techniques)used in association step of data mining include

Suppose that a data set contains a variable EDUCATION,which has 7 discrete levels.EDUCATION can be represented by binary variables

In discriminant analysis the averages for the independent variables for a group define the

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

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

A way to detecting and avoiding overfitting is to

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

Prediction step in data mining is an option available in

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

Data mining tasks fall in the following categories

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

The goal of discriminant analysis is

Suppose the Fisher classification scores for an observation have been converted to the following probabilities: i)0.6 for Group 1 and ii)0.4 for Group 2.The observation will be classified to

Suppose that a data set contains a variable EDUCATION,which has 7 discrete levels.EDUCATION is an example of

A correlation coefficient

Techniques)used in association step of data mining include

Suppose that a data set contains a variable EDUCATION,which has 7 discrete levels.EDUCATION can be represented by binary variables

In discriminant analysis the averages for the independent variables for a group define the

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

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