Deck 10: Data Mining

Question 1

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?

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

Answer 1

19/25 = 76%

Question 2

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 correctly?

A)90%
B)80%
C)85%
D)100%

Answer 2

85%

Question 3

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

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

Answer 3

A

Question 4

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 verbal test score value of the group centroid for group 1?

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

Question 5

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?

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

Question 6

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 verbal test score value of the group centroid for group 2?

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

Question 7

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

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

Question 8

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

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 9

Which of the following is not true regarding discriminant analysis?

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 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.What number of observations is classified incorrectly?

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

Question 11

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?

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

Question 12

The goal of discriminant analysis is

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 13

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

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

Question 14

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 big and positive.This means that

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 15

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 big and negative.This means that

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 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 2?

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

Question 17

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

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

Question 18

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

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 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 is the quantitative test score value of the group centroid for group 1?

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

Question 20

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

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

Question 21

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 straight line distance between 6,4)and 2,9)?

A)3.20
B)6.40
C)9
D)41

Question 22

In preparation for mining an analyst should

A)explore the relationships between variables
B)verify completeness and accuracy of the data
C)clean the data addressing missing values,errors,etc.
D)all of the above

Question 23

An Excel add-in tool used for data mining is called

A)XL Miner
B)GS4
C)XML
D)Data Miner

Question 24

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 correctly?

A)100%
B)85.71%
C)95%
D)90%

Question 25

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

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

Question 26

Mahalanobis distance is a measure of

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 27

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

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 28

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?

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

Question 29

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

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 30

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?

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 31

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 is the quantitative test score value of the group centroid for group 1?

A)697.71
B)647.86
C)587.67
D)650.43

Question 32

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 is the verbal test score value of the group centroid for group 1?

A)697.71
B)647.86
C)587.67
D)650.43

Question 33

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 number of observations is classified incorrectly?

A)19
B)20
C)7
D)1

Question 34

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 is the verbal test score value of the group centroid for group 3?

A)697.71
B)647.86
C)587.67
D)605.17

Question 35

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.

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 36

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

A)697.71
B)647.86
C)587.67
D)650.43

Question 37

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 number of observations is classified correctly?

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

Question 38

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?

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

Question 39

Normalization of data involves

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 40

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.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?

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 41

A way to detecting and avoiding overfitting is to

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 42

Techniques)used in prediction step of data mining include

A)regression analysis
B)the k'th largest neighbor technique
C)neural networks
D)all of the above

Question 43

Data mining tasks fall in the following categories

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

Question 44

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

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

Question 45

Plots useful in data mining analysis can be accessed in Excel using the add-in

A)XLMiner
B)Charts
C)Data Analysis
D)Vusual Basic

Question 46

In the step of data mining,a researcher attempts to estimate to which discrete group an observation belongs to

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

Question 47

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

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

Question 48

Techniques)used in classification step of data mining include

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

Question 49

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

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

Question 50

Oversampling forces a classification method to

A)discriminate between groups
B)classifying records correctly
C)classifying records incorrectly
D)perform a large number of iterations

Question 51

Useful data mining techniques can be found in Excel under drop menu

A)Data/Data Analysis)
B)Regression
C)Histogram
D)Insert/Chart

Question 52

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

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 53

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

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

Question 54

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

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 55

In the step of data mining,a researcher attempts to form logical groupings of data in the set

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

Question 56

A correlation coefficient

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 57

In classification techniques the dependent variable is

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

Question 58

Techniques)used in association step of data mining include

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

Question 59

Overfitting refers to

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 60

Suppose that two variables are found to be significantly correlated.A researcher may

A)remove one variable from the data set
B)replace the two variables by their product
C)replace the two variables by their squared difference
D)remove both variables from the data set

Question 61

The first step in creating a classification tree involves

A)recursively partitioning the independent variables using the outcome of previous partitions
B)creating a subset of variables
C)clustering the variables into a larger superset
D)deciding the number of partitions

Question 62

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

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

Question 63

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

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

Question 64

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

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

Question 65

The Fisher classification scores can be converted to

A)a linear function for each of the groups in the classification problem
B)probabilities of group membership
C)a uniform distribution
D)a half-space

Question 66

The parameters of the logistic regression model

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

Question 67

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

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 68

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

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

Question 69

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

A)are very sensitive to the sample-specific characteristics of the training data
B)place all observations into the most frequently occurring group in the training data
C)are robust
D)are reliable

Question 70

_ and must be chosen each time a partition is subdivided

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 71

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

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 72

The k-nearest neighbor classification technique

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 73

Logistic regression is a classification technique that

A)outperforms other techniques acros a variety of data collections
B)is not reliable
C)is not robust
D)is not feasible for most data sets

Question 74

The Fisher linear discriminant function

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 75

A provides a visual summary of the improvements that a data mining project provides on a binary classification problem compared to a random guess

A)lift chart
B)Pareto chart
C)Gantt chart
D)histogram

Question 76

Standardization of a variable

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 77

The objective of classification tree algorithms is to

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 78

Two common ways of measuring impurity are and _

A)the Gini index and the entropy measure
B)the fraction of pure data and the enthaply index
C)the fraction of contaminated data and the entropy index
D)the fraction of real data and the data purity coefficient

Question 79

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

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

Question 80

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

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