Question 1

Data groups exhibit dissimilar characteristics (heterogeneity) within cluster (intra-cluster) and homogeneity or similarities between groups (inter-cluster).

Accepted Answer

A) True 
 B)False  False

Question 2

Describe how Best Buy used insights gained through market segmentation to achieve a 10 percent growth in sales.

Accepted Answer

Best Buy used insights gained through market segmentation to achieve a 10 percent growth in sales by identifying and targeting specific customer segments with tailored marketing strategies. By understanding the unique needs and preferences of different customer groups, Best Buy was able to create personalized marketing campaigns, product offerings, and customer experiences that resonated with each segment. This approach allowed Best Buy to effectively reach and engage with a wider range of customers, ultimately driving an increase in sales. Additionally, by focusing on specific segments, Best Buy was able to allocate resources more efficiently and optimize their marketing efforts, leading to improved sales performance. Overall, the use of market segmentation enabled Best Buy to better understand and connect with their customers, resulting in a significant growth in sales.

Question 3

In which of the following functions is the distance measured equivalent to the true straight line distance between two points?

Accepted Answer

A)  The Euclidean distance 
B)  Jaccard's coefficient 
C)  The Manhattan distance 
D)  Matching coefficient 
A)  The Euclidean distance 
B)  Jaccard's coefficient 
C)  The Manhattan distance 
D)  Matching coefficient A

Question 4

Which of the following is true of the agglomerative clustering approach?

Accepted Answer

A)  At the end of the process, all observations are included in a single cluster. 
B)  The most dissimilar observations are sequentially separated from the initial 100-observation cluster. 
C)  The process starts with a single cluster of 100 and quickly ends up with 100 different clusters. 
D)  All observations are initially assigned to a single cluster. 
A)  At the end of the process, all observations are included in a single cluster. 
B)  The most dissimilar observations are sequentially separated from the initial 100-observation cluster. 
C)  The process starts with a single cluster of 100 and quickly ends up with 100 different clusters. 
D)  All observations are initially assigned to a single cluster.

Question 5

A typical silhouette score ranges between ________.

Accepted Answer

A)  +1 and ?1 
B)  +10 and ?10 
C)  +5 and ?5 
D)  +100 and ?100 
A)  +1 and ?1 
B)  +10 and ?10 
C)  +5 and ?5 
D)  +100 and ?100

Question 6

Summarize practitioner Kaitlin Marvin's views about the use of market segmentation and cluster analysis to improve sales.

Accepted Answer

Practitioner Kaitlin Marvin believes tha

Question 7

In the ________ method of linking individual observations both within and between clusters, similarity is defined by the group average of observations from one cluster to all observations from another cluster.

Accepted Answer

A)  bell crank linkage 
B)  single linkage 
C)  average linkage 
D)  complete linkage 
A)  bell crank linkage 
B)  single linkage 
C)  average linkage 
D)  complete linkage

Question 8

In hierarchical clustering, approaches such as ________ are most often used when numerical variables are analyzed.

Accepted Answer

A)  Matching coefficient or the Manhattan distance 
B)  Jaccard's coefficient or the Euclidean distance 
C)  the Euclidean distance or the Manhattan distance 
D)  Matching coefficient or Jaccard's coefficient 
A)  Matching coefficient or the Manhattan distance 
B)  Jaccard's coefficient or the Euclidean distance 
C)  the Euclidean distance or the Manhattan distance 
D)  Matching coefficient or Jaccard's coefficient

Question 9

Compare and contrast hierarchical and k-means cluster analysis techniques of market segmentation.

Accepted Answer

Hierarchical and k-means cluster analysi

Question 10

Explain how a dendrogram is used to illustrate all four measurement approaches for determining clusters in hierarchical clustering.

Accepted Answer

A dendrogram is a diagram that illustrat

Question 11

In the ________ method of linking individual observations both within and between clusters, similarity is defined by the maximum distance between observations in two different clusters.

Accepted Answer

A)  complete linkage 
B)  average linkage 
C)  single linkage 
D)  parallel motion linkage 
A)  complete linkage 
B)  average linkage 
C)  single linkage 
D)  parallel motion linkage

Question 12

When running k-means clustering, it is best to begin with a dataset in which each variable is measured differently.

Accepted Answer

A) True 
 B)False

Question 13

K-means clustering uses the mean value for each cluster and minimizes the distance to individual observations.

Accepted Answer

A) True 
 B)False

Question 14

Compare and contrast agglomerative and divisive clustering approaches within the hierarchical clustering method of market segmentation.

Accepted Answer

Agglomerative and divisive clustering ar

Question 15

Which of the following statements is true of the clustering process?

Accepted Answer

A)  It enables companies to assign groups of customers to their network. 
B)  It enables marketers to forecast revenue for next quarter. 
C)  It enables marketers to identify hidden structures in data. 
D)  It typically assigns names and definitions to unrelated sections of data. 
A)  It enables companies to assign groups of customers to their network. 
B)  It enables marketers to forecast revenue for next quarter. 
C)  It enables marketers to identify hidden structures in data. 
D)  It typically assigns names and definitions to unrelated sections of data.

Question 16

Segmenting a market using shared characteristics is called cluster analysis.

Accepted Answer

A) True 
 B)False

Question 17

The Jaccard's coefficient approach of measuring similarity between observations

Accepted Answer

A)  measures the distance as the true straight line distance between two points. 
B)  is a path with right turns as if one is walking a grid in a city. 
C)  makes calculations based on how dissimilar two observations are from each other. 
D)  measures the similarity between two observations with values that represent the minimum differences between two points. 
A)  measures the distance as the true straight line distance between two points. 
B)  is a path with right turns as if one is walking a grid in a city. 
C)  makes calculations based on how dissimilar two observations are from each other. 
D)  measures the similarity between two observations with values that represent the minimum differences between two points.

Question 18

Distinguishing clusters from the larger population or dataset is necessary for learning and responding to different engagement or buying behaviors.

Accepted Answer

A) True 
 B)False

Question 19

In the Manhattan distance approach of measuring similarity between observations

Accepted Answer

A)  the distance is measured as the true straight line distance between two points. 
B)  the distance between two points is a path with right turns as if one is walking a grid in a city. 
C)  the similarity between two observations is measured with values that represent the minimum differences between two points. 
D)  the similarity is based on how dissimilar two observations are from each other. 
A)  the distance is measured as the true straight line distance between two points. 
B)  the distance between two points is a path with right turns as if one is walking a grid in a city. 
C)  the similarity between two observations is measured with values that represent the minimum differences between two points. 
D)  the similarity is based on how dissimilar two observations are from each other.

Question 20

In the k-means clustering algorithm, what happens after observations are randomly assigned to a cluster?

Accepted Answer

A)  Observations are reassigned. 
B)  Cluster centroids are recalculated. 
C)  Cluster centroids are determined. 
D)  Initial k clusters are determined. 
A)  Observations are reassigned. 
B)  Cluster centroids are recalculated. 
C)  Cluster centroids are determined. 
D)  Initial k clusters are determined.

Data groups exhibit dissimilar characteristics (heterogeneity) within cluster (intra-cluster) and homogeneity or similarities between groups (inter-cluster).

Describe how Best Buy used insights gained through market segmentation to achieve a 10 percent growth in sales.

In which of the following functions is the distance measured equivalent to the true straight line distance between two points?

Which of the following is true of the agglomerative clustering approach?

A typical silhouette score ranges between ________.

Summarize practitioner Kaitlin Marvin's views about the use of market segmentation and cluster analysis to improve sales.

In the ________ method of linking individual observations both within and between clusters, similarity is defined by the group average of observations from one cluster to all observations from another cluster.

In hierarchical clustering, approaches such as ________ are most often used when numerical variables are analyzed.

Compare and contrast hierarchical and k-means cluster analysis techniques of market segmentation.

Explain how a dendrogram is used to illustrate all four measurement approaches for determining clusters in hierarchical clustering.

In the ________ method of linking individual observations both within and between clusters, similarity is defined by the maximum distance between observations in two different clusters.

When running k-means clustering, it is best to begin with a dataset in which each variable is measured differently.

K-means clustering uses the mean value for each cluster and minimizes the distance to individual observations.

Compare and contrast agglomerative and divisive clustering approaches within the hierarchical clustering method of market segmentation.

Which of the following statements is true of the clustering process?

Segmenting a market using shared characteristics is called cluster analysis.

The Jaccard's coefficient approach of measuring similarity between observations

Distinguishing clusters from the larger population or dataset is necessary for learning and responding to different engagement or buying behaviors.

In the Manhattan distance approach of measuring similarity between observations

In the k-means clustering algorithm, what happens after observations are randomly assigned to a cluster?

Introduction to Marketing Analytics

Data Management

Exploratory Data Analysis Using Cognitive Analytics

Data Visualization

Regression Analysis

Neural Networks

Automated Machine Learning

Market Basket Analysis

Natural Language Processing - Text Mining and Sentiment Analysis

Social Network Analysis

Web Analytics

Filters

Exam 8: Cluster Analysis

Data groups exhibit dissimilar characteristics (heterogeneity) within cluster (intra-cluster) and homogeneity or similarities between groups (inter-cluster).

Describe how Best Buy used insights gained through market segmentation to achieve a 10 percent growth in sales.

In which of the following functions is the distance measured equivalent to the true straight line distance between two points?

Which of the following is true of the agglomerative clustering approach?

A typical silhouette score ranges between ________.

Summarize practitioner Kaitlin Marvin's views about the use of market segmentation and cluster analysis to improve sales.

In the ________ method of linking individual observations both within and between clusters, similarity is defined by the group average of observations from one cluster to all observations from another cluster.

In hierarchical clustering, approaches such as ________ are most often used when numerical variables are analyzed.

Compare and contrast hierarchical and k-means cluster analysis techniques of market segmentation.

Explain how a dendrogram is used to illustrate all four measurement approaches for determining clusters in hierarchical clustering.

In the ________ method of linking individual observations both within and between clusters, similarity is defined by the maximum distance between observations in two different clusters.

When running k-means clustering, it is best to begin with a dataset in which each variable is measured differently.

K-means clustering uses the mean value for each cluster and minimizes the distance to individual observations.

Compare and contrast agglomerative and divisive clustering approaches within the hierarchical clustering method of market segmentation.

Which of the following statements is true of the clustering process?

Segmenting a market using shared characteristics is called cluster analysis.

The Jaccard's coefficient approach of measuring similarity between observations

Distinguishing clusters from the larger population or dataset is necessary for learning and responding to different engagement or buying behaviors.

In the Manhattan distance approach of measuring similarity between observations

In the k-means clustering algorithm, what happens after observations are randomly assigned to a cluster?

Introduction to Marketing Analytics

Data Management

Exploratory Data Analysis Using Cognitive Analytics

Data Visualization

Regression Analysis

Neural Networks

Automated Machine Learning

Market Basket Analysis

Natural Language Processing - Text Mining and Sentiment Analysis

Social Network Analysis

Web Analytics

Filters