# In this blog you will find the correct answer of the Coursera quiz Business Analytics for Decision Making Coursera Week 1 Quiz mixsaver always try to bring the best blogs and best coupon codes

Week 1 Quiz

1.
Question 1
Which of the following is true of cluster
analysis?

1 point

• It is a data analysis
technique to discover trends in time-series data
• It is a data mining tool that
is used to create homogeneous groups
• It is a data visualization
tool in market research
• It is model for customer
behavior in the organic and natural products industry

2.
Question 2
Which
of the following settings are appropriate applications of cluster
analysis? (select all that apply)

1 point

• A
recommender system that seeks to predict the rating or preference that a user
would give to an item (e.g., a movie, a book, or a restaurant).
• A delivery scheduling
system that assigns delivery trucks to customers in the same general
geographical area
• A cable
company seeking to identify the number and type of TV packages to offer (e.g.,
• An inventory management
system for retail pharmacies that attempts to minimize both the probability of
running out of stock and the inventory carrying cost.

3.
Question 3
Which of the following statements is true of principal component analysis (PCA) and cluster analysis?

1 point

• PCA and cluster analysis are incompatible techniques, only one of them can be applied to the same data
• PCA is a data reduction technique and cluster analysis is a dimensionality reduction technique
• Cluster analysis is a data reduction technique and PCA is a dimensionality reduction technique
• The main goal of cluster analysis is to identify redundant variables and the main goal of PCA is to create homogeneous groups of observations

4.
Question 4
Cluster analysis is considered an unsupervised learning technique because it operates on historical observations that are not labeled. That is, it is not known to which group historical observations belong and therefore it is not known how many groups there are.

1 point

• True
• False

5.
Question 5
If the Euclidean distance were to be represented in a right angle triangle, which of the following would be considered the distance between two objects of a cluster?

1 point

• Hypotenuse
• Small leg
• Long leg
• Average of the sum of both legs

6.
Question 6
Which of the following is the definition of distance between two clusters in a complete linkage clustering?

1 point

• The average of distances between all pairs of objects, where each pair is made up of one object of each group
• The distance between the most distant pair of objects, one from each group
• The sum of squares of the distance between clusters
• The distance between the value of the shortest link between the clusters

7.
Question 7
Which of the following is true of hierarchical clustering?

1 point

• All clusters must have the same number of objects
• No single cluster can have all objects
• Each step of the procedure consists of merging the two closest clusters
• All clusters must have more than one object in them

8.
Question 8
Which of the following is true of clustering methods?

1 point

• The k-means method is an exact procedure that finds the optimal (i.e., the best)
• The best clustering approach when dealing with very large data sets is to solve the optimization problem using Excel’s Solver
• The k-means method and hierarchical clustering always arrive at the same solution, that is, they always produce the same set of clusters
• Finding the best set of clusters is complicated because the number of ways of partitioning the observations into k groups is very large and this is why approximation methods such as k-means and hierarchical clustering are used.

Week 1 Application Assignment – Clustering

1.
Question 1
Assignment Overview
In this assignment you will practice what we learned in video 5 of this module. In Part 1 of the assignment, which is optional, you will be provided with a set of demographic data on 49 of America’s largest cities and will have an opportunity apply k-means clustering to city groups for marketing purposes. In the Part 2 of the assignment, you will be asked a series of questions that will prompt you to describe demographic structure of the clusters, and identify cities where to conduct a test for a new product.

Assignment Prompt
A large consumer goods company wants to select 4 U.S. cities where to test a new product. The company wants each city to represent a particular market segment, as defined by their demographic structure. The company has collected demographic data on 49 of America’s largest cities (see the Cities Excel file below). The demographic data consist of six attributes: 1) percentage of African-American population (% Black), 2) percentage of Hispanic population (% Hispanic), 3) percentage of Asian-American population (% Asian), 4) median age, 5) unemployment rate, and 6) per capita income.

Which cluster represents cities with no particular dominant minority group, with average age, employment rate, and income?

1 point

• Cluster 1
• Cluster 2
• Cluster 3
• Cluster 4

2.
Question 2
Which cluster consists of cities with a large Asian
population who is older and wealthy.

1 point

• Cluster 1
• Cluster 2
• Cluster 3
• Cluster 4

3.
Question 3
Which
cluster includes cities with a large population of African-Americans.

1 point

• Cluster 1
• Cluster 2
• Cluster 3
• Cluster 4

4.
Question 4
The
company would like to choose one city to represent each market in order to test
the new product. As discussed in the module, a representative object for a
cluster could be chosen as the one that is closest to the centroid. The
worksheet KMC_Clusters generated by XLMiner contains a table with the distances
from each city to the centroid of each cluster. To identify the city to
represent each cluster, we just need to find the city with the minimum distance
to each of the centroids. Which cities
would you recommend to choose to represent each cluster?

1 point

• Cluster 1: Seattle, Cluster 2: Memphis,
Cluster 3: Las Vegas, and Cluster 4: San Antonio
• Cluster
1: San Francisco, Cluster 2: Philadelphia, Cluster 3: Toledo, and Cluster 4:
Los Angeles
• Cluster 1: San Francisco, Cluster 2:
Philadelphia, Cluster 3: Omaha, and Cluster 4: Los Angeles
• Cluster 1: San Jose, Cluster 2: Detroit,
Cluster 3: Las Vegas, and Cluster 4: El Paso