Create a scatter plot of the data in such a way that points of different clusters have different colors.

Learning Goal: I’m working on a jquery / prototype question and need an explanation and answer to help me learn.Part 1: Clustering of Trader Joe’s Stores with k-MeansTraderJoes.csv Download TraderJoes.csv
Suppose you work for the supply-chain analytics division of Trader Joe’s, a national chain of specialty grocery stores. Trader Joe’s is considering a redesign of its supply chain. You know that Trader Joe’s uses frequent truck shipments from its distribution centers to its retail stores. To keep costs low, retail stores are typically located near a distribution center. The file Trader Joes contains data on the location of Trader Joe’s retail stores. You would like to use k-means clustering with k=8 to estimate the preferred locations for a proposal to use eight distribution centers to support its retail stores.Import the data to JMP Pro and set the column data types properly.
Apply k-means with 8 clusters using variables Latitude and Longitude only. Make sure to select “save the clusters” and “save colors to table”.
Create a scatter plot of the data in such a way that points of different clusters have different colors. Inspect the clusters.
Calculate the WSS for each cluster.
Copy the “Cluster Summary Tables” and “Cluster Means” table to a Word file. Copy also the table with the WSS. In the same Word file, and based on the work done above, answer the following questions:If Trader Joe’s establishes 8 distribution centers, how many retail stores does the k-means approach suggest assigning to each distribution center?
Where would you suggest to locate (approximately) each of the 8 distributions centers?
What are the drawbacks to directly applying this solution to assign retail stores to distribution centers?
What is the cluster with smallest WSS? Why is that?
Part 2: Clustering Colleges with k-Means.FBS.csv Download FBS.csv
The Football Bowl Subdivision (FBS) level of the National Collegiate Athletic Association (NCAA) consists of over 100 schools. Most of these schools belong to one of several conferences, or collections of schools, that compete with each other on a regular basis in collegiate sports. Suppose the NCAA has commissioned a study that will propose the formation of conferences based on the similarities of the constituent schools. The file FBS contains data on schools that belong to the Football Bowl Subdivision. Each row in this file contains information on a school. The variables include football stadium capacity, latitude, longitude, athletic department revenue, endowment, and undergraduate enrollment.Import the data and make sure the column data types are set properly.
Apply k-means clustering with k = 9 using football stadium capacity, latitude, longitude, endowment, and enrollment as variables. Make sure to select “save the clusters” and “save colors to table”.
Follow the steps in this video (Links to an external site.) to create a geographical map of the schools using the coordinates. Make sure schools of different clusters are marked with different colors.Before you proceed to the next question, it may be useful to format each column of the “Cluster Means” table, so that all values in a column have the same number of decimal places. This makes it easier to analyze and compare data. To do that, with the mouse on each column, right-click and select “Format column…”.
Copy the “Cluster Summary Tables” and “Cluster Means” table to a Word file. In the same Word file, and based on the work done above, answer the following questions:
Describe the smallest cluster(s). Why do you think such cluster(s) were created?
Describe the largest cluster.
What problems do you see with the plan for defining the school membership of the 9 conferences directly with these 9 clusters?
Requirements: in form of clusters   |   .doc file

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