Collaborate with your team to complete the following requirements: Prepare a document bearing the course title, date, names of all team members, faculty member's name, and title “Week 3 Team Assignment.” Download the SkiResorts.xlsx data file. Import it into a data frame in RStudio. Name the data frame descriptively. Issue the command: set.seed(7) Create a 3-cluster k-means model that includes all the numeric attributes in the data set (columns 4 through 11). Create a centroid table comprised of the cluster sizes and the centers for each attribute. Name this data object descriptively. Create a new data frame comprised of the original data set and the cluster number for each ski resort. Name this data frame descriptively. In your team assignment document, write a report of your k-means cluster analysis. Address all the following points in your report: Using images and specific values from the centroid table, discuss the average attribute values of small, medium, and large resorts. Discuss the average number of lifts across the 3 different clusters. What about this attribute is different from the other attributes’ means? Examine the lift ticket price averages. Discuss how elevation, snowfall, acreage, and other variables appear to impact the cost of tickets. The cluster containing the smallest, least expensive ski areas has more resorts than the other two clusters combined. Discuss the average attributes of this largest cluster of ski destinations. Examine the 64 resorts that are classified into the third cluster. Discuss the attributes that best characterize these resorts. Evaluate the ticket price attribute for these resorts. Although the average ticket price for these resorts is over $106 per day, there are some resorts that cost less than $50. Examine the means for these resorts and discuss why you believe these resorts were categorized alongside the other cluster of 3 resorts despite their lower cost. Discuss as a group what additional insights your team has made using the k-means analysis of this data. Summarize your group discussion, including where team members may have disagreed. Discuss as a group how k-means data analysis tools can be used by different organizations and how valuable each team member found this tool to be. Conclude with a summary of this group discussion, including where team members may have disagreed.

Collaborate with your team to complete the following requirements: Prepare a document bearing the course title, date, names of all team members, faculty member's name, and title “Week 3 Team Assignment.” Download the SkiResorts.xlsx data file. Import it into a data frame in RStudio. Name the data frame descriptively. Issue the command: set.seed(7) Create a 3-cluster k-means model that includes all the numeric attributes in the data set (columns 4 through 11). Create a centroid table comprised of the cluster sizes and the centers for each attribute. Name this data object descriptively. Create a new data frame comprised of the original data set and the cluster number for each ski resort. Name this data frame descriptively. In your team assignment document, write a report of your k-means cluster analysis. Address all the following points in your report: Using images and specific values from the centroid table, discuss the average attribute values of small, medium, and large resorts. Discuss the average number of lifts across the 3 different clusters. What about this attribute is different from the other attributes’ means? Examine the lift ticket price averages. Discuss how elevation, snowfall, acreage, and other variables appear to impact the cost of tickets. The cluster containing the smallest, least expensive ski areas has more resorts than the other two clusters combined. Discuss the average attributes of this largest cluster of ski destinations. Examine the 64 resorts that are classified into the third cluster. Discuss the attributes that best characterize these resorts. Evaluate the ticket price attribute for these resorts. Although the average ticket price for these resorts is over $106 per day, there are some resorts that cost less than $50. Examine the means for these resorts and discuss why you believe these resorts were categorized alongside the other cluster of 3 resorts despite their lower cost. Discuss as a group what additional insights your team has made using the k-means analysis of this data. Summarize your group discussion, including where team members may have disagreed. Discuss as a group how k-means data analysis tools can be used by different organizations and how valuable each team member found this tool to be. Conclude with a summary of this group discussion, including where team members may have disagreed.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

Collaborate with your team to complete the following requirements:

Prepare a document bearing the course title, date, names of all team members, faculty member's name, and title “Week 3 Team Assignment.”
Download the SkiResorts.xlsx data file. Import it into a data frame in RStudio. Name the data frame descriptively.
Issue the command: set.seed(7)
Create a 3-cluster k-means model that includes all the numeric attributes in the data set (columns 4 through 11).
Create a centroid table comprised of the cluster sizes and the centers for each attribute. Name this data object descriptively.
Create a new data frame comprised of the original data set and the cluster number for each ski resort. Name this data frame descriptively.
In your team assignment document, write a report of your k-means cluster analysis. Address all the following points in your report:
Using images and specific values from the centroid table, discuss the average attribute values of small, medium, and large resorts.
Discuss the average number of lifts across the 3 different clusters. What about this attribute is different from the other attributes’ means?
Examine the lift ticket price averages. Discuss how elevation, snowfall, acreage, and other variables appear to impact the cost of tickets.
The cluster containing the smallest, least expensive ski areas has more resorts than the other two clusters combined. Discuss the average attributes of this largest cluster of ski destinations.
Examine the 64 resorts that are classified into the third cluster. Discuss the attributes that best characterize these resorts. Evaluate the ticket price attribute for these resorts. Although the average ticket price for these resorts is over $106 per day, there are some resorts that cost less than $50. Examine the means for these resorts and discuss why you believe these resorts were categorized alongside the other cluster of 3 resorts despite their lower cost.
Discuss as a group what additional insights your team has made using the k-means analysis of this data. Summarize your group discussion, including where team members may have disagreed.
Discuss as a group how k-means data analysis tools can be used by different organizations and how valuable each team member found this tool to be. Conclude with a summary of this group discussion, including where team members may have disagreed.

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