Performance Lawn Equipment Chapter $10$ Case Study:

In this chapter, four data mining approaches were introduced. These four approaches include: $1)$ Cluster analysis, $2)$ Classification, $3)$ Association, $4)$ Cause$-$and$-$effect modeling. For each one of these approaches, multiple techniques were discussed. The bullet points shown below provides a summary of the approaches along with some of their respective techniques.

$$ Cluster Analysis

o Single Linkage Clustering

o Complete Linkage Clustering

o Average Linkage Clustering

o Average Group Linkage Clustering

o Ward$$s Hierarchical Clustering

$$ Classification

o Intuitive Classification Technique with Classification Matrix

o K$-$Nearest Neighbors $($k$-$NN$)$

o Discriminant Analysis

o Logistic Regression

$$ Association

o Creating or Identifying Association Rules

o Strength of Association Measures through confidence and lift

$$ Cause and Effect Modeling

o Creating cause and effect correlation matrices with conditional formatting

o Designing cause and effect models from the developed correlation matrix

The worksheet Purchasing Survey in the Performance Lawn Care database provides data related to predicting the level of business $($Usage Level$)$ obtained from a third$-$party survey of purchasing managers of customers Performance Lawn Care. The seven PLE attributes rated by each respondent are

$1.$ Delivery speed$$the amount of time it takes to deliver the product one an order is confirmed

$2.$ Price level$$the perceived level of price charged by PLE

$3.$ Price flexibility$$the perceived willingness of PLE representatives to negotiate price on all types of purchases

$4.$ Manufacturing image$$the overall image of the manufacturer

$5.$ Overall service$$the overall level of service necessary for maintain a satisfactory relationship between PLE and the purchaser

$6.$ Sales force image$$the overall image of the PLE$$s sales force

$7.$ Product quality$$perceived level of quality

Responses to these seven variables were obtained using a graphic rating scale, where a $10-$centimeter line was drawn between endpoints labeled $$poor$$ and $$excellent$.$ Respondents indicated their perceptions using a mark on the line, which was measured from the left endpoint. The result was a scale from $0$ to $10$ rounded to one decimal place

Two measures were obtained that reflected the outcomes of the respondent$$s purchase relationships with PLE:

$1.$ Usage level$$how much of the firm$$s total product is purchased from PLE, measured on a $100-$point scale, ranging from $0\%$ to $100\%$

$2.$ Satisfaction level$$how satisfied the purchaser is with past purchases from PLE, measure on the same graphic rating scale as perceptions $1$ through $7$

The data also includes four characteristics of the responding firms:

$1.$ Size of firm$$size relative to others in this market $(0=$ small; $1=$ large$)$

$2.$ Purchasing structure$$the purchasing method used in a particular company $(1=$ centralized procurement, $0=$ decentralized procurement$)$

$3.$ Industry$$the industry classification of the purchaser $[1=$ retail$($resale such as Home Depot$),0=$ private $($non$-$resale, such as a landscaper$)]$

$4.$ Buying type$$a variable that has three categories $(1=$ new purchase, $2=$ modified rebuy, $3=$ straight rebuy$).$

Elizabeth Burke requests that the analytics group implement several data mining tools to understand more about the purchasing dynamics within PLE. Specifically, she wants each group to design $4$ questions.

$$ Each of the $4$ questions should be answered by implementing $1$ of the data mining approaches listed on page $1.$

$$ All data$-$mining approaches must be implemented in the report. Hence, your group may not use more than $1$ data$-$mining approach to answer two or more questions.

$$ The questions may be very broad or specific, such as $$In what ways may it be more beneficial to split observations up into several clusters?$$

$$ For each question $($data mining approach$),$ implement two unique techniques listed below the respective data mining approach found on page $1($see bullet points$).$ I.E$.$ If I am going to address my question in the above bullet point, then I can choose to implement Single Linkage Clustering and Complete Linkage Clustering.

$$ Provide discussions throughout your report on your analysis and finally how you answered your questions.

$$ It is strongly encouraged that you use the Analytic Solver Platform Software on this case study as the software will save you a lot of time for multiple data mining approaches.

$$ This is an open$-$ended case study. There are a plethora of correct questions, answers, discussions. Be creative!

Main Task: Write a formal report summarizing your results for the entire exercise listed above. When completing the case study project, it is required that in each report $(1$ report per group$),$ you include an introduction that reminds the manager Ms$.$ Burke about what the report contains, and a conclusion that summarizes the main findings of the analysis you conducted. The introduction and conclusion should be $2$ moderate