SUMMARY STATISTICS REVIEW

MIS2502

Data Analytics

Bottom line

• In large sets of data, these patterns aren’t obvious• And we can’t just figure it out in our head

• We need analytics software

• We’ll be using SAS to perform these three analyses on large sets of data• Decision Trees• Clustering• Association Rules

• Do most players make more or less than the mean? Explain.• Are player salaries normally distributed? Explain.•What do you learn about player salaries based on the standard deviation being greater than the mean?

SAS #1 – Intro • Start up SAS • Modify an existing Project • Create a new Diagram within that Project • Define Data Set AAEM61.Organics for that Project • Modify Data Set AAEM61.Organics

• DemCluster:Reject • TargetAmt: Reject • TargetBuy: Target – Binary

Analysis (during Data Source Definition) Target Buy - Proportion who Purchase

Explore Data Source Organics • DemGender (BAR Chart) • DemAge (Summary Stats Max) • Distribution of DemAffl - Mode v Mean

File >New > Diagram File>New>Data Source

SAS #1- Data Source Wizard

SAS #1- Data Source Wizard : Step 2Browse to SharedData>Libraries> AAEM >Organics OK

SAS #1- Data Source Wizard : Next for Steps 3, 4 and 5 . Basic and then make changes

SAS #1- Data Source Wizard : Next for Steps 7, 8, 9 and 10, then Finish.

SAS #1 – Explore Right Click on file and Choose Explore

This will open the Summary Statistics window

SAS #1 – Explore Default Explore Window

SAS #1 – Explore using Bar Chart Actions>Plot>BarChart>

SAS #1 – Explore Sample Statistics

SAS #1 - Explore Using Histogram Actions>Plot>Histogram>

Highlight bars to get Stats

SAS #1 - Explore Using Histogram Actions>Plot>Histogram>

Answer will vary depending on fetch size and sample method but not by much…

SAS Homework 2 Review Decision Trees

• Using Organics Data Set from exercise #1. If Organics is wrong then your Decision Tree will be wrong

• Partition: 50% Training, 50% Validation• Add a Decision Tree using defaults (max number branches

2)• Evaluate default Decision Tree using Average Square Error • Add another Decision Tree but this time customize by

changing the max number of branches from 2 to 3• Assess this Decision Tree using Average Square Error • Compare default Tree (2 branch max) to customized Tree (3

branch max) and determine which model is ‘better’• Answer some questions regarding the customized Decision

Tree

Partition

Decision Tree – Diagram

• Right Click> Run after adding objects

• Difference is Maximum Branch

Assessing the 1st Decision Tree

• 2 branches • Age is 1st branch

Assessing the 1st Decision Tree using average square error subtree assessment plot

• The line is the Optimal Leaf count for that tree

• Note the Leaf Count and the Validation: Average Square Error

• View>Model>Subtree Assessment Plot

Assessing the 2nd Decision Tree

Assessing the 2nd Decision Tree

• Note change in average square error

• In general, less error is better

Navigating the Tree What is the probability that a 39.5 year old male with an affluence grade of 15 buys organics ? • Age = 39.5, • AfflGrade > 11.5• Gender = M• Look at the ‘Validation’ stats • 1 = buy, 0 = no buy

• View> Explorer