Transcript Data Modeling - Temple Fox MIS

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 2
Browse 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
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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
• View>Model>Subtree Assessment Plot
• The line is the Optimal
Leaf count for that tree
• Note the Leaf Count
and the Validation:
Average Square Error
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