This assignment will allow you to experience two categories of Business Analytics, those termed Management Science and Statistical Analysis and Data Mining and Predictive Analysis. The SAS Institute has developed a group of tools attached to its well‐known statistical package for Business Analytics. A powerful one is SAS Enterprise Miner®. Even though it is a software package that may not come naturally as an extension of other software packages used in organizations, its powerful knowledge engine makes it a tool that provides, those who learn it, an enormous support for decision‐making at the highest levels in business.
Based on your Last Name you will analyze 500 entries of 13 variables:
Last Name Starts with File Target Variable
A - I Data Set A F2022.csv Variable 1
J - Z Data Set B F2022.csv Variable 1
Deliverables:
ETL [10 marks]
Work with the data set assigned to you and prepare it to be used in SAS. It is recommended that you transform the data. Any transformations performed need to be explained in a separate sheet named Legend. Load the transformed data to SAS.
Stats Report [30 marks]
Prepare a Statistical report in SAS Enterprise Guide®. [Your reports must be exported as PDF. They need to include a footnote clearly indicating that is your work.]
• Including Mean, Standard Deviation Minimum, Maximum, Range, Median & number of observations. Do a quick analysis to see if there are differences between those stats when the Target variable is True or False.
• Perform Correlation Analysis between the Target variable and all other variables. Clearly identify the variables that are significantly correlated with the Target.
• Explore any additional associations among the set of variables identified in the correlation analysis by generating a Scatter Plot Matrix. Use the Target variable to visually identify patterns.
Analytics Report [50 marks]
Based on the findings in SAS Enterprise Guide® use SAS Enterprise Miner® to estimate the target variable using two different methods: Decision Tree & Logistic Regression. The target variable is binary and has no missing values. Please use a 2‐to‐1 ratio between Training and Validation. There is no need for Testing. When needed, use the last 5 digits of your student number as seed to run your models.
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