Business Knowledge Series course
Presented by Howard S. Friedman, Ph.D., Professor, Columbia University, and Partner, DataMed Solutions LLC; or Paul W. Thurman, DBA, Professor, Columbia University
This course focuses on testing whether the results of a program can be attributed to a given cause. For example, was the increase in customer sales due to mailing of sales flyers? Was the health improvement due to the new medication? What conclusion can be drawn? The following cases are examined: randomized controlled experiments and observational studies that require adjustment to reduce bias by using propensity score analysis through either propensity score matching or propensity score adjustment.
Learn how to
- identify situations in which the simple method of multiple linear regression is inadequate
- apply quasi-experimental analysis methods to real-world data for the following techniques: Propensity Score Matching and Propensity Score Adjustment.
Who should attend
Data analysts or statisticians, in the fields of finance, telecommunications, pharmaceuticals, retail, and the public sector, who have an understanding of basic statistics and SAS programming
Formats available | Standard Duration (duration can vary, see event schedule for details) | | |
Classroom: |
2.0 days | | |
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Before attending this course, you should complete or have the equivalent working experience of the following courses:
This course addresses Base SAS software.