Linda Nozick is a professor in Cornell’s School of Civil and Environmental Engineering. She served as director of the school for more than a decade. Prior to that role, Nozick was the director of the college’s Systems Engineering Program, which she co-founded. Nozick has been the recipient of several awards including a CAREER award from the National Science Foundation and a Presidential Early Career Award for Scientists and Engineers from President Clinton for “the development of innovative solutions to problems associated with the transportation of hazardous waste.” She is a past U.S. Presidential appointee to the U.S Nuclear Waste Technical Review Board. Nozick has authored over 100 peer-reviewed publications, many focused on transportation, the movement of hazardous materials, and the modeling of critical infrastructure systems. Nozick holds a B.S. in systems analysis and engineering from the George Washington University and a M.S.E and Ph.D. in systems engineering from the University of Pennsylvania.
Regression Analysis and Discrete Choice ModelsCornell Course
Course Overview
A story can play an important role in understanding data. It can help distill complex information into something manageable- something we can think about easily, relate to, and use to make decisions. For many problems that we encounter globally, however, a story that describes what already happened is not enough precision for the job we want to perform. Often, we would like to use available data to make numerically accurate predictions about what might happen in the future. This task requires the construction of mathematical models that are well suited to our real-world problems.
In this course, you will explore several types of statistical models used with data to make predictions. These models bring with them a whole batch of important concerns, such as estimation and validation, that make the entire process into both an art and a science. You will implement each of these techniques using the free and open-source statistical programming language R with real-world data sets. The focus will be on making these methods accessible for you in your own work.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Understanding Data Analytics
- Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
- Finding Patterns in Data Using Cluster and Hotspot Analysis
Key Course Takeaways
- Fit a linear regression model
- Measure the goodness of fit of a regression model
- Measure the prediction quality of a regression model
- Create and interpret confidence and prediction intervals
- Validate a linear regression model
- Diagnose problems with linear regression models
- Create and interpret logistics regression models
How It Works
Course Author
Who Should Enroll
- Current and aspiring data scientists
- Analysts
- Engineers
- Researchers
- Technical managers
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