Sumanta Basu is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. Broadly, his research interests are structure learning and the prediction of large systems from data, with a particular emphasis on developing learning algorithms for time series data. Professor Basu also collaborates with biological and social scientists on a wide range of problems, including genomics, large-scale metabolomics, and systemic risk monitoring in financial markets. His research is supported by multiple awards from the National Science Foundation and the National Institutes of Health. At Cornell, Professor Basu teaches “Introductory Statistics” for graduate students outside the Statistics Department and “Computational Statistics” for Statistics Ph.D. students. He also serves as a faculty consultant at Cornell Statistical Consulting Unit, which assists the broader Cornell community with various aspects of analyzing empirical research. Professor Basu received his Ph.D. from the University of Michigan and was a postdoctoral scholar at the University of California, Berkeley, and Lawrence Berkeley National Laboratory. Before he received his Ph.D, Professor Basu was a business analyst, working with large retail companies on the design and data analysis of their promotional campaigns.
Modeling Interactions Between PredictorsCornell Course
Course Overview
In this course, you will explore strategies for incorporating categorical predictors in a regression model, including using dummy variables to represent different categories. You will inspect binary and nonbinary categorical variables and discover how to interpret the estimated coefficients of dummy variables.
As you progress through the course, you will practice modeling and interpreting interactions between categorical and quantitative predictors in a linear model. Finally, you will focus on defining and implementing decision trees, which are advantageous for capturing complex interactions between predictors that linear models may be unable to capture. By the end of the course, you will be equipped to transform categorical variables into numerical variables, fit regression models with categorical predictors, interpret dummy variable coefficients, and use decision trees for modeling complex relationships between predictors.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Nonlinear Regression Models
Key Course Takeaways
- Articulate the importance of dummy variables for including categorical predictors in models
- Define a dummy variable, include dummy variables in a model, and interpret the estimated coefficients
- Include interactions between categorical and quantitative predictors in a linear model
- Define a decision tree, describe the advantage of using a decision tree over a linear model, and implement decision trees in R
How It Works
Course Author
Who Should Enroll
- Current and aspiring data scientists and analysts
- Business decision makers
- Marketing analysts
- Consultants
- Executives
- Anyone seeking to gain deeper exposure to data science
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