CIS602 - Modeling Interactions Between Predictors

Course Description

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

Faculty Author

Benefits to the Learner

• 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

Target Audience

• Current and aspiring data scientists and analysts
• Business decision makers
• Marketing analysts
• Consultants
• Executives
• Anyone seeking to gain deeper exposure to data science

Applies Towards the Following Certificates

• Data Science Modeling : Core Courses