CEEM584 - Regression Analysis and Discrete Choice Models

Course Description

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

Faculty Author

Benefits to the Learner

• 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

Target Audience

• Current and aspiring data scientists
• Analysts
• Engineers
• Researchers
• Technical managers

Applies Towards the Following Certificates

• AI 360 : Core Courses
• Data Science : Core Courses