Learn from data science expert Michael Grogan in this tutorial that teaches you how to use regression analysis and R to uncover high-value business insights hidden inside large datasets. The course reviews the meaning of regression analysis; shows you how to use R to conduct regression analysis techniques on cross-sectional and time series datasets; discusses standard regression techniques such as Ordinary Least Squares (OLS) and Logistic Regressions; and surveys the various violations of OLS and how these can be corrected. By the end of the course, you'll understand the theory behind regression analysis and how to put this theory into practice. Learners should have a basic understanding of statistics, familiarity with data types (i.e., nominal, ordinal, interval, and scale), and preferably some prior experience with R.
- Learn to uncover key business insights hidden inside data using regression analysis and R
- Gain hands-on experience running linear and logistic regressions using R
- Understand how to interpret statistical output and derive meaning from results
- Explore methods that screen and correct for violations of Ordinary Least Squares assumptions
Michael Grogan is a data scientist who specializes in R, Python, and Shiny. As a consultant, Michael provides data science solutions to clients in healthcare, finance, and government. As an educator, Michael creates data science tutorials for organizations such as Data Science Central, Sitepoint, and O'Reilly Media. He holds a Master's degree in business economics from University College Cork.
Table of Contents
- Introduction to Regression Analysis
- OLS Violations and Logistic Regressions
- Working With Time Series
- Wrap Up and Thank You 00:01:24
- Title: Regression Analysis and Hypothesis Testing in R
- Release date: January 2018
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492028543