About this Course
The second course in the program presents methods and strategies for developing both simple and complex models in R, allowing you to analyze relationships in multivariate data using linear regression. We’ll explore how to measure the models’ fit, establish confidence intervals on the model parameters, identify outlier data, and understand what the model parameters represent. At the end of the course, you’ll be able to use R to analyze and display how well the models represent complex data, as well as be able to interpret and report on the results.
Prerequisites:
What You’ll Learn
- How to formulate an appropriate model to answer a scientific question
- How to interpret and graphically display and report results
- How to use and interpret results from multiple linear regression models
- How to use and interpret results from Analysis of Covariance (ANCOVA) and 1- and 2-way ANOVA models
- Details about data transformations, dummy variables and outlier detection
Get Hands-On Experience
You’ll complete a data analysis project using real-world examples to examine data, display and report results, and draw appropriate conclusions.