Data Modeling and Prediction with R

About this course

Learn how to move from exploring data to modeling it with confidence. In this course, you’ll build and interpret linear and logistic regression models in R to uncover relationships, make predictions, and quantify uncertainty. You’ll begin by learning how to fit and interpret simple and multiple linear regression models, then advance to modeling categorical outcomes with logistic regression. Finally, you’ll explore bootstrapping and hypothesis testing to understand and communicate the uncertainty in your results. By the end of this course, you’ll be able to use statistical modeling to make and explain data-driven decisions – an essential skill for data scientists, analysts, and anyone working with real-world data.

What you will learn

• Fit and interpret linear and logistic regression models to examine relationships between predictors and outcomes.
• Evaluate model performance and recognize limitations such as overfitting.
• Apply bootstrapping and hypothesis testing to quantify and communicate uncertainty in model results.