Course
Introduction to Regression in R
IntermediateSkill Level
Updated 08/2024RProbability & Statistics4 hr14 videos52 Exercises4,050 XP76,320Statement of Accomplishment
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Prerequisites
Introduction to Data Visualization with ggplot2Introduction to Statistics in R1
Simple Linear Regression
You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.
2
Predictions and model objects
In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.
3
Assessing model fit
In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand the leverage and influence of each observation used to create the model.
4
Simple logistic regression
Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.
Introduction to Regression in R
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FAQs
Is this course suitable for beginners?
Yes. The first chapter starts by defining regression and explaining how linear and logistic models differ, so no prior experience with statistical modeling is required.
What can I do after completing this course?
You will be able to fit linear and logistic regression models in R, make predictions on new data, quantify model performance, and diagnose problems with model fit.
Which tools and datasets are used in this course?
All exercises use R. You will work with real-world datasets covering Taiwanese house prices, Facebook ad clicks, motor insurance claims, and fish sizes.
How does the course handle logistic regression differently from linear regression?
The final chapter focuses on logistic regression for classification tasks, including predicting the likelihood a customer closes a bank account and evaluating results with confusion matrices.
Who is this course designed for?
Anyone who wants to build a foundation in statistical modeling, including data analysts and R users who work with tabular data and want to move beyond summary statistics into predictive modeling.
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