Course
Inference for Linear Regression in R
AdvancedSkill Level
Updated 12/2021RProbability & Statistics4 hr15 videos59 Exercises4,650 XP15,729Statement of Accomplishment
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Prerequisites
Foundations of Inference in RIntermediate Regression in R1
Inferential ideas
In the first chapter, you will understand how and why to perform inferential (instead of descriptive only) analysis on a regression model.
2
Simulation-based inference for the slope parameter
In this chapter you will learn about the ideas of the sampling distribution using simulation methods for regression models.
3
t-Based Inference For the Slope Parameter
In this chapter you will learn about how to use the t-distribution to perform inference in linear regression models. You will also learn about how to create prediction intervals for the response variable.
4
Technical Conditions in linear regression
Additionally, you will consider the technical conditions that are important when using linear models to make claims about a larger population.
5
Building on Inference in Simple Linear Regression
This chapter covers topics that build on the basic ideas of inference in linear models, including multicollinearity and inference for multiple regression models.
Inference for Linear Regression in R
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FAQs
What prior knowledge do I need before taking Inference for Linear Regression in R?
You should be comfortable with dplyr, ggplot2, hypothesis testing, and intermediate regression in R. This is an advanced course that builds on both statistical inference and linear modeling foundations.
How does this course differ from Introduction to Regression in R?
Introduction to Regression focuses on fitting models, while this course focuses on making inferences from those models, including confidence intervals, prediction intervals, and significance testing for slope parameters.
Will I learn simulation-based methods or only classical t-based inference?
You will learn both. The course covers simulation-based inference for sampling distributions and t-based inference for constructing confidence intervals and testing slope significance.
What is the broom package used for in this course?
The broom package is used to tidy model output into clean data frames, making it easier to work with regression results alongside dplyr and ggplot2 in your analysis pipeline.
Does the course cover inference for multiple regression or only simple linear regression?
It covers both. The final chapter extends inference to multiple regression models and addresses multicollinearity, building on the simple linear regression concepts from earlier chapters.
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