Linear Regression and Modeling

Duke University via Coursera

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Overview

Linear Regression and Modeling is a comprehensive course designed to introduce learners to one of the most fundamental techniques in statistical analysis and machine learning—linear regression. This course focuses on the core principles, mathematical foundations, and real-world applications of linear regression for predictive modeling. Learners begin by understanding the relationship between independent and dependent variables, followed by exploring how to fit a linear model to data using methods like least squares. The course covers key concepts such as intercepts, slopes, residuals,...

Syllabus

About Linear Regression and Modeling

This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Please take several minutes to browse them through. Thanks for joining us in this course!

Linear Regression

In this week well introduce linear regression. Many of you may be familiar with regression from reading the news, where graphs with straight lines are overlaid on scatterplots. Linear models can be used for prediction or to evaluate whether there is a linear relationship between two numerical variables.

More about Linear Regression

Welcome to week 2! In this week, we will look at outliers, inference in linear regression and variability partitioning. Please use this week to strengthen your understanding on linear regression. Don't forget to post your questions, concerns and suggestions in the discussion forum!

Multiple Regression

In this week, well explore multiple regression, which allows us to model numerical response variables using multiple predictors (numerical and categorical). We will also cover inference for multiple linear regression, model selection, and model diagnostics. There is also a final project included in this week. You will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.