Course Summary
This course teaches you how to build and evaluate linear regression models, one of the most widely used statistical models in the business world. You will gain a solid understanding of the theory behind linear regression and learn how to apply it to real-world data.Key Learning Points
- Learn how to build and evaluate linear regression models
- Understand the theory behind linear regression
- Apply linear regression to real-world data
Related Topics for further study
Learning Outcomes
- Ability to apply linear regression to real-world data
- Understanding of the theory behind linear regression
- Experience building and evaluating linear regression models
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with a programming language (Python recommended)
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on projects
Similar Courses
- Multiple Linear Regression Analysis in Excel
- Applied Data Science: Machine Learning
Related Education Paths
Related Books
Description
This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.
Outline
- About Linear Regression and Modeling
- Introduction to Statistics with R
- About Statistics with R Specialization
- More about Linear Regression and Modeling
- Linear Regression
- Introduction
- Correlation
- Residuals
- Least Squares Line
- Prediction and Extrapolation
- Conditions for Linear Regression
- R Squared
- Regression with Categorical Explanatory Variables
- Lesson Learning Objectives
- Lesson Learning Objectives
- Week 1 Suggested Readings and Practice
- Week 1 Practice Quiz
- Week 1 Quiz
- More about Linear Regression
- Outliers in Regression
- Inference for Linear Regression
- Variability Partitioning
- Lesson Learning Objectives
- Week 2 Suggested Readings and Exercises
- About Lab Choices
- Week 1 & 2 Lab Instructions (RStudio)
- Week 1 & 2 Lab Instructions (RStudio Cloud)
- Week 2 Practice Quiz
- Week 2 Quiz
- Week 1 & 2 Lab
- Multiple Regression
- Introduction
- Multiple Predictors
- Adjusted R Squared
- Collinearity and Parsimony
- Inference for MLR
- Model Selection
- Diagnostics for MLR
- Lesson Learning Objectives
- Lesson Learning Objectives
- Week 3 Suggested Readings and Exercises
- Week 3 Lab Instructions (RStudio)
- Week 3 Lab Instructions (RStudio Cloud)
- Week 3 Practice Quiz
- Week 3 Quiz
- Week 3 Lab
- Final Project
- Project Files and Rubric
Summary of User Reviews
Learn about Linear Regression Model on Coursera. The course has received positive reviews overall. Many users appreciated the clear and concise explanations provided throughout the course.Key Aspect Users Liked About This Course
Clear and concise explanationsPros from User Reviews
- Great introduction to linear regression
- Well-organized course structure
- Instructor is knowledgeable and engaging
- Plenty of practice problems to reinforce concepts
- Accessible to beginners without a strong math background
Cons from User Reviews
- Some users found the course too basic
- Limited real-world applications discussed
- No peer feedback on assignments
- Some parts of the course can be repetitive
- No certificate of completion without paying for it
Mine Çetinkaya-Rundel
4.7 Raiting
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