By the end of this course, learners will be able to define core concepts of Linear Regression, construct simple and multiple regression models, apply dummy variable techniques, and evaluate model performance using statistical tests. Participants will also develop the ability to optimize models through backward elimination and validate predictive accuracy on new datasets.
Linear Regression with R: Build & Optimize
Linear Regression with R: Build & Optimize
This course is part of AI Machine Learning with R & Python Projects Specialization
Instructor: EDUCBA
Included with 
Gain insight into a topic and learn the fundamentals.
5 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Define regression concepts and build simple/multiple models in R.
Apply dummy variables, statistical tests, and model validation.
Optimize models with backward elimination for predictive accuracy.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
October 2025
Assessments
6 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the AI Machine Learning with R & Python Projects Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 2 modules in this course
This module introduces the foundational concepts of Linear Regression, focusing on how regression equations are formed, how variables relate, and how to build simple models. Learners will explore the basics of regression algorithms, interpret key equations, and practice constructing and visualizing regression lines with training data.
What's included
7 videos3 assignments1 plugin
This module expands regression learning into advanced techniques, including multiple linear regression, dummy variable encoding, model evaluation, and feature selection methods. Learners will apply regression to new datasets, test model generalization, and implement optimization strategies such as backward elimination for improved accuracy.
What's included
8 videos3 assignments
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Explore more from Machine Learning
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
More questions
Financial aid available,