This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse.
Modern Regression Analysis in R
New year. Big goals. Bigger savings. Unlock a year of unlimited access to learning with Coursera Plus for $199. Save now.
Modern Regression Analysis in R
This course is part of Statistical Modeling for Data Science Applications Specialization
Instructor: Brian Zaharatos
7,102 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.4
(30 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 45 hours
Learn at your own pace
Build toward a degree
What you'll learn
Articulate some recommended practices for ethical behavior and communication in statistics and data science.
Interpret important components of the MLR model, including the “systematic” and “random” components of the model.
Describe and implement testing-based procedures for model selections and select a “best” model based on a given procedure.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
11 quizzes
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 Statistical Modeling for Data Science Applications 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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 6 modules in this course
In this module, we will introduce the basic conceptual framework for statistical modeling in general, and for linear regression models in particular.
What's included
8 videos3 readings2 quizzes2 programming assignments1 peer review1 discussion prompt1 ungraded lab
In this module, we will learn how to fit linear regression models with least squares. We will also study the properties of least squares, and describe some goodness of fit metrics for linear regression models.
What's included
9 videos2 quizzes1 programming assignment1 peer review1 ungraded lab
In this module, we will study the uses of linear regression modeling for justifying inferences from samples to populations.
What's included
8 videos1 reading2 quizzes1 programming assignment2 peer reviews1 ungraded lab
In this module, we will identify how models can predict future values, as well as construct interval estimates for those values. We will also explore the relationship between statistical modelling and causal explanations.
What's included
6 videos1 quiz1 programming assignment1 peer review1 ungraded lab
In this module, we will learn how to diagnose issues with the fit of a linear regression model. In particular, we will use formal tests and visualizations to decide whether a linear model is appropriate for the data at hand.
What's included
6 videos2 quizzes1 programming assignment1 peer review1 ungraded lab
In this module, we will study methods for model selection and model improvement.. In particular, we will learn when and how to apply model selection techniques such as forward selection and backward selection, criterion-based methods, and will learn about the problem of multicollinearity (also called collinearity).
What's included
10 videos2 quizzes1 programming assignment1 peer review1 ungraded lab
Instructor
Instructor ratings
4.8 (12 ratings)Offered by
Recommended if you're interested in Probability and Statistics
University of Colorado Boulder
SkillUp EdTech
Rice University
Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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."
Learner reviews
4.4
30 reviews
- 5 stars
76.66%
- 4 stars
10%
- 3 stars
0%
- 2 stars
6.66%
- 1 star
6.66%
Showing 3 of 30
DK
5
Reviewed on Apr 29, 2024
A lot of work with several peer reviews, but it get you into R for Regression Analysis. Well laid out course. need knowledge of Linear algrebra for this course.
New to Probability and Statistics? Start here.
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
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.