We may earn an affiliate commission when you visit our partners.

Regression Models

Roger D. Peng, PhD, Brian Caffo, PhD, and Jeff Leek, PhD

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

What's inside

Syllabus

Week 1: Least Squares and Linear Regression

This week, we focus on least squares and linear regression.

Week 2: Linear Regression & Multivariable Regression

This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.

Week 3: Multivariable Regression, Residuals, & Diagnostics

This week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.

Week 4: Logistic Regression and Poisson Regression

This week, we will work on generalized linear models, including binary outcomes and Poisson regression.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores essential data science tools for modeling outcomes

Taught by recognized experts in the field of data science

Covers the latest statistical analysis techniques for regression

In-depth coverage of multivariable regression for complex data

Examines applications of regression models beyond their traditional uses

Prerequisites may be necessary for full comprehension

Save this course

Save Regression Models to your list so you can find it easily later:

Save

Reviews summary

Regression course: well-received study

Learners say this course is an in-depth introduction to regression models, particularly linear regression, with applications in R. Many students describe the course content as engaging, thorough, and well-suited for beginners. Students consistently commend the instructor, Brian Caffo, for his expertise and approachable teaching style. The course features interactive exercises and a final project that encourages practical application of the concepts learned.

The feedback on assignments is helpful and provides opportunities for improvement.

"These videos are better than the previous ones in this specialization but it gets too repetitive and long and boring. The swirl activities are the way to go!"

"I really like both lectures and the project work to be accomplished during the 4 weeks."

"This course materials are engaging and thorough and the quizzes are at a reasonable level of difficulty."

The course is suitable for learners with little to no prior knowledge of regression models.

"I have been involved with regression models for a long time.I was amazed on the capabilities that have been developed in R."

"I liked this because I have almost no background on this sort of thing and it forced me to go waaay back and revisit and deepen my knowledge of modeling and statistics as well."

"I give this 5 stars as the material taught is essential for a Data Scientist, and was presented well."

Brian Caffo's expertise is apparent throughout the course, and his teaching style is praised for being approachable and engaging.

"Excellent course. The instructor is very knowledgeable and covers the most important aspects of regression models."

"The instructor explains the course in detail. He speaks word by word and if you are an international student, you will be able to understand what he says."

"The professor Brian Caffo is excelent and the best!"

The course features interactive exercises and a final project that provide opportunities for practical application of the concepts learned.

"I really enjoyed the course. Even though I had already learned linear regression and logistic regression from a computer science perspective, I still learned a lot since the course approaches these subjects from a statistical view."

"I really liked the way the professors teach a concept; starting from the need for it to step by step logical derivation for it."

"Regression analysis has been a very insteresting course. I've learned a lot, and was happy to do my graphs and analysis in R!"

The course content is comprehensive and covers a wide range of topics in regression models, including linear regression, multiple regression, and logistic regression.

"Very detailed and complete course with heavy theorical concepts which are all very useful for data science applications"

"The course materials are engaging and thorough and the quizzes are at a reasonable level of difficulty."

"This course serves as a great introduction to regression modelling"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Regression Models with these activities:

Review Linear Algebra and Calculus

Show steps

Focus on vector spaces, matrices, and differential and integral calculus to solidify understanding required for the course.

Browse courses on Linear Algebra

Show steps

Review key concepts from linear algebra
Review differentiation and integration techniques from calculus
Complete practice problems to reinforce understanding

Complete Regression Analysis Tutorial

Show steps

Follow a structured and comprehensive tutorial to build a strong foundation in regression analysis

Browse courses on Regression Analysis

Show steps

Find a reputable online or in-person tutorial
Complete the tutorial modules and practice exercises
Apply the concepts to practice datasets

Participate in Study Groups and Discussions

Show steps

Engage with peers to discuss course concepts, problem-solve, and reinforce learning through collaborative interactions.

Show steps

Find or form a study group with other students
Meet regularly to discuss course material
Collaborate on problem-solving and assignments
Share perspectives and insights

Four other activities

Expand to see all activities and additional details

Show all seven activities

Solve Linear Regression Practice Problems

Show steps

Engage in repetitive exercises to enhance proficiency in solving linear regression problems.

Browse courses on Linear Regression

Show steps

Find a collection of practice problems
Solve the problems using the techniques covered in the course
Check answers and identify areas for improvement

Develop a Regression Analysis Cheat Sheet

Show steps

Consolidate key concepts and formulas by creating a cheat sheet for quick reference during study and practice sessions.

Browse courses on Regression Analysis

Show steps

Identify the essential concepts and formulas
Organize and present the information in a clear and concise manner
Review and update the cheat sheet regularly

Attend Workshops on Regression Modeling

Show steps

Expand knowledge and skills by participating in workshops led by experts in the field.

Browse courses on Regression Modeling

Show steps

Identify relevant workshops offered by universities, industry organizations, or online platforms
Register for and attend the workshops
Engage with instructors and fellow attendees
Apply the techniques and insights gained in the course

Analyze a Dataset with Regression Techniques

Show steps

Apply regression analysis to a real-world dataset to gain hands-on experience and reinforce understanding.

Browse courses on Regression Analysis

Show steps

Choose a dataset that aligns with the concepts covered in the course
Clean and prepare the data
Apply regression techniques to analyze the data
Interpret the results and draw meaningful conclusions
Write a report or presentation summarizing the findings

Career center

Learners who complete Regression Models will develop knowledge and skills that may be useful to these careers:

Data Scientist

Working as a Data Scientist involves collecting, storing, and interpreting data on behalf of organizations. Regression models, being a statistical analysis tool, are integral to your ability to draw accurate conclusions from data. This course will help you build a foundation in regression models, and build comfort in using them. This course will support you in developing proficiency in data analysis, a key skill for this role.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians collect and interpret data, using mathematical and statistical techniques. Regression models are incredibly important for your success as a Statistician, and this course will provide you with the tools you need to succeed. Because this course is geared toward a deep study of regression models, it is an ideal choice for somebody wanting to work as a Statistician in the future.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts are experts in using mathematical and statistical models to solve financial problems. Regression models are an important math tool in a Quantitative Analyst's toolkit, and mastering these models can help you progress in your career. This course will help you build a foundation in applying regression models to the financial industry, which can give you a strong advantage.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

Data Analysts examine and interpret data to help businesses make decisions. Being comfortable using regression models is a huge asset in this role, and this course can help you gain that comfort. While this course alone will not qualify you for a role as a Data Analyst, it will help you build the skills you need to move toward your goal.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models. Regression models are a basic tool for any Machine Learning Engineer, and having a strong foundation in this area is important for your career progression. This course will help you understand the fundamentals of regression models, which will allow you to enter this role with confidence.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to help businesses solve problems and improve their operations. Regression models are a useful tool for Business Analysts, and having a foundational understanding of them is helpful. While this course on its own will not qualify you for a role as a Business Analyst, it can help you build a foundation for your future career as one.

See salaries and explore the career path for Business Analyst

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. Regression models are an important tool for actuaries, and having a clear understanding of them is helpful for your career. While this course alone will not qualify you for a role as an actuary, it can help you build the foundation for your future career as one.

See salaries and explore the career path for Actuary

Financial Analyst

Financial Analysts use data to make investment recommendations. Regression models are an important tool in finance, and mastering them is important for your growth in this role. This course will help you build a foundation in regression models, which will support your goal of becoming a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers use data to understand consumer behavior and trends. Regression models are a tool that can enhance your ability to understand data as a Market Researcher. While this course alone will not qualify you for a role as a Market Researcher, it can help you build some of the skills you need to succeed in this role.

See salaries and explore the career path for Market Researcher

Economist

Economists study the production, distribution, and consumption of goods and services. Regression models are a tool that can be used in economic analysis, and being familiar with them can be helpful. This course can help build your foundation in regression models, which will support your goal of becoming an Economist.

See salaries and explore the career path for Economist

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to improve business operations. Regression models can be helpful as an Operations Research Analyst, and a foundation in them is helpful. This course will help you build your foundation in this area, which can contribute to your success in this role.

See salaries and explore the career path for Operations Research Analyst

Risk Manager

Risk Managers identify, assess, and mitigate risks. Regression models can be a tool in a Risk Manager's toolkit, and a foundation can be helpful for your career. This course may provide a helpful foundation for regression models.

See salaries and explore the career path for Risk Manager

Insurance Analyst

Insurance Analysts use data to assess risk and determine insurance rates. Regression models can be a tool in the role of an Insurance Analyst, and understanding them can help you succeed. This course may provide a helpful foundation in regression models, which can aid in your career goals.

See salaries and explore the career path for Insurance Analyst

Healthcare Analyst

Healthcare Analysts use data to improve the efficiency and quality of healthcare. Regression models can be a tool in the role of a Healthcare Analyst, and this course may be helpful for building a foundation in this area.

See salaries and explore the career path for Healthcare Analyst

Consultant

Consultants are problem-solvers who use data to help organizations improve their performance. Regression models are a tool that can contribute to your success as a Consultant. While this course will not qualify you for this role, it can help you begin to build a foundation.

See salaries and explore the career path for Consultant

Reading list

We've selected 17 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Regression Models.

An R Companion to Applied Regression

Save

Provides a comprehensive overview of regression analysis and generalized linear models. It valuable resource for learners who want to learn how to build and interpret regression models for a variety of research questions.

An R Companion to Applied Regression

Paperback

$$$

An R Companion to Applied Regression

Paperback

Regression Models

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Regression course: well-received study

Activities

Career center

Reading list

Share

Similar courses