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Regression Models

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

4.4

Based on 3,373 ratings

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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.

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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

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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

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Reviews summary

Robust regression models for data scientists

According to students, this Regression Models course from Johns Hopkins University offers a strong theoretical foundation in statistical analysis, essential for aspiring data scientists. Learners praise the clear explanations of complex topics like least squares and generalized linear models, making seemingly difficult concepts accessible. The instructor's expertise shines through, guiding students through key concepts such as residuals and model comparison. While the course provides practical applications, some note it assumes some prior knowledge of statistics or R programming. Overall, it's considered challenging but rewarding, effectively building essential skills in regression analysis for data professionals.

Good for theory, could use more hands-on coding.

"The course was heavy on theory, which was great, but I had to seek out additional practice problems for coding."

"I would have loved more actual case studies or projects to apply the models in R."

"While the theoretical understanding is robust, I felt a slight gap in hands-on practical implementation examples."

Complex statistical concepts explained clearly.

"The instructor did an excellent job breaking down difficult concepts into understandable pieces."

"I found the lectures very engaging and the explanations for things like diagnostics were spot on."

"His teaching style made even the most abstract statistical ideas seem logical and intuitive."

Provides deep understanding of regression models.

"This course really solidified my understanding of the underlying principles of linear and logistic regression."

"I appreciate how the course focused on the 'why' behind the models, not just the 'how' to use them."

"The explanations of least squares and inference were incredibly thorough and built a solid base for advanced topics."

Highly valuable for data science careers.

"This course is a must for anyone serious about a career in data science, especially for understanding models deeply."

"I've already applied what I learned about generalized linear models in my job, truly beneficial."

"It bridges the gap between statistical theory and practical analytical work beautifully."

Requires some background in stats or R.

"I struggled a bit without a stronger background in R; the course assumes you're familiar with it."

"Coming into this, I wish I had brushed up on my basic statistics knowledge, as it moved quickly."

"If you're completely new to statistics, you might find the pace a bit fast, but it's manageable with extra effort."

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

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

Browse courses on Linear Algebra

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

Complete Regression Analysis Tutorial

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

Browse courses on Regression Analysis

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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

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

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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

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Solve Linear Regression Practice Problems

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

Browse courses on Linear Regression

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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

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

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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

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

Browse courses on Regression Modeling

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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

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

Browse courses on Regression Analysis

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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