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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
Read more
Syllabus
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
Week 2: Linear Regression & Multivariable Regression
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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
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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
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
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
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.
Statistician
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.
Quantitative Analyst
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.
Data 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.
Machine Learning Engineer
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.
Business Analyst
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.
Actuary
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.
Financial Analyst
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.
Market Researcher
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.
Economist
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.
Operations Research Analyst
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.
Risk Manager
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.
Insurance Analyst
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.
Healthcare 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.
Consultant
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.
For more career information including salaries, visit:
OpenCourser.com/course/sm67iz/regression
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.
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.
Provides a broad overview of statistical learning methods, including linear regression, logistic regression, and decision trees. It valuable resource for learners who want to gain a deeper understanding of the theory and practice of statistical learning.
Is an excellent resource for learners who want to gain a broad overview of machine learning methods. It covers a wide range of topics, including linear regression, logistic regression, and decision trees.
Provides a rigorous treatment of generalized linear models. It valuable resource for learners who want to gain a deeper understanding of the theory and practice of generalized linear modeling.
Provides a unique perspective on regression analysis. It emphasizes the importance of understanding the underlying assumptions of regression models and how to interpret the results of regression analyses.
Provides a comprehensive overview of causal inference. It covers a wide range of topics, including graphical models, structural equation modeling, and counterfactuals. It valuable resource for students and practitioners of data science.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy gradients. It valuable resource for students and practitioners of data science.
Provides a comprehensive overview of statistical learning methods, including linear regression, logistic regression, and tree-based methods. It valuable reference for students and practitioners of data science.
Provides a comprehensive overview of generalized linear models. It covers a wide range of topics, including logistic regression, Poisson regression, and negative binomial regression. It valuable reference for students and practitioners of data science.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative models. It valuable resource for students and practitioners of data science.
Save
Provides a practical introduction to data science. It covers a wide range of topics, including data wrangling, data analysis, and data visualization. It valuable resource for students and practitioners of data science.
Provides a practical introduction to linear regression. It covers a wide range of topics, including model selection, diagnostics, and interpretation. It valuable resource for students and practitioners of data science.
Provides a practical introduction to regression modeling. It covers a wide range of topics, including linear regression, logistic regression, and survival analysis. It valuable resource for students and practitioners of data science.
Provides a comprehensive overview of natural language processing. It covers a wide range of topics, including text classification, sentiment analysis, and machine translation. It valuable resource for students and practitioners of data science.
Save
Provides a comprehensive overview of computer vision. It covers a wide range of topics, including image processing, object detection, and scene understanding. It valuable resource for students and practitioners of data science.
Save
Provides a comprehensive overview of big data. It covers a wide range of topics, including data science, machine learning, and data visualization. It valuable resource for students and practitioners of data science.
Provides a practical introduction to data science for business. It covers a wide range of topics, including data wrangling, data analysis, and data visualization. It valuable resource for students and practitioners of data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sm67iz/regression
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Rating
4.4
Effort
53 hours to complete
Via
Coursera
Institution
Johns Hopkins University
Instructors
Roger D. Peng, PhD
Brian Caffo, PhD
Jeff Leek, PhD
Language
English
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