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Regression Models
Roger D. Peng, PhD,
Brian Caffo, PhD,
and
Jeff Leek, PhD
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/sm67iz/regression
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
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
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
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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"
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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Coursera
Institution
Johns Hopkins University
Instructors
Roger D. Peng, PhD
Brian Caffo, PhD
Jeff Leek, PhD
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English
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This course belongs to a
collection
called
Data Science. It's comprised of 11 courses:
Good to know
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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