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Rafael Irizarry
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part ofourProfessional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R.
Read more
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part ofourProfessional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R.
Read more
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part ofourProfessional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R.
In data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball. We will try to determine which measured outcomes best predict baseball runs by using linear regression.
We will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.
Register for this course and see more details by visiting:
OpenCourser.com/course/qmyf9c/data
What's inside
Learning objectives
- How linear regression was originally developed by galton
- What is confounding and how to detect it
- How to examine the relationships between variables by implementing linear regression in r
- How linear regression was originally developed by galton
- What is confounding and how to detect it
- How to examine the relationships between variables by implementing linear regression in r
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Geared toward data scientists and those working on predictive modeling and statistical modeling for baseball analytics
Most relevant to those with prior knowledge of baseball statistics; assumes foundational familiarity with R and data science
Taught by Rafael Irizarry, an expert in biostatistics and computational biology
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Reviews summary
Disappointing intro to linear regression
According to students, this introductory course in linear regressions offers a lot of knowledge, but its poorly explained content and difficult-to-run R code leave much to be desired. Although videos are engaging and concise, and assessments are well-applied, many learners ultimately find this disappointing course to be not worth the cost.
Assessments are well-applied.
"Assessments are a clear application of the lesson concepts, without bugs and at the right complexity level."
Videos are engaging and to the point.
"Videos are concise, to the point."
"Videos recaps with Key Points listing and videos examples codes are extremely useful."
Content not well-explained.
"Mr. Irizarry is the worst teacher I’ve ever seen."
"The professor is not able to explain the content in an understandable way."
"None of his examples run."
R code provided often does not run.
"The problem is, there's no evidence that the professor knows how to program in R."
"Some of the problems seem to stem from the professor using his own private R packages, which define commonly-used R commands differently than in 'standard' R."
"Even if you download the packages that students have guessed, there are still some examples that nobody has figured out how to run, and these threads have been going on for months."
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 Data Science: Linear Regression with these
activities:
Organize course materials for effective review
Show steps
Enhance your retention by consolidating course materials.
Browse courses on
Study Skills
Show steps
-
Create a system for organizing notes, assignments, and other course materials.
-
Review materials regularly to reinforce your understanding.
Review Matrix Algebra and Vector Calculus
Show steps
Strengthen your foundation in matrix algebra and vector calculus, which are essential mathematical concepts used in linear regression.
Browse courses on
Matrix Algebra
Show steps
-
Review textbooks or online resources on matrix algebra and vector calculus.
-
Practice solving problems related to these concepts.
-
Attend office hours or consult with a tutor if you encounter difficulties.
Review introductory statistics
Show steps
Review basic statistical concepts to prepare for the course's emphasis on quantifying relationships between variables.
Browse courses on
Statistics
Show steps
-
Revisit the basics of probability distributions, measures of central tendency, and hypothesis testing.
-
Solve practice problems on statistical concepts to assess your understanding.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Review concepts of confounding in data analysis
Show steps
Strengthen your foundational knowledge of confounding to enhance your understanding of the course.
Browse courses on
Confounding
Show steps
-
Revisit previous coursework or materials on confounding.
-
Solve practice problems or review examples to reinforce your comprehension.
Read 'Moneyball' by Michael Lewis
Show steps
Gain insights into the practical application of linear regression in real-world decision-making.
View
The Big Short: Inside the Doomsday Machine
on Amazon
Show steps
-
Read and analyze the book, paying attention to how linear regression is used in baseball.
-
Identify key concepts and their relevance to the course's learning objectives.
Solve Linear Regression Practice Problems
Show steps
Practice solving linear regression problems to reinforce your understanding of the concepts and techniques covered in the course.
Browse courses on
Linear Regression
Show steps
-
Review the course materials on linear regression.
-
Find a set of practice problems online or in a textbook.
-
Work through the problems step-by-step, showing all your calculations.
-
Check your answers against the provided solutions or consult with a tutor or instructor if needed.
Explore tutorials on linear regression in R
Show steps
Expand your knowledge and skills in using R for linear regression analysis.
Browse courses on
Linear Regression
Show steps
-
Identify and access online tutorials or resources on linear regression in R.
-
Follow the tutorials and complete the exercises to enhance your practical understanding.
Participate in Study Groups or Discussion Forums
Show steps
Enhance your understanding and critical thinking skills by engaging in discussions with peers, exchanging ideas, and challenging perspectives.
Show steps
-
Join or create a study group with classmates.
-
Participate in discussions, asking questions, sharing insights, and providing feedback.
-
Engage with classmates in online discussion forums.
Solve linear regression practice problems
Show steps
Sharpen your skills in applying linear regression through targeted practice.
Browse courses on
Linear Regression
Show steps
-
Solve a set of linear regression practice problems covering different scenarios.
-
Review your answers and identify areas where you need further clarification.
-
Seek support from the course forum or instructors for any difficulties encountered.
Follow Tutorials on Implementing Linear Regression in R
Show steps
Enhance your practical skills by following guided tutorials that demonstrate how to implement linear regression models in R.
Browse courses on
R Programming
Show steps
-
Identify online tutorials or video courses that provide step-by-step guidance on linear regression in R.
-
Follow the tutorials carefully, implementing the code and reproducing the results.
-
Experiment with different datasets and scenarios to gain a deeper understanding of the process.
Help peers with linear regression concepts
Show steps
Reinforce your understanding by assisting others and engaging in discussions.
Browse courses on
Linear Regression
Show steps
-
Participate in course forums and answer questions related to linear regression.
-
Provide clear explanations and examples to support your peers' learning.
Create a Data Visualization of a Linear Regression Model
Show steps
Solidify your understanding of linear regression by creating a data visualization that represents a fitted model, helping you interpret and communicate the results effectively.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and fit a linear regression model to it.
-
Use a data visualization tool to create a scatter plot or other appropriate visualization that represents the fitted model.
-
Add labels, annotations, and a title to make the visualization clear and informative.
-
Interpret the visualization to identify patterns, trends, and insights.
Develop a linear regression model for a real-world dataset
Show steps
Demonstrate your understanding of linear regression by applying it to a practical problem.
Browse courses on
Linear Regression
Show steps
-
Identify a real-world dataset that aligns with the course objectives.
-
Clean and prepare the dataset for analysis.
-
Develop a linear regression model using R.
-
Evaluate the model's performance and present your findings.
Organize course materials for effective review
Show steps
Enhance your retention by consolidating course materials.
Browse courses on
Study Skills
Show steps
Show steps
Show steps
- Create a system for organizing notes, assignments, and other course materials.
- Review materials regularly to reinforce your understanding.
Review Matrix Algebra and Vector Calculus
Show steps
Strengthen your foundation in matrix algebra and vector calculus, which are essential mathematical concepts used in linear regression.
Browse courses on
Matrix Algebra
Show steps
Show steps
Show steps
- Review textbooks or online resources on matrix algebra and vector calculus.
- Practice solving problems related to these concepts.
- Attend office hours or consult with a tutor if you encounter difficulties.
Review introductory statistics
Show steps
Review basic statistical concepts to prepare for the course's emphasis on quantifying relationships between variables.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Revisit the basics of probability distributions, measures of central tendency, and hypothesis testing.
- Solve practice problems on statistical concepts to assess your understanding.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Review concepts of confounding in data analysis
Show steps
Strengthen your foundational knowledge of confounding to enhance your understanding of the course.
Browse courses on
Confounding
Show steps
Show steps
Show steps
- Revisit previous coursework or materials on confounding.
- Solve practice problems or review examples to reinforce your comprehension.
Read 'Moneyball' by Michael Lewis
Show steps
Gain insights into the practical application of linear regression in real-world decision-making.
View
The Big Short: Inside the Doomsday Machine
on Amazon
Show steps
Show steps
Show steps
- Read and analyze the book, paying attention to how linear regression is used in baseball.
- Identify key concepts and their relevance to the course's learning objectives.
Solve Linear Regression Practice Problems
Show steps
Practice solving linear regression problems to reinforce your understanding of the concepts and techniques covered in the course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Review the course materials on linear regression.
- Find a set of practice problems online or in a textbook.
- Work through the problems step-by-step, showing all your calculations.
- Check your answers against the provided solutions or consult with a tutor or instructor if needed.
Explore tutorials on linear regression in R
Show steps
Expand your knowledge and skills in using R for linear regression analysis.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify and access online tutorials or resources on linear regression in R.
- Follow the tutorials and complete the exercises to enhance your practical understanding.
Participate in Study Groups or Discussion Forums
Show steps
Enhance your understanding and critical thinking skills by engaging in discussions with peers, exchanging ideas, and challenging perspectives.
Show steps
Show steps
Show steps
- Join or create a study group with classmates.
- Participate in discussions, asking questions, sharing insights, and providing feedback.
- Engage with classmates in online discussion forums.
Solve linear regression practice problems
Show steps
Sharpen your skills in applying linear regression through targeted practice.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Solve a set of linear regression practice problems covering different scenarios.
- Review your answers and identify areas where you need further clarification.
- Seek support from the course forum or instructors for any difficulties encountered.
Follow Tutorials on Implementing Linear Regression in R
Show steps
Enhance your practical skills by following guided tutorials that demonstrate how to implement linear regression models in R.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Identify online tutorials or video courses that provide step-by-step guidance on linear regression in R.
- Follow the tutorials carefully, implementing the code and reproducing the results.
- Experiment with different datasets and scenarios to gain a deeper understanding of the process.
Help peers with linear regression concepts
Show steps
Reinforce your understanding by assisting others and engaging in discussions.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Participate in course forums and answer questions related to linear regression.
- Provide clear explanations and examples to support your peers' learning.
Create a Data Visualization of a Linear Regression Model
Show steps
Solidify your understanding of linear regression by creating a data visualization that represents a fitted model, helping you interpret and communicate the results effectively.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and fit a linear regression model to it.
- Use a data visualization tool to create a scatter plot or other appropriate visualization that represents the fitted model.
- Add labels, annotations, and a title to make the visualization clear and informative.
- Interpret the visualization to identify patterns, trends, and insights.
Develop a linear regression model for a real-world dataset
Show steps
Demonstrate your understanding of linear regression by applying it to a practical problem.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify a real-world dataset that aligns with the course objectives.
- Clean and prepare the dataset for analysis.
- Develop a linear regression model using R.
- Evaluate the model's performance and present your findings.
Career center
Learners who complete Data Science: Linear Regression will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for developing and implementing statistical models to solve business problems. They use their knowledge of machine learning, data mining, and statistics to extract insights from data. Many Data Scientists work specifically in the field of Linear Regression. This course is an excellent way to build a foundation in Linear Regression and other statistical models used by Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models. They use their knowledge of mathematics, computer science, and statistics to build models that can learn from data and make predictions. Many Machine Learning Engineers specialize in Linear Regression. This course provides an excellent overview of Linear Regression and how it can be used to build accurate and robust models.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. They use their knowledge of statistics and data analysis techniques to identify trends and patterns in data. Many Data Analysts use Linear Regression to build models that can predict future outcomes. This course is a great way to learn about Linear Regression and how it can be used to solve business problems.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use their knowledge of probability, statistics, and data analysis techniques to draw conclusions about data. Many Statisticians use Linear Regression to model relationships between variables. This course provides a comprehensive overview of Linear Regression and how it can be used to answer research questions.
Business Analyst
Business Analyst
Business Analysts are responsible for identifying and solving business problems. They use their knowledge of business, technology, and data analysis techniques to develop solutions that improve business performance. Many Business Analysts use Linear Regression to analyze data and make recommendations for improvement. This course provides a solid foundation in Linear Regression and how it can be used to solve business problems.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making recommendations for investment decisions. They use their knowledge of finance, economics, and data analysis techniques to evaluate the financial performance of companies. Many Financial Analysts use Linear Regression to build models that can predict future financial performance. To be successful in this role, having a background in Statistics or Mathematics is a real plus. This course provides an excellent overview of Linear Regression and how it can be used to analyze financial data.
Market Researcher
Market Researcher
Market Researchers are responsible for conducting research to understand consumer behavior. They use their knowledge of marketing, psychology, and data analysis techniques to collect and analyze data about consumer needs and preferences. Many Market Researchers use Linear Regression to analyze data and make recommendations for marketing campaigns. This course provides a foundation in Linear Regression and how it can be used to understand consumer behavior.
Epidemiologist
Epidemiologist
Epidemiologists are responsible for investigating the causes and spread of disease. They use their knowledge of public health, medicine, and data analysis techniques to identify and track outbreaks of disease. Many Epidemiologists use Linear Regression to analyze data and make recommendations for preventing and controlling disease. This course provides a foundation in Linear Regression and how it can be used to investigate the causes and spread of disease.
Actuary
Actuary
Actuaries are responsible for assessing risk and uncertainty. They use their knowledge of mathematics, statistics, and data analysis techniques to develop models that can predict the likelihood of future events. Many Actuaries use Linear Regression to build models that can predict the likelihood of insurance claims. This course provides an overview of Linear Regression and how it can be used to assess risk and uncertainty.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for developing and implementing mathematical models to solve business problems. They use their knowledge of mathematics, computer science, and data analysis techniques to build models that can improve business efficiency. Many Operations Research Analysts use Linear Regression to build models that can optimize business processes.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and implementing software applications. They use their knowledge of computer science, mathematics, and data analysis techniques to build software that meets the needs of users. Many Software Engineers use Linear Regression to build models that can improve the performance of software applications.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and implementing mathematical models to solve financial problems. They use their knowledge of mathematics, statistics, and data analysis techniques to build models that can predict the performance of financial assets. Many Quantitative Analysts use Linear Regression to build models that can predict the returns of stocks and bonds. This course provides a strong foundation in Linear Regression and how it can be used to solve financial problems.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and managing risk. They use their knowledge of risk management, finance, and data analysis techniques to develop plans that mitigate risk and protect the organization from financial losses. Many Risk Managers use Linear Regression to build models that can predict the likelihood of financial losses. This course provides a foundation in Linear Regression and how it can be used to identify and manage risk.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They use their knowledge of marketing, product development, and data analysis techniques to create products that meet the needs of customers. Many Product Managers use Linear Regression to build models that can predict the demand for products. This course provides a foundation in Linear Regression and how it can be used to understand customer needs and develop successful products.
Consultant
Consultant
Consultants are responsible for providing advice to clients on a variety of topics. They use their knowledge of business, technology, and data analysis techniques to help clients solve problems and improve their performance. Many Consultants use Linear Regression to build models that can help clients make better decisions.
For more career information including salaries, visit:
OpenCourser.com/course/qmyf9c/data
Reading list
We've selected 16 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
Data Science: Linear Regression.
Provides a comprehensive introduction to statistical methods, covering topics such as hypothesis testing, regression analysis, and ANOVA. It good choice for those who want to learn more about the practical aspects of statistical methods.
Provides a comprehensive introduction to linear regression analysis, covering topics such as model selection, diagnostics, and forecasting. It good choice for those who want to learn more about the practical aspects of linear regression.
Provides a comprehensive introduction to regression analysis, covering topics such as model selection, diagnostics, and forecasting. It good choice for those who want to learn more about the practical aspects of linear regression.
Provides a practical introduction to regression analysis, covering topics such as model selection, diagnostics, and forecasting. It good choice for those who want to learn more about the practical aspects of linear regression.
Provides a comprehensive introduction to linear regression models, covering topics such as model selection, diagnostics, and forecasting. It good choice for those who want to learn more about the practical aspects of linear regression.
Provides a comprehensive introduction to generalized linear models, covering topics such as model selection, diagnostics, and forecasting. It good choice for those who want to learn more about the practical aspects of generalized linear models.
Provides a comprehensive introduction to mixed effects models, covering topics such as model selection, diagnostics, and forecasting. It good choice for those who want to learn more about the practical aspects of mixed effects models.
Provides a comprehensive introduction to multivariate statistical methods, covering topics such as factor analysis, principal component analysis, and discriminant analysis. It good choice for those who want to learn more about the practical aspects of multivariate statistical methods.
More accessible introduction to statistical learning than Elements of Statistical Learning, covering similar topics in a more user-friendly way. It good choice for those who are new to machine learning or who want to learn more about its practical applications.
This graduate-level textbook that provides a comprehensive overview of statistical learning methods, including linear regression. Additionally, it is commonly found on reading lists for universities and industry professionals.
Provides a practical introduction to data science, covering topics such as data collection, analysis, and visualization. It good choice for those who want to learn more about how data science can be used to solve business problems.
Provides a practical introduction to machine learning using R. It good choice for those who want to learn how to use R to build and train machine learning models.
Provides a comprehensive overview of statistical learning methods, including sections on linear regression. Additionally, it provides more advanced topics in machine learning, such as support vector machines and neural networks.
Provides a practical introduction to linear regression, with focuses on data analysis and model building.
Provides a comprehensive overview of statistical methods for data analysis, including linear regression. Additionally, it contains a provides a good balance of theoretical and practical content.
Provides a comprehensive guide to linear models using the R programming language. More specifically, it provides a unique perspective on model building in R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qmyf9c/data
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Effort
1 to 2 hours per week for 8 weeks
Level
Introductory
Via
edX
Institution
Harvard University
Instructor
Rafael Irizarry
Sessions
October 16, 2024
- June 18, 2025
April 17, 2024
- December 18, 2024
Language
English
Transcripts
English
Español
Français
Português
Deutsch
中文
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This course belongs to a
collection
called
Data Science. It's comprised of nine courses:
Good to know
Geared toward data scientists and those working on predictive modeling and statistical modeling for baseball analytics
Most relevant to those with prior knowledge of baseball statistics; assumes foundational familiarity with R and data science
Taught by Rafael Irizarry, an expert in biostatistics and computational biology
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