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Mine Çetinkaya-Rundel
This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.
Register for this course and see more details by visiting:
OpenCourser.com/course/xt7xik/linear
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What's inside
Syllabus
About Linear Regression and Modeling
This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Please take several minutes to browse them through. Thanks for joining us in this course!
Read more
Syllabus
About Linear Regression and Modeling
This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Please take several minutes to browse them through. Thanks for joining us in this course!
Read more
Linear Regression
In this week we’ll introduce linear regression. Many of you may be familiar with regression from reading the news, where graphs with straight lines are overlaid on scatterplots. Linear models can be used for prediction or to evaluate whether there is a linear relationship between two numerical variables.
More about Linear Regression
Welcome to week 2! In this week, we will look at outliers, inference in linear regression and variability partitioning. Please use this week to strengthen your understanding on linear regression. Don't forget to post your questions, concerns and suggestions in the discussion forum!
Multiple Regression
In this week, we’ll explore multiple regression, which allows us to model numerical response variables using multiple predictors (numerical and categorical). We will also cover inference for multiple linear regression, model selection, and model diagnostics. There is also a final project included in this week. You will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops fundamental theory behind linear regression, which is core to data analysis
Emphasizes the application of learned concepts through data examples
Utilizes the free and widely used statistical software R and RStudio
Covers a comprehensive range of topics, from basic to advanced concepts in linear regression
Provides a strong foundation for further study in statistics and data analysis
Taught by Mine Çetinkaya-Rundel, an experienced instructor in the field
Save this course
Save Linear Regression and Modeling to your list so you can find it easily later:
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Reviews summary
Well-received linear regression course
Learners say this course provides a comprehensive overview of linear regression, with a focus on building models and interpreting results. It is well-structured and engaging, with accessible explanations and practical exercises. The course is particularly strong in its coverage of regression diagnostics and model evaluation. Overall, learners find this course to be a valuable resource for understanding and applying linear regression techniques.
Rigorous final project that reinforces learning
"The final project was certainly time-consuming and challenging, but extremely worth it to integrate the material well."
"The project at the end of the course is REALLY good, you can learn a lot from the analysis and investigation you need to do on it."
"The final project is interesting and you can learn a lot while doing it"
Emphasis on hands-on exercises and real-world applications
"The labs and quizzes are really important."
"The final project is focused enough not to be overwhelming, and allows the learner to play around with the concepts"
"A great primer on linear regression with labs that help to establish understanding"
Easy-to-follow explanations of complex concepts
"Awesome course with very clear material!"
"The professor does an excellent job of making the material very intuitive."
"Very good course and material overall"
Thorough coverage of foundational concepts and techniques
"The course checked off many boxes: theoretical concepts, assumptions, R code, interpreting the output, thought-provoking questions, non-trivial quizzes and interesting data analysis project."
"Excellent course that gives a comprehensive view into Linear Regression and Modelling."
"I feel this specialization and this course are far more rigorous than the statistics class I took in college."
Engaged and supportive online community
"the message boards are empty and it's very unlikely that you will get any questions answered."
"The forums are completely unhelpful."
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 Linear Regression and Modeling with these
activities:
Watch tutorials on linear regression with R
Show steps
Watching tutorials will provide you with additional instruction and examples of how to use linear regression in R.
Browse courses on
Linear Regression
Show steps
-
Search for tutorials on linear regression with R on YouTube or other video platforms.
-
Watch several tutorials to learn different approaches and techniques.
Read 'Linear Models with R'
Show steps
Reading this book will introduce you to the fundamentals of linear regression and provide a solid foundation for the course.
View
Linear Models with Python (Chapman & Hall/CRC...
on Amazon
Show steps
-
Read chapters 1-3 to understand the basics of linear regression.
-
Work through the exercises in chapters 1-3 to practice your understanding.
Running practice files in R
Show steps
Running practice files in R will help you become more proficient in using the software and applying linear regression techniques.
Browse courses on
Linear Regression
Show steps
-
Download the practice files from the course website.
-
Open RStudio and load the practice files.
-
Run the practice files and check your output against the expected results.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Form a study group with other students
Show steps
Forming a study group will allow you to discuss the material with other students and get different perspectives.
Browse courses on
Linear Regression
Show steps
-
Find a few other students who are taking the course.
-
Meet regularly to discuss the material, work on problems, and quiz each other.
Practice multiple linear regression problems
Show steps
Practicing multiple linear regression problems will help you develop your skills in using this technique.
Browse courses on
Multiple Linear Regression
Show steps
-
Find a dataset with multiple independent variables.
-
Fit a multiple linear regression model to the dataset using R.
-
Interpret the results of the model.
Answer questions in the course discussion forum
Show steps
Answering questions in the discussion forum will help you reinforce your understanding of the material and help other students in the course.
Browse courses on
Linear Regression
Show steps
-
Read through the discussion forum and identify questions that you can answer.
-
Write thoughtful and informative responses to the questions.
Final project: Analyze a dataset using linear regression
Show steps
The final project will allow you to apply your knowledge of linear regression to a real-world dataset and demonstrate your understanding of the concepts covered in the course.
Browse courses on
Linear Regression
Show steps
-
Choose a dataset that is relevant to your interests.
-
Explore the dataset and identify potential relationships between variables.
-
Fit a linear regression model to the dataset.
-
Interpret the results of the model and draw conclusions.
-
Write a report summarizing your findings.
Watch tutorials on linear regression with R
Show steps
Watching tutorials will provide you with additional instruction and examples of how to use linear regression in R.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Search for tutorials on linear regression with R on YouTube or other video platforms.
- Watch several tutorials to learn different approaches and techniques.
Read 'Linear Models with R'
Show steps
Reading this book will introduce you to the fundamentals of linear regression and provide a solid foundation for the course.
View
Linear Models with Python (Chapman & Hall/CRC...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to understand the basics of linear regression.
- Work through the exercises in chapters 1-3 to practice your understanding.
Running practice files in R
Show steps
Running practice files in R will help you become more proficient in using the software and applying linear regression techniques.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Download the practice files from the course website.
- Open RStudio and load the practice files.
- Run the practice files and check your output against the expected results.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Form a study group with other students
Show steps
Forming a study group will allow you to discuss the material with other students and get different perspectives.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Find a few other students who are taking the course.
- Meet regularly to discuss the material, work on problems, and quiz each other.
Practice multiple linear regression problems
Show steps
Practicing multiple linear regression problems will help you develop your skills in using this technique.
Browse courses on
Multiple Linear Regression
Show steps
Show steps
Show steps
- Find a dataset with multiple independent variables.
- Fit a multiple linear regression model to the dataset using R.
- Interpret the results of the model.
Answer questions in the course discussion forum
Show steps
Answering questions in the discussion forum will help you reinforce your understanding of the material and help other students in the course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Read through the discussion forum and identify questions that you can answer.
- Write thoughtful and informative responses to the questions.
Final project: Analyze a dataset using linear regression
Show steps
The final project will allow you to apply your knowledge of linear regression to a real-world dataset and demonstrate your understanding of the concepts covered in the course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Choose a dataset that is relevant to your interests.
- Explore the dataset and identify potential relationships between variables.
- Fit a linear regression model to the dataset.
- Interpret the results of the model and draw conclusions.
- Write a report summarizing your findings.
Career center
Learners who complete Linear Regression and Modeling will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use statistical methods to extract meaning from data. They use their findings to help organizations make informed decisions. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Data Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use statistical and financial models to evaluate investments. They use their findings to make recommendations to clients. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Financial Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They use their findings to improve efficiency and effectiveness. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as an Operations Research Analyst.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They use their findings to make recommendations to clients about how to manage their risks. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as an Actuary.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze data in the field of biology. They use their findings to help scientists understand the causes and treatments of diseases. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Biostatistician.
Statistician
Statistician
Statisticians use statistical methods to analyze data in a variety of fields. They use their findings to help businesses, governments, and other organizations make informed decisions. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Statistician.
Data Scientist
Data Scientist
Data Scientists use statistical and machine learning methods to extract meaning from data. They use their findings to help organizations make informed decisions. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Data Scientist.
Market Researcher
Market Researcher
Market Researchers use statistical methods to collect and analyze data about consumers. They use their findings to help businesses develop and market their products and services. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Market Researcher.
Economist
Economist
Economists use statistical and economic models to analyze economic data. They use their findings to make recommendations to governments and businesses about economic policy. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as an Economist.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical and mathematical models to analyze financial data. They use their findings to make recommendations to clients about how to invest their money. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Quantitative Analyst.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use statistical methods to analyze data and improve the performance of their applications. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Software Engineer.
Business Analyst
Business Analyst
Business Analysts use statistical and business methods to analyze data and identify opportunities for improvement. They use their findings to help businesses make informed decisions. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Business Analyst.
Consultant
Consultant
Consultants use statistical and business methods to help businesses solve problems and improve their performance. They use their findings to make recommendations to clients about how to improve their operations. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Consultant.
Marketing Manager
Marketing Manager
Marketing Managers use statistical and marketing methods to develop and implement marketing campaigns. They use their findings to track the success of their campaigns and make improvements. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Marketing Manager.
Product Manager
Product Manager
Product Managers use statistical and product management methods to develop and manage products. They use their findings to make decisions about product features, pricing, and marketing. This course introduces the fundamentals of linear regression, a statistical technique that can be used to model the relationship between two or more variables. By taking this course, you will gain the skills necessary to analyze data and draw meaningful conclusions. This course is a valuable resource for anyone who wants to enter or advance their career as a Product Manager.
For more career information including salaries, visit:
OpenCourser.com/course/xt7xik/linear
Reading list
We've selected 15 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
Linear Regression and Modeling .
Provides a comprehensive overview of statistical learning methods, including linear regression. It valuable resource for anyone who wants to learn more about the theory and practice of statistical modeling.
Provides a practical guide to fitting linear models in R. It valuable resource for anyone who wants to use R for data analysis.
Provides an introduction to regression modeling with a focus on actuarial and financial applications. It valuable resource for anyone who wants to learn more about the use of regression models in these fields.
Provides a comprehensive overview of generalized linear models. It valuable resource for anyone who wants to learn more about the theory and practice of generalized linear modeling.
Provides a practical guide to statistical methods used in medical research. It valuable resource for anyone who wants to learn more about the use of statistical methods in this field.
Provides a practical guide to fitting linear models. It valuable resource for anyone who wants to learn more about the theory and practice of linear modeling.
Provides a comprehensive overview of linear regression analysis. It valuable resource for anyone who wants to learn more about the theory and practice of linear regression.
Provides a practical guide to regression analysis. It valuable resource for anyone who wants to learn more about the theory and practice of regression analysis.
Provides a comprehensive overview of statistical learning methods. It valuable resource for anyone who wants to learn more about the theory and practice of statistical modeling.
Provides a practical guide to machine learning. It valuable resource for anyone who wants to learn more about the theory and practice of machine learning.
Provides a practical guide to data science. It valuable resource for anyone who wants to learn more about the theory and practice of data science.
Provides a practical guide to machine learning with R. It valuable resource for anyone who wants to learn more about the theory and practice of machine learning with R.
Provides a comprehensive overview of reinforcement learning. It valuable resource for anyone who wants to learn more about the theory and practice of reinforcement learning.
Provides a comprehensive overview of Bayesian data analysis. It valuable resource for anyone who wants to learn more about the theory and practice of Bayesian data analysis.
Provides a comprehensive overview of causal inference in statistics. It valuable resource for anyone who wants to learn more about the theory and practice of causal inference in statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xt7xik/linear
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Effort
4 weeks of study, 5-7 hours/week
Level
Introductory
Via
Coursera
Institution
Duke University
Instructor
Mine Çetinkaya-Rundel
Language
English
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Good to know
Develops fundamental theory behind linear regression, which is core to data analysis
Emphasizes the application of learned concepts through data examples
Utilizes the free and widely used statistical software R and RStudio
Covers a comprehensive range of topics, from basic to advanced concepts in linear regression
Provides a strong foundation for further study in statistics and data analysis
Taught by Mine Çetinkaya-Rundel, an experienced instructor in the field
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