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Alex Bottle
and
Victoria Cornelius
Welcome to Linear Regression in R for Public Health!
Read more
Welcome to Linear Regression in R for Public Health!
Read more
Welcome to Linear Regression in R for Public Health!
Public Health has been defined as “the art and science of preventing disease, prolonging life and promoting health through the organized efforts of society”. Knowing what causes disease and what makes it worse are clearly vital parts of this. This requires the development of statistical models that describe how patient and environmental factors affect our chances of getting ill. This course will show you how to create such models from scratch, beginning with introducing you to the concept of correlation and linear regression before walking you through importing and examining your data, and then showing you how to fit models. Using the example of respiratory disease, these models will describe how patient and other factors affect outcomes such as lung function.
Linear regression is one of a family of regression models, and the other courses in this series will cover two further members. Regression models have many things in common with each other, though the mathematical details differ.
This course will show you how to prepare the data, assess how well the model fits the data, and test its underlying assumptions – vital tasks with any type of regression.
You will use the free and versatile software package R, used by statisticians and data scientists in academia, governments and industry worldwide.
Register for this course and see more details by visiting:
OpenCourser.com/course/kf18bd/linear
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What's inside
Syllabus
INTRODUCTION TO LINEAR REGRESSION
Before jumping ahead to run a regression model, you need to understand a related concept: correlation. This week you’ll learn what it means and how to generate Pearson’s and Spearman’s correlation coefficients in R to assess the strength of the association between a risk factor or predictor and the patient outcome. Then you’ll be introduced to linear regression and the concept of model assumptions, a key idea underpinning so much of statistical analysis.
Read more
Syllabus
INTRODUCTION TO LINEAR REGRESSION
Before jumping ahead to run a regression model, you need to understand a related concept: correlation. This week you’ll learn what it means and how to generate Pearson’s and Spearman’s correlation coefficients in R to assess the strength of the association between a risk factor or predictor and the patient outcome. Then you’ll be introduced to linear regression and the concept of model assumptions, a key idea underpinning so much of statistical analysis.
Read more
Linear Regression in R
You’ll be introduced to the COPD data set that you’ll use throughout the course and will run basic descriptive analyses. You’ll also practise running correlations in R. Next, you’ll see how to run a linear regression model, firstly with one and then with several predictors, and examine whether model assumptions hold.
Multiple Regression and Interaction
Now you’ll see how to extend the linear regression model to include binary and categorical variables as predictors and learn how to check the correlation between predictors. Then you’ll see how predictors can interact with each other and how to incorporate the necessary interaction terms into the model and interpret them. Different kinds of interactions exist and can be challenging to interpret, so we will take it slowly with worked examples and opportunities to practise.
MODEL BUILDING
The last part of the course looks at how to build a regression model when you have a choice of what predictors to include in it. It describes commonly used automated procedures for model building and shows you why they are so problematic. Lastly, you’ll have the chance to fit some models using a more defensible and robust approach.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Useful for public health professionals to understand risk factors for respiratory disease
Covers the basics of linear regression in R, including model building and interpretation
Taught by experts in public health with experience in respiratory disease research
Requires familiarity with R and basic statistical concepts
May not be suitable for those with no prior knowledge of regression analysis
Save this course
Save Linear Regression in R for Public Health to your list so you can find it easily later:
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Reviews summary
Practical linear regression with r
According to students, this course is excellent and well-paced, offering practical knowledge of linear regression using the R programming language. Learners find the videos, readings, and exercises engaging and informative, allowing them to build a solid understanding of the subject's concepts and applications. The instructors are knowledgeable and professional, providing clear and helpful feedback. Overall, learners highly recommend this course for those seeking to develop their skills in linear regression and R for public health research.
Suitable for learners with little or no prior experience in linear regression or R
"Wonderful course. Anyone with any background can attend this course."
"This course is excellent- if you want a solid understanding of the basics, this is as good as it gets."
Hands-on coding exercises provide practical experience
"The course offered a systematic and efficient way to learn linear regression in R."
"Great Refresher or first course. Videos were the right length. The difficulty the right level. "
"I love this course. I can understand clearly how linear regression work and apply this in real situation"
Provides prompt and helpful feedback on assignments
"Excellent course, both in the structure and in the way of teaching"
"The feedback was also incredibly valuable and helped us understand possible mistakes."
Uses real-world datasets to illustrate concepts
"This was a great course. The best part: hands-on experience with real-world dataset."
"I really enjoyed this course, which broke down the steps of how to build a multivariable regression model, from examining individual data, exploring its correlations, fitting a linear regression model, coding all of these in R, and even provided tips on how to put all of these together in a research paper."
Covers a wide range of topics, including statistics, R coding, and data analysis
"This course is good value for money"
"Great introduction to regression with R"
"Excellent course, excellent Porfessor. "
Instructors are knowledgeable, professional, and engaging
"Excellent course!!"
"Exceptional course"
"Best teaching team online"
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 in R for Public Health with these
activities:
Review Previous Statistics Course Material
Show steps
Reviewing your previous statistics course material will ensure that you have a strong foundation before beginning this course on linear regression.
Browse courses on
Statistics
Show steps
-
Gather your notes, assignments, quizzes, and exams from your previous statistics course.
-
Review your notes, focusing on the key concepts of statistics.
-
Work through the practice problems in your textbook or online resources.
Watch Video Tutorials on Linear Regression
Show steps
Watching video tutorials can help you learn the basics of linear regression.
Browse courses on
Linear Regression
Show steps
-
Search for video tutorials on linear regression.
-
Watch the tutorials and take notes.
Join a Study Group
Show steps
Joining a study group can help you learn from and collaborate with other students.
Browse courses on
Linear Regression
Show steps
-
Find a study group that meets regularly.
-
Attend the study group meetings and participate in the discussions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Linear Regression Practice Problems
Show steps
Completing practice problems will help you master the concepts of linear regression.
Browse courses on
Linear Regression
Show steps
-
Find practice problems online or in a textbook.
-
Work through the problems step-by-step.
-
Check your answers against the provided solutions.
Write a Summary of Linear Regression
Show steps
Writing a summary of linear regression will help you understand the key concepts and organize your thoughts.
Browse courses on
Linear Regression
Show steps
-
Identify the key concepts of linear regression.
-
Write a brief summary of each concept.
-
Organize your summary into a logical structure.
Read Applied Linear Regression
Show steps
Reading Applied Linear Regression will provide you with a comprehensive understanding of linear regression methods.
View
Applied Linear Statistical Models 5ed (Pb 2013)
on Amazon
Show steps
-
Purchase or borrow a copy of Applied Linear Regression.
-
Read the book, focusing on the chapters that cover linear regression.
Run Linear Regression on Real-World Data
Show steps
Gain practical experience by running linear regression on real-world data.
Browse courses on
Linear Regression
Show steps
-
Find a dataset that is relevant to your interests.
-
Clean and prepare the data.
-
Run a linear regression model on the data.
Create a Presentation on Linear Regression
Show steps
Creating a presentation will solidify your understanding of linear regression and allow you to share your knowledge with others.
Browse courses on
Linear Regression
Show steps
-
Organize your thoughts and ideas on linear regression.
-
Create a presentation using slides or other visual aids.
-
Practice your presentation and deliver it to an audience.
Review Previous Statistics Course Material
Show steps
Reviewing your previous statistics course material will ensure that you have a strong foundation before beginning this course on linear regression.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Gather your notes, assignments, quizzes, and exams from your previous statistics course.
- Review your notes, focusing on the key concepts of statistics.
- Work through the practice problems in your textbook or online resources.
Watch Video Tutorials on Linear Regression
Show steps
Watching video tutorials can help you learn the basics of linear regression.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Search for video tutorials on linear regression.
- Watch the tutorials and take notes.
Join a Study Group
Show steps
Joining a study group can help you learn from and collaborate with other students.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Find a study group that meets regularly.
- Attend the study group meetings and participate in the discussions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Linear Regression Practice Problems
Show steps
Completing practice problems will help you master the concepts of linear regression.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Find practice problems online or in a textbook.
- Work through the problems step-by-step.
- Check your answers against the provided solutions.
Write a Summary of Linear Regression
Show steps
Writing a summary of linear regression will help you understand the key concepts and organize your thoughts.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify the key concepts of linear regression.
- Write a brief summary of each concept.
- Organize your summary into a logical structure.
Read Applied Linear Regression
Show steps
Reading Applied Linear Regression will provide you with a comprehensive understanding of linear regression methods.
View
Applied Linear Statistical Models 5ed (Pb 2013)
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of Applied Linear Regression.
- Read the book, focusing on the chapters that cover linear regression.
Run Linear Regression on Real-World Data
Show steps
Gain practical experience by running linear regression on real-world data.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Find a dataset that is relevant to your interests.
- Clean and prepare the data.
- Run a linear regression model on the data.
Create a Presentation on Linear Regression
Show steps
Creating a presentation will solidify your understanding of linear regression and allow you to share your knowledge with others.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Organize your thoughts and ideas on linear regression.
- Create a presentation using slides or other visual aids.
- Practice your presentation and deliver it to an audience.
Career center
Learners who complete Linear Regression in R for Public Health will develop knowledge and skills
that may be useful to these careers:
Epidemiologist
Epidemiologist
Epidemiologists study the causes and patterns of disease, injury, and other health problems in populations. They use statistical methods to analyze data and develop models to understand how these factors affect health. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing health data, which can be essential for Epidemiologists in their work. The course also covers topics such as model building and interpretation, which are important for Epidemiologists in developing and evaluating models to understand health outcomes.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze studies that investigate health-related questions. They also develop and apply statistical models to understand the causes and patterns of disease, injury, and other health problems. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing health data, which is essential for Biostatisticians in their work. The course also covers topics such as model building and interpretation, which are important for Biostatisticians in developing and evaluating models to understand health outcomes.
Public Health Analyst
Public Health Analyst
Public Health Analysts use data to understand and improve the health of populations. They work with data from a variety of sources, including surveys, censuses, and health records. They use statistical methods to analyze data and develop models to identify trends and patterns, and to assess the effectiveness of public health programs. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing health data, which is essential for Public Health Analysts in their work. The course also covers topics such as model building and interpretation, which are important for Public Health Analysts in developing and evaluating models to understand health outcomes.
Health Economist
Health Economist
Health Economists use economic principles to analyze the costs and benefits of health care interventions. They use statistical methods to evaluate the effectiveness of health care programs and to identify ways to improve the efficiency of health care delivery. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing health data, which is essential for Health Economists in their work. The course also covers topics such as model building and interpretation, which are important for Health Economists in developing and evaluating models to understand the costs and benefits of health care interventions.
Data Scientist
Data Scientist
Data Scientists use statistical methods to analyze data and develop models to solve business problems. They work with data from a variety of sources, including surveys, censuses, and social media. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing data, which is essential for Data Scientists in their work. The course also covers topics such as model building and interpretation, which are important for Data Scientists in developing and evaluating models to solve business problems.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. They work in a variety of fields, including public health, business, and government. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing data, which is essential for Statisticians in their work. The course also covers topics such as model building and interpretation, which are important for Statisticians in developing and evaluating models to understand data.
Research Analyst
Research Analyst
Research Analysts use statistical methods to analyze data and develop models to understand trends and patterns. They work in a variety of fields, including public health, business, and government. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing data, which is essential for Research Analysts in their work. The course also covers topics such as model building and interpretation, which are important for Research Analysts in developing and evaluating models to understand trends and patterns.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical methods to analyze data and develop models to make investment decisions. They work in the financial industry, where they help investors make informed decisions about where to invest their money. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing data, which is essential for Quantitative Analysts in their work. The course also covers topics such as model building and interpretation, which are important for Quantitative Analysts in developing and evaluating models to make investment decisions.
Actuary
Actuary
Actuaries use statistical methods to assess risk and uncertainty. They work in the insurance industry, where they help insurance companies set premiums and create policies. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing data, which is essential for Actuaries in their work. The course also covers topics such as model building and interpretation, which are important for Actuaries in developing and evaluating models to assess risk and uncertainty.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical methods to analyze data and develop models to improve the efficiency of operations. They work in a variety of industries, including manufacturing, logistics, and healthcare. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing data, which is essential for Operations Research Analysts in their work. The course also covers topics such as model building and interpretation, which are important for Operations Research Analysts in developing and evaluating models to improve the efficiency of operations.
Market Research Analyst
Market Research Analyst
Market Research Analysts use statistical methods to analyze data and develop models to understand consumer behavior. They work in the marketing industry, where they help businesses develop and implement marketing campaigns. The course Linear Regression in R for Public Health provides a strong foundation in statistical methods for analyzing data, which is essential for Market Research Analysts in their work. The course also covers topics such as model building and interpretation, which are important for Market Research Analysts in developing and evaluating models to understand consumer behavior.
Financial Analyst
Financial Analyst
Financial Analysts use statistical methods to analyze data and develop models to make investment decisions. They work in the financial industry, where they help investors make informed decisions about where to invest their money. The course Linear Regression in R for Public Health provides a foundation in statistical methods for analyzing data, which can be useful for Financial Analysts in their work. The course covers topics such as model building and interpretation, which are important for Financial Analysts in developing and evaluating models to make investment decisions.
Business Analyst
Business Analyst
Business Analysts use statistical methods to analyze data and develop models to improve the efficiency of business operations. They work in a variety of industries, including manufacturing, retail, and healthcare. The course Linear Regression in R for Public Health provides a foundation in statistical methods for analyzing data, which can be useful for Business Analysts in their work. The course covers topics such as model building and interpretation, which are important for Business Analysts in developing and evaluating models to improve the efficiency of business operations.
Data Analyst
Data Analyst
Data Analysts use statistical methods to analyze data and develop models to solve business problems. They work in a variety of industries, including retail, healthcare, and finance. The course Linear Regression in R for Public Health provides a foundation in statistical methods for analyzing data, which can be useful for Data Analysts in their work. The course covers topics such as model building and interpretation, which are important for Data Analysts in developing and evaluating models to solve business problems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including technology, healthcare, and finance. The course Linear Regression in R for Public Health provides a foundation in statistical methods, which can be useful for Software Engineers in their work. The course covers topics such as model building and interpretation, which are important for Software Engineers in developing and evaluating software applications.
For more career information including salaries, visit:
OpenCourser.com/course/kf18bd/linear
Reading list
We've selected ten 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 in R for Public Health .
This textbook provides a comprehensive overview of statistical learning methods and techniques, including linear regression. It offers a gentle introduction to the underlying concepts and algorithms and provides helpful examples and exercises.
This textbook provides an introduction to causal inference, which is essential for understanding the causal relationships between variables in regression models. It offers a clear and accessible explanation of key concepts such as confounding, selection bias, and instrumental variables.
Offers a practical guide to linear regression modeling, with a focus on applications in public health. It covers the fundamentals of linear regression, including model fitting, diagnostics, and interpretation.
Save
Provides a comprehensive overview of regression methods for time series analysis, which is an extension of linear regression used to analyze data collected over time. It covers advanced topics such as autoregressive integrated moving average (ARIMA) models and forecasting methods.
Provides a comprehensive overview of generalized linear models, which are an extension of linear regression that can be used to model nonlinear relationships and non-normal data. It covers topics such as logistic regression, Poisson regression, and negative binomial regression.
This textbook provides a practical guide to regression analysis, with a focus on real-world examples. It covers basic concepts such as model fitting, diagnostics, and interpretation, as well as advanced topics such as variable selection and model building.
This textbook provides a comprehensive introduction to linear regression analysis, with a focus on applications in engineering and business. It covers basic concepts such as model fitting, diagnostics, and interpretation, as well as advanced topics such as multicollinearity and model building.
This advanced textbook provides a comprehensive treatment of linear models, including regression analysis. It offers a theoretical foundation for linear regression and covers advanced topics such as model selection and shrinkage methods.
This advanced textbook provides a comprehensive treatment of linear regression analysis, including both theoretical and applied aspects. It covers topics such as model selection, diagnostics, and forecasting, which may be useful for those interested in a deeper understanding of linear regression.
This textbook covers regression modeling techniques with a focus on actuarial and financial applications. It provides advanced topics such as generalized linear models, survival analysis, and time series analysis, which may be useful for those interested in extending their knowledge of linear regression.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kf18bd/linear
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Effort
4 weeks of study 3-5 hours per week
Level
Intermediate
Via
Coursera
Institution
Imperial College London
Instructors
Alex Bottle
Victoria Cornelius
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Statistical Analysis with R for Public Health. It's comprised of four courses:
- Logistic Regression in R for Public Health
- Introduction to Statistics & Data Analysis in Public Health
- Linear Regression in R for Public Health (viewing)
- Survival Analysis in R for Public Health
Good to know
Useful for public health professionals to understand risk factors for respiratory disease
Covers the basics of linear regression in R, including model building and interpretation
Taught by experts in public health with experience in respiratory disease research
Requires familiarity with R and basic statistical concepts
May not be suitable for those with no prior knowledge of regression analysis
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