Linear Regression in R for Public Health from Coursera

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.

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.

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Read about what's good

what should give you pause

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

Reviews summary

Linear regression in r for public health

According to learners, this course provides a strong foundation in linear regression using R, specifically tailored for public health applications. Many appreciate the practical, hands-on R examples that mirror real-world data analysis tasks. The course content is generally considered clear and well-structured, building concepts logically. While the majority of feedback is highly positive, some students note that the course may feel fast-paced or require some prior familiarity with R programming or basic statistics to fully keep up.

Course structure and explanations are clear.

"Explanations were clear and concise, making complex topics accessible."

"The instructor explained the concepts well, breaking down linear regression logically."

"Found the structure easy to follow, building from basics to more complex ideas."

"The course material is presented in a very organized manner."

"Concepts were explained simply enough even for someone relatively new to statistics."

Content is highly relevant to public health.

"Highly relevant content for public health professionals working with quantitative data."

"The focus on public health applications made the material particularly engaging and useful."

"Appreciated the use of a public health dataset throughout the course."

"Directly applicable to my work in public health research."

Hands-on R coding is highly practical.

"The hands-on R coding and projects are the strongest part of the course for me."

"I learned how to apply linear regression models using R effectively for public health data analysis."

"The practical examples provided were very helpful in understanding how to implement the concepts in R."

"Applying the methods in R using the provided dataset made the theory much clearer and applicable."

"Excellent step-by-step guidance on running regressions in R."

May require prior R or stats background.

"Moves quite fast, might be challenging if you are completely new to R or statistics."

"Could be more helpful if more foundational R knowledge was assumed."

"Some parts feel a bit rushed if you need time to digest the statistical theory."

"I struggled slightly without a stronger background in R programming."

"Recommend having basic statistical understanding before starting this course."

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

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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

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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

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Watching video tutorials can help you learn the basics of linear regression.

Browse courses on Linear Regression

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Search for video tutorials on linear regression.
Watch the tutorials and take notes.

Join a Study Group

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Joining a study group can help you learn from and collaborate with other students.

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Find a study group that meets regularly.
Attend the study group meetings and participate in the discussions.

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Solve Linear Regression Practice Problems

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Completing practice problems will help you master the concepts of linear regression.

Browse courses on Linear Regression

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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

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Writing a summary of linear regression will help you understand the key concepts and organize your thoughts.

Browse courses on Linear Regression

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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

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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

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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

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Gain practical experience by running linear regression on real-world data.

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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

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Creating a presentation will solidify your understanding of linear regression and allow you to share your knowledge with others.

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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

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.

See salaries and explore the career path for Epidemiologist

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Public Health Analyst

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.

See salaries and explore the career path for Health Economist

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Research 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.

See salaries and explore the career path for Quantitative Analyst

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.

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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.

See salaries and explore the career path for Operations 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.

See salaries and explore the career path for Market Research 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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Software Engineer