Logistic Regression in R for Public Health from Coursera

Welcome to Logistic Regression in R for Public Health!

Why logistic regression for public health rather than just logistic regression? Well, there are some particular considerations for every data set, and public health data sets have particular features that need special attention. In a word, they're messy. Like the others in the series, this is a hands-on course, giving you plenty of practice with R on real-life, messy data, with predicting who has diabetes from a set of patient characteristics as the worked example for this course. Additionally, the interpretation of the outputs from the regression model can differ depending on the perspective that you take, and public health doesn’t just take the perspective of an individual patient but must also consider the population angle. That said, much of what is covered in this course is true for logistic regression when applied to any data set, so you will be able to apply the principles of this course to logistic regression more broadly too.

By the end of this course, you will be able to:

Explain when it is valid to use logistic regression

Define odds and odds ratios

Run simple and multiple logistic regression analysis in R and interpret the output

Evaluate the model assumptions for multiple logistic regression in R

Describe and compare some common ways to choose a multiple regression model

This course builds on skills such as hypothesis testing, p values, and how to use R, which are covered in the first two courses of the Statistics for Public Health specialisation. If you are unfamiliar with these skills, we suggest you review Statistical Thinking for Public Health and Linear Regression for Public Health before beginning this course. If you are already familiar with these skills, we are confident that you will enjoy furthering your knowledge and skills in Statistics for Public Health: Logistic Regression for Public Health.

We hope you enjoy the course!

What's inside

Syllabus

Introduction to Logistic Regression

Welcome to Statistics for Public Health: Logistic Regression for Public Health! In this week, you will be introduced to logistic regression and its uses in public health. We will focus on why linear regression does not work with binary outcomes and on odds and odds ratios, and you will finish the week by practising your new skills. By the end of this week, you will be able to explain when it is valid to use logistic regression, and define odds and odds ratios. Good luck!

Logistic Regression in R

In this week, you will learn how to prepare data for logistic regression, how to describe data in R, how to run a simple logistic regression model in R, and how to interpret the output. You will also have the opportunity to practise your new skills. By the end of this week, you will be able to run simple logistic regression analysis in R and interpret the output. Good luck!

Running Multiple Logistic Regression in R

Now that you're happy with including one predictor in the model, this week you'll learn how to run multiple logistic regression, including describing and preparing your data and running new logistic regression models. You will have the opportunity to practise your new skills. By the end of the week, you will be able to run multiple logistic regression analysis in R and interpret the output. Good luck!

Assessing Model Fit

Welcome to the final week of the course! In this week, you will learn how to assess model fit and model performance, how to avoid the problem of overfitting, and how to choose what variables from your data set should go into your multiple regression model. You will put all the skills you have learned throughout the course into practice. By the end of this week, you will be able to evaluate the model assumptions for multiple logistic regression in R, and describe and compare some common ways to choose a multiple regression model. Good luck!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers logistic regression which is essential in public health to understand predictors and outcomes of health related issues affecting populations

Taught in R which is highly relevant statistical programming tool in public health

Requires prior knowledge of hypothesis testing, p values, and R

Learn on real-life, messy data, with predicting who has diabetes from a set of patient characteristics

Reviews summary

Highly rated logistic regression course

Learners say that this course is an excellent introduction to logistic regression in R for public health data. The material is comprehensive and the examinations are challenging but fair. More than a few learners mentioned that they enjoyed the instructor's sense of humor, which helped to make learning more enjoyable. Overall, students largely praised this course and highly recommended it to other learners.

The coursework is challenging, but learners find it rewarding.

"This is a great course though it was very challenging."

"It may take enough time for you to understand each concept clearly, but i think it is worth learning."

The course content is high quality and well organized.

"Good, easy to follow introduction to logistic regression"

"The course was worth the time and efforts."

The course is practical and teaches learners how to apply logistic regression in R.

"The course needs more exercises to practice R!"

"Great practical way to learn how to do logistic regression in R."

The instructor is knowledgeable and engaging.

"The instructor was very clear and succinct; I found it easy to follow."

"The facilitator made it so easy to understand."

"The instructor did a great job explaining the concepts without bogging down in the details."

Some learners experienced technical issues with the R exercises.

"I had a problem with running the logistic regression in R since the missing observations that R gave me was not the same as given in this course."

The lectures could include more visualizations.

"Too much was left for the lecturer to tell and not enough visualizations provided in the video."

"Use more visualizations in the lecture. Like explaining the summary outout in R."

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 Logistic Regression in R for Public Health with these activities:

Organize your notes, assignments, and quizzes

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Stay organized and improve your ability to review and retain course materials.

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Create a system for organizing your notes, assignments, and quizzes.
Regularly review and update your organized materials.

Review Applied Logistic Regression

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Gain a deeper understanding of logistic regression by reviewing this book, which provides a comprehensive overview of the topic.

View Applied Logistic Regression on Amazon

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Read the book's introduction and first chapter to familiarize yourself with the basics of logistic regression.
Focus on the chapters that cover the specific topics you are interested in learning more about, such as model building, interpretation, and diagnostics.
Complete the practice exercises and problems at the end of each chapter to test your understanding.

Simulate patient dataset

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Improve your understanding of logistic regression by simulating a patient dataset and running regression analysis on it.

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Generate a simulated dataset of patient characteristics and outcomes.
Import the dataset into R and prepare it for analysis.
Run a logistic regression model on the dataset.
Interpret the results of the model, including the coefficients, p-values, and odds ratios.

One other activity

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Join a study group or participate in online discussion forums

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Engage with classmates or other learners to discuss course topics, share insights, and get feedback on your understanding.

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Join an online discussion forum or study group dedicated to the course.
Participate in discussions by asking questions, sharing your thoughts, and responding to others.
Review the contributions of others and provide feedback to help everyone learn.

Career center

Learners who complete Logistic Regression in R for Public Health will develop knowledge and skills that may be useful to these careers:

Public Health Statistician

Public Health Statisticians use statistical methods to collect, analyze, and interpret data to improve public health. The skills you will learn in "Logistic Regression in R for Public Health" will be essential for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in public health research. You will learn how to use logistic regression to identify factors that are associated with health outcomes, such as diabetes or heart disease. Additionally, you will learn how to assess the model fit and performance, which is crucial for ensuring that your results are accurate and reliable.

See salaries and explore the career path for Public Health Statistician

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. The skills you will learn in "Logistic Regression in R for Public Health" will be essential for success in this role. This course will help you build a foundation in logistic regression, a statistical technique that is commonly used in public health research. You will learn how to use logistic regression to identify factors that are associated with health outcomes, such as diabetes or heart disease. Additionally, you will learn how to assess the model fit and performance, which is crucial for ensuring that your results are accurate and reliable.

See salaries and explore the career path for Data Analyst

Biostatistician

Biostatisticians apply statistical methods to solve problems in medicine and public health. The skills you will learn in "Logistic Regression in R for Public Health" will be essential for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in biostatistics. You will learn how to use logistic regression to identify factors that are associated with health outcomes, such as diabetes or heart disease. Additionally, you will learn how to assess the model fit and performance, which is crucial for ensuring that your results are accurate and reliable.

See salaries and explore the career path for Biostatistician

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. The skills you will learn in "Logistic Regression in R for Public Health" will be essential for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in quantitative analysis. You will learn how to use logistic regression to identify factors that are associated with health outcomes, such as diabetes or heart disease. Additionally, you will learn how to assess the model fit and performance, which is crucial for ensuring that your results are accurate and reliable.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. The skills you will learn in "Logistic Regression in R for Public Health" will be essential for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in actuarial science. You will learn how to use logistic regression to identify factors that are associated with health outcomes, such as diabetes or heart disease. Additionally, you will learn how to assess the model fit and performance, which is crucial for ensuring that your results are accurate and reliable.

See salaries and explore the career path for Actuary

Epidemiologist

Epidemiologists investigate the causes of disease and other health problems in populations. The skills you will learn in "Logistic Regression in R for Public Health" will be essential for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in epidemiology. You will learn how to use logistic regression to identify factors that are associated with health outcomes, such as diabetes or heart disease. Additionally, you will learn how to assess the model fit and performance, which is crucial for ensuring that your results are accurate and reliable.

See salaries and explore the career path for Epidemiologist

Data Scientist

Data Scientists use statistical and programming skills to analyze data and extract insights. The skills you will learn in "Logistic Regression in R for Public Health" will be essential for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in data science. You will learn how to use logistic regression to identify factors that are associated with health outcomes, such as diabetes or heart disease. Additionally, you will learn how to assess the model fit and performance, which is crucial for ensuring that your results are accurate and reliable.

See salaries and explore the career path for Data Scientist

Financial Analyst

Financial Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. The skills you will learn in "Logistic Regression in R for Public Health" may be useful for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in financial analysis.

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Healthcare Consultant

Healthcare Consultants provide advice to healthcare organizations on how to improve their operations and performance. The skills you will learn in "Logistic Regression in R for Public Health" may be useful for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in healthcare consulting.

See salaries and explore the career path for Healthcare Consultant

Risk Analyst

Risk Analysts use mathematical and statistical models to assess risk and make decisions about how to mitigate it. The skills you will learn in "Logistic Regression in R for Public Health" may be useful for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in risk analysis.

See salaries and explore the career path for Risk Analyst

Insurance Analyst

Insurance Analysts use mathematical and statistical models to assess risk and determine insurance premiums. The skills you will learn in "Logistic Regression in R for Public Health" may be useful for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in insurance analysis.

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Survey Researcher

Survey Researchers design and conduct surveys to collect data about populations. The skills you will learn in "Logistic Regression in R for Public Health" may be useful for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in survey research.

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Market Researcher

Market Researchers conduct research to identify and understand the needs of customers. The skills you will learn in "Logistic Regression in R for Public Health" may be useful for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in market research.

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Health Policy Analyst

Health Policy Analysts develop and evaluate policies that affect the health of populations. The skills you will learn in "Logistic Regression in R for Public Health" may be useful for success in this role. This course will help you build a foundation in logistic regression, a statistical technique commonly used in health policy research.

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Medical Writer

Medical Writers create educational materials for patients, healthcare professionals, and the general public. The skills you will learn in "Logistic Regression in R for Public Health" may be useful for success in this role. This course will help you develop your understanding of public health research and statistics, which is essential for writing accurate and informative medical materials.

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