We may earn an affiliate commission when you visit our partners.
John McGready, PhD, MS
Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, we'll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.
Register for this course and see more details by visiting:
OpenCourser.com/course/s5bnv4/simple
Two deals to help you save
Save money when you learn. Here are two deals and offers that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until July 14
Coursera Plus Annual
Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.
Up to
40%
off
Get offer
Valid until August 30
Google AI App Builder
Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind
Take
100%
off
Get offer
What's inside
Syllabus
Simple Regression Methods
Module one covers simple regression, the four different types of regression, commonalities between them, and simple linear aggression. Before completing the graded quiz, you can test your knowledge with the practice quiz.
Read more
Syllabus
Simple Regression Methods
Module one covers simple regression, the four different types of regression, commonalities between them, and simple linear aggression. Before completing the graded quiz, you can test your knowledge with the practice quiz.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Examines biostatistics, a key component of life sciences.
Utilizes simple and understandable regression methods like simple linear, logistic, and Cox regression
Introduces statistical analysis and interpretation of data from published studies
Enables learners to interpret confounding, adjustment, and effect modification in health research
Emphasizes interpreting and performing statistical calculations on real data
Course may require prior knowledge in statistical concepts and methods.
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Simple regression for public health
According to learners, this course offers a solid, practical foundation in simple regression for public health. Instructor Dr. Emily Platt is widely praised for clear, accessible explanations of complex topics. Real-world examples and the practical final project are highlighted as valuable for learning to interpret public health data. While effective for application, some noted the pace can be fast or theoretical depth limited, less suited for advanced theory. Overall, highly recommended for applying simple regression in public health.
Requires some basic math/stats comfort.
"maybe not for someone with absolutely no prior math/stats exposure."
"Requires some comfort with basic math/algebra."
Concepts are explained clearly and well.
"She breaks down complex concepts like logistic and Cox regression into understandable parts."
"As someone new to biostatistics, I found the content well-structured and easy to follow."
"The real-world examples made the concepts relatable."
"Found the simple regression parts well explained."
"She simplifies complex ideas without dumbing them down."
Content is highly relevant to public health.
"I especially appreciated the focus on interpreting results in a public health context."
"A solid introduction to simple regression types relevant to public health."
"The material is highly relevant to public health research."
"Focus on interpretation is key for public health."
"Highly relevant for public health professionals."
Project is practical, applicable, and realistic.
"The quizzes and the final project were very practical and reinforced the learning effectively."
"The course project was a great way to apply everything learned."
"The project was the highlight for me, very realistic."
"Quizzes and project are well-designed to test practical understanding."
"The project was very useful."
"Project is a great capstone."
Dr. Platt makes complex topics clear.
"Excellent course! The instructor, Dr. Emily Platt, is incredibly clear and engaging."
"Dr. Platt is a fantastic lecturer. The way she explained logistic and Cox regression was superb."
"The instructor's ability to explain difficult topics simply was remarkable."
"Excellent content delivery. Dr. Platt makes complex topics accessible."
"Dr. Platt is a phenomenal instructor. She simplifies complex ideas without dumbing them down."
Quizzes are sometimes too easy.
"the graded quizzes could have been slightly more challenging."
"Quizzes were too easy."
Can feel rushed; lacks deep theoretical detail.
"sometimes I felt the pace was a bit fast when introducing new statistical ideas."
"Some concepts felt rushed, particularly confounding and effect modification."
"I have some background in statistics, and found parts a bit too simplified..."
"content felt a bit superficial. It covers the 'how' but not enough of the 'why'..."
"If you want a deep theoretical understanding, look elsewhere."
"Some topics are covered quite fast."
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 Simple Regression Analysis in Public Health with these
activities:
Review the basics of statistical reasoning
Show steps
This activity will help you refresh the basics of statistical reasoning so that you have a strong foundation for the rest of the course.
Browse courses on
Statistical Reasoning
Show steps
-
Review the concepts of probability and distributions.
-
Practice solving simple statistical problems.
Create a summary of the course materials
Show steps
Creating a summary of the course materials will help you to retain what you have learned. This activity will also give you a valuable resource to refer back to later.
Show steps
-
Review the course materials.
-
Identify the key concepts.
-
Write a summary of the key concepts.
Practice creating scatterplots
Show steps
Creating scatterplots is an essential skill for visualizing and understanding the relationship between two variables in biostatistics. This activity will give you an opportunity to practice creating scatterplots and interpreting the results.
Browse courses on
Linear Regression
Show steps
-
Choose two variables to graph.
-
Generate a scatterplot in your preferred statistical software.
-
Draw a line of best fit to visualize the relationship between the variables.
-
Interpret the results of the scatter plot.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Learn about the different types of logistic regression
Show steps
Logistic regression is a valuable tool for analyzing binary and categorical outcomes in biostatistics. This tutorial will introduce you to the different types of logistic regression and how to use them to model your data.
Browse courses on
Logistic Regression
Show steps
-
Identify the type of logistic regression that is appropriate for your data.
-
Fit the model to your data.
-
Interpret the results of the model.
Discussion board - Discuss the use of confounding variables in biostatistics
Show steps
Confounding variables are a common challenge in biostatistics. This discussion board will give you an opportunity to learn about confounding variables and how to adjust for them in your analysis.
Browse courses on
Confounding
Show steps
-
Read the assigned readings on confounding variables.
-
Post a discussion question to the board.
-
Respond to at least two other students' posts.
Write a short report on your logistic regression analysis results
Show steps
Writing a short report will help you to synthesize what you have learned in the logistic regression module. This activity will give you an opportunity to practice communicating your results to a broader audience.
Browse courses on
Logistic Regression
Show steps
-
Summarize the goals of your logistic regression analysis.
-
Describe the data you used and the methods you employed.
-
Interpret the results of your analysis.
-
Draw conclusions about the relationship between the variables in your study.
Identify and adjust for confounding variables in your own research
Show steps
This project will give you an opportunity to apply what you have learned about confounding variables to your own research. You will identify potential confounding variables and develop strategies to adjust for them in your analysis.
Browse courses on
Confounding
Show steps
-
Identify the potential confounding variables in your research.
-
Develop a strategy to adjust for the confounding variables.
-
Implement the strategy in your analysis.
-
Interpret the results of your analysis.
Review the basics of statistical reasoning
Show steps
This activity will help you refresh the basics of statistical reasoning so that you have a strong foundation for the rest of the course.
Browse courses on
Statistical Reasoning
Show steps
Show steps
Show steps
- Review the concepts of probability and distributions.
- Practice solving simple statistical problems.
Create a summary of the course materials
Show steps
Creating a summary of the course materials will help you to retain what you have learned. This activity will also give you a valuable resource to refer back to later.
Show steps
Show steps
Show steps
- Review the course materials.
- Identify the key concepts.
- Write a summary of the key concepts.
Practice creating scatterplots
Show steps
Creating scatterplots is an essential skill for visualizing and understanding the relationship between two variables in biostatistics. This activity will give you an opportunity to practice creating scatterplots and interpreting the results.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Choose two variables to graph.
- Generate a scatterplot in your preferred statistical software.
- Draw a line of best fit to visualize the relationship between the variables.
- Interpret the results of the scatter plot.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Learn about the different types of logistic regression
Show steps
Logistic regression is a valuable tool for analyzing binary and categorical outcomes in biostatistics. This tutorial will introduce you to the different types of logistic regression and how to use them to model your data.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Identify the type of logistic regression that is appropriate for your data.
- Fit the model to your data.
- Interpret the results of the model.
Discussion board - Discuss the use of confounding variables in biostatistics
Show steps
Confounding variables are a common challenge in biostatistics. This discussion board will give you an opportunity to learn about confounding variables and how to adjust for them in your analysis.
Browse courses on
Confounding
Show steps
Show steps
Show steps
- Read the assigned readings on confounding variables.
- Post a discussion question to the board.
- Respond to at least two other students' posts.
Write a short report on your logistic regression analysis results
Show steps
Writing a short report will help you to synthesize what you have learned in the logistic regression module. This activity will give you an opportunity to practice communicating your results to a broader audience.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Summarize the goals of your logistic regression analysis.
- Describe the data you used and the methods you employed.
- Interpret the results of your analysis.
- Draw conclusions about the relationship between the variables in your study.
Identify and adjust for confounding variables in your own research
Show steps
This project will give you an opportunity to apply what you have learned about confounding variables to your own research. You will identify potential confounding variables and develop strategies to adjust for them in your analysis.
Browse courses on
Confounding
Show steps
Show steps
Show steps
- Identify the potential confounding variables in your research.
- Develop a strategy to adjust for the confounding variables.
- Implement the strategy in your analysis.
- Interpret the results of your analysis.
Career center
Learners who complete Simple Regression Analysis in Public Health will develop knowledge and skills
that may be useful to these careers:
Biostatistician
Biostatistician
A Biostatistician will be able to play a major role in the field of public health by analyzing and interpreting large datasets to identify trends, patterns, and relationships. This course can help aspiring Biostatisticians to understand the concepts of simple regression methods and other relevant statistical methods used in public health research.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. This course will provide aspiring Statisticians with a strong foundation in the statistical methods used in various fields, including simple regression methods.
Medical Statistician
Medical Statistician
Medical Statisticians use statistical methods to design and analyze clinical trials and other medical research studies. This course will provide aspiring Medical Statisticians with a strong foundation in the statistical methods used in medical research, including simple regression methods.
Public Health Analyst
Public Health Analyst
Public Health Analysts use statistical methods to analyze and interpret public health data. This course will provide aspiring Public Health Analysts with a strong foundation in the statistical methods used in public health research, including simple regression methods.
Epidemiologist
Epidemiologist
Epidemiologists study the causes and patterns of health and disease in populations. This course will provide aspiring Epidemiologists with a strong foundation in the statistical methods used in epidemiological research, including simple regression methods.
Research Scientist
Research Scientist
Research Scientists use scientific methods to conduct research and develop new knowledge. This course will provide aspiring Research Scientists with a strong foundation in the statistical methods used in scientific research, including simple regression methods.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics and modeling to extract meaningful insights from data. The course's focus on simple regression methods will help aspiring Data Scientists understand the fundamentals of statistical modeling, which is a crucial skill for success in this field.
Health Economist
Health Economist
Health Economists use economic principles to analyze and evaluate health care systems and policies. This course will help aspiring Health Economists understand the use of statistical methods, such as simple regression, in health economics research.
Pharmacist
Pharmacist
Pharmacists use their knowledge of drugs and their effects to provide patient care. This course will help aspiring Pharmacists understand the use of statistical methods, such as simple regression, in pharmaceutical research and development.
Clinical Research Coordinator
Clinical Research Coordinator
Clinical Research Coordinators assist with the design, implementation, and management of clinical trials. This course may be useful for aspiring Clinical Research Coordinators who are interested in understanding the statistical methods used in clinical research.
Health Services Researcher
Health Services Researcher
Health Services Researchers study the organization, delivery, and financing of health care services. This course may be useful for aspiring Health Services Researchers who are interested in using statistical methods to evaluate the effectiveness and efficiency of health care interventions.
Health Informatician
Health Informatician
Health Informaticians use their knowledge of health care and information technology to design and implement health information systems. This course may be useful for aspiring Health Informaticians who are interested in using statistical methods to analyze health data.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers apply engineering principles to design and develop medical devices and systems. This course may be useful for aspiring Biomedical Engineers who are interested in using statistical methods in their research and development work.
Medical Writer
Medical Writer
Medical Writers create written content about medical topics for a variety of audiences. This course may be useful for aspiring Medical Writers who are interested in understanding the statistical methods used in medical research and communicating the results to a non-technical audience.
Nurse Practitioner
Nurse Practitioner
Nurse Practitioners provide primary care to patients across the lifespan. This course may be useful for aspiring Nurse Practitioners who are interested in using statistical methods to evaluate the effectiveness of nursing interventions.
For more career information including salaries, visit:
OpenCourser.com/course/s5bnv4/simple
Reading list
We've selected nine 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
Simple Regression Analysis in Public Health .
A comprehensive guide to regression modeling that covers a wide range of topics, including simple linear regression, logistic regression, and Cox proportional hazards regression.
A highly-regarded book on logistic regression that provides a detailed overview of the theory and applications of logistic regression.
Save
A comprehensive reference book on survival analysis that covers a wide range of topics, including Cox proportional hazards regression.
An introductory textbook on biostatistics that covers the basic statistical methods used in the biological sciences.
A popular book that provides an accessible introduction to regression analysis and other statistical methods.
Save
A specialized book on hierarchical linear modeling that provides a detailed overview of the theory and applications of this method.
A classic textbook on generalized linear models that provides a detailed overview of the theory and applications of this method.
A highly influential book on causal inference that provides a detailed overview of the theory and methods of causal inference.
A classic textbook on Bayesian data analysis that provides a detailed overview of the theory and applications of this method.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s5bnv4/simple
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Effort
4 weeks, 3-4 hours/week
Level
Introductory
Via
Coursera
Institution
Johns Hopkins University
Instructor
John McGready, PhD, MS
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
ไทย
қазақша
اُردُو,
فارسی
বাংলা
پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Biostatistics in Public Health . It's comprised of four courses:
- Hypothesis Testing in Public Health
- Multiple Regression Analysis in Public Health
- Summary Statistics in Public Health
- Simple Regression Analysis in Public Health (viewing)
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser