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Philip S. Boonstra
and
Bhramar Mukherjee
This course introduces learners to the analysis of binary/dichotomous outcomes. Learners will become familiar with fundamental tests for two-group comparisons and statistical inference plus prediction more broadly using logistic regression. They will understand the connection between prevalence, risk ratios, and odds ratios. By the end of this course, learners will be able to understand how binary outcomes arise, how to use R to compare proportions between two groups, how to fit logistic regressions in R, how to make predictions using logistic regression, and how to assess the quality of these predictions. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises.
Register for this course and see more details by visiting:
OpenCourser.com/course/cj95nz/logistic
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What's inside
Syllabus
Simple Comparisons of Binary Outcomes
This module introduces you to binary outcomes, including how they arise, how to calculate proportions, and how to compare proportions between two groups.
Read more
Syllabus
Simple Comparisons of Binary Outcomes
This module introduces you to binary outcomes, including how they arise, how to calculate proportions, and how to compare proportions between two groups.
Read more
Introducing Logistic Regression
In this module, you will be introduced to the ubiquitous logistic regression, one of the most common tools for measuring the association between one or more predictors and a binary outcome.
Assessing the Predictive Accuracy of Logistic Regression Models
This module introduces you to tools for assessing the quality of a fitted logistic regression model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Targeted at those new to analysis of binary outcomes
Taught by Philip S. Boonstra and Bhramar Mukherjee, both experienced in this subject matter
Covers a range of concepts from simple comparisons of binary outcomes to logistic regression
Includes hands-on exercises and activities to reinforce learning
Part of a series of courses on statistical inference and prediction in R
Save this course
Save Logistic Regression and Prediction for Health Data to your list so you can find it easily later:
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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 and Prediction for Health Data with these
activities:
Review R Basics
Show steps
R is a popular language used for statistical computing and graphics. Become familiar with R before the start of the course.
Show steps
-
Take an online tutorial on R Basics
-
Install R and R Studio
-
Start practicing with the R console
Practice Calculating Proportions
Show steps
Calculating proportions is a fundamental skill for analyzing binary outcomes. Practice this skill before tackling more complex concepts.
Browse courses on
Proportions
Show steps
-
Find a dataset with binary outcomes
-
Calculate the proportion of positive outcomes
-
Compare your results to the expected proportions
-
Repeat the process for different datasets
Form a Study Group
Show steps
Studying with peers can enhance your learning experience and provide a support system for tackling challenging topics.
Show steps
-
Find classmates who are also enrolled in the course
-
Set up regular meeting times
-
Review course materials together
-
Discuss key concepts
-
Work on practice problems
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explain Logistic Regression to a Classmate
Show steps
Explaining a complex concept to someone else helps reinforce your own understanding. Try explaining logistic regression to a classmate who is not familiar with the concept.
Browse courses on
Logistic Regression
Show steps
-
Review the basics of logistic regression
-
Prepare a simple example to illustrate the concept
-
Explain the steps involved in fitting a logistic regression model
-
Discuss the interpretation of the coefficients
-
Have your classmate ask questions and provide feedback
Review Logistic Regression with tidymodels
Show steps
tidymodels is a powerful R package for modeling. Become familiar with using tidymodels for logistic regression.
Browse courses on
Logistic Regression
Show steps
-
Install the tidymodels and broom packages
-
Load your data into R
-
Create a logistic regression model using tidymodels
-
Interpret the model results using broom
Create a Dataset for Binary Outcomes
Show steps
Creating your own dataset can help you understand the concepts of binary outcomes and how to work with them.
Show steps
-
Identify a research question that involves binary outcomes
-
Design a data collection plan
-
Collect the data
-
Clean and prepare the data
-
Create a codebook or documentation
Review: Regression Models for Binary Outcomes
Show steps
This book provides a comprehensive overview of logistic regression and other statistical models for analyzing binary outcomes.
View
Regression Models for Categorical Dependent...
on Amazon
Show steps
-
Read Chapter 1: Introduction
-
Read Chapter 2: Linear Probability Models
-
Read Chapter 3: Logistic Regression Models
-
Work through the practice problems
Predict Binary Outcomes Using Logistic Regression
Show steps
Apply your knowledge of logistic regression to build a model that can predict binary outcomes.
Browse courses on
Logistic Regression
Show steps
-
Identify a dataset with binary outcomes
-
Prepare the data for modeling
-
Build and fit a logistic regression model
-
Evaluate the model's performance
-
Make predictions using the model
Review R Basics
Show steps
R is a popular language used for statistical computing and graphics. Become familiar with R before the start of the course.
Show steps
Show steps
Show steps
- Take an online tutorial on R Basics
- Install R and R Studio
- Start practicing with the R console
Practice Calculating Proportions
Show steps
Calculating proportions is a fundamental skill for analyzing binary outcomes. Practice this skill before tackling more complex concepts.
Browse courses on
Proportions
Show steps
Show steps
Show steps
- Find a dataset with binary outcomes
- Calculate the proportion of positive outcomes
- Compare your results to the expected proportions
- Repeat the process for different datasets
Form a Study Group
Show steps
Studying with peers can enhance your learning experience and provide a support system for tackling challenging topics.
Show steps
Show steps
Show steps
- Find classmates who are also enrolled in the course
- Set up regular meeting times
- Review course materials together
- Discuss key concepts
- Work on practice problems
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explain Logistic Regression to a Classmate
Show steps
Explaining a complex concept to someone else helps reinforce your own understanding. Try explaining logistic regression to a classmate who is not familiar with the concept.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Review the basics of logistic regression
- Prepare a simple example to illustrate the concept
- Explain the steps involved in fitting a logistic regression model
- Discuss the interpretation of the coefficients
- Have your classmate ask questions and provide feedback
Review Logistic Regression with tidymodels
Show steps
tidymodels is a powerful R package for modeling. Become familiar with using tidymodels for logistic regression.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Install the tidymodels and broom packages
- Load your data into R
- Create a logistic regression model using tidymodels
- Interpret the model results using broom
Create a Dataset for Binary Outcomes
Show steps
Creating your own dataset can help you understand the concepts of binary outcomes and how to work with them.
Show steps
Show steps
Show steps
- Identify a research question that involves binary outcomes
- Design a data collection plan
- Collect the data
- Clean and prepare the data
- Create a codebook or documentation
Review: Regression Models for Binary Outcomes
Show steps
This book provides a comprehensive overview of logistic regression and other statistical models for analyzing binary outcomes.
View
Regression Models for Categorical Dependent...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1: Introduction
- Read Chapter 2: Linear Probability Models
- Read Chapter 3: Logistic Regression Models
- Work through the practice problems
Predict Binary Outcomes Using Logistic Regression
Show steps
Apply your knowledge of logistic regression to build a model that can predict binary outcomes.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Identify a dataset with binary outcomes
- Prepare the data for modeling
- Build and fit a logistic regression model
- Evaluate the model's performance
- Make predictions using the model
Career center
Learners who complete Logistic Regression and Prediction for Health Data will develop knowledge and skills
that may be useful to these careers:
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to biological, medical, and public health research. Logistic regression is used extensively in biostatistics because it allows for the modeling of binary outcomes. This course will provide the foundation in logistic regression needed to build a successful career in biostatistics.
Epidemiologist
Epidemiologist
Epidemiologists investigate the distribution and patterns of health-related states or events (including disease), and the factors that influence them. This course introduces logistic regression, a key tool for understanding the relationship between risk factors and health outcomes, which is fundamental to epidemiological research.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare. Logistic regression is a commonly used statistical technique in healthcare, and this course provides a solid foundation in the topic.
Clinical Research Associate
Clinical Research Associate
A Clinical Research Associate (CRA) plays an essential part in the clinical development process of pharmaceuticals, biologics, and medical devices. CRAs manage all study-related activities at clinical investigation sites to ensure the rights, safety, and well-being of human subjects, as well as the quality and integrity of the clinical research data. The course's emphasis on logistic regression can help CRAs understand the underlying relationship between patient characteristics and clinical outcomes, which is crucial for effective study design and analysis.
Research Scientist
Research Scientist
Research scientists conduct research to advance scientific knowledge. They use various methods, including logistic regression, to analyze data and draw conclusions. This course provides a strong foundation in logistic regression, a valuable skill for researchers in healthcare and related fields.
Data Analyst
Data Analyst
Data analysts uncover insights from data using various statistical and analytic techniques. Those in healthcare leverage their knowledge to discover patterns, trends, and relationships in data related to patient care, treatment, and outcomes. The course's focus on logistic regression provides data analysts with a tool to predict patient outcomes and identify factors that influence health.
Public Health Officer
Public Health Officer
Public health officers protect and improve the health of communities. They develop and implement policies and programs to prevent disease and promote healthy behaviors. Logistic regression is used in public health to identify risk factors for disease and to evaluate the effectiveness of public health interventions.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design, develop, and maintain machine learning models. Logistic regression is a foundational machine learning algorithm that is widely used in healthcare applications, such as disease prediction and personalized medicine.
Quality Improvement Specialist
Quality Improvement Specialist
Quality improvement specialists work to improve the quality of healthcare services. They use various tools and techniques, including logistic regression, to identify areas for improvement and to track progress.
Physician
Physician
Physicians diagnose and treat diseases and injuries. They also provide preventive care and counseling to patients. Logistic regression is used in medicine to predict the likelihood of developing certain diseases and to identify risk factors for poor health outcomes.
Health Economist
Health Economist
Health economists use economic principles to evaluate the cost and effectiveness of health care interventions. Logistic regression is commonly used in health economics to predict the likelihood of health events and to estimate the impact of health policies.
Pharmacist
Pharmacist
Pharmacists dispense medications and provide drug information to patients. They also monitor patient progress and make recommendations to other healthcare professionals. Logistic regression is used in pharmacy to predict the likelihood of adverse drug reactions and to optimize medication regimens.
Regulatory Affairs Specialist
Regulatory Affairs Specialist
Regulatory Affairs Specialists ensure that medical products are safe and effective before they are marketed. They also work to ensure that these products comply with all applicable laws and regulations. Logistic regression is used in regulatory affairs to evaluate the safety and efficacy of medical products.
Toxicologist
Toxicologist
Toxicologists study the effects of toxic substances on humans and the environment. They use a variety of methods, including logistic regression, to assess the risks associated with exposure to toxic substances.
Medical Writer
Medical Writer
Medical writers create content that informs and educates healthcare professionals and patients about medical products, treatments, and research findings. Understanding logistic regression can help medical writers accurately interpret and communicate research results related to binary health outcomes.
For more career information including salaries, visit:
OpenCourser.com/course/cj95nz/logistic
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
Logistic Regression and Prediction for Health Data.
This renowned book provides a comprehensive introduction to Bayesian data analysis, including applications in health research, offering an alternative perspective on statistical modeling.
This comprehensive book offers a detailed treatment of logistic regression models, providing theoretical insights, practical guidance, and real-world examples, making it a valuable resource for advanced learners.
Provides an advanced treatment of regression analysis, including multilevel and hierarchical models, offering insights into complex data structures.
Provides a readable and concise introduction to logistic regression, covering both the theoretical underpinnings and practical applications, making it an excellent resource for students and practitioners.
Provides an in-depth examination of regression models for categorical dependent variables, including logistic regression, offering a theoretical and empirical perspective.
This classic book provides a comprehensive treatment of multiple regression analysis, including logistic regression as a special case.
This comprehensive book covers a wide range of regression methods, including logistic regression, offering a mathematical and statistical approach, suitable for advanced learners.
Focuses on the use of dummy variables in regression analysis, explaining how to code and interpret categorical variables, essential for analyzing data with binary outcomes.
Provides a practical guide to performing logistic regression using SAS, offering step-by-step instructions and examples specific to SAS software.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cj95nz/logistic
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Effort
3 weeks
Level
Intermediate
Via
Coursera
Institution
University of Michigan
Instructors
Philip S. Boonstra
Bhramar Mukherjee
Language
English
This course belongs to a
collection
called
Data Science for Health Research. It's comprised of three courses:
- Linear Regression Modeling for Health Data
- Logistic Regression and Prediction for Health Data (viewing)
- Arranging and Visualizing Data in R
Good to know
Targeted at those new to analysis of binary outcomes
Taught by Philip S. Boonstra and Bhramar Mukherjee, both experienced in this subject matter
Covers a range of concepts from simple comparisons of binary outcomes to logistic regression
Includes hands-on exercises and activities to reinforce learning
Part of a series of courses on statistical inference and prediction in R
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