Regression Models in Healthcare from edX

In this course, you will begin learning about more advanced multivariate statistical methods that are regularly used in healthcare data analysis. You will also practice applying these statistical methods to examples from the healthcare industry. The topics covered in this course will prepare you for interpreting data and making data-informed decisions in real-world healthcare settings. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.

This self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).

The course is comprised of 4 modules that you should complete in order, as each subsequent module builds on the previous one.

Module 1: Non-Linear Trends
Module 2: Interacting Variables and Finding Outliers
Module 3: Logistic Regression
Module 4: Logistic Regression Variants

What's inside

Learning objectives

By the end of this course, you will be able to:
Use nonlinear regressions with quadratic and logarithmic dependent and independent variables.
Use interactions between variables in regression models and interpret the results.
Find potentially problematic data points in a regression model.

Implement logistic regression models and interpret their results.
Perform diagnostic tests for logistic regression models to determine their validity.
Use ordinal, multinomial, and poisson logistic regression models and interpret their results.

By the end of this course, you will be able to:
Use nonlinear regressions with quadratic and logarithmic dependent and independent variables.
Use interactions between variables in regression models and interpret the results.
Find potentially problematic data points in a regression model.
Implement logistic regression models and interpret their results.
Perform diagnostic tests for logistic regression models to determine their validity.
Use ordinal, multinomial, and poisson logistic regression models and interpret their results.

Syllabus

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what should give you pause

and possible dealbreakers

Offers advanced knowledge and skills in multivariate statistical methods

Focuses on application and usage of healthcare-related statistical methods

Provides practice opportunities for applying these methods to healthcare examples

Prepares learners to interpret data and make informed decisions in real-world healthcare settings

Covers 4 specific modules to teach non-linear trends, interacting variables, logistic regression, and logistic regression variants

Requires learners to take modules in order as they build on one another

Reviews summary

Regression models: healthcare application & theory

According to learners, this course offers a largely positive experience, particularly for those looking to apply advanced regression models in healthcare. Students frequently highlight the course's ability to provide a strong balance between mathematical underpinning and practical application. Many find the structured modules and clear explanations effective for grasping complex statistical concepts. While the course is highly valued for its direct relevance to healthcare data analysis, some students note that a foundational understanding of statistics is highly recommended for optimal engagement.

Quizzes and final assessment reinforce learning effectively.

"The module quizzes really helped reinforce my understanding of each topic and identify areas for review."

"The summative assessment covered all learning objectives thoroughly, ensuring I had a comprehensive grasp of the material."

"I liked that the assessments tested both conceptual understanding and practical application of the models."

Content is well-organized and builds progressively.

"The 4 modules build logically on each other, making complex topics easier to grasp and retain."

"I found the video content and step-by-step follow-along activities very helpful for understanding and practicing."

"The written content was clear and complemented the video lectures well, supporting my learning journey."

Good blend of mathematical theory and practical implementation.

"I appreciated the discussion of mathematical underpinning alongside practical application, providing a holistic view."

"It strikes a good balance between understanding the formulae and knowing how to apply them effectively in various scenarios."

"The course explains necessary assumptions well, which is crucial for correct application of statistical methods."

Directly applicable methods for healthcare data analysis.

"I found the statistical methods directly applicable to real-world healthcare datasets, which was incredibly valuable."

"The examples provided helped me understand how to interpret data and make data-informed decisions in my professional setting."

"This course truly taught me to apply regression models to solve challenges specific to the healthcare industry."

Assumes prior statistical understanding for optimal learning.

"While well-explained, I feel a solid background in basic statistics and regression is essential to keep up with the pace."

"Some parts were quite challenging, especially if you're not already comfortable with foundational statistical concepts."

"I would recommend reviewing basic probability and regression analysis before starting this course to maximize your learning."

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 Regression Models in Healthcare with these activities:

Review foundational concepts in statistics prior to starting the course

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Refreshing your knowledge of foundational statistics will provide a strong foundation for success in this course.

Browse courses on Statistics

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Review basic concepts of probability and statistics.
Practice solving basic statistics problems.
Identify any areas where you need additional support.

Identify a mentor or expert in the field of healthcare data analysis

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Having a mentor can provide you with valuable guidance and support throughout your learning journey.

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Identify potential mentors who have experience in healthcare data analysis.
Reach out to mentors and express your interest in learning from them.
Establish regular communication and seek guidance on your progress.

Organize and review course materials

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By organizing and reviewing course materials regularly, you can improve your retention and understanding of the course content.

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Create a system for organizing your notes, assignments, and other course materials.
Review your course materials on a regular basis.
Identify any areas where you need further clarification or support.

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Practice problems related to nonlinear regressions with quadratic and logarithmic dependent and independent variables

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Practice problems will reinforce your understanding of the concepts covered in Module 1.

Browse courses on Multivariate Statistics

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Identify the type of nonlinear regression model (quadratic or logarithmic) that is appropriate for the given data.
Estimate the parameters of the nonlinear regression model using statistical software.
Interpret the results of the nonlinear regression analysis.

Discuss the use of interactions between variables in regression models

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Discussing with peers will help you clarify your understanding of Module 2 concepts and identify potential challenges.

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Identify the variables that might interact with each other in the given regression model.
Create interaction terms between the variables.
Interpret the results of the regression analysis with interaction terms.

Explore online tutorials on logistic regression variants

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Online tutorials will provide additional resources to enhance your understanding of the different types of logistic regression models covered in Module 4.

Browse courses on Multivariate Statistics

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Search for online tutorials on ordinal logistic regression.
Review the concepts of ordinal logistic regression.
Apply ordinal logistic regression to a real-world dataset.
Repeat the same steps for multinomial and Poisson logistic regression.

Create a presentation on logistic regression models

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Creating a presentation will help you synthesize the information covered in Module 3 and develop strong communication skills.

Browse courses on Logistic Regression

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Review the concepts of logistic regression.
Identify the steps involved in building a logistic regression model.
Apply logistic regression to a real-world dataset.
Create presentation slides that effectively communicate the key concepts.
Deliver the presentation to an audience.

Contribute to open-source projects related to healthcare data analysis

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Contributing to open-source projects can provide you with practical experience and help you build your portfolio.

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Identify open-source projects related to healthcare data analysis.
Review the project documentation and identify areas where you can contribute.
Make meaningful contributions to the project.
Collaborate with other contributors and learn from their experiences.

Career center

Learners who complete Regression Models in Healthcare will develop knowledge and skills that may be useful to these careers:

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Regression Models in Healthcare

What's inside

Learning objectives

Syllabus

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Reviews summary

Regression models: healthcare application & theory

Activities

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