University of Michigan

Linear Regression Modeling for Health Data

University of Michigan

Linear Regression Modeling for Health Data

This course is part of Data Science for Health Research Specialization

Philip S. Boonstra
Bhramar Mukherjee

Instructors: Philip S. Boonstra

Access provided by Kalinga Institute of Industrial Technology

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Become knowledgeable about the concept of statistical modeling and the basics of statistical inference

  • Recognize, fit, and interpret a simple linear regression model

  • Develop intuition to fit and interpret a multiple regression model

Details to know

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Assessments

9 assignments

Taught in English

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This course is part of the Data Science for Health Research Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
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There are 3 modules in this course

This module gives you a first look at the world of statistical modeling. It begins with a high-level overview of different philosophies on the question of 'what is a statistical model' and introduces you to the core ideas of traditional statistical inference and reasoning. At the end of the module, you will have an introductory understanding of important terms such as 'sample-to-population' (STOP) principle, sampling variation, and measures of statistical uncertainty. You will also get your first look at the ever popular t-test.

What's included

11 videos7 readings3 assignments3 discussion prompts

This module takes you beyond t-test into linear regression. By the end of the module, you will understand how linear regression is a generalization of the t-test.

What's included

13 videos6 readings4 assignments2 discussion prompts

A key reason that linear regression is so powerful is that it allows to adjust for multiple predictors at the same time. In Module 3, you will learn how to fit regression models for multiple predictors. You will see how to interpret the resulting model and how to use it to answer different questions about your data.

What's included

8 videos3 readings2 assignments1 discussion prompt

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Instructors

Philip S. Boonstra
University of Michigan
4 Courses 4,152 learners

Offered by

University of Michigan

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