This course provides learners with 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 learners to the core ideas of traditional statistical inference and reasoning. Learners will get their first look at the ever-popular t-test and delve further into linear regression. They will also learn how to fit and interpret regression models for a continuous outcome with multiple predictors. 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.
Linear Regression Modeling for Health Data
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Linear Regression Modeling for Health Data
This course is part of Data Science for Health Research Specialization
Instructors: Philip S. Boonstra
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
13 hours to complete
3 weeks at 4 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
Skills you'll gain
Details to know
Shareable certificate
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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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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
Offered by
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