Imperial College London
Linear Regression in R for Public Health
Imperial College London

Linear Regression in R for Public Health

This course is part of Statistical Analysis with R for Public Health Specialization

Taught in English

Some content may not be translated

Alex Bottle
Victoria Cornelius

Instructors: Alex Bottle

14,657 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(493 reviews)

|

97%

Intermediate level

Recommended experience

14 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

What you'll learn

  • Describe when a linear regression model is appropriate to use

  • Read in and check a data set's variables using the software R prior to undertaking a model analysis

  • Fit a multiple linear regression model with interactions, check model assumptions and interpret the output

Details to know

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Assessments

11 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(493 reviews)

|

97%

Intermediate level

Recommended experience

14 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

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This course is part of the Statistical Analysis with R for Public Health Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

Before jumping ahead to run a regression model, you need to understand a related concept: correlation. This week you’ll learn what it means and how to generate Pearson’s and Spearman’s correlation coefficients in R to assess the strength of the association between a risk factor or predictor and the patient outcome. Then you’ll be introduced to linear regression and the concept of model assumptions, a key idea underpinning so much of statistical analysis.

What's included

7 videos9 readings5 quizzes2 discussion prompts1 plugin

You’ll be introduced to the COPD data set that you’ll use throughout the course and will run basic descriptive analyses. You’ll also practise running correlations in R. Next, you’ll see how to run a linear regression model, firstly with one and then with several predictors, and examine whether model assumptions hold.

What's included

3 videos8 readings2 quizzes3 discussion prompts

Now you’ll see how to extend the linear regression model to include binary and categorical variables as predictors and learn how to check the correlation between predictors. Then you’ll see how predictors can interact with each other and how to incorporate the necessary interaction terms into the model and interpret them. Different kinds of interactions exist and can be challenging to interpret, so we will take it slowly with worked examples and opportunities to practise.

What's included

4 videos9 readings2 quizzes

The last part of the course looks at how to build a regression model when you have a choice of what predictors to include in it. It describes commonly used automated procedures for model building and shows you why they are so problematic. Lastly, you’ll have the chance to fit some models using a more defensible and robust approach.

What's included

5 videos7 readings2 quizzes2 discussion prompts1 plugin

Instructors

Instructor ratings
4.9 (90 ratings)
Alex Bottle
Imperial College London
6 Courses62,726 learners

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4.8

493 reviews

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AO
5

Reviewed on Sep 11, 2023

RR
5

Reviewed on Dec 8, 2020

JN
4

Reviewed on Jun 7, 2020

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