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

Logistic Regression in R for Public Health

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

Taught in English

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Alex Bottle

Instructor: Alex Bottle

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Course

Gain insight into a topic and learn the fundamentals

4.8

(350 reviews)

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92%

Intermediate level

Recommended experience

12 hours (approximately)
Flexible schedule
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Prepare for a degree

What you'll learn

  • Describe a data set from scratch using descriptive statistics and simple graphical methods as a first step for advanced analysis using R software

  • Interpret the output from your analysis and appraise the role of chance and bias as potential explanations

  • Run multiple logistic regression analysis in R and interpret the output

  • Evaluate the model assumptions for multiple logistic regression in R

Details to know

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Assessments

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(350 reviews)

|

92%

Intermediate level

Recommended experience

12 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
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There are 4 modules in this course

Welcome to Statistics for Public Health: Logistic Regression for Public Health! In this week, you will be introduced to logistic regression and its uses in public health. We will focus on why linear regression does not work with binary outcomes and on odds and odds ratios, and you will finish the week by practising your new skills. By the end of this week, you will be able to explain when it is valid to use logistic regression, and define odds and odds ratios. Good luck!

What's included

3 videos7 readings2 quizzes2 discussion prompts1 plugin

In this week, you will learn how to prepare data for logistic regression, how to describe data in R, how to run a simple logistic regression model in R, and how to interpret the output. You will also have the opportunity to practise your new skills. By the end of this week, you will be able to run simple logistic regression analysis in R and interpret the output. Good luck!

What's included

2 videos4 readings2 quizzes1 discussion prompt

Now that you're happy with including one predictor in the model, this week you'll learn how to run multiple logistic regression, including describing and preparing your data and running new logistic regression models. You will have the opportunity to practise your new skills. By the end of the week, you will be able to run multiple logistic regression analysis in R and interpret the output. Good luck!

What's included

1 video6 readings1 quiz2 discussion prompts

Welcome to the final week of the course! In this week, you will learn how to assess model fit and model performance, how to avoid the problem of overfitting, and how to choose what variables from your data set should go into your multiple regression model. You will put all the skills you have learned throughout the course into practice. By the end of this week, you will be able to evaluate the model assumptions for multiple logistic regression in R, and describe and compare some common ways to choose a multiple regression model. Good luck!

What's included

3 videos10 readings3 quizzes1 discussion prompt1 plugin

Instructor

Instructor ratings
4.8 (63 ratings)
Alex Bottle
Imperial College London
6 Courses63,974 learners

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Recommended if you're interested in Public Health

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