About this Course

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Flexible deadlines
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Shareable Certificate
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100% online
Start instantly and learn at your own schedule.
Intermediate Level

We advise that you first take the previous courses in the series, particularly Introduction to Statistics, though this is not essential.

Approx. 11 hours to complete
English

What you will learn

  • Run Kaplan-Meier plots and Cox regression in R and interpret the output

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

  • Describe and compare some common ways to choose a multiple regression model

Skills you will gain

Understand common ways to choose what predictors go into a regression modelRun and interpret Kaplan-Meier curves in RConstruct a Cox regression model in R
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level

We advise that you first take the previous courses in the series, particularly Introduction to Statistics, though this is not essential.

Approx. 11 hours to complete
English

Offered by

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Imperial College London

Start working towards your Master's degree

This course is part of the 100% online Global Master of Public Health from Imperial College London. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

Week
1

Week 1

4 hours to complete

The Kaplan-Meier Plot

4 hours to complete
4 videos (Total 16 min), 11 readings, 3 quizzes
Week
2

Week 2

2 hours to complete

The Cox Model

2 hours to complete
3 videos (Total 18 min), 4 readings, 2 quizzes
Week
3

Week 3

2 hours to complete

The Multiple Cox Model

2 hours to complete
1 video (Total 6 min), 7 readings, 1 quiz
Week
4

Week 4

3 hours to complete

The Proportionality Assumption

3 hours to complete
3 videos (Total 11 min), 7 readings, 3 quizzes

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About the Statistical Analysis with R for Public Health Specialization

Statistical Analysis with R for Public Health

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