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#### Shareable Certificate

Earn a Certificate upon completion

#### 100% online

Start instantly and learn at your own schedule.

#### Intermediate Level

You'll need to have taken the Statistical Thinking and Linear Regression courses in this series or have equivalent knowledge.

#### English

Subtitles: English

### What you will 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

### Skills you will gain

Logistic RegressionR Programming

#### Shareable Certificate

Earn a Certificate upon completion

#### 100% online

Start instantly and learn at your own schedule.

#### Intermediate Level

You'll need to have taken the Statistical Thinking and Linear Regression courses in this series or have equivalent knowledge.

#### English

Subtitles: English

## 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.

Week
1

## Week 1

2 hours to complete

## Introduction to Logistic Regression

2 hours to complete
3 videos (Total 12 min), 7 readings, 2 quizzes
3 videos
Introduction to Logistic Regression5m
Odds and Odds Ratios3m
About Imperial College & the team5m
How to be successful in this course5m
Data set and Glossary10m
Why does linear regression not work with binary outcomes?10m
Odds Ratios and Examples from the Literature10m
2 practice exercises
Logistic Regression10m
End of Week Quiz10m
Week
2

## Week 2

3 hours to complete

## Logistic Regression in R

3 hours to complete
2 videos (Total 11 min), 4 readings, 2 quizzes
2 videos
Logistic Regression in R5m
How to Describe Data in R20m
Results of Cross Tabulation20m
Practice in R: Simple Logistic Regression15m
Feedback - Output and Interpretation from Simple Logistic Regression35m
2 practice exercises
Cross Tabulation30m
Interpreting Simple Logistic Regression30m
Week
3

## Week 3

3 hours to complete

## Running Multiple Logistic Regression in R

3 hours to complete
1 video (Total 4 min), 6 readings, 1 quiz
1 video
Describing your Data and Preparing to Run Multiple Logistic Regression35m
Practice in R: Describing Variables20m
Feedback20m
Practice in R: Running Multiple Logistic Regression15m
Feedback: Multiple Regression Model
Feedback on the Assessment10m
1 practice exercise
Running A New Logistic Regression Model30m
Week
4

## Week 4

5 hours to complete

## Assessing Model Fit

5 hours to complete
3 videos (Total 17 min), 10 readings, 3 quizzes
3 videos
Overfitting and Non-convergence6m
Summary of the Course3m
Model Fit in Logistic Regression10m
How to Interpret Model Fit and Performance Information in R10m
Summary of Different Ways to Run Multiple Regression10m
Practice in R: Applying Backwards Elimination30m
Feedback: Backwards Elimination20m
Practice in R: Run a Model with Different Predictors30m
Feedback on the New Model10m
Further Reading on Model Selection Methods20m
R Code for the Whole Module20m
3 practice exercises
Quiz on R’s Default Output for the Model30m
Overfitting and Model Selection20m
End of Course Quiz

## About the Statistical Analysis with R for Public Health Specialization

Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health. In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around. This specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019. The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data....