About this Course

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Learner Career Outcomes

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got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Flexible deadlines
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Intermediate Level

You should know the basics of types of variables, distributions, hypothesis testing, p values and confidence intervals using R, though I'll recap.

Approx. 15 hours to complete
English
Subtitles: English

What you will 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

Skills you will gain

Correlation And DependenceLinear RegressionR Programming

Learner Career Outcomes

50%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

You should know the basics of types of variables, distributions, hypothesis testing, p values and confidence intervals using R, though I'll recap.

Approx. 15 hours to complete
English
Subtitles: English

Offered by

Imperial College London logo

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

5 hours to complete

INTRODUCTION TO LINEAR REGRESSION

5 hours to complete
7 videos (Total 34 min), 9 readings, 5 quizzes
7 videos
Pearson’s Correlation Part I3m
Pearson’s Correlation Part II6m
Intro to Linear Regression: Part I4m
Intro to Linear Regression: Part II3m
Linear Regression and Model Assumptions: Part I6m
Linear Regression and Model Assumptions: Part II5m
9 readings
About Imperial College London & the Team10m
How to be successful in this course10m
Grading policy10m
Data set and Glossary10m
Additional Reading10m
Linear Regression Models: Behind the Headlines5m
Linear Regression Models: Behind the Headlines: Written Summary20m
Warnings and precautions for Pearson's correlation20m
Introduction to Spearman correlation15m
5 practice exercises
Linear Regression Models: Behind the Headlines10m
Correlations30m
Spearman Correlation20m
Practice Quiz on Linear Regression20m
End of Week Quiz20m
Week
2

Week 2

4 hours to complete

Linear Regression in R

4 hours to complete
3 videos (Total 11 min), 8 readings, 2 quizzes
3 videos
Fitting the linear regression3m
Multiple Regression4m
8 readings
Recap on installing R10m
Assessing distributions and calculating the correlation coefficient in R 10m
Feedback10m
How to fit a regression model in R10m
Feedback15m
Fitting the Multiple Regression in R30m
Feedback10m
Summarising correlation and linear regression30m
2 practice exercises
Linear Regression20m
End of Week Quiz20m
Week
3

Week 3

4 hours to complete

Multiple Regression and Interaction

4 hours to complete
4 videos (Total 17 min), 9 readings, 2 quizzes
4 videos
Introduction to Key Dataset Features: Part II2m
Interactions between binary variables4m
Interactions between binary and continuous variables5m
9 readings
How to assess key features of a dataset in R20m
How to check your data in R10m
Good Practice Steps20m
Practice with R: Run a Good Practice Analysis30m
Practice with R: Run Multiple Regression30m
Feedback10m
Practice with R: Running and interpreting a multiple regression30m
Feedback15m
Additional Reading10m
2 practice exercises
Fitting and interpreting model results20m
Interpretation of interactions20m
Week
4

Week 4

3 hours to complete

MODEL BUILDING

3 hours to complete
5 videos (Total 16 min), 7 readings, 2 quizzes
5 videos
Variable Selection3m
Developing a Model Building Strategy6m
Summary of developing a Model Building Strategy56s
Summary of Course1m
7 readings
Feedback10m
Further details of limitations of stepwise10m
How many predictors can I include?10m
Practice with R: Developing your model
Practice with R: Fitting the final model10m
Feedback on developing the model10m
Final R Code20m
2 practice exercises
Problems with automated approaches20m
End of Course Quiz20m

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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....
Statistical Analysis with R for Public Health

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