Este curso faz parte do Programa de cursos integrados Statistical Analysis with R for Public Health

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Programa de cursos integrados Statistical Analysis with R for Public Health

Imperial College London

Informações sobre o curso

Welcome to Linear Regression in R for Public Health!
Public Health has been defined as “the art and science of preventing disease, prolonging life and promoting health through the organized efforts of society”. Knowing what causes disease and what makes it worse are clearly vital parts of this. This requires the development of statistical models that describe how patient and environmental factors affect our chances of getting ill. This course will show you how to create such models from scratch, beginning with introducing you to the concept of correlation and linear regression before walking you through importing and examining your data, and then showing you how to fit models. Using the example of respiratory disease, these models will describe how patient and other factors affect outcomes such as lung function.
Linear regression is one of a family of regression models, and the other courses in this series will cover two further members. Regression models have many things in common with each other, though the mathematical details differ.
This course will show you how to prepare the data, assess how well the model fits the data, and test its underlying assumptions – vital tasks with any type of regression.
You will use the free and versatile software package R, used by statisticians and data scientists in academia, governments and industry worldwide.

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

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

Sugerido: 4 weeks of study 3-5 hours per week ...

Legendas: Inglês

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

Correlation And DependenceLinear RegressionR Programming

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

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

Sugerido: 4 weeks of study 3-5 hours per week ...

Legendas: Inglês

Semana

1Before 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....

7 vídeos (total de (Total 34 mín.) min), 9 leituras, 5 testes

Pearson’s Correlation Part I3min

Pearson’s Correlation Part II6min

Intro to Linear Regression: Part I4min

Intro to Linear Regression: Part II3min

Linear Regression and Model Assumptions: Part I6min

Linear Regression and Model Assumptions: Part II5min

About Imperial College London & the Team10min

How to be successful in this course10min

Grading policy10min

Data set and Glossary10min

Additional Reading10min

Reading: Linear Regression Models: Behind the Headlines5min

Linear Regression Models: Behind the Headlines: Written Summary20min

Warnings and precautions for Pearson's correlation20min

Introduction to Spearman correlation15min

Linear Regression Models: Behind the Headlines10min

Correlations30min

Spearman Correlation20min

Practice Quiz on Linear Regression20min

End of Week Quiz20min

Semana

2You’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....

3 vídeos (total de (Total 11 mín.) min), 8 leituras, 2 testes

Recap on installing R10min

Assessing distributions and calculating the correlation coefficient in R 10min

Feedback10min

How to fit a regression model in R10min

Feedback15min

Fitting the Multiple Regression in R30min

Feedback10min

Summarising correlation and linear regression30min

Linear Regression20min

End of Week Quiz20min

Semana

3Now 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....

4 vídeos (total de (Total 17 mín.) min), 9 leituras, 2 testes

Introduction to Key Dataset Features: Part II2min

Interactions between binary variables4min

Interactions between binary and continuous variables5min

How to assess key features of a dataset in R20min

How to check your data in R10min

Good Practice Steps20min

Practice with R: Run a Good Practice Analysis30min

Practice with R: Run Multiple Regression30min

Feedback10min

Practice with R: Running and interpreting a multiple regression30min

Feedback15min

Additional Reading10min

Fitting and interpreting model results20min

Interpretation of interactions20min

Semana

4The 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....

5 vídeos (total de (Total 16 mín.) min), 7 leituras, 2 testes

Variable Selection3min

Developing a Model Building Strategy6min

Summary of developing a Model Building Strategy56s

Summary of Course1min

Feedback10min

Further details of limitations of stepwise10min

How many predictors can I include?10min

Practice with R: Developing your model

Practice with R: Fitting the final model10min

Feedback on developing the model10min

Final R Code20min

Problems with automated approaches20min

End of Course Quiz20min

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges.
Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology....

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....

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