本課程是 Statistical Analysis with R for Public Health 專項課程 專項課程的一部分

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課程信息

Welcome to Logistic Regression in R for Public Health!
Why logistic regression for public health rather than just logistic regression? Well, there are some particular considerations for every data set, and public health data sets have particular features that need special attention. In a word, they're messy. Like the others in the series, this is a hands-on course, giving you plenty of practice with R on real-life, messy data, with predicting who has diabetes from a set of patient characteristics as the worked example for this course. Additionally, the interpretation of the outputs from the regression model can differ depending on the perspective that you take, and public health doesn’t just take the perspective of an individual patient but must also consider the population angle. That said, much of what is covered in this course is true for logistic regression when applied to any data set, so you will be able to apply the principles of this course to logistic regression more broadly too.
By the end of this course, you will be able to:
Explain when it is valid to use logistic regression
Define odds and odds ratios
Run simple and multiple logistic regression analysis in R and interpret the output
Evaluate the model assumptions for multiple logistic regression in R
Describe and compare some common ways to choose a multiple regression model
This course builds on skills such as hypothesis testing, p values, and how to use R, which are covered in the first two courses of the Statistics for Public Health specialisation. If you are unfamiliar with these skills, we suggest you review Statistical Thinking for Public Health and Linear Regression for Public Health before beginning this course. If you are already familiar with these skills, we are confident that you will enjoy furthering your knowledge and skills in Statistics for Public Health: Logistic Regression for Public Health.
We hope you enjoy the course!

立即開始，按照自己的計劃學習。

根據您的日程表重置截止日期。

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

建議：3-5 hours/week...

字幕：英語（English）

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

Logistic RegressionR Programming

立即開始，按照自己的計劃學習。

根據您的日程表重置截止日期。

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

建議：3-5 hours/week...

字幕：英語（English）

週

1Welcome 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!...

3 個視頻 （總計 12 分鐘）, 7 個閱讀材料, 2 個測驗

About Imperial College & the team5分鐘

How to be successful in this course5分鐘

Grading policy5分鐘

Data set and Glossary10分鐘

Additional Reading10分鐘

Why does linear regression not work with binary outcomes?10分鐘

Odds Ratios and Examples from the Literature10分鐘

Logistic Regression10分鐘

End of Week Quiz10分鐘

週

2In 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! ...

2 個視頻 （總計 11 分鐘）, 4 個閱讀材料, 2 個測驗

How to Describe Data in R20分鐘

Results of Cross-tabulation20分鐘

Practice in R: Simple Logistic Regression15分鐘

Feedback: Output and Interpretation from Simple Logistic Regression35分鐘

Cross Tabulation30分鐘

Interpreting Simple Logistic Regression30分鐘

週

3Now 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!...

1 個視頻 （總計 4 分鐘）, 6 個閱讀材料, 1 個測驗

Describing your Data and Preparing to Run Multiple Logistic Regression35分鐘

Practice in R: Describing Variables20分鐘

Feedback20分鐘

Practice in R: Running Multiple Logistic Regression15分鐘

Feedback: Multiple Regression Model10分鐘

Feedback on the Assessment10分鐘

Running A New Logistic Regression Model30分鐘

週

4Welcome 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! ...

3 個視頻 （總計 17 分鐘）, 10 個閱讀材料, 3 個測驗

Overfitting and Non-convergence6分鐘

Summary of the Course3分鐘

Model Fit in Logistic Regression10分鐘

How to Interpret Model Fit and Performance Information in R10分鐘

Further Reading on Model Fit20分鐘

Summary of Different Ways to Run Multiple Regression10分鐘

Practice in R: Applying Backwards Elimination30分鐘

Feedback: Backwards Elimination20分鐘

Practice in R: Run a Model with Different Predictors30分鐘

Feedback on the New Model10分鐘

Further Reading on Model Selection Methods20分鐘

R Code for the Whole Module20分鐘

Quiz on R’s Default Output for the Model30分鐘

Overfitting and Model Selection20分鐘

End of Course Quiz

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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