面向公共卫生的R语言逻辑回归

Logistic Regression in R for Public Health

1190 次查看
伦敦帝国学院
Coursera
  • 完成时间大约为 11 个小时
  • 中级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

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

课程概况

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!

课程大纲

Introduction to Logistic Regression

Welcome 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!

Logistic Regression in R

In 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!

Running Multiple Logistic Regression in R

Now 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!

Assessing Model Fit

Welcome 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!

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