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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/logistic-regression-r-public-health
课程评论：没有评论
课程名称：《R语言中的逻辑回归与公共卫生》 课程概述： 欢迎参加《R语言中的逻辑回归与公共卫生》课程！本课程将强调在公共卫生领域中逻辑回归的重要性，特别是在处理复杂的公共卫生数据时。公共卫生数据通常较为凌乱，因此需要特别关注。课程将通过实际案例（例如根据患者特征预测糖尿病）来帮助您掌握R语言的应用。 课程内容： 1. **逻辑回归简介**：学习逻辑回归在公共卫生中的应用，了解为何线性回归不适用于二元结果。 2. **在R中进行逻辑回归**：掌握数据准备、描述和运行简单逻辑回归模型的技能，能够解释结果输出。 3. **在R中运行多元逻辑回归**：学习如何运行多元逻辑回归，如何描述和准备数据，并进行新模型的实践。 4. **评估模型拟合度**：了解如何评估模型的拟合度及其性能，避免过拟合问题，并选择合适的变量输入多元回归模型。 预期成果： 完成本课程后，您将能够： - 解释逻辑回归的有效使用场景 - 定义赔率和赔率比 - 在R中进行简单和多元逻辑回归分析并解释输出 - 评估在R中多元逻辑回归模型的假设 - 描述和比较选择多元回归模型的常用方法 建议学员在参加本课程前，复习《公共卫生统计思维》和《公共卫生线性回归》中的相关内容，以便更好地掌握课程内容。我们希望您享受这门课程！
Name:Introduction to Logistic Regression
Description: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!
Name:Logistic Regression in R
Description: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!
Name:Running Multiple Logistic Regression in R
Description: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!
Name:Assessing Model Fit
Description: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!
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!
