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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/linear-regression-r-public-health
课程评论：没有评论
课程名称：公共卫生中的线性回归R语言应用 课程概述：欢迎来到公共卫生中的线性回归R语言应用课程！公共卫生被定义为“通过社会的组织努力来预防疾病、延长生命和促进健康的艺术和科学”。了解疾病的成因及其加重因素是至关重要的，这需要开发统计模型来描述患者和环境因素如何影响我们生病的几率。本课程将教你如何从零开始创建这样的模型，从介绍相关性和线性回归的概念入手，逐步引导你导入和检查数据，然后展示如何拟合模型。通过呼吸系统疾病的案例，这些模型将描述患者及其他因素如何影响肺功能等结果。 线性回归属于一系列回归模型中的一种，本系列课程还将覆盖其他两种模型。回归模型有许多相似之处，尽管数学细节有所不同。本课程将向你展示如何准备数据、评估模型的拟合优度以及检验模型的基本假设，这些都是任何回归分析中至关重要的任务。你将使用自由且多功能的软件包R，广泛应用于学术界、政府和工业界的统计学家和数据科学家。 课程大纲： 1. 引言到线性回归 - 本周你将了解相关性这一概念，并学习如何在R中生成皮尔逊和斯皮尔曼的相关系数，以评估风险因素或预测因子与患者结果之间的关联强度。接着，你将被介绍到线性回归和模型假设的概念，这些都是统计分析的关键思想。 2. R中的线性回归 - 你将被介绍到将贯穿整个课程的COPD数据集，并进行基本描述性分析。并练习在R中运行相关分析。接下来，你将学习如何运行线性回归模型，首先使用一个预测因子，然后用多个预测因子，检查模型假设是否成立。 3. 多重回归与交互作用 - 本部分你将看到如何扩展线性回归模型，包含二元和分类变量作为预测因子，并学习如何检查预测因子之间的相关性。随后，你会了解预测因子之间如何相互作用，以及如何将必要的交互作用项纳入模型并进行解释。由于不同类型的交互作用的解释可能比较复杂，我们会通过实例和练习来慢慢讲解。 4. 模型构建 - 课程的最后部分将探讨当你可以选择包含哪些预测因子时，如何建立回归模型。你将了解常用的自动化模型构建程序及其潜在问题。最后，你将有机会使用更为稳健和可靠的方法来拟合模型。
Name:INTRODUCTION TO LINEAR REGRESSION
Description:Before 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.
Name:Linear Regression in R
Description:You’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.
Name:Multiple Regression and Interaction
Description:Now 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.
Name:MODEL BUILDING
Description:The 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.
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.
