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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/regression-modeling-practice
课程评论：没有评论
课程名称：实践中的回归建模 课程概述： 本课程重点介绍数据分析中最重要的工具之一：回归分析。您将使用SAS或Python，首先学习线性回归，然后了解在两个变量之间没有明显线性关系时如何进行调整。您将研究多种预测变量，并能够识别混杂变量，以更好地讲述有关结果的故事。课程内容包括回归分析的基础假设、如何解释回归系数，以及如何使用回归诊断图和其他工具来评估回归模型的质量。课程期间，您将与其他人分享自己开发的回归模型及其背后的故事。 课程大纲： 第一部分：回归介绍 本部分提供有关主要数据类型的概念背景，帮助您选择最合适的统计分析方法，并理解数据集的局限性。同时介绍混杂变量的概念，您将获得描述数据的经验，包括关于样本、研究数据收集程序和数据管理步骤的书写。 第二部分：线性回归基础 在这一部分，我们将讨论检验混杂的必要性，并通过实例说明混杂变量如何影响解释变量与响应变量之间的关联。您将使用基础线性回归分析测试和解释数值响应变量的关联，学习如何利用线性回归模型预测观察到的响应变量，并了解线性回归模型的统计假设及最佳编码实践。 第三部分：多重回归 多重回归分析是一个扩展研究问题的工具，通过添加多个定量和/或分类的解释变量，进行更严谨的关联测试。您将应用并解释定量响应变量的多重回归分析，学习如何使用置信区间考虑 estimations 的误差，同时学会如何在回归模型中处理非线性关联，并使用回归诊断技术评估多重回归模型的预测能力。 第四部分：逻辑回归 本部分将讨论在未来继续进行数据分析时的一些重要事项，并教授如何在多重回归分析中测试具有多个类别的分类解释变量。您将接触到二元响应变量的逻辑回归分析，该模型是多重回归模型的另一种形式，用于处理二元响应变量。您将获得测试和解释逻辑回归模型的经验，包括使用赔率比和置信区间来确定解释变量与响应变量之间关联的强度。
Part: 1
Title:Introduction to Regression
Description:This session starts where the Data Analysis Tools course left off. This first set of videos provides you with some conceptual background about the major types of data you may work with, which will increase your competence in choosing the statistical analysis that’s most appropriate given the structure of your data, and in understanding the limitations of your data set. We also introduce you to the concept of confounding variables, which are variables that may be the reason for the association between your explanatory and response variable. Finally, you will gain experience in describing your data by writing about your sample, the study data collection procedures, and your measures and data management steps.
Part: 2
Title:Basics of Linear Regression
Description:In this session, we discuss more about the importance of testing for confounding, and provide examples of situations in which a confounding variable can explain the association between an explanatory and response variable. In addition, now that you have statistically tested the association between an explanatory variable and your response variable, you will test and interpret this association using basic linear regression analysis for a quantitative response variable. You will also learn about how the linear regression model can be used to predict your observed response variable. Finally, we will also discuss the statistical assumptions underlying the linear regression model, and show you some best practices for coding your explanatory variables
Part: 3
Title:Multiple Regression
Description:Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model. In this session, you will apply and interpret a multiple regression analysis for a quantitative response variable, and will learn how to use confidence intervals to take into account error in estimating a population parameter. You will also learn how to account for nonlinear associations in a linear regression model. Finally, you will develop experience using regression diagnostic techniques to evaluate how well your multiple regression model predicts your observed response variable.
Part: 4
Title:Logistic Regression
Description:In this session, we will discuss some things that you should keep in mind as you continue to use data analysis in the future. We will also teach also you how to test a categorical explanatory variable with more than two categories in a multiple regression analysis. Finally, we introduce you to logistic regression analysis for a binary response variable with multiple explanatory variables. Logistic regression is simply another form of the linear regression model, so the basic idea is the same as a multiple regression analysis. But, unlike the multiple regression model, the logistic regression model is designed to test binary response variables. You will gain experience testing and interpreting a logistic regression model, including using odds ratios and confidence intervals to determine the magnitude of the association between your explanatory variables and response variable.
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
