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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/quantifying-relationships-regression-models
课程评论：没有评论
课程名称：利用回归模型量化关系 课程概述：本课程将向您介绍线性回归模型，这是一种强大的工具，研究人员可以用它来测量多个变量之间的关系。我们将首先探索双变量回归模型的组成部分，该模型评估自变量与因变量之间的关系。在此基础上，我们将讨论如何创建和解释多变量模型、二元因变量模型和交互模型。我们还将考虑如何在模型中恰当地纳入分类变量和虚拟变量等不同类型的变量。总体来说，我们还将讨论回归模型在描述性与因果推断方面的多种用途以及这一分析工具的局限性。到课程结束时，您应该能够解读和批判性地评估多变量回归分析。 课程大纲： 1. 回归模型：是什么，以及我们为何需要它们 描述：图表虽然对可视化关系有用，但不能提供变量之间关系的精确度量。本模块将介绍相关性作为衡量两个变量之间关系的初始手段，并讨论预测误差作为评估估计准确性的框架。最终，模块将介绍线性回归模型，这是一种我们可以用来精确测量变量之间关系的强大工具。 2. 拟合和评估双变量回归模型 描述：在掌握回归分析的基础知识后，接下来的步骤是考虑如何评估和修改基本的回归模型。本模块将介绍模型拟合的常见衡量标准和回归分析的三大核心假设，并探索使用二元（即虚拟）处理变量进行回归分析的特殊情况。 3. 多变量回归模型 描述：双变量回归模型是统计学的重要组成部分，但在实际应用中通常不足以作为描述性、因果或预测推断的有用模型。本模块将介绍多变量回归分析，并解释解读和评估多变量分析结果的适当方法。 4. 多变量模型的扩展 描述：一旦您掌握了普通最小二乘法（OLS）多变量模型，您就可以学习多种回归建模技术。本模块将重点讨论交互项和二元因变量模型这两种工具。了解不同的回归建模工具将使您在研究问题时能够选择最适合的工具。 通过本课程的学习，您将能够从基础开始，逐步深入理解回归分析，并在未来的分析工作中自信地运用所学知识。
Name:Regression Models: What They Are and Why We Need Them
Description:While graphs are useful for visualizing relationships, they don't provide precise measures of the relationships between variables. Suppose you want to determine how an outcome of interest is expected to change if we change a related variable. We need more than just a scatter plot to answer this question. What should you do, for example, if you want to calculate whether air quality changes when vehicle emissions decline? Or if you want to calculate how consumer purchasing behavior changes if a new tax policy is implemented? To calculate these predicted effects, we can use a regression model. This module will first introduce correlation as an initial means of measuring the relationship between two variables. The module will then discuss prediction error as a framework for evaluating the accuracy of estimates. Finally, the module will introduce the linear regression model, which is a powerful tool we can use to develop precise measures of how variables are related to each other.
Name:Fitting and Evaluating a Bivariate Regression Model
Description:Now that you've got a handle on the basics of regression analysis, the next step is to consider how to evaluate and modify a basic regression model. This module will introduce you to a common measure of model fit and the three core assumptions of regression analysis. In addition, we'll explore the special circumstance of conducting a regression analysis with a binary (AKA dummy) treatment variable. Dummy variables, which take on two values, are frequently used in statistics. Understanding how to use and interpret dummy variables provides a foundation for developing a multivariate regression model, which we'll get to in the next module.
Name:Multivariate Regression Models
Description:The bivariate regression model is an essential building block of statistics, but it is usually insufficient in practice as a useful model for descriptive, causal or predictive inference. This is because there are usually multiple variables that impact a particular dynamic. Whether you are modeling political behavior, environmental processes or drug treatment outcomes, it is almost always necessary to account for multiple influences on an outcome of interest. This module will introduce the multivariate model of regression analysis and explain the appropriate ways to interpret and evaluate the results from a multivariate analysis.
Name:Extensions of the Multivariate Model
Description:Once you've mastered the OLS multivariate model, you're ready to learn about a wide array of regression modeling techniques. Remember, researchers should always employ modeling tools that best enable them to answer the question at hand. This module will focus on two tools in particular, interaction terms and models for binary dependent variables. Keep in mind, however, that there are numerous regression modeling tools that you can learn and implement based on the research question you're trying to answer. After you've developed a solid understanding of regression basics, you should feel capable of expanding this knowledge base as you move forward as a producer and consumer of analytics.
This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables. We’ll begin by exploring the components of a bivariate regression model, which estimates the relationship between an independent and dependent variable. Building on this foundation, we’ll then discuss how to create and interpret a multivariate model, binary dependent variable model and interactive model. We’ll also consider how different types of variables, such as categorical and dummy variables, can be appropriately incorporated into a model. Overall, we’ll discuss some of the many different ways a regression model can be used for both descriptive and causal inference, as well as the limitations of this analytical tool. By the end of the course, you should be able to interpret and critically evaluate a multivariate regression analysis.
