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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/anova-and-experimental-design
课程评论：没有评论
课程名称：方差分析与实验设计 课程概述：本课程是统计建模的第二门课程，将引导学生学习方差分析（ANOVA）、协方差分析（ANCOVA）及实验设计的相关内容。ANOVA和ANCOVA被视为一种线性回归模型，提供了为数据科学应用设计实验的数学基础。课程强调与设计相关的重要概念，如随机化、分块、因子设计和因果关系。同时，课程还将关注实验过程中出现的伦理问题。 该课程可作为科罗拉多大学博尔德分校（CU Boulder）数据科学硕士（MS-DS）学位的一部分，在Coursera平台上获得学术学分。MS-DS是一个跨学科的学位项目，汇集了来自应用数学、计算机科学、信息科学等多个部门的教师。该项目的入学采用基于绩效的评估，没有申请流程，适合具有计算机科学、信息科学、数学和统计学等广泛背景的个人。想了解更多关于MS-DS项目的信息，请访问https://www.coursera.org/degrees/master-of-science-data-science-boulder。 课程大纲： 1. 名称：方差分析与实验设计概述 描述：在本模块中，我们将介绍实验设计的基本概念框架，并定义模型，以回答有关组均值与连续变量之间差异的有意义问题。这些模型包括单因素方差分析（ANOVA）和协方差分析（ANCOVA）模型。 2. 名称：方差分析背景下的假设检验 描述：在本模块中，我们将学习在ANOVA/ANCOVA背景下，统计假设检验和置信区间如何帮助回答有关组均值与连续变量之间差异的有意义问题。 3. 名称：双因素方差分析与交互作用 描述：在本模块中，我们将研究双因素方差分析模型，并利用真实数据回答研究问题。 4. 名称：实验设计：基本概念和设计 描述：在本模块中，我们将研究基本的实验设计概念，如随机化、处理设计、重复和分块。我们还将探讨基本的因子设计作为改进单因素方法的一种手段。通过将这些概念与ANOVA和ANCOVA模型相结合，我们将进行有意义的实验。
Name:Introduction to ANOVA and Experimental Design
Description:In this module, we will introduce the basic conceptual framework for experimental design and define the models that will allow us to answer meaningful questions about the differences between group means with respect to a continuous variable. Such models include the one-way Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) models.
Name:Hypothesis Testing in the ANOVA Context
Description:In this module, we will learn how statistical hypothesis testing and confidence intervals, in the ANOVA/ANCOVA context, can help answer meaningful questions about the differences between group means with respect to a continuous variable.
Name:Two-Way ANOVA and Interactions
Description:In this module, we will study the two-way ANOVA model and use it to answer research questions using real data.
Name:Experimental Design: Basic Concepts and Designs
Description:In this module, we will study fundamental experimental design concepts, such as randomization, treatment design, replication, and blocking. We will also look at basic factorial designs as an improvement over elementary “one factor at a time” methods. We will combine these concepts with the ANOVA and ANCOVA models to conduct meaningful experiments.
This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Some attention will also be given to ethical issues raised in experimentation. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
