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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/linear-models-2
课程评论：没有评论
课程名称：数据科学高级线性模型 2：统计线性模型 课程概述：欢迎参加数据科学高级线性模型课程2：统计线性模型。本课程将从线性代数和数学的角度介绍最小二乘法。在开始课程之前，请确保您具备以下基础知识： - 基本的线性代数和多元微积分知识。 - 基本的统计学和回归模型知识。 - 对证明基础数学的初步了解。 - R编程语言的基本知识。 完成本课程后，学生将对线性代数在回归建模中的应用有扎实的基础，从而大幅增强应用数据科学家对回归模型的整体理解。 课程大纲： 1. **简介和期望值**：本模块涵盖课程的基本内容以及先修知识。接着介绍多元向量的期望值基本概念，并总结普通最小二乘估计的矩性质。 2. **多元正态分布**：在本模块中，我们将从独立同分布的正态分布开始，逐步构建多元与奇异正态分布。 3. **分布结果**：本模块重点介绍多元回归中出现的基本分布结果。 4. **残差**：我们将重新审视残差，并考虑其分布结果。同时介绍所谓的PRESS残差，并演示如何在不重新拟合模型的情况下进行计算。
Name:Introduction and expected values
Description:In this module, we cover the basics of the course as well as the prerequisites. We then cover the basics of expected values for multivariate vectors. We conclude with the moment properties of the ordinary least squares estimates.
Name:The multivariate normal distribution
Description:In this module, we build up the multivariate and singular normal distribution by starting with iid normals.
Name:Distributional results
Description:In this module, we build the basic distributional results that we see in multivariable regression.
Name:Residuals
Description:In this module we will revisit residuals and consider their distributional results. We also consider the so-called PRESS residuals and show how they can be calculated without re-fitting the model.
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. - A basic understanding of statistics and regression models. - At least a little familiarity with proof based mathematics. - Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.
