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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/modern-regression-analysis-in-r
课程评论：没有评论
课程名称：R中的现代回归分析 课程概述：本课程将为数据科学提供一套基础的统计建模工具。特别是，学生将学习线性统计模型的方法、理论和应用，涵盖参数估计、残差诊断、拟合优度以及变量选择和模型比较的各种策略。课程还将关注统计模型的误用及其伦理影响。 该课程可以作为科罗拉多大学博尔德分校数据科学硕士（MS-DS）学位的一部分进行学习，MS-DS是一个跨学科的学位项目，整合了来自应用数学、计算机科学、信息科学等多个学科的教师资源。该项目采用基于表现的录取方式，无需申请流程，非常适合具有计算机科学、信息科学、数学和统计学等领域多样本科学士教育或专业经验的个人。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. 模块名称：统计模型简介 描述：本模块将介绍统计建模的一般基础概念框架，特别是线性回归模型。 2. 模块名称：线性回归参数估计 描述：本模块将学习如何使用最小二乘法拟合线性回归模型。还将研究最小二乘法的性质，并描述线性回归模型的一些拟合优度指标。 3. 模块名称：线性回归推断 描述：本模块将研究线性回归建模用于推导样本到总体推论的应用。 4. 模块名称：线性回归分析中的预测与解释 描述：本模块将识别模型如何预测未来值，并构建这些值的区间估计。同时，我们将探讨统计建模与因果解释之间的关系。 5. 模块名称：回归诊断 描述：本模块将学习如何诊断线性回归模型拟合中的问题。具体来说，我们将使用正式检验和可视化技术判断线性模型是否适合当前数据。 6. 模块名称：模型选择与多重共线性 描述：本模块将研究模型选择和模型改进的方法。具体来说，我们将学习何时以及如何应用模型选择技术，例如前向选择和后向选择、基于准则的方法，并了解多重共线性（也称为共线性）的问题。
Name:Introduction to Statistical Models
Description:In this module, we will introduce the basic conceptual framework for statistical modeling in general, and for linear regression models in particular.
Name:Linear Regression Parameter Estimation
Description:In this module, we will learn how to fit linear regression models with least squares. We will also study the properties of least squares, and describe some goodness of fit metrics for linear regression models.
Name:Inference in Linear Regression
Description:In this module, we will study the uses of linear regression modeling for justifying inferences from samples to populations.
Name:Prediction and Explanation in Linear Regression Analysis
Description:In this module, we will identify how models can predict future values, as well as construct interval estimates for those values. We will also explore the relationship between statistical modelling and causal explanations.
Name:Regression Diagnostics
Description:In this module, we will learn how to diagnose issues with the fit of a linear regression model. In particular, we will use formal tests and visualizations to decide whether a linear model is appropriate for the data at hand.
Name:Model Selection and Multicollinearity
Description:In this module, we will study methods for model selection and model improvement.. In particular, we will learn when and how to apply model selection techniques such as forward selection and backward selection, criterion-based methods, and will learn about the problem of multicollinearity (also called collinearity).
This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse. 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
