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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/linear-regression-model
课程评论：没有评论
课程名称：线性回归与建模 概述：本课程介绍了简单和多重线性回归模型。这些模型可以帮助你评估数据集中变量与连续响应变量之间的关系。例如，教授的外貌吸引力与学生评价分数之间是否存在关系？我们能否根据母亲的某些特征预测孩子的考试成绩？在本课程中，你将学习线性回归背后的基本理论，并通过数据示例学习如何拟合、检查和利用回归模型来分析多个变量之间的关系，使用免费的统计软件R和RStudio。 课程大纲： 1. 线性回归与建模概述：此模块介绍Coursera的专业课程和本课程的基础知识，包括“使用R的统计学”专业和“线性回归与建模”课程。 2. 线性回归：在这一周，我们将介绍线性回归。许多人可能对回归有所了解，通过新闻阅读到图表上显示的散点图和直线的关系。线性模型可以用于预测或评估两个数值变量之间是否存在线性关系。 3. 关于线性回归的更多内容：欢迎来到第二周！这一周我们将研究异常值、线性回归中的推断和变异性划分。请利用这一周加强对线性回归的理解，并在讨论论坛中发布你的问题、关注和建议。 4. 多重回归：在这一周，我们将探讨多重回归，它允许我们使用多个预测变量（数值和分类）对数值响应变量进行建模。我们还将涵盖多重线性回归的推断、模型选择以及模型诊断。这一周还包括一个期末项目，你将使用提供的数据集完成并报告一个数据分析问题。请仔细阅读项目说明以完成自我评估。
Name:About Linear Regression and Modeling
Description:This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Please take several minutes to browse them through. Thanks for joining us in this course!
Name:Linear Regression
Description:In this week we’ll introduce linear regression. Many of you may be familiar with regression from reading the news, where graphs with straight lines are overlaid on scatterplots. Linear models can be used for prediction or to evaluate whether there is a linear relationship between two numerical variables.
Name:More about Linear Regression
Description:Welcome to week 2! In this week, we will look at outliers, inference in linear regression and variability partitioning. Please use this week to strengthen your understanding on linear regression. Don't forget to post your questions, concerns and suggestions in the discussion forum!
Name:Multiple Regression
Description:In this week, we’ll explore multiple regression, which allows us to model numerical response variables using multiple predictors (numerical and categorical). We will also cover inference for multiple linear regression, model selection, and model diagnostics. There is also a final project included in this week. You will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.
This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.
