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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/regression-models
课程评论：没有评论
课程名称：回归模型 课程概述：线性模型是根据线性假设将结果与一组感兴趣的预测变量关联起来的一种方法。回归模型是线性模型的一个子集，是数据科学家工具箱中最重要的统计分析工具。本课程涵盖回归分析、最小二乘法及通过回归模型进行推断。此外，还将介绍回归模型的特殊情况，包括方差分析（ANOVA）和协方差分析（ANCOVA）。课程将探讨残差分析和变异性，并涵盖现代模型选择思维及回归模型的新颖应用，如散点图平滑。 课程大纲： - 第1周：最小二乘法与线性回归 - 本周将重点讨论最小二乘法和线性回归的基础知识。 - 第2周：线性回归与多元回归 - 本周将完成线性回归的学习，然后开始多元回归的第一部分内容。 - 第3周：多元回归、残差与诊断 - 本周将继续多元回归的学习，通过实例加深理解，并探讨残差分析、诊断、方差膨胀及模型比较等主题。 - 第4周：逻辑回归与泊松回归 - 本周将学习广义线性模型，包括二元结果和泊松回归的应用。 本课程为希望深入理解回归分析及其应用的学习者提供了全面的知识基础。
Name:Week 1: Least Squares and Linear Regression
Description:This week, we focus on least squares and linear regression.
Name:Week 2: Linear Regression & Multivariable Regression
Description:This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.
Name:Week 3: Multivariable Regression, Residuals, & Diagnostics
Description:This week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.
Name:Week 4: Logistic Regression and Poisson Regression
Description:This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
