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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/regression-modeling-sas
课程评论：没有评论
课程名称：回归建模基础 课程概述：本课程是为使用SAS软件进行统计分析的用户设计的入门课程，重点包括t检验、方差分析（ANOVA）和线性回归，同时简要介绍了逻辑回归。 课程大纲： 1. 课程概述（回顾统计学：假设检验） - 本模块介绍课程内容及分析数据的背景，并帮助学员准备进行实践练习所需的数据。 2. 模型构建与效应选择 - 本模块探索几种模型选择工具，旨在限制候选模型的数量，以便于学员根据自己的专业知识和研究优先级选择合适的模型。 3. 模型拟合后的推断 - 学员将学习如何验证模型假设，以及在进行线性回归时诊断可能遇到的问题。包括检查残差、识别在数据中极为不同的异常值，以及识别对回归模型产生不当影响的关键观察值。此外，学员还将学习如何诊断多重共线性，以避免模型中的标准误差膨胀和参数不稳定。 4. 评分与预测的模型构建 - 本模块指导学员如何从推断性统计转向预测建模。学员将学习如何使用诚实评估来评估模型，而不是依赖p值。在选择最佳表现模型后，学员还将了解如何部署模型以预测新数据。 5. 类别数据分析 - 本模块侧重于使用假设检验寻找预测因子与二元响应之间的关联。学员将构建逻辑回归模型并学习如何表征响应与预测因子之间的关系，最后学员将学习如何使用逻辑回归建立模型或分类器，以预测未知案例。 本课程致力于帮助学员掌握回归分析的基础知识和实践技巧，提高他们在SAS环境下进行统计分析的能力。
Name:Course Overview (Review from Introduction to Statistics: Hypothesis Testing)
Description:In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.
Name:Model Building and Effect Selection
Description:In this module you explore several tools for model selection. These tools help limit the number of candidate models so that you can choose an appropriate model that's based on your expertise and research priorities.
Name:Model Post-Fitting for Inference
Description:In this module you learn to verify the assumptions of the model and diagnose problems that you encounter in linear regression. You learn to examine residuals, identify outliers that are numerically distant from the bulk of the data, and identify influential observations that unduly affect the regression model. Finally, you learn to diagnose collinearity to avoid inflated standard errors and parameter instability in the model.
Name:Model Building for Scoring and Prediction
Description:In this module you learn how to transition from inferential statistics to predictive modeling. Instead of using p-values, you learn about assessing models using honest assessment. After you choose the best performing model, you learn about ways to deploy the model to predict new data.
Name:Categorical Data Analysis
Description:In this module you look for associations between predictors and a binary response using hypothesis tests. Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. Finally, you learn how to use logistic regression to build a model, or classifier, to predict unknown cases.
This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.
