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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-last-statistical-model-youll-ever-need-in-sas/
课程评论：没有评论
本课程名为“SAS 中你将需要的最后一个统计模型”，旨在教授学员如何使用广义线性混合模型（GLMM）框架在 SAS 中进行统计分析，并超越传统的统计检验方法。 课程内容涵盖： * **GLMM 的优势与 SAS Studio 的获取与使用（第一讲）**：解释为何选择 GLMM，并指导学员获取和使用免费版的 SAS Studio。 * **GLMM 详解（第二讲）**：深入介绍 GLMM 的定义、模型示例、组成部分、假设条件及注意事项，并提供一个简单的 SAS 示例。 * **SAS 中 PROC GLIMMIX 的应用（第三讲）**：详细讲解 PROC GLIMMIX 过程，包括其核心语句、其他语句及常用选项。 * **PROC GLIMMIX 输出解读与数据可视化（第四讲）**：指导学员如何解读 PROC GLIMMIX 的输出表格，并将输出数据进行整理，制作条形图和散点图。 * **传统统计检验与 GLMM 的对比（第五至十讲）**：通过相同的数据集，逐一对比和演示多种传统统计检验（如卡方检验、t 检验、ANOVA、ANCOVA、线性回归、逻辑回归、泊松回归）与 GLMM 的应用，重点关注模型设定和结果解读。 * **GLMM 的局限性与进阶学习（第十一讲）**：探讨 GLMM 无法实现的统计检验，并提供进一步学习的建议和推荐参考资料。 完成本课程后，学员将能够自信地将 GLMM 作为一种新的统计分析范式来应用。
This course will teach you how to use the generalized linear mixed model framework to replicate (and go beyond) the classical statistical tests such as t-test, ANOVA, and regression in SAS. In the 1st lecture, I start by asking why use generalized linear mixed models (GLMMs)? Next, I explain how to get and use a free version of SAS, SAS Studio. The 2nd lecture goes over the details of GLMMs. This includes definitions, model examples, parts, assumptions, and caveats. The section ends with a quick example in SAS. Lecture #3 covers how to run GLMMs in SAS using PROC GLIMMIX. It starts with an overview of the procedure, then delves into the core statements, other statements, and common statement options. Lecture #4 focuses on the output table and datasets produced by PROC GLIMMIX. The first half covers table interpretation. The second half of the course wrangles data outputs to graph bar plots and scatterplots. Lectures #5-10 go through side-by-side examples of standard statistical tests compared to generalized linear mixed models, using the same datasets. Model specification and output interpretation is considered for each. The tests covered are: Chi-Square Test of IndependenceTwo-Way T-testOne-Way T-testPaired T-testOne-Way ANOVATwo-Way ANOVARepeated-Measures ANOVAANCOVASimple Linear RegressionMultiple Linear RegressionLogistic Regression (Categorical)Logistic Regression (Numerical)Poisson RegressionLinear Mixed ModelsGeneralized Linear Mixed Models In the 11th and final lecture, I explain what tests you can't do with a GLMM format, provide next steps to take your learning, and list useful references. By the end of the course, you will be confidently using GLMMs as a new paradigm for statistical analysis.
