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所在平台: Udemy |
课程主页: https://www.udemy.com/course/logistic-regression-using-sas-stat/
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课程名称：SAS统计下的应用逻辑回归 课程概述： 欢迎来到使用SAS Stat进行逻辑回归项目的课程！在本课程中，您将深入了解逻辑回归分析的基础知识及其在现实场景中的应用，特别是在保险数据集的背景下。逻辑回归是一种强大的统计技术，广泛用于二分类任务，例如预测事件发生的可能性。通过本课程，您将学习如何使用逻辑回归技术分析和建模数据，最终能够建立、评估和解读逻辑回归模型，从而根据数据驱动的洞察做出明智的决策。 无论您是希望提升统计分析技能的初学者，还是希望扩展SAS Stat中逻辑回归知识的经验丰富的数据分析师，本课程都提供了有价值的见解和实用知识，以提升您在预测建模方面的能力。准备好与SAS Stat一起踏上逻辑回归的旅程吧！ 课程内容简介： 第一部分：介绍 在这一部分，学生将接受有关使用SAS Stat进行逻辑回归项目的介绍。第一讲提供了逻辑回归项目的概述，为后续讲座的理解奠定基础。第二讲深入讲解并探索保险数据集，提供关于学生在整个课程中将要处理的数据的见解。 第二部分：逻辑回归演示 在这一部分，学生将获得逻辑回归的实际经验。第三讲和第四讲展示了逻辑回归的演示，分为两部分以帮助全面理解。第五讲介绍了处理缺失值的技巧，第六讲和第七讲则重点解决分类输入，这在逻辑回归建模中至关重要。 第三部分：变量聚类 在这一部分，学生将学习变量聚类，这是一种简化复杂数据集的重要技术。第八讲、第九讲和第十讲深入探讨变量聚类，并提供逐步实施的指导。第十一讲和第十二讲进一步探索变量筛选技术，以识别回归模型中最具影响力的变量。 第四部分：子集选择 子集选择对于建立有效的逻辑回归模型至关重要。从第十三讲到第二十一讲，涵盖了子集选择的各个方面，包括其理论依据和实际实施。学生将学习如何选择最相关的变量子集，以优化模型的预测能力。此外，第二十一讲介绍了对数几率图，提供关于预测变量与响应变量对数几率之间关系的深入见解。 本课程将使学生掌握使用SAS Stat进行逻辑回归分析所需的知识和技能，从数据探索到模型解读，全面提升您的统计分析能力。
Welcome to the Logistic Regression Project using SAS Stat course! In this course, you will delve into the fundamentals of logistic regression analysis and its application in real-world scenarios using SAS Stat. Logistic regression is a powerful statistical technique commonly used for binary classification tasks, such as predicting the likelihood of an event occurring or not.Throughout this course, you will learn how to analyze and model data using logistic regression techniques, specifically tailored to the context of insurance datasets. By the end of the course, you will have a solid understanding of how to build, evaluate, and interpret logistic regression models, making informed decisions based on data-driven insights.Whether you're a beginner looking to enhance your statistical analysis skills or an experienced data analyst seeking to expand your knowledge of logistic regression in SAS Stat, this course offers valuable insights and practical knowledge to advance your proficiency in predictive modeling. Get ready to embark on a journey into the world of logistic regression with SAS Stat!Section 1: IntroductionIn this section, students will receive an introduction to the logistic regression project using SAS Stat. Lecture 1 provides an overview of the logistic regression project, setting the stage for understanding the subsequent lectures. Lecture 2 delves into the explanation and exploration of the insurance dataset, offering insights into the data students will be working with throughout the course.Section 2: Logistic Regression DemonstrationStudents will gain hands-on experience with logistic regression in this section. Lecture 3 and Lecture 4 present a demonstration of logistic regression, divided into two parts for comprehensive understanding. Lecture 5 covers techniques for handling missing values, while Lecture 6 and Lecture 7 focus on dealing with categorical inputs, an essential aspect of logistic regression modeling.Section 3: Variable ClusteringIn this section, students will learn about variable clustering, an important technique for simplifying complex datasets. Lecture 8, Lecture 9, and Lecture 10 delve into variable clustering, offering a step-by-step guide to its implementation. Lecture 11 and Lecture 12 further explore variable screening techniques to identify the most influential variables for the regression model.Section 4: Subset SelectionSubset selection is crucial for building an effective logistic regression model. Lecture 13 to Lecture 21 cover various aspects of subset selection, including its rationale and practical implementation. Students will learn how to select the most relevant subsets of variables to optimize the predictive power of their models. Additionally, Lecture 21 introduces logit plots, providing insights into the relationship between predictor variables and the log-odds of the response variable.This course equips students with the knowledge and skills needed to perform logistic regression analysis effectively using SAS Stat, from data exploration to model interpretation.
