Logistic Regression using SAS - Indepth Predictive Modeling

所在平台: Udemy

课程主页: https://www.udemy.com/course/logistic-regression-credit-scoring-modelling-using-sas/

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课程名称:使用SAS进行逻辑回归 - 深入的预测建模 课程概述: 本课程专注于使用SAS进行信用评分和逻辑回归模型构建。它承诺以清晰明了的方式解释相关概念,并探讨分析师面临的实际问题。课程内容涵盖以下主题: - 如何明确目标并确保数据充足? - 如何决定性能窗口? - 数据处理的步骤是什么? - 如何进行变量选择? - 如何处理数值变量和字符变量? - 如何科学地处理多重共线性? - 如何理解模型的强度? - 如何验证模型? - 如何解释SAS输出并相应地开发下一个SAS代码? 课程将通过实例数据集进行逐步指导,帮助学员掌握模型开发的全过程。 课程材料: 本课程包括屏幕录制的视频(视听内容)、演示文稿的PDF文件、Excel数据供练习使用、包含代码的Word文档,以及包含逐步模型开发练习细节的Excel文档。 课程时长: 该课程预计完成时间为30小时。 课程结构: 课程分为七个部分,逐步解释模型开发的过程。 为什么选择这门课程? 本课程旨在帮助学生和分析专业人士获得清晰的理解,提升在相关工作中的就业竞争力,并通过有效的输出在他们的统计或分析职业中获得成功。

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What is this course all about? This course is all about credit scoring / logistic regression model building using SAS. It explains There course promises to explain concepts in a crystal clear manner. It goes through the practical issue faced by analyst. Some of the discussion item would be How to clarify objective and ensure data sufficiency? How do you decide the performance window? How do you perform data treatment How to go for variable selection? How to deal with numeric variables and character variables? How do you treat multi collinerity scientifically? How do you understand the strength of your model? How do you validate your model? How do you interpret SAS output and develop next SAS code accordingly? Step by step workout - model development on an example data set What kind of material is included? It consists of video recording of screen (audio visual screen capture), pdf of presentations, Excel data for workout, word document containing code and Excel document containing step by step model development workout details How long the course will take to complete? Approximately 30 hours How is the course structured? It has seven sections, which step by step explains model development Why Take this course? The course is more intended towards students / analytics professionals to Get crystal clear understanding Get jobs in this kind of work by clearing interview with confidence Be successful at their statistical or analytical profession due to the quality output they produce

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