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Advanced Logistic Regression Analysis with SAS Stat: Theory and Practice

Unlock the power of logistic regression analysis with SAS Stat in our advanced course. Learn theoretical concepts and practical implementation techniques. Explore variable clustering and subset selection for optimized models. Master logistic regression analysis for insightful data-driven decision-making.

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What you'll get

  • 4+ Hours
  • 1 Courses
  • Course Completion Certificates
  • One year access
  • Self-paced Courses
  • Technical Support
  • Mobile App Access
  • Case Studies

Synopsis

  • Theoretical foundations of logistic regression analysis.
  • Practical implementation techniques using SAS Stat.
  • Exploration and interpretation of datasets for logistic regression analysis.
  • Handling missing values and categorical inputs in logistic regression models.
  • Variable clustering methods for identifying related predictor variables.
  • Subset selection techniques for optimizing logistic regression models.
  • How to interpret and analyze model output effectively.
  • Creating logit plots to visualize relationships between variables.
  • Best practices for model evaluation and validation.
  • Application of logistic regression analysis to real-world datasets for data-driven decision-making.
  • Know in detail about regression analysis
  • Explain logistic regression and its benefits
  • Understand about the key components of logistic regression
  • Know how to interpret logistic regression analysis output produced by SAS

Content

Description

Introduction: This comprehensive course is designed to equip participants with the knowledge and skills to master logistic regression analysis using SAS Stat. It begins with an in-depth introduction to logistic regression concepts, focusing on their application within SAS Stat. Participants will explore the intricacies of logistic regression through a practical lens, starting with an overview of a logistic regression project involving an insurance dataset. They will learn how to navigate and explore datasets effectively to prepare them for analysis.

Logistic Regression Demonstration: Participants will engage in practical demonstrations of logistic regression analysis using SAS Stat. Through a series of detailed tutorials, they will learn how to conduct logistic regression analysis step-by-step. This includes handling missing values, encoding categorical inputs, and interpreting model output. Participants will gain proficiency in applying logistic regression techniques within SAS Stat to derive meaningful insights from their data.

Variable Clustering: In this section, participants will delve into advanced techniques for variable selection using SAS Stat. They will learn how to perform variable clustering to identify groups of related variables and streamline the model-building process. By understanding the relationships between variables, participants will be able to make informed decisions about which variables to include in their logistic regression models.

Subset Selection: The course concludes with a focus on subset selection methods for logistic regression analysis. Participants will explore various subset selection techniques, learning how to identify the most influential variables for predicting the outcome of interest. Through hands-on exercises and tutorials, participants will gain practical experience in applying subset selection methods within SAS Stat to optimize their logistic regression models.

Throughout the course, participants will have access to comprehensive resources and support, enabling them to deepen their understanding of logistic regression analysis and apply their newfound skills confidently in real-world projects and research endeavors. By mastering logistic regression analysis with SAS Stat, participants will enhance their analytical capabilities and unlock new opportunities for data-driven decision-making.

Requirements

  • Students or anyone taking this course should have some familiarity with SAS. There are no basic skills required to take this course.

Target Audience

  • Researchers, Forensic statisticians, Data Miners, Environmental Scientists, Epidemiologists
  • Anyone who is interested in modeling data and estimate the probabilities of given outcomes

* One Time Payment & Get One year access

Offer ends in:

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