Advanced Logistic Regression Analysis: Theory, Methods, and SAS Implementation
Understand the key components of logistic regression and develop a logistic regression model using SAS. Unlock the intricacies of logistic regression analysis in our advanced course. Explore regression fundamentals and predicting probabilities with expert guidance. Master logistic regression principles and methodologies, including SAS implementation. Gain practical skills for model fit assessment and data-driven decision-making.
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What you'll get
- 2+ 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.
- Regression analysis fundamentals and its significance in modeling relationships between variables.
- Various methods for predicting probabilities in regression analysis.
- Key concepts and principles of logistic regression modeling.
- Reasons for choosing logistic regression over ordinary least squares (OLS) regression for binary outcomes.
- Implementation of logistic regression models using SAS software.
- Techniques for assessing model fit and evaluating the performance of logistic regression models.
- Practical approaches for interpreting logistic regression results and making informed decisions.
- Strategies for model refinement and optimization using SAS methodologies.
- Application of logistic regression analysis to real-world datasets for predictive modeling and risk assessment.
- Develop a logistic regression model using SAS
- Know in detail about regression analysis
- Explain logistic regression and its benefits
- Understand about the key components of logistic regression
- Know about the different methods of finding the probabilities
- Learn how to interpret the modeling results and present it to others
- Know how to interpret logistic regression analysis output produced by SAS
Content
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MODULE 1: Essentials Training
Courses No. of Hours Certificates Details Logistic Regression for Beginners 1h 58m ✔
Description
Introduction: This introductory section provides participants with a comprehensive overview of the course objectives and the importance of logistic regression analysis in predictive modeling and risk assessment.
Regression Analysis: Participants will delve into the fundamentals of regression analysis, understanding its significance in modeling relationships between variables. Through a series of lectures, they will explore different aspects of regression analysis, including its key concepts and methodologies.
Predicting Probabilities: In this section, participants will learn about various methods for predicting probabilities in regression analysis. Through detailed discussions and examples, they will gain insights into the different approaches and their applications in real-world scenarios.
Logistic Regression: Participants will be introduced to logistic regression, focusing on its fundamental principles and key concepts. They will explore why logistic regression is preferred over ordinary least squares (OLS) regression for modeling binary outcomes and gain an understanding of the modeling process.
SAS Methodology: This section provides participants with a practical overview of logistic regression analysis using SAS software. Through step-by-step demonstrations and tutorials, participants will learn how to implement logistic regression models in SAS, assess model fit, and interpret results effectively.
Model Fit and Conclusion: The course concludes with a discussion on model fit and a recap of key concepts covered throughout the course. Participants will gain insights into assessing the goodness-of-fit of logistic regression models and receive a summary of the methodologies learned.
By the end of the course, participants will have acquired a deep understanding of logistic regression analysis, its methodologies, and practical implementation using SAS software. They will be equipped with the skills to apply logistic regression techniques confidently in various domains, making informed decisions based on data-driven insights.
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.
Course Ratings
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Very explanatory & timely information structured about logistic regression.I didnt had any basic knowledge about logistic regression. this course helped me in getting first hand basic information right from scratch about regression. Best part was structured information.
Pawan Soni