Multiple Linear Regression, Logistic Regression, and Survival Analysis Course Description
In this comprehensive 5-hour seminar, participants will delve into the core principles and applications of multiple regression, logistic regression, and Cox regression. Designed for professionals across various industries, this training provides a deep understanding of how to model and interpret complex data sets. Learn to apply multiple regression techniques to predict continuous outcomes, use logistic regression for binary outcomes, and employ Cox regression for survival analysis. Through practical examples and interactive sessions, gain the skills necessary to make data-driven decisions and enhance your analytical capabilities. Join us to transform your data analysis approach and unlock powerful insights from your data.
Why Should You Attend
Enhance Your Analytical Skills: This seminar provides in-depth training on multiple regression, logistic regression, and Cox regression, equipping you with the essential tools to analyze complex data sets accurately and efficiently.
Practical Application: Through real-world examples and hands-on exercises, you'll learn to apply these regression techniques to solve practical problems in your field, making the training highly relevant and immediately useful.
Career Advancement: Gaining proficiency in advanced statistical methods can significantly boost your professional profile, opening up opportunities for career growth and advancement in data-driven roles across various industries.
Expert Guidance: Learn from an experienced instructor who will provide clear explanations, answer your questions, and offer insights into best practices and common pitfalls in regression analysis.
Stay Competitive: In today's data-centric world, having advanced data analysis skills is crucial. This training will help you stay ahead of the curve by mastering techniques that are highly valued in the job market.
Learning Objectives
- Understand the Fundamentals: Gain a solid understanding of multiple regression, logistic regression, and Cox regression, including their underlying assumptions and applications.
- Data Preparation: Learn how to properly prepare and clean data for regression analysis, ensuring accurate and reliable results.
- Model Building: Develop the skills to build and fit regression models using statistical software, including the interpretation of coefficients and other key metrics.
- Results Interpretation: Master the interpretation of regression results, including understanding p-values, confidence intervals, odds ratios, and hazard ratios.
- Diagnostics and Validation: Learn to perform diagnostic checks and validation techniques to assess the goodness-of-fit and robustness of your regression models.
- Communicating Results: Enhance your ability to effectively communicate the results of your regression analyses to non-statistical audiences, including visualizing data and presenting findings clearly.
These learning objectives will ensure that participants leave the seminar with a comprehensive skill set in regression analysis, ready to tackle complex data challenges in their professional roles.
Agenda
Introduction and Overview
- Introduction to the seminar
- Welcome and objectives
- Brief overview of topics to be covered
- Housekeeping and seminar logistics
Session 1: Multiple Regression
- Basics of Multiple Regression
- Definition and applications
- Assumptions of multiple regression
- Conducting Multiple Regression Analysis
- Data preparation and exploration
- Running the analysis in statistical software
- Interpreting Results
- Coefficients, significance, and goodness of fit
- Practical examples
- Q&A
Session 2: Logistic Regression
- Introduction to Logistic Regression
- When and why to use logistic regression
- Differences from multiple regression
- Conducting Logistic Regression Analysis
- Data requirements and preparation
- Running logistic regression in statistical software
- Interpreting Results
- Odds ratios, coefficients, and model fit
- Case studies and examples
- Q&A
Session 3: Cox Regression
- Understanding Cox Regression
- Introduction to survival analysis
- Kaplan-Meier curves and log-rank test
- Basics of Cox proportional hazards model
- Conducting Cox Regression Analysis
- Data preparation for survival analysis
- Running Cox regression in statistical software
- Interpreting Results
- Hazard ratios and model diagnostics
- Practical examples and case studies
- Q&A
Conclusion and Wrap-up
- Summary of Key Points
- Recap of major topics covered
- Final thoughts and additional resources
- Feedback and Next Steps
- How to apply what was learned
- Further learning opportunities
- Thank you and closing remarks
Who Will Benefit
- Healthcare and Medical Research
- Pharmaceutical Industry
- Academia and Research
- Finance and Economics
- Marketing and Market Research
- Public Health and Policy Making
- Engineering and Technology
- Environmental Science
- Government and Nonprofit Organizations
Elaine Eisenbeisz
Statistician ( 30 + yrs exp.)
Owner & Principal of Omega Statistics
Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.
In addition to her technical expertise, Elaine possesses a talent for conveying statistical concepts and results in a way that people can intuitively understand.
