Introduction:

The "Foundations of Data and Models: Regression Analytics" training program is designed to provide participants with a solid understanding of the fundamental concepts and techniques used in regression analysis. The program will cover the theory and practical applications of various regression models, including linear and nonlinear regression, as well as advanced techniques such as regularization and model selection.

Objectives:

• Understand the theory and assumptions underlying different types of regression models

• Learn how to fit, interpret, and evaluate regression models using real-world data

• Gain hands-on experience with popular software tools for regression analysis

• Develop the ability to critically evaluate the assumptions and limitations of regression models

• Learn how to use regression models to make predictions and draw inferences about relationships between variables

Target Audience:

This program is intended for professionals who are interested in learning more about regression analysis, including data analysts, statisticians, data scientists, and researchers.

Outline:

Day 1:

Introduction to Regression Analysis

• Overview of the course and the concepts of regression analysis

• Simple linear regression

• Assumptions and limitations of linear regression

Day 2:

Multiple Linear Regression

• Multiple linear regression

• Interaction terms and polynomial regression

• Model selection and evaluation

Day 3:

Nonlinear Regression

• Nonlinear regression

• Logistic regression

• Generalized linear models

Day 4:

Advanced Regression Techniques

• Regularization techniques (Ridge, Lasso, Elastic Net)

• Model selection and evaluation

Day 5:

Applications and Case Studies

• Hands-on exercises using real-world data

• Use of regression models in different fields

• Interpretation and drawing inferences from regression models