Foundations of Data and Models Regression Analytics #254028

Upon completion of this course, participants will be able to:

• Understand Core Concepts: Define and explain key regression principles including correlation, linear relationships, and model assumptions

• Build Regression Models: Construct and estimate both simple and multiple regression models using statistical software

• Interpret Results: Analyze and explain coefficient estimates, significance levels, and model fit statistics

• Validate Models: Check regression assumptions and diagnose potential problems with model specification

• Apply Predictive Analytics: Use regression models to generate predictions and quantify uncertainty

• Communicate Findings: Present regression results effectively to technical and non-technical audiences

• Implement Ethical Practices: Recognize limitations and potential misuses of regression analysis

This course is designed for professionals who work with data and need to understand relationships and make predictions:

• Business Analysts and Data Analysts

• Market Researchers and Marketing Analysts

• Financial Analysts and Risk Analysts

• Operations and Supply Chain Professionals

• Academic Researchers and Students

• Quality Assurance and Process Improvement Specialists

• Anyone who needs to interpret or create predictive models in their work

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of Regression Analysis

• AM: Introduction to Predictive Analytics

  • Overview of regression analysis and its applications

  • Understanding variables: dependent vs. independent variables

  • Correlation analysis and visualization

  • Introduction to statistical software (R/Python/SPSS)

• PM: Simple Linear Regression

  • The linear regression model: equation and components

  • Ordinary Least Squares (OLS) estimation

  • Interpretation of coefficients and intercept

  • Hands-on Exercise: Fitting and interpreting a simple regression model

Day 2: Multiple Regression and Model Building

• AM: Multiple Regression Fundamentals

  • Extending to multiple independent variables

  • Partial regression coefficients interpretation

  • Model specification and variable selection

  • Categorical independent variables (dummy variables)

• PM: Model Assessment and Goodness of Fit

  • R-squared and adjusted R-squared

  • F-test for overall model significance

  • Standard error of estimate

  • Hands-on Exercise: Building and comparing multiple regression models

Day 3: Assumptions and Diagnostics

• AM: Regression Assumptions

  • Linearity, independence, homoscedasticity, normality

  • Detecting assumption violations: residual analysis

  • Identifying outliers and influential points

• PM: Addressing Violations and Problems

  • Transformations for non-linearity (log, polynomial)

  • Handling heteroscedasticity

  • Dealing with multicollinearity (VIF)

  • Hands-on Exercise: Diagnostic testing and remediation

Day 4: Advanced Topics and Model Extensions

• AM: Model Selection and Validation

  • Stepwise selection procedures

  • Cross-validation techniques

  • Overfitting and underfitting concepts

  • Introduction to regularization (Ridge/Lasso)

• PM: Special Applications

  • Interaction effects between variables

  • Nonlinear regression approaches

  • Introduction to logistic regression for binary outcomes

  • Hands-on Exercise: Implementing advanced techniques

Day 5: Applications and Capstone Project

• AM: Communicating Results

  • Effective visualization of regression results

  • Presenting findings to different audiences

  • Ethical considerations in regression analysis

  • Common pitfalls and how to avoid them

• PM: Capstone Project and Review

  • Comprehensive Exercise: Participants work through a complete regression analysis from data preparation to interpretation

  • Project presentations and feedback

  • Course review and Q&A

  • Resources for continued learning