REGRESSION METHODS AND MODELS

INTRODUCTION

Regression analysis is a fundamental statistical technique used to model relationships between variables, identify patterns, and make predictions. Whether analyzing trends in business, healthcare, or research, regression models provide valuable insights into how independent variables influence dependent outcomes.

This one-day course introduces participants to simple and multiple linear regression, as well as logistic regression methods. The session covers correlation techniques, model assumptions, data transformations, and interpretation of statistical outputs. Participants will explore real-world applications of regression analysis and understand the potential of regression models as predictive tools.

This John Varlow | Training and Consultancy course will cover:

✔ Understanding independent, dependent, confounding, and influencing variables
✔ Exploring correlation methods, including correlation coefficients and R-squared
✔ Understanding the line of best fit using least squares methods
✔ Introduction to simple and multiple linear regression and their equations
✔ Transforming non-linear data to fit a linear model
✔ Understanding binomial logistic regression and how to interpret outputs
✔ Introduction to multinomial logistic regression methods
✔ Exploring practical applications of regression models in predictive analysis

COURSE OBJECTIVES

By the end of this one-day course, participants will:

✔ Differentiate between independent, dependent, confounding, and influencing variables
✔ Understand correlation techniques and their role in regression analysis
✔ Learn how to apply least squares methods to determine the line of best fit
✔ Develop an understanding of simple and multiple linear regression models
✔ Explore how non-linear data transformations can improve model accuracy
✔ Gain insight into binomial and multinomial logistic regression
✔ Interpret statistical outputs from regression models
✔ Understand how regression techniques can be used for prediction and decision-making

TRAINING METHODOLOGY

This session is interactive and application-driven, combining theoretical explanations with hands-on examples. Participants will engage in:

✔ Step-by-step demonstrations of regression models
✔ Hands-on exercises analyzing real-world datasets
✔ Discussions on model selection, interpretation, and limitations
✔ Practical examples of regression techniques applied in different industries

By the end of the session, participants will have a strong foundational understanding of regression analysis and how it can be used in real-world predictive modeling.

WHO SHOULD ATTEND?

This course is designed for professionals who need to analyze relationships between variables, build predictive models, and interpret regression results. It is particularly relevant for:

✔ Researchers & Academics working with statistical modeling
✔ Business & Data Analysts using regression for decision-making
✔ Healthcare & Epidemiology Professionals analyzing patient outcomes
✔ Finance, Risk, and Operations Specialists forecasting trends
✔ Policy Makers & Public Sector Professionals working with quantitative data

Pre-requisites:

✔ Basic numeracy skills
✔ Completion of "Introduction to Descriptive Statistics" or equivalent understanding
✔ Completion of "Introduction to Inferential Statistics" or equivalent understanding

COURSE OUTLINE

1. Understanding Variables and Correlation

✔ Defining independent, dependent, confounding, and influencing variables
✔ Exploring correlation coefficients (Pearson’s r) and R-squared values
✔ Limitations of correlation vs. causation

2. The Line of Best Fit & Least Squares Method

✔ Introduction to least squares regression
✔ How to calculate and interpret the line of best fit

3. Simple & Multiple Linear Regression

✔ Understanding simple linear regression equations
✔ Introduction to multiple linear regression and its assumptions
✔ Interpreting regression coefficients and model fit statistics

4. Transforming Non-Linear Data

✔ Identifying non-linear relationships in data
✔ Techniques for transforming data to fit a linear model

5. Introduction to Logistic Regression

✔ Understanding binomial logistic regression
✔ How to interpret odds ratios, coefficients, and model fit

6. Multinomial Logistic Regression

✔ Expanding logistic regression to categorical outcomes
✔ Identifying when to use multinomial regression models

7. Applying Regression Models to Real-World Data

✔ Using regression for predictive analysis
✔ Recognizing limitations and assumptions of regression models

DURATION

⏳ 1 Day

This course provides a practical yet comprehensive introduction to regression analysis, equipping participants with the skills to apply, interpret, and critically assess regression models in their work.

CERTIFICATION

Upon successful completion of this course, participants will receive a John Varlow | Training and Consultancy Certificate of Completion.