About this Event
Group Discounts:
- Save 10% when registering 3 or more participants
- Save 15% when registering 10 or more participants
About This Course
- Duration: 1 Full Day (8 Hours)
- Delivery Mode: Classroom (In-Person)
- Language: English
- Credits: 8 PDUs / Training Hours
- Certification: Course Completion Certificate
- Refreshments: Lunch, snacks, and beverages included
Course Overview
This 1 Day intensive program introduces you to the essential concepts of regression analysis using Python. You will explore simple and multiple linear regression, diagnostics, transformations, qualitative predictors, collinearity, and real-world modelling challenges.
The course focuses on practical understanding, helping you build, interpret, evaluate, and troubleshoot regression models efficiently.
Using a blend of theory, examples, and structured activities, you will learn how to apply regression to real datasets, assess model validity, and improve model performance in various scenarios.
Learning Objectives
By the end of the course, you will be able to:
- Understand and apply simple and multiple linear regression.
- Interpret regression output and evaluate model performance.
- Diagnose model problems using residual analysis and assumptions.
- Use indicator variables and interactions effectively.
- Apply transformations to improve model fit and reliability.
- Identify and manage collinearity issues.
- Build regression models using Python with confidence.
Target Audience
This course is ideal for:
- Data science beginners with basic Python knowledge
- Students and professionals exploring statistical modelling
- Business analysts and researchers working with data
- Anyone looking to strengthen analytical and regression skills
Why Choose This Course?
This program simplifies complex regression topics into a clear, practical 1-day format.
The trainer brings real-world data science experience, ensuring examples and explanations remain practical, relevant, and easy to understand.
You gain structured exposure to essential and intermediate-level regression concepts without being overwhelmed by advanced mathematics.
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Want to train your entire team?
We provide customized in-house sessions tailored to your industry datasets, modelling goals, and team skill levels.
The content can be adapted to finance, marketing, healthcare, operations, or research environments.
Ideal for teams aiming to improve analytics capability in one focused day.
📧 Contact us today to schedule a customized in-house, face-to-face session:
Agenda
Module 1: Introduction to Regression Analysis
Info: • Understand what regression analysis is and why it is used.
• Explore publicly available datasets for modelling.
• Learn key steps in performing regression analysis.
• Icebreaker
Module 2: Simple Linear Regression Essentials
Info: • Learn covariance, correlation, and basic model structure.
• Explore parameter estimation and hypothesis testing.
• Understand predictions and evaluating model fit.
• Case Study
Module 3: Multiple Linear Regression Concepts
Info: • Learn interpretation of regression coefficients.
• Understand centring, scaling, and estimator properties.
• Explore inference and prediction in multiple regression.
• Simulation
Module 4: Regression Diagnostics
Info: • Understand standard regression assumptions.
• Learn residual types, normality, linearity, and influence.
• Identify outliers and assess model violations.
• Role Play
Module 5: Qualitative Predictors & Indicator Variables
Info: • Learn how to include categorical predictors in regression.
• Understand interaction variables and seasonal indicators.
• Explore multi-equation regression systems.
• Case Study
Module 5: Qualitative Predictors & Indicator Variables
Info: • Learn how to include categorical predictors in regression.
• Understand interaction variables and seasonal indicators.
• Explore multi-equation regression systems.
• Case Study
Module 6: Transformations & Model Improvement
Info: • Apply transformations for linearity and variance stabilization.
• Detect heteroscedasticity and apply weighted least squares.
• Use log and power transformations to enhance model accuracy.
• Activity
Module 7: Problem of Correlated Errors
Info: • Understand autocorrelation and its impact on regression models.
• Learn the Durbin–Watson statistic and its limitations.
• Explore transformations and indicator variables to correct correlation.
• Simulation
Module 8: Collinearity & Advanced Regression Concepts
Info: • Understand principal components and constrained estimation.
• Explore principal component regression and biased coefficients.
• Learn ridge regression basics and model stabilization strategies.
• Group Discussion
Event Venue & Nearby Stays
Regus ON, Burlington - North Service Road, 4145 North Service Road #2nd Floor, Burlington, Canada
CAD 633.68 to CAD 807.56
