About this Event
Group Discounts:
Save 10% when registering 3 or more participants
Save 15% when registering 10 or more participants
About the 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 will be provided during the session
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 this course, you will:
- 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:
- 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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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 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 IL, Chicago - 111 W. Jackson, 111 W. Jackson Suite 1700, Chicago, United States
USD 532.73 to USD 695.54
