Research and Data Analysis

Advanced Regression Analysis in Linear and Non-Linear Models Training Course

Introduction

In the age of data-driven decision-making, mastering regression analysis is essential for professionals aiming to gain accurate insights and predict future outcomes. Advanced Regression Analysis in Linear and Non-Linear Models Training Course is designed to equip analysts, data scientists, and business professionals with advanced statistical tools and modeling techniques. This comprehensive course covers both linear regression models and the increasingly important non-linear regression techniques using real-world datasets and case studies from diverse domains. Learners will explore multiple regression, logistic regression, polynomial regression, splines, generalized additive models, and machine learning-based regression methods using R, Python, and other industry-standard tools.

As organizations move toward predictive analytics and AI-powered forecasting, understanding the limitations and strengths of different regression techniques becomes vital. Through a hands-on, practical learning approach, participants will gain the expertise to build robust models, validate assumptions, detect outliers, manage multicollinearity, and apply advanced diagnostics. By the end of the training, learners will be able to apply sophisticated modeling strategies to drive results and support strategic initiatives across sectors such as finance, healthcare, marketing, and policy-making.

Programme Curriculum

Advanced Regression Analysis in Linear and Non-Linear Models Training Course

Introduction

Course Objectives

Master advanced linear regression techniques and diagnostics.
Explore various non-linear regression models for real-world data.
Implement regularization methods like Lasso and Ridge regression.
Utilize Python and R for regression modeling and visualization.
Detect and handle outliers, leverage points, and influential data.
Apply variable selection and feature engineering strategies.
Perform model validation using cross-validation and bootstrapping.
Understand and apply logistic and Poisson regression models.
Use splines and GAMs for flexible model fitting.
Integrate machine learning techniques into regression workflows.
Interpret model coefficients and assess multicollinearity.
Apply regression analysis to time-series and panel data.
Translate regression outputs into actionable business insights.

Target Audiences

Data Analysts
Business Intelligence Professionals
Economists
Data Scientists
Healthcare Analysts
Academic Researchers
Financial Risk Managers
Policy and Government Analysts

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of Advanced Regression

Review of simple and multiple linear regression
Residual analysis and assumptions
Introduction to multicollinearity
Variable transformation and scaling
Dealing with missing values
Case Study: Predicting housing prices using Boston dataset

Module 2: Model Diagnostics and Improvement

Checking linearity and homoscedasticity
Cook’s Distance and leverage
VIF and correlation matrix
Interaction terms and their interpretation
Model refinement techniques
Case Study: Marketing campaign effectiveness analysis

Module 3: Logistic Regression for Binary Outcomes

Odds ratio and logit function
Model fitting and interpretation
ROC curve and AUC
Confusion matrix and metrics
Multinomial and ordinal regression
Case Study: Predicting customer churn

Module 4: Polynomial and Spline Regression

Polynomial model creation and fitting
Identifying optimal polynomial degree
Natural and B-splines in R/Python
Advantages of smooth curves
Model complexity vs. interpretability
Case Study: Modeling population growth trends

Module 5: Generalized Linear Models (GLM)

Introduction to exponential family
Poisson regression for count data
Quasi-Poisson and negative binomial
Link functions and canonical forms
Goodness-of-fit measures
Case Study: Emergency room visit prediction

Module 6: Regularization Techniques

Ridge vs. Lasso regression
Elastic Net implementation
Cross-validation for lambda selection
Bias-variance tradeoff
Feature selection benefits
Case Study: Stock return forecasting

Module 7: Regression with Machine Learning

Decision Trees and Random Forest regression
Gradient boosting (XGBoost)
Hyperparameter tuning
Model interpretation with SHAP
Comparing ML and traditional regression
Case Study: Energy consumption prediction

Module 8: Feature Engineering and Selection

Categorical encoding (one-hot, label)
Interaction and polynomial features
Recursive feature elimination (RFE)
PCA for dimensionality reduction
Feature importance in tree-based models
Case Study: Health insurance premium modeling

Training Methodology

Instructor-led lectures with practical labs
Hands-on coding with Python and R
Live case studies and problem-solving sessions
Interactive quizzes and peer discussions
Real-world datasets and capstone project guidance
Certificate of completion and final assessment

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Regression analysis linear & non-linear models

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000