Foundations of Data and Models: Regression Analytics

This DIXONTECH course provides professionals with a strong foundation in data modeling and regression analytics, equipping them with the knowledge to analyze relationships between variables and predict business outcomes. Participants will learn to apply linear, multiple, and logistic regression techniques, interpret model outputs, and validate analytical assumptions using modern tools and datasets for evidence-based decision-making.

TRAINING TOPICS:

• Introduction to Data and Analytical Models

• Fundamentals of Regression Analysis

• Building and Validating Regression Models

• Advanced Regression and Predictive Techniques

• Applying Regression Models in Business Decisions

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO:

By the end of this DIXONTECH training, participants will:

• Understand the principles of regression analytics

• Build and interpret linear and multiple regression models

• Test hypotheses and validate analytical assumptions

• Apply regression for forecasting and prediction

• Evaluate model performance using key metrics

• Use Excel, Python, or R for model development

• Translate statistical models into business insights

TRAINING IS TAILORED TO:

This course is designed for:

• Data analysts and researchers

• Business intelligence professionals

• Financial and market analysts

• Statisticians and academic professionals

• Project and operations managers

• Professionals involved in forecasting and modeling

• Decision-makers relying on analytical predictions

TRAINING METHODOLOGY:

DIXONTECH applies a practice-oriented approach, combining lectures, demonstrations, and hands-on data labs. Participants will work with real datasets, applying regression techniques using analytical tools such as Excel, Python, and R. Group discussions and practical exercises reinforce technical understanding and decision-making applications.

DAY 1: Introduction to Data and Analytical Models

• Understanding analytical and statistical modeling concepts

• Defining dependent and independent variables

• Types of data: continuous, categorical, and ordinal

• The role of regression in data analytics

• Relationship between correlation and causation

• Overview of model-building stages

• Identifying patterns and relationships in datasets

DAY 2: Fundamentals of Regression Analysis

• Simple linear regression model structure

• Understanding regression coefficients and intercepts

• Interpreting slope and correlation strength

• Measuring model fit (R² and adjusted R²)

• Assumptions of linear regression

• Residual analysis and outlier detection

• Case study: revenue prediction using regression

DAY 3: Building and Validating Regression Models

• Multiple regression and interaction terms

• Dummy variables and categorical predictors

• Checking for multicollinearity and heteroscedasticity

• Model selection and variable importance

• Model validation using training and test datasets

• Cross-validation and performance metrics

• Practical lab: developing a predictive regression model

DAY 4: Advanced Regression and Predictive Techniques

• Logistic regression for binary outcomes

• Polynomial and nonlinear regression models

• Stepwise and regularized regression (LASSO, Ridge)

• Forecasting using time-series regression

• Regression trees and ensemble approaches overview

• Interpreting advanced regression diagnostics

• Model optimization and fine-tuning methods

DAY 5: Applying Regression Models in Business Decisions

• Translating regression results into insights

• Predictive analytics for business applications

• Reporting regression outcomes effectively

• Linking models to KPIs and performance dashboards

• Common errors and misinterpretations in regression

• Ethical and governance aspects of data modeling

• Final project: applied regression business case