Foundations of Data and Models Regression Analytics

Course overview

Course outline

1. 1Day 1: Introduction to Data and Statistical Foundations

   • Understanding data types (categorical, numerical, structured)
   • Data collection and preparation basics
   • Descriptive statistics (mean, median, variance, etc.)
   • Data visualization fundamentals
   • Correlation and relationship between variables
   • Introduction to analytical thinking
2. 2Day 2: Introduction to Regression Analysis

   • Concept of regression modeling
   • Simple linear regression
   • Relationship between dependent and independent variables
   • Interpreting slope and intercept
   • Error terms and model fit
   • Practical exercises using sample data
3. 3Day 3: Multiple Regression Analysis

   • Expanding to multiple variables
   • Building multiple regression models
   • Understanding coefficients and variable impact
   • Multicollinearity concept (intro level)
   • Model interpretation techniques
   • Hands-on regression modeling practice
4. 4Day 4: Model Evaluation and Performance

   • Measuring model accuracy (R² and error metrics)
   • Residual analysis and interpretation
   • Detecting model weaknesses
   • Overfitting and underfitting concepts
   • Improving model reliability
   • Basic validation techniques
5. 5Day 5: Practical Applications of Regression Modeling

   • Applying regression to real-world datasets
   • Forecasting and prediction basics
   • Using regression outputs for decision-making
   • Common pitfalls in data analysis
   • Best practices in reporting results
   • Final practical exercise and review