Date & Time: 4th, April 2026 | 2:30 PM CET Via Microsoft Teams
Regression is more than just “drawing a line through dots.” It is the foundational language of predictive modeling and the secret behind how businesses forecast trends, evaluate risks, and make data-driven decisions.
In Session 5 of our Data Science In Action Bootcamp, we move from exploration to estimation. We will dive deep into the mechanics of Linear Regression, teaching you not just how to build a model, but how to ensure it is statistically sound and reliable.
What You Will Achieve:
By the end of this session, you will be able to:
- Understand the Concept of Regression Analysis: Grasp the theoretical framework behind predicting dependent variables.
- Apply the Ordinary Least Squares (OLS) Method: Master the mathematical engine that minimizes the sum of squared residuals.
- Develop and Interpret Regression Models: Build models from scratch and translate coefficients into business insights.
- Evaluate Model Fit and Statistical Significance: Use $R^2$, p-values, and F-statistics to prove your model actually works.
- Use Statistical Software for Regression Analysis: Execute professional-grade workflows using industry-standard tools (R Software).
- Interpret Residuals and Model Errors: Interrogate your "leftover" data to identify bias and improve accuracy.
Who Should Attend?
Linear Regression is the “gateway” to Machine Learning. Whether you are aiming to be a Data Scientist, a Financial Analyst, or a Researcher, mastering the OLS method and Residual Analysis is non-negotiable for building credible, defensible models.
