Welcome to the course on Statistical Modeling and Regression! This topic is fundamental in data science, machine learning, and empirical research. Let's dive into the key concepts and applications.

🔍 Course Overview

Statistical modeling involves creating mathematical representations of real-world processes to analyze data and make predictions. Regression, a core technique, helps quantify relationships between variables.

  • Linear Regression: Models relationships using a straight line.
  • Multiple Regression: Extends linear regression to include multiple predictors.
  • Nonlinear Regression: Deals with curves and complex patterns.
  • Logistic Regression: Used for classification tasks.

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Statistical_Model

🎯 Learning Objectives

By the end of this course, you'll be able to:

  1. Understand the principles of statistical modeling.
  2. Apply regression techniques to real datasets.
  3. Interpret regression results and evaluate model performance.
  4. Explore advanced topics like regularization and model selection.

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Regression_Analysis

📘 Course Structure

  • Week 1: Introduction to statistical models and basic regression concepts.
  • Week 2: Linear regression and its assumptions.
  • Week 3: Multiple regression and variable selection.
  • Week 4: Advanced regression techniques (e.g., Ridge, Lasso).
  • Week 5: Model evaluation and case studies.

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Machine_Learning

📚 Recommended Resources

For hands-on practice, try implementing regression models using Python or R. 🐍📊

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