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University of California, Berkeley

Linear Regression

University of California, Berkeley via edX

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Overview

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This course provides learners with advanced tools for modeling and prediction in business analytics. Building on prior coursework, students apply regression methods to identify relationships between variables, forecast performance, and uncover causal patterns.

Through case studies in marketing, operations, economics, and technology, learners will:

  • Develop simple and multiple regression models in Python.
  • Test for non-linearity, discontinuities, trends, and seasonality.
  • Incorporate interaction effects to capture complex business dynamics.
  • Evaluate model validity, assumptions, and fit.
  • Use regression outcomes to guide decision-making across industries.

This course blends theory with practical application, giving students the chance to work directly with business datasets. By the end, learners will be able to construct predictive models that improve strategy and decision-making in a variety of contexts.

Syllabus

  1. Simple and multiple linear regression
  2. Non-linearity, discontinuity, and seasonal effects
  3. Interaction terms and complex modeling
  4. Model evaluation and diagnostic tools
  5. Business applications of regression analysis

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