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5.15: Summary

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    138276
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    Linear regression is a cornerstone of business analytics. It provides a clear framework for understanding how changes in input variables affect outcomes and supports predictive forecasting. By modeling relationships based on data, business leaders can reduce uncertainty and improve planning.

    Simple linear regression is useful for quick insights with one predictor, while multiple regression captures more realistic business scenarios involving several inputs. The key to leveraging regression is understanding the outputs—especially coefficients, R², adjusted R², and p-values—and validating the model’s assumptions.

    Different tools support different needs: Excel for accessibility, Python or RStudio for statistical depth, Tableau for communication, and ChatGPT for concept clarification. Used solo or in combination, they empower MBA students and managers to incorporate data into their decision-making process effectively.

    Ultimately, regression is not just a math technique—it’s a storytelling tool. When done correctly, it allows business professionals to craft evidence-based narratives that inform action and strategy.


    This page titled 5.15: Summary is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Elbert L. Hearon, M.B.A., M.S..

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