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Book Overview & Buying
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Table Of Contents
Python in Excel for Data Analytics
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In this chapter, you learned how to use Python in Excel to perform statistical inference in a way that is practical, visual, and grounded in real decision-making. Working with the fuel mileage dataset throughout, you ran core inferential tests including independent samples t-tests, ANOVA with post-hoc analysis, and Chi-square tests of independence. You also learned how to interpret p-values alongside effect sizes to assess both statistical and practical significance.
You also explored resampling with bootstrapping as a flexible, data-driven approach to quantifying uncertainty. By generating confidence intervals and visualizing bootstrap distributions, you moved beyond single point estimates and learned to communicate variability in a way that is more intuitive for stakeholders.
Throughout the chapter, the emphasis was on pairing statistical results with domain understanding rather than treating significance tests as automatic decision rules. A low p-value tells you a difference...