This video follows from where we left off in Part 2 in this series on the details of Logistic Regression. Last time we saw how to fit a squiggly line to the data. This time we'll learn how to evaluate if that squiggly line is worth anything. In short, we'll calculate the R-squared value and it's associated p-value.
NOTE: This StatQuest assumes that you are already familiar with Part 1 in this series, Logistic Regression Details Pt1: Coefficients:
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Correction:
13:58 The formula at should be 2[(LL(saturated) - LL(overall)) - (LL(saturated) - LL(fit))]. I got the terms flipped.
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