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7_REGMODS/Regression Models Course Notes HTML.Rmd

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7_REGMODS/Regression Models Course Notes.Rmd

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### Derivation for Least Squares = Empirical Mean (Finding the Minimum)
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* Let $X_i =$ **regressor**/**predictor**, and $Y_i =$ **outcome**/**result** so we want to minimize the the squares: $$\sum_{i=1}^n (Y_i - \mu)^2$$
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* Let $X_i$ = **regressor**/**predictor**, and $Y_i =$ **outcome**/**result** so we want to minimize the the squares: $$\sum_{i=1}^n (Y_i - \mu)^2$$
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* Proof is as follows $$
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\begin{aligned}
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\sum_{i=1}^n (Y_i - \mu)^2 & = \sum_{i=1}^n (Y_i - \bar Y + \bar Y - \mu)^2 \Leftarrow \mbox{added} \pm \bar Y \mbox{which is adding 0 to the original equation}\\

7_REGMODS/Regression_Models_Course_Notes.html

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8_PREDMACHLEARN/Practical Machine Learning Course Notes HTML.Rmd

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8_PREDMACHLEARN/Practical_Machine_Learning_Course_Notes.html

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9_DEVDATAPROD/Developing Data Products Course Notes HTML.Rmd

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9_DEVDATAPROD/Developing Data Products Course Notes.Rmd

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