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What is $\\hat{y}_i$?

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The model's prediction of $y_i$.

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The mean of $y_i$.

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The $i^{\\text{th}}$ $y$ value.

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The variance of the $y$ values.

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What can the formula be rearranged to?

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$R^2 = \\dfrac{\\sum(y_i-\\bar{y})^2-(y_i-\\hat{y}_i)^2}{\\sum(y_i-\\bar{y})^2}$

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$R = 1 - \\dfrac{\\sum(y_i-\\hat{y}_i)}{\\sum(y_i - \\bar{y})}$

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$R^2 = 1 - \\dfrac{\\sum(\\hat{y}_i-\\bar{y})^2}{\\sum(y_i-\\bar{y})^2}$

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$R^2 = 1 - \\dfrac{\\sum\\hat{y}_i^2}{\\sum\\bar{y}^2}$

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What is the largest value $R$ can take?

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Given the formula for the coefficient of determination of a regression model,

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\\[ R^2 = 1 - \\dfrac{\\sum(y_i-\\hat{y}_i)^2}{\\sum(y_i-\\bar{y})^2}, \\]

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answer the following questions

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