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Covariance and variance doesn't make sense for realised (non-stochastic) variables. So removed the expressions with them and expanded the last term. |
→Partitioning in simple linear regression: Just moved some sums around to make it a little more readable |
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Therefore,
:<math>\sum_{i=1}^n 2(\hat{y}_i-\bar{y})(y_i-\hat{y}_i) = 2\hat{b}\sum_{i=1}^n (x_i-\bar{x})(y_i-\hat{y}_i) = 2\hat{b}\sum_{i=1}^n (x_i-\bar{x})((y_i - \bar{y}) - \hat{b}(x_i - \bar{x}))</math>
:<math>2b\left(\sum_{
==Partitioning in the general ordinary least squares model==
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