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Refers to a general estimation technique that selects the parameter value to minimize the squared difference between two quantities, such as the observed value of a variable, and the expected value of that observation conditioned on the parameter value. Gaussian linear models are fit by least squares and least squares is the idea underlying the use of mean-squared-error (MSE) as a way of evaluating an estimator.

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Show regression line passes through points $(X_1,\bar{Y_2}),(X_2,\bar{Y_2})$

To find out if a point lies on the line, we can plug the values in for x and y just like in regular algebra. Recall the simple regression line formula is: $$\hat{y} = \hat{\theta}_0 + \hat{\theta}_1 x …
Kiernynn Grantham-Crum's user avatar