Variance, Covariance Matrix, Linear Regression, Standard Error, Confidence Interval.
i.e. e’e. We also know that the fitted model’s equation can be written as: y = Xβ_cap + e, or, in terms of e, e=(y — Xβ_cap). Thus, we need to minimize the following so as to fit the linear model using the least squares technique: (Image by Author) To do so, we will take the partial derivative of