# Matrix regression proof that $\hat \beta = (X' X)^{-1} X' Y = {\hat \beta_0 \choose \hat \beta_1}$

Matrix regression proof that $$\hat \beta = (X' X)^{-1} X' Y = {\hat \beta_0 \choose \hat \beta_1}$$

where $$\beta$$ is the least square estimator of $$\hat\beta$$ of $$\beta$$

attempt

So I know $${\hat \beta_0 \choose \hat \beta_1} = {\overline{Y} - \hat \beta_1 \overline{X} \choose \frac{\sum_{i=1}^{n} (X_i - \overline{X})(Y_i - \overline{X})}{\sum_{i=1}^{n}(X_i - \overline{X})^2}}$$

Not really sure how to start as I don't know what formulas there are to reduce any of this. And if this was answered elsewhere please duplicate I was trying to search but couldn't

• – Minus One-Twelfth Jun 30 at 8:08
• The steps there are basically 1) Recall that the least squares estimator is chosen to minimise (with respect to $\beta$) the function $$S(\beta):= (y-X\beta)^T (y - X\beta);$$ 2) expand this to show that $$S(\beta) = y^T y - 2y^T X \beta + \beta^T X^T X \beta;$$ 3) use matrix calculus to find the $\beta$ that minimises this (calculate $\frac{\partial S}{\partial \beta}$, set to $\mathbf{0}$ and solve for $\beta$). – Minus One-Twelfth Jun 30 at 8:13

In a slight variant on @MinusOne-Twelfth's comment,$$\frac{\partial}{\partial\beta_i}(y-X\beta)_j=-X_{ji}\implies\frac{\partial}{\partial\beta_i}\sum_j(y-X\beta)_j^2=2\sum_jX_{ij}^T(X\beta-y)_j=2(X^\prime X\beta-X^\prime y)_i.$$Setting this to $$0$$ for all $$i$$,$$X^\prime X\beta=X^\prime y\implies\beta=(X^\prime X)^{-1}X^\prime y.$$
• I'm confused how finding $\beta$ equals ${\beta_0 \choose \beta_1}$? – bob Jun 30 at 23:04
• @bob The easiest option is to double-check $X^{\prime}X\left(\begin{array}{c} \beta_{0}\\ \beta_{1} \end{array}\right)=X^{\prime}y$. – J.G. Jul 1 at 5:28
Our goal is to minimize $$f(\beta) = \frac12 \| X \beta - Y \|^2.$$ Notice that $$f = g \circ h$$, where $$h(\beta) = X \beta - Y$$ and $$g(u) = \frac12 \| u \|^2$$. The derivatives of $$g$$ and $$h$$ are given by $$g'(u) = u^T, \quad h'(\beta) = X.$$ By the chain rule, we have \begin{align} f'(\beta) &= g'(h(\beta)) h'(\beta) \\ &= (X \beta - Y)^T X. \end{align} The gradient of $$f$$ is $$\nabla f(\beta) = f'(\beta)^T = X^T( X \beta - Y).$$ Setting the gradient of $$f$$ equal to $$0$$, we discover that $$X^T X \beta = X^T Y.$$