linearny model

$y_i = \sum_j=0^k \beta_j x_{ij} + \epsilon_i$

$\YY = \XX_B + \Epsilon$

(y_1, ..., y_M)^T -- vektor pozorovani

x_{i,j} * (\beta_1, ..., \beta_k)^T + (\epsilon_1, ...\epsilon_n)^T
i = 1...k
j= 1...n

\epsilon_i \sim N(0, \sigma^2)

linearna regresia - prekladanie priamky y = ax + b hodnotami

vektor rezidui: namerane minus odcitane hodnoty