$$\text { Find the least-squares line } y = \beta _ { 0 } + \beta _ { z } x \text { that best fits the given data. }$$

$$\text { Find the least-squares line } y = \beta _ { 0 } + \beta _ { z } x \text { that best fits the given data. }$$
-Given: The data points (-3, 2) , (-2, 5) , (0, 5) , (2, 3) , (3, 3) . Suppose the errors in measuring the y-values of the last two data points are greater than for the
Other points. Weight these data points half as much as the rest of the data. $$X = \left[ \begin{array} { r r } 1 & - 3 \\1 & - 2 \\1 & 0 \\1 & 2 \\1 & 3\end{array} \right] , \beta = \left[ \begin{array} { l } \beta _ { 1 } \\\beta _ { 2 }\end{array} \right] , y = \left[ \begin{array} { l } 2 \\5 \\5 \\3 \\3\end{array} \right]$$

A) $y = 0.18 + 4.1 x$
B) $y = 0.8 + 0.60 x$
C) $y = 4.5 + 0.60 x$
D) $\mathrm { y } = 4.1 + 0.18 \mathrm { x }$