Assume That the Data Looks as Follows:
Y = $\left( \begin{array} { l } Y _ { 1 } \\Y _ { 2 } \\O \\Y _ { n }\end{array}\right)$

Essay

Assume that the data looks as follows:
Y = $\left( \begin{array} { l } Y _ { 1 } \\Y _ { 2 } \\O \\Y _ { n }\end{array}\right)$ , U = $\left( \begin{array} { c } u _ { 1 } \\u _ { 2 } \\\mathrm { O } \\u _ { n }\end{array} \right)$ , X = $\left( \begin{array} { l } X _ { 11 } \\X _ { 12 } \\\mathrm { O } \\X _ { 1 n }\end{array} \right)$ , and β = (β₁)
Using the formula for the OLS estimator $\hat { \beta }$ = ( $X ^ { \prime }$ X)^-1
$X ^ { \prime }$ ^Y, derive the formula for $\hat { \beta }$ ₁, the only slope in this "regression through the origin."