Retail Price Data for N = 60 Hard Disk Drives

Retail price data for n = 60 hard disk drives were recently reported in a computer magazine. Three variables were recorded for each hard disk drive:
$\begin{aligned} y = & \text { Retail PRICE (measured in dollars) } \\ x _ { 1 } = & \text { Microprocessor SPEED (measured in megahertz) } \\ & \text { (Values in sample range from } 10 \text { to } 40 \text { ) } \\ x _ { 2 } = & \text { CHIP size (measured in computer processing units) } \\ & \text { (Values in sample range from } 286 \text { to } 486 \text { ) } \end{aligned}$
A first-order regression model. was fit to the data. Part of the printout follows:
$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$ Parameter Estimates
$\quad$$\quad$$\quad$$\quad$$\quad$ PARAMETER STANDARD $\quad$ T FOR 0 :
VARIABLE DF ESTIMATE ERROR PARAMETER $= 0$ PROB $> | \mathrm { T } |$
$\begin{array} { l r l l l l } \text { INTERCEPT } &1 & - 373.526392 & 1258.1243396 & - 0.297 & 0.7676 \\\text { SPEED } & 1 & 104.838940 & 22.36298195 & 4.688 & 0.0001 \\\text { CHIP } & 1 & 3.571850 & 3.89422935 & 0.917 & 0.3629\end{array}$


$\text { Identify and interpret the estimate of } \beta_{2} \text {. }$