Retail Price Data for N = 60 Hard Disk Drives $y =$

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: $y =$ Retail PRICE (measured in dollars)
$x _ { 1 } =$ Microprocessor SPEED (measured in megahertz)
(Values in sample range from 10 to 40 )
$x _ { 2 } = \mathrm { CHIP }$ size (measured in computer processing units)
(Values in sample range from 286 to 486 )
A first-order regression model was fit to the data. Part of the printout follows:
Parameter Estimates
$\begin{array}{lrllll}{\text { PARAMETER STANDARD }} & \text { T FOR 0: } \\\text { VARIABLE } & \text { DF } & \text { ESTIMATE } & \text { ERROR PARAMETER }=0 \text { PROB }>\text { |T| } =\\\text { INTERCEPT } 1 & -373.526392 & 1258.1243396-0.297 & 0.7676 \\\text { SPEED }\quad\quad\quad1 & 104.838940 & 22.36298195 4.688 & 0.0001 \\\text { CHIP } \quad\quad\quad\quad1 & 3.571850 & 3.89422935 0.917 & 0.3629\end{array}$

Identify and interpret the estimate for the SPEED $\beta$ -coefficient, $\hat { \beta } _ { 1 }$ .
A) $\hat { \beta } _ { 1 } = 105$ ; For every 1-megahertz increase in SPEED, we estimate PRICE $( y )$ to increase $\$ 105$ , holding CHIP fixed.
B) $\hat { \beta } _ { 1 } = 105$ ; For every $\$ 1$ increase in PRICE, we estimate SPEED to increase 105 megahertz, holding CHIP fixed.
C) $\hat { \beta } _ { 1 } = 3.57 ;$ For every 1 -megahertz increase in SPEED, we estimate PRICE to increase $\$ 3,57$ , holding CHIP fixed.
D) $\hat { \beta } _ { 1 } = 3.57$ ; For every $\$ 1$ increase in PRICE, we estimate SPPED to increase by about 4 megahertz, holding CHIP fixed.