The following data represent the number of flash drives sold per day at a localcomputer shop and their prices. Price Units Sold34 336 432 635 530 938 240 1a. Develop the estimated regression equation that could be used to predict thequantity sold given the price. Interpret the slope. b. Did the estimated regression equation provide a good fit? Explain. c. Compute the sample correlation coefficient between the price and the number offlash drives sold. Use a= 0.01 to test the relationship between price and units sold. d. How many units can be sold per day if the price of flash drive is set to $28.

a)3145 x 0.01 = 31.45  3145- 31.45 = 3113.55

Compute the sample correlation 3113.55 -? we find the least square pressing at least 15x on the calculator then minus this from 3113.55 to find a better fit and minimum regression.

We add the differences of units then divide by distribution as seen below.

b) unsure.

c) = (see below) just test each number shown unit sold per day / price then x can show the differences in each number from day 1 to day 2.

d) = 16 sold.

Step-by-step explanation:

a) We count the units up and deduct from it from the equation p is recognized as units sold. R1 is cost R2 is total days.

b) The line of best fit is described by the equation ŷ = bX + a, where b is the slope of the line and a is the intercept (i.e., the value of Y when X = 0).

c) r 2= decimal ; the regression equation has accounted for percentage of the total sum of squares. You cna do this one.

d) = 16 sold at $28 each. - Why ? We using 7 day data and prove a how many units can be sold p/d if the price of flash drive is set to $28 each per unit.

Day 1 = 34  /  28  =  1      =    1.21428571429 = 1 no difference day prior.

Day 2 = 336   / 28   = 12  = 12 = difference day prior is 11

Day 3 = 432  / 28  = 15    =  15.4285714286   = 15 difference day prior is 3

Day 4 = 635  / 28 =  23   =  22.6785714286  = 23 difference day prior is 8

Day 5  = 530  /  28 = 19    =  18.9285714286  = 19 difference day prior is minus - 4

Day 6 = 938  / 28  =  34   = 33.5 = 34 difference day prior is 15

Day 7 = 240  / 28  =   9    =   8.57142857143 = 9 difference day prior is minus -25

Total days 7 = Total revenue / price = average units sold

Average units sold total = 1+ 12+15 +23 +19+34+9 = 113 rounded.

Average units sold total = 1.21428571429 + 12 + 15.4285714286

+ 22.6785714286

+18.9285714286

+ 33.5

+ 8.57142857143 =  112.321428572 units sold weekly when priced at $28

To answer D we divide this by 7  to show;

112.321428572/ 7 = 16.0459183674

Daily units sold = 16

it is a secret for it not to be compromised sorry

step-by-step explanation: