The scatter plot shows a regression line of advertising dollars spent and sales. If the company were to spend $6,500 on advertising, what would be the predicted sales? A) $110,000 B) $114,000 C) $118,000 D)$122,000

if the company were to spend $7000 on advertising the predicted sales would be 128000 approx.

Step-by-step explanation:

X axis data is  advertising in in continuous in thousand dollars.

Y axis data is sales which is discrete in thousand dollars.

Since the number could in graph show extreme result in graph.

The counts are taken in lower digits.

X axis is assuming 5.7,5.85,5.95,6,6.05,6.15,6.3,6.4,6.45,6.5,6.55

Y axis is assuming 108,109,113,111,110,115,114,119,118,116,118.

After plotting this in the graph it is as per the diagrams.

Algorithm for Linear regression is OLS ( ordinary least sqaure)

formula is y = mx + c, y being the dependent variable...

x being the dependent variable and c is constant.

A constant is there is minimum start to sales in every product.

Calculate slope m which is m =  N Σ(xy) − Σx ΣyN/ Σ(x2) − (Σx)2

calculate intercept which is c = b =  Σy − m Σx /N

now put it in y = mx + c , as per the above data y= 18.412x

answer is $ 128884 whic is closer to 128000

idk but i found this "the graph of y = f(x-2) is the graph of y = f(x) translated two units rightward.  

so, since the point (9,2) lies on the graph of y = f(x), the point (11,2) lies on the graph of y = f(x-2)."

step-by-step explanation:

link is

step-by-step explanation:

this is not possible to solve with the info given.   you need at least an one angle somewhere.