I think the correct answer would be B. If the residuals for brand A form an increasing curve, and the residuals for brand B form a U-shaped pattern, then neither of the data is likely to be linear. In order to be linear, the residuals of both data set should be, more or less, linear or approaching linearity in nature. Therefore, the linear regression that was done would not give good results since it is only applicable to linear data sets. Also, you can say that the relation of the data sets of the products are not linear. It would be best to do a curve fitting for both sets by using different functions like parabolic functions.
Answer:
3 stands for the main amount.
Step-by-step explanation:
Answer:
(0,-2), (5,0) and (10,2).
Step-by-step explanation:
Given equation is 
.
Now we need to find 3 pairs of solutions in (x,y) form for the given equation.
As is a linear equation so we are free to pick any number for x like x=0, 5, 10
Plug x=0 into , we get:
Hence first solution is (0,-2)
We can repeat same process with x=5 and 10 to get the other solutions.
Hence final answer is (0,-2), (5,0) and (10,2).
Answer:
The probability of the bus getting there BEFORE 8:20 isn't likely
I took an average of all of the numbers and its shows that he bus will be there about 8:20.25
