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:
Linear, Corresponding, and Vertical
Step-by-step explanation:
5 and 8 would have the same degree. So would 1 and 4, and 2 and 3, and 6 and 7 (lot's of "and's" because it's lots of the same degrees...)
11x+2 = 9x+6. Subtract 2 from both sides. 11x = 9x+6. subtract 9x from both sides. 2x = 4, divide both sides by 2. x = 2. Substitute the x with 2 and check to see it it works. The numbers should equal each other.
Answer:
Approx. 6 b/m
Step-by-step explanation:
Find out How many minutes in a year which is 525600 then divide that by 3,000,000 which makes 5.7077 so rounded is 6.
Answer:
40
Step-by-step explanation:
it would be first bodmas so
(15) /3 * root 64
5 * root 64
5*8
40
