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.
To find the answer for this question, you need to convert your percent to a fraction. (33/100) You will multiply 60 by 33/100, and get 19.8. That shows you the discount you are getting. Finally, you need to subtract 19.8 from 60, getting you a final answer of $40.2.
Answer:
a) 34mi
b) 10mi
c) 44mi
d) by Calculating the amount of miles it would take John to go to school plus the amount of miles it would take Sara to go to school. Then add the sum of the amount of miles it took for both to go to school.
Step-by-step explanation:
a) explanation:





b) explanation:





c) explanation: we already have all the data from John's house to Sara's House.
John's House to Sara's House = John's House to School + Sara's House to School.
Sara's House to School (Question #1) = 34mi
John's House to School (Question #2) = 10mi
34(mi) + 10(mi) = 44(mi)
d) explanation: explanation is on the answer