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.
The equation that has the same solution as 2.3p – 10.1 = 6.5p – 4 – 0.01p are as follows;
<h3>How to rewrite an equation? </h3>
2.3p – 10.1 = 6.5p – 4 – 0.01p
The equation that has the same solution as above can be found as follows;
2.3p – 10.1 = 6.5p – 4 – 0.01p
combine like terms
2.3p – 10.1 = 6.5p – 0.01p - 4
2.3p – 10.1 = 6.49p – 4
Multiply through by 100.
Therefore,
230p - 1010 = 649p - 400
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That the answer, I hope that will help you
7 inches per what per mile per foot what
-7 - 12 = -1(7 + 12) = -1(19) = -19
Your answer is A.
