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:
- L(t) = 727.775 -51.875cos(2π(t +11)/365)
- 705.93 minutes
Step-by-step explanation:
a) The midline of the function is the average of the peak values:
(675.85 +779.60)/2 = 727.725 . . . minutes
The amplitude of the function is half the difference of the peak values:
(779.60 -675.85)/2 = 51.875 . . . minutes
Since the minimum of the function is closest to the origin, we choose to use the negative cosine function as the parent function.
Where t is the number of days from 1 January, we want to shift the graph 11 units to the left, so we will use (t+11) in our function definition.
Since the period is 365 days, we will use (2π/365) as the scale factor for the argument of the cosine function.
Our formula is ...
L(t) = 727.775 -51.875cos(2π(t +11)/365)
__
b) L(55) ≈ 705.93 minutes
Answer: 16x-25 over 4(4x-5)(2x-3)
Step-by-step explanation:
Answer:
f(x) = 4
Step-by-step explanation:
Let us assume that the linear function f(x) = ax + b
Here, we have to find the value of a and b to know the function.
Given that f(-10) = 4 and f(-2) = 4
So, -10a + b = 4 ....... (1) and
-2a + b = 4 .......... (2)
From equations (1) and (2) we get a = 0 and b = 4.
So, the linear function is f(x) = 4. (Answer)
Answer:
A. x > 13
Step-by-step explanation:
x-3>10
x>10+3
x>13
