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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:
C
Step-by-step explanation:
The elevati0on of sea level is 0 km and the elevation of the summit is 10 km
Answer:
95% confidence interval: (0.325 ,0.383)
Step-by-step explanation:
We are given the following in the question:
Sample size, n = 231
Sample mean = 0.354 ppm
Sample standard deviation = 0.231 ppm
95% confidence interval:
Putting the values, we get,
Answer:
The total number of feet travelled by the motorcycle is 396 feet
Step-by-step explanation:
Using the constant speed relationship with the distance:
v = s/t
From the above equation the distance travelled will be:
s = v * t (i)
We are given with the v, that is:
v = 45 miles per hour
Converting in to feet per second.
v = (45 * 5280)/3600 feet / s
v = 66 feet per second
Now, put that v and t in equation (i)
s = 66 * 6
s = 396 feet
