Which assumption will work best for a large data set with a normal distribution? a.Most of the data points are close to the mini
mum value of the set. b. Most of the data points are close to the maximum value of the set. c. Most of the data points are close to the mean value of the set d. The data points are distributed uniformly over the range of the set.
I think the correct answer would be option C. The best assumption that will work for a large data set having a normal distribution would be that most of the data points are close to the mean value of the set. Having this assumption, we can use freely the common measures of central tendency as statistical tools.
Option C) Most of the data points are close to the mean value of the set
Step-by-step explanation:
We are given the following information in the question:
Properties of Normal Distribution
It is a probability distribution which is symmetric about the mean that means data near the mean.
In graph form, normal distribution will appear as a bell curve.
In a normal distribution, Mean = Mode = Median
We have to choose the best assumption that will work for a large data set with normal distribution.
Option C) Most of the data points are close to the mean value of the set
It is one of the properties of the normal distribution. Most of the data point cannot be close to the minimum or maximum value and nor the data can be distributed uniformly over the range of the set.
The answer would be 42 degrees because the total of a triangle is 180 degrees. So you would subtract 180 and 42 (180-42) and that gives you 138 and since the two angles that are touching are the same
