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.
If two expressions are equal to each other, and you add the same value to both sides of the equation, the equation will remain equal. When you solve an equation, you find the value of the variable that makes the equation true. In order to solve the equation, you isolate the variable.
