As a rule of thumb, the sampling distribution of the sample proportion can be approximated by a normal probability distribution whenever the sample size is large.
<h3>What is the Central limit theorem?</h3>
- The Central limit theorem says that the normal probability distribution is used to approximate the sampling distribution of the sample proportions and sample means whenever the sample size is large.
- Approximation of the distribution occurs when the sample size is greater than or equal to 30 and n(1 - p) ≥ 5.
Thus, as a rule of thumb, the sampling distribution of the sample proportions can be approximated by a normal probability distribution when the sample size is large and each element is selected independently from the same population.
Learn more about the central limit theorem here:
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The correct answer is the first option -
ninth root of x squared.
To see how I got that answer, take a look at the attachment below.
Answer:
(-5, 3)
Step-by-step explanation:
B = 2M - A
B = 2(-2, 5) -(1, 7) = (-4-1, 10-7) = (-5, 3)
<u>Given</u>
- a = m(2) + 2
- value of a when m = -3
<u>Substitute m with -3</u>
a = -3*2 + 2
a = -6 + 2
a = -4
<u>Answer</u>
The value of a when m = -3 is -4
