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:
brainly.com/question/13652429
#SPJ4
Let the unknown be x
Both the base number are 3, so we equate the exponent:
Answer: 1/6
Answer:
sin (x) ≥ 0, between 0° and 180° or 0 and π,
sin(x) ≥ 1/2, between 30° and 150° or π/6 and 5π/6
Step-by-step explanation:
This is how I would do it. Subtract sin(x) from both sides
2
- sin(x) ≥ 0 , then factor out sin(x)
sin(x) [2 sin(x) - 1] ≥0, then set each factor ≥ 0
sin(x) ≥ 0 and 2 sin(x) - 1 ≥ 0
sin (x) ≥ 0, between 0° and 180° or 0 and π
2 sin(x) ≥ 1
sin(x) ≥ 1/2, between 30° and 150° or π/6 and 5π/6
Answer:
False
Step-by-step explanation:
They will have different angle measures and un-proportional sides, making them non similar.
