Answer:
D. 30
Step-by-step explanation:
Having a population that doesn't follow normal distribution (skewed) can still have sampling distribution that is completely normal. This fact is presented in the Central Limit Theorem.
Central Limit Theorem: states that we can have a normal distribution of sample means even if the original population doesn't follow normal distribution, we just need to take a large sample.
So how much sample size do we need?
There is no straight forward answer to this rather we have to analyse the situation closely!
1. If the population distribution is already normal then a smaller sample size would be enough to ensure normal distribution.
2. If the population distribution is very skewed than a larger number of sample size is needed to ensure normal distribution. The rule of thumb is to take sample size equal to or more than 30 to be on safer side. This is the case in this problem hence option D fits the best.
Step-by-step explanation:
<u>Properties used</u>
- logₐ a = 1
- log aᵇ = b log a
- log ab = log a + log b
See the steps below
- - log (4*10⁻³) =
- - (log 4 + log 10⁻³) = (log 4 ≈ 0.6 rounded)
- - (0.6 - 3*log 10) =
- - (0.6 - 3*1) =
- - (0.6 - 3) =
- - (- 2.4) =
- 2.4
Answer: subtraction property equality.
Step-by-step explanation:
Answer: Coterminal Angles are angles who share the same initial side and terminal sides. Finding coterminal angles is as simple as adding or subtracting 360° or 2π to each angle, depending on whether the given angle is in degrees or radians.
64 you divide 120 by 75% and get 160 40$ of that is 64
