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.
Answer:
Interval notation: 
Step-by-step explanation:
<u>First inequality:</u>
<u />
Therefore, this inequality restricts:
<u>Second inequality:</u>
Therefore, this inequality restricts:
Therefore, with both of these restrictions together, we have:
.
On Monday the town got 2 3/4 (2.75) inches of snow.
On Tuesday it got 1 1/2 (1.5) times as much: 2.75 x 1.5 = 1.375 (inch)
⇒ On Tuesday the town got 1.375 inch of snow.
On Wednesday it got 7/8 (0.875) inch of snow<span>.
</span>After Monday, Tuesday and Wednesday, the town got:
2.75 + 1.375 + 0.875 = 5 (inches of snow)
For the rest of the week, the town got:
15 1/2 - 5 = 10 1/2 = 10.5 (inches of snow)
Answer:
X = 4 pls mark me brainliest
