Estimating the Probability of Earning a Certain Amount in a Weekend as a Waiter

In summary, the Central Limit Theorem is a statistical concept that states that the sampling distribution of the means of multiple samples from a population will approximate a normal distribution, regardless of the underlying distribution of the population. It is important because it allows us to make inferences about a larger population based on a sample, and can be applied to all types of data as long as the sample size is large enough. The minimum sample size needed for the Central Limit Theorem to hold depends on the underlying distribution of the population, and it does not guarantee that the sample mean will be equal to the population mean, but as the sample size increases, the sample mean will become a better estimate of the population mean.
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h91907
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A waiter believes the distribution of his tips has a model that is slightly skewed to the left, with a mean of $\$8.20$ and a standard deviation of ​$\$5.60$. He usually waits on about 60 parties over a weekend of work. ​a) Estimate the probability that he will earn at least ​$\$600$. ​b) How much does he earn on the best 1​% of such​ weekends?

I worked out A and got 0.0064. I am having an issue on B. I don't understand how you obtain z​.
 
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  • #2
I have figured out on my own. 1-00.1= .99 find it on the z-scale!
 

Related to Estimating the Probability of Earning a Certain Amount in a Weekend as a Waiter

What is the Central Limit Theorem?

The Central Limit Theorem is a statistical concept that states that regardless of the underlying distribution of a population, the sampling distribution of the means of multiple samples from that population will approximate a normal distribution.

Why is the Central Limit Theorem important?

The Central Limit Theorem is important because it allows us to make inferences about a larger population based on a sample. This is useful in research and data analysis, as it allows us to draw conclusions and make predictions with a certain level of confidence.

Can the Central Limit Theorem be applied to all types of data?

Yes, the Central Limit Theorem can be applied to all types of data, as long as the sample size is large enough. However, it works best for data that is normally distributed.

What is the minimum sample size needed for the Central Limit Theorem to hold?

The minimum sample size needed for the Central Limit Theorem to hold depends on the underlying distribution of the population. In general, a sample size of at least 30 is recommended for the Central Limit Theorem to work effectively.

Does the Central Limit Theorem guarantee that the sample mean will be equal to the population mean?

No, the Central Limit Theorem does not guarantee that the sample mean will be equal to the population mean. However, as the sample size increases, the sample mean will become a better estimate of the population mean.

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