Central Limit Theorem question

In summary, the conversation discusses a problem involving the weight of a mint produced by a machine as a random variable with a mean of 10gm and a variance of 2gm^2. The problem asks for the approximate probability that a bag with contents weighing 1000g will have more than 98 but less than 103 mints, as well as the target weight for the contents so that there will be at least 100 mints in each bag with a probability of 0.99. The concept of the central limit theorem is also mentioned as a potential solution to the problem.
  • #1
murph563
2
0
Hi i was wondering if you could help me with the following question people?:
The weight of a mint produced by a machine is a random variable with mean 10gm variance 2gm^2.
1. What is the approximate probability that a bag with contents weighing 1000g will have more than 98 but less than 103 mints?
2. find the target weight for the contents so that there will be at least 100 mints in each bag with a probability of 0.99
I don't know what to put for n, and how to go about answering the question help people?
 
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  • #2
What have you been able to do with the problem? (Even if you've only identified a formula or theorem that might be useful, and have an idea how it might be useful, that's still something)

And what is this "n" of which you speak?
 
  • #3
No, the first part of the question says that 100 mints are in a bag and give the probability that the bag will weigh between 980 and 1100 grams, i get that part cos u take n to be 100, but i don't know how ud go about answering these next few parts
 
  • #4
Okay, here's a start: since you titled this "Central Limit Theorem", the central limit theorem must play some part here. What does the central limit theorem say?
 

What is the Central Limit Theorem?

The Central Limit Theorem (CLT) is a fundamental concept in statistics that states that when independent random variables are added, their sum tends to be normally distributed. This means that if we take a sample of any distribution, the distribution of the sample means will tend to follow 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 population based on a sample. It also forms the basis for many statistical tests and models, such as hypothesis testing and confidence intervals.

What are the assumptions for the Central Limit Theorem?

The Central Limit Theorem assumes that the sample is drawn from a population with a finite variance, and that the sample size is sufficiently large (usually n > 30). Additionally, the samples must be independent and identically distributed.

How do you apply the Central Limit Theorem in practice?

In practice, the Central Limit Theorem can be applied by taking a random sample from a population and calculating the mean of the sample. This mean can then be used to make inferences about the population mean using the normal distribution.

What are the limitations of the Central Limit Theorem?

The Central Limit Theorem is not applicable to all distributions, as it only holds true for certain types of distributions. Additionally, the sample size must be sufficiently large for the CLT to hold. If the sample size is too small, the distribution of the sample means may not follow a normal distribution.

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