What is the Probability of Hereditary Conditions in a Gaussian Distribution?

In summary: Thanks for catching it!Right you are. I taught statistics a number of times, some years ago, but this one slipped my mind. Thanks for catching it!
  • #1
conrad dunk
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Homework Statement


Please help!

I'm new to Gaussian and I've been on this problem for hours, I can't crack it at all (no pun intended) can anyone provide a detailed walk through the answers?

On average 5% of eggs contain a hereditary condition. Use Gaussian distribution to find the probability thatIn a box of ten eggs none carry the hereditary condition
Batch of 500 there are 20 that carry the hereditary condition
In a batch of 10000 there are LESS than 1000 faulty!

Homework Equations


I can't use equation tool on this ancient phone

The Attempt at a Solution


Completely stumped
 
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  • #2
Some of the problem uses the binomial distribution. Parts of the problem are most easily calculated simply using the binomial without even approximating it by a Gaussian. The last part is most readily done by approximating the binomial to a Gaussian with same ## \mu ## and ## \sigma ##. You should know that ## \sigma ## for a binomial is ## \sigma=\sqrt{Npq} ##.
 
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  • #3
conrad dunk said:

Homework Statement


Please help!

I'm new to Gaussian and I've been on this problem for hours, I can't crack it at all (no pun intended) can anyone provide a detailed walk through the answers?

On average 5% of eggs contain a hereditary condition. Use Gaussian distribution to find the probability thatIn a box of ten eggs none carry the hereditary condition
Batch of 500 there are 20 that carry the hereditary condition
In a batch of 10000 there are LESS than 1000 faulty!

Homework Equations


I can't use equation tool on this ancient phone

The Attempt at a Solution


Completely stumped

What, exactly, do you know about the Gaussian distribution? Where are you stuck?

Do you know what mean and variance represent? Do you know how to find the appropriate mean and variance in this problem? Do you know how to transform a problem with arbitrary mean and variance to an equivalent problem with mean = 0 and variance = 1? Do you know what needs to be done after that?
 
  • #4
I need to find the variance and mean and standard deviation from the Gaussian distribution
 
  • #5
conrad dunk said:
I need to find the variance and mean and standard deviation from the Gaussian distribution

No, you don't. You have it backwards. You need to find the appropriate mean and variance; THEN you can figure out what is the correct Gaussian distribution for this problem.
 
  • #6
How do I find that from the supplied data? Sorry I'm really stuck
 
  • #7
conrad dunk said:
How do I find that from the supplied data? Sorry I'm really stuck
Post #2 from @Charles Link is a very strong hint. The underlying distribution is a binomial distribution -- an egg either has the hereditary defect or it doesn't, and the problem here is to approximate this binomial distribution with a normal (or Gaussian) distribution. The p (= ##\mu##) that Charles link mentions is pretty much given in the problem, and he has given an formula for finding ##\sigma##.
 
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  • #8
Mark44 said:
Post #2 from @Charles Link is a very strong hint. The underlying distribution is a binomial distribution -- an egg either has the hereditary defect or it doesn't, and the problem here is to approximate this binomial distribution with a normal (or Gaussian) distribution. The p (= ##\mu##) that Charles link mentions is pretty much given in the problem, and he has given an formula for finding ##\sigma##.
Yes, I agree. One minor correction ## \mu=Np ##.
 
  • #9
Charles Link said:
Yes, I agree. One minor correction ## \mu=Np ##.
Right you are. I taught statistics a number of times, some years ago, but this one slipped my mind.
 
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1. What is a Gaussian distribution?

A Gaussian distribution, also known as a normal distribution, is a type of probability distribution that is commonly used in statistics to describe a large number of random variables. It is characterized by a bell-shaped curve and is often used to analyze data that is continuous and symmetrically distributed around a mean value.

2. How is a Gaussian distribution different from other types of distributions?

Unlike other distributions, such as the uniform or binomial distribution, a Gaussian distribution is completely determined by its mean and standard deviation. This means that once these two parameters are known, the entire distribution can be described. Additionally, the majority of observations in a Gaussian distribution fall within three standard deviations from the mean, making it a very useful tool for analyzing data.

3. What is the significance of the mean and standard deviation in a Gaussian distribution?

The mean represents the central tendency of the distribution, or where the data is most likely to be clustered. The standard deviation, on the other hand, measures the spread of the data around the mean. A larger standard deviation indicates a wider spread of the data, while a smaller standard deviation indicates a more narrow range of values.

4. How is a Gaussian distribution used in real-world applications?

Gaussian distributions are commonly used in various fields, such as economics, psychology, and natural sciences, to model and analyze data. This distribution is also used in statistical tests, such as the t-test and ANOVA, to determine the likelihood of obtaining a particular result by chance. Additionally, many physical phenomena, such as the distribution of particle velocities, follow a Gaussian distribution.

5. Can a Gaussian distribution be skewed?

Although a Gaussian distribution is symmetric around the mean, it is possible for it to be skewed if the data contains outliers or if the underlying population is not normally distributed. In these cases, the mean may not be a good representation of the central tendency and the spread of the data may not be accurately described by the standard deviation.

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