Probability Question - Nonstandard Normal Distributions

In summary, the weight of eggs from a certain type of hen is normally distributed with a mean of 6.5 grams and a standard deviation of 2 grams. The probability that the average weight of a random sample of 25 eggs will be less than 6 grams is determined by taking the original standard deviation of 2 grams and dividing it by the square root of 25, resulting in a standard deviation of 0.4 grams for the sample mean. This means that the probability can be calculated using the normal distribution with a mean of 6.5 grams and a standard deviation of 0.4 grams.
  • #1
Love_to_Learn
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Homework Statement

The weight of eggs produced by a certain type of hen varies according to a distribution that is approximately normal with mean 6.5 grams and standard deviation 2 grams.

What is the probability that the average of a random sample of the weights of 25 eggs will be less than 6 grams



Homework Equations


P(X<6)=P((6-6.5)/σ)



The Attempt at a Solution

- The part I can't figure out is how to arrive at sigma. This is a problem from a practice exam, so I already know that sigma is 0.40. If I'm understanding correctly, then that would make V(X) = 4/25. I just can't figure out how to arrive at these conclusion from the data that is given. I'm pretty stumped.
 
Last edited:
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  • #2
your problem asks about the probability the MEAN of a sample will be a certain size. what do you know about distributions of sample means?
 
  • #3
statdad said:
your problem asks about the probability the MEAN of a sample will be a certain size. what do you know about distributions of sample means?

Not much.

I think I figured out why sigma is what it is though. Since the SD is 2 grams, it follows that V(X) = 4. Since I'm trying to find out what the average of X is, I divide 4 by 25. that is how I get the 4/25. From there sigma is easy. I think that is kind of close anyway.
 
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  • #4
You've essentially got it. If you take a sample of size [tex] n [/tex] from a normally distributed population, then [tex] \overline X [/tex] has a normal distribution. For the
distribution of [tex] \overline X [/tex],

[tex]
\mu = \text{ original population mean}
[/tex]

and

[tex]
\sigma = \frac{\text{Original standard deviation}}{\sqrt n}
[/tex]

As long as the original population itself has a normal distribution, this is true
for any sample size.
 

1. What is a nonstandard normal distribution?

A nonstandard normal distribution refers to a probability distribution that does not follow the traditional bell-shaped curve of a standard normal distribution. This means that the mean, standard deviation, and shape of the distribution differ from those of a standard normal distribution.

2. How is a nonstandard normal distribution different from a standard normal distribution?

A nonstandard normal distribution differs from a standard normal distribution in terms of its mean, standard deviation, and shape. While a standard normal distribution has a mean of 0 and a standard deviation of 1, a nonstandard normal distribution can have any mean and standard deviation. Additionally, a standard normal distribution has a symmetrical, bell-shaped curve, while a nonstandard normal distribution may have a different shape.

3. What are some examples of nonstandard normal distributions?

Some examples of nonstandard normal distributions include the chi-square distribution, the t-distribution, and the F-distribution. These distributions are commonly used in statistical analysis to model real-world data that does not follow a standard normal distribution.

4. How do you calculate probabilities for nonstandard normal distributions?

To calculate probabilities for nonstandard normal distributions, you can use statistical software or look up the values in a table specific to the distribution. Alternatively, you can use the standard normal distribution and convert the values using the formula z = (x - μ) / σ, where z is the standardized value, x is the observed value, μ is the mean, and σ is the standard deviation.

5. Why are nonstandard normal distributions important?

Nonstandard normal distributions are important in statistics because they allow us to model and analyze data that does not follow a standard normal distribution. This is crucial in real-world applications where data can be complex and varied. By understanding and using nonstandard normal distributions, we can make more accurate predictions and decisions based on data analysis.

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