Calculating Probability with a Normal Distribution

In summary: Thank you for your input!In summary, the bakery has a 50/50 chance of having all 78 cakes ready in less than 380 minutes.
  • #1
splitendz
32
0
Hi Guy's,

I have problems answering questions like this...(i'll just make up a question)

The time it takes to bake a cake in a bakery shop is a random variable that has a normal distribution with a mean of 4.5 minutes and standard deviation of 1 minute.

Lets suppose this bakery has been given an order to make 78 cakes. What is the probability that the baker will have in less than 380 minutes all 78 cakes ready?

My first instinct is to treat this as a poisson distribution but if I was to evaulate P[N < 79] it would simply take too long.

Any ideas would be great :)
 
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  • #2
The sample mean is distributed about normally. In this case it is distributed exactly normally, because the observations have the normal distribution. The sample mean has the same mean as the population mean, and its standard deviation is the population's standard deviation divided by the square root of the sample size. With u the mean baking time for his sample of 78 cakes, the mean of u is 4.5 and the standard deviation of u is 1/sqrt(78). From the distribution of the mean, you can easily determine the distribution of the sum.
 
  • #3
BT - why talk about the sample mean?

The sum of n normal distributions(mu,sigma) is distributed normally with mean equal to n * mu, standard deviation = sqrt(n)*sigma. That's a fairly standard result due to the fact that when adding two independent random variables with normal distributions, sum their means, and add their variances for the distribution of their sum.
 
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  • #4
Or, you could do it your way.

I was talking about the sample mean because maybe his pie baking times are _approximately_ normal, and not _exactly_ normal. By the Central Limit Theorem you can say that the sample mean is still about normal. Not that it makes a difference in this case, but that's why I approached it by the sample mean.
 
  • #5
How will knowing the sample distribution of the sum assist in determining the probability of the baker making 78 cakes in under 380 minutes?
 
  • #6
The total time it takes to bake the 78 cakes is the sum of the sample of the 78 baking times of each of the cakes.
 
  • #7
Ah, I think I get it. The mean for a population and a sample are the same and the standard deviation is given by 1/sqrt(N) as you said. Once you have the stanard deviation and the mean for the sample distribution you can then standardise the normal distribution and find the probability accordingly. In this case it would be the probability that the time it takes to bake a cake is less than 380/78 given mu = 4.5 and a standard deviation of 1/sqrt(78) = 0.11. Right?
 

1. What is a normal distribution?

A normal distribution, also known as a bell curve, is a type of probability distribution that is symmetrical and has a characteristic bell-shaped curve. It is often used to represent a wide range of natural phenomena, such as height and weight of a population or test scores of students.

2. How is a normal distribution calculated?

A normal distribution is calculated using the mean, standard deviation, and a mathematical formula known as the normal curve equation. The formula is (1/σ√2π) * e^(-1/2((x-µ)/σ)^2), where µ represents the mean and σ represents the standard deviation.

3. What are the properties of a normal distribution?

Some of the key properties of a normal distribution include its symmetrical shape, with the mean, median, and mode all being equal. It also has a mean of 0 and a standard deviation of 1, and approximately 68% of the data falls within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations.

4. What is the significance of the normal distribution in statistics?

The normal distribution is significant in statistics because it is used as a model to represent many natural phenomena and it allows for the calculation of probabilities based on known parameters. It is also used as a basis for many statistical tests and analyses, making it a fundamental concept in the field of statistics.

5. What are some real-world examples of a normal distribution?

Some common examples of a normal distribution in the real world include the distribution of heights and weights in a population, the distribution of test scores in a classroom, and the distribution of IQ scores in a population. It can also be seen in natural phenomena such as the distribution of animal sizes in a species or the distribution of rainfall amounts in a specific region.

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