Stats: Normal distribution, std dev, mean z score -- find x

In summary, the question is how to find the value that will be greater than or equal to 99% of all observations given a set of random numbers and a mean. The solution involves using the z-score of 2.576 for 99% probability and the formula x = (standard deviation * z-score) + mean, assuming a uniform distribution on numbers 1-100.
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Homework Statement


So the question is, given a set of random numbers, find the mean and the value that will be >= 99% of the occurances. So for a set of random numbers between say, 1-100, if the mean is 50, how do I find out what number will be >= 99% of all observations of the time.

Havent taken stats in a long time. No book provided.

Homework Equations



99% has a z score of 2.576
Somehow I have to turn that into a number using standard deviation.

The Attempt at a Solution


z=(x-m)/stdev
x=(stdev*z)+mean
is x the number I am looking for?

Thanks all!
 
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  • #2
What do you mean by "random numbers between say, 1-100"? That is, what probability distribution are you assuming for choosing the random numbers? Most likely, you are assuming all numbers are "equally likely" so that you are using the "uniform distribution" on numbers 1-100. According to Quora (https://www.quora.com/What-is-the-mean-and-variance-of-uniform-distribution) the uniform distribution on 1-100 has mean (100+1)/2= 50.5 and variance (100- 1)^2/12= 816.75, so standard deviation sqrt(816.75).
 
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Good to see you back, Halls.
 

Related to Stats: Normal distribution, std dev, mean z score -- find x

1. What is a normal distribution?

A normal distribution is a type of probability distribution where the data is symmetrically distributed around the mean, creating a bell-shaped curve. It is commonly used in statistics to describe natural phenomena such as height, weight, and test scores.

2. What is standard deviation and why is it important?

Standard deviation is a measure of how spread out a set of data is from the mean. It is important because it allows us to quantify the variability of a dataset and make comparisons between different datasets. A smaller standard deviation means the data is less spread out, while a larger standard deviation indicates more variability.

3. How do you calculate the mean of a dataset?

The mean is calculated by adding up all the values in a dataset and dividing it by the total number of values. This gives us the average value of the dataset and is often represented by the symbol "μ" for the population mean or "x̄" for the sample mean.

4. What is a z-score and how is it calculated?

A z-score, also known as a standard score, is a measure of how many standard deviations a particular data point is above or below the mean. It is calculated by subtracting the mean from the data point and then dividing it by the standard deviation. This allows us to compare data points from different datasets on the same scale.

5. How do you use z-scores to find a specific data point?

To find a specific data point using z-scores, we first need to convert the data point to a z-score by subtracting the mean and dividing it by the standard deviation. Then, we can use a z-score table or a statistical calculator to find the corresponding value on the standard normal distribution. Finally, we can convert the z-score back to the original data scale by multiplying it by the standard deviation and adding it to the mean.

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