Statistics: Standard Deviation for a Normal Distribution

In summary, to ensure a maximum failure rate of 1 out of 250,000 parts, the failed parts must be at least 4.46518 standard deviations away from the mean in a normal distribution. This can be determined by using the inverse norm function and accounting for the two-sided nature of the error rate. This means that the company must aim for a quality goal that is beyond the 99.7% threshold (27 failures per 10,000) to achieve their desired failure rate.
  • #1
jdawg
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Homework Statement


A company allows a maximum failure rate of 1 out of 250,000 parts. To insure this quality goal, failed parts must be how many standard deviations from the mean? Use Excel to solve.

Homework Equations


z= (X-μ)/σ

The Attempt at a Solution


Hi! So I'm assuming that this is a normal distribution. I'm a little confused, I kind of feel like there wasn't enough information provided to find how many standard deviations need to be away from the mean.

So far I've tried finding the z-value using excel and assuming that 1/250000 is my alpha value:
=NORM.S.INV(1/250000) = -4.46518

I was thinking about trying to plug it into this formula to find σ:
z= (X-μ)/σ

Am I on the right track with this? I wasn't given an X or a μ, so I don't know how I would go about solving this.
 
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  • #2
You are likely correct that you should be assuming a normal distribution. However you are not determining your z-value correctly. You need to account for the two sided nature of the error rate. Your alpha is the area under the bell curve on both sides of the mean z standard deviations out. So the area on each tail is alpha/2.
The inverse norm function is the inverse CDF and so gives the upper bound on the area under the bell curve of the input value.

Visualize the bell curve with the two tails, alpha/2 is the area of the upper tail (above the critical z value) and so 1 - alpha/2 is the area to the left of the critical z value.
You can then take inverse norm of (1-alpha/2) or equivalently the negative of the inverse norm of alpha/2.

Remember your Z score is standardized in terms of the mean and SD so it is the number of standard deviations above the mean so once you find the critical z-value that is your answer.
 
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  • #3
Thank you! Super helpful!
 
  • #4
Just for a bit of context, remember the (1-2-3) -68-95-99.7 rule, which will give you 27 failures per 10,000. Times 25 that is 675 per 250,000. So you have some way to go beyond that.
 

FAQ: Statistics: Standard Deviation for a Normal Distribution

What is Standard Deviation for a Normal Distribution?

Standard Deviation for a Normal Distribution is a measure of how spread out the data is from the mean (average) in a normal distribution. It is calculated by finding the square root of the variance, which is the average of the squared differences from the mean.

How is Standard Deviation for a Normal Distribution calculated?

Standard Deviation for a Normal Distribution is calculated by taking the square root of the variance. The variance is calculated by finding the average of the squared differences from the mean, which is then divided by the number of data points in the distribution.

What is a "normal distribution"?

A normal distribution is a type of probability distribution where the data is symmetrically distributed around the mean. It is also known as a bell curve due to its shape. In a normal distribution, the mean, median, and mode are all equal.

Why is Standard Deviation important in statistics?

Standard Deviation is important in statistics because it gives an idea of the variation or spread of the data. A small standard deviation indicates that the data points are close to the mean, while a large standard deviation indicates that the data points are spread out. It is also used to calculate confidence intervals and to compare the variability of different data sets.

Can Standard Deviation be negative?

No, Standard Deviation cannot be negative. Since it is the square root of the variance, which is always a positive value, the Standard Deviation will also always be positive.

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