Understanding Normal Distribution Curve for Quality Control in CNC Machining

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In summary, the purpose of the normal distribution curve is to understand and analyze independent errors in quality control applications, specifically in machining components on a CNC machine. The assumption of a normal distribution is often used for convenience and ease of analysis, but it is important to verify its validity in each case. Additionally, there are many real-world examples of the application of the normal distribution in various fields.
  • #1
Analysis
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Dear all

For better conceptual understanding purpose i am posting this query

What is the purpose of normal distribution curve?
We are using this curve quality control applications also.
say for component we are machining in cnc machine.
The dimension limit is 25.00/25.025
we are taking sample and measuring the dimensions periodically.

Assume all the samples are taking are accepted one case.
otherwise some of the compnenets not confiming in second case.
How this situation related to normal distribution curve?
And what we are infering from this?

Thanks
Prakash
 
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  • #2
The normal distribution usage come from the central limit theorem (one of the basic theorems in probability theory). Roughly the theorem states that if you have something involving a lot of independent errors, the distribution will be normal.
 
  • #3
Laziness is another reason. The normal distribution is very well behaved and lends well to analysis. Sometimes it is valid to assume a normal distribution, sometimes not. It is a good idea to verify the validity of a normality assumption. The normality assumption is valid if the true distribution is indistinguishable from normal, and is usually valid if the corresponding normal distribution is conservative compared to the true distribution.
 
  • #4
D H said:
Laziness is another reason. The normal distribution is very well behaved and lends well to analysis. Sometimes it is valid to assume a normal distribution, sometimes not. It is a good idea to verify the validity of a normality assumption. The normality assumption is valid if the true distribution is indistinguishable from normal, and is usually valid if the corresponding normal distribution is conservative compared to the true distribution.

Thanks for your reply.
My querey is application oriented i am in search of application case study examples.

Prakash
 

What is the difference between probability and statistics?

Probability is the branch of mathematics that deals with the likelihood of an event occurring. It involves calculating the chances of different outcomes based on a set of known conditions or assumptions. Statistics, on the other hand, is the science of collecting, analyzing, and interpreting data to make conclusions or predictions about a larger population. While probability focuses on predicting the likelihood of future events, statistics focuses on understanding and describing past events.

How do you calculate probability?

Probability is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. This can be represented as a fraction, decimal, or percentage. For example, if you roll a six-sided die, the probability of rolling a 3 is 1/6 or 0.1667 or 16.67%.

What is the difference between descriptive and inferential statistics?

Descriptive statistics involves summarizing and describing data using measures such as mean, median, and standard deviation. It is used to organize and present data in a meaningful way. Inferential statistics, on the other hand, involves using data from a sample to make conclusions or predictions about a larger population. It uses techniques such as hypothesis testing and confidence intervals to make inferences.

What is a normal distribution?

A normal distribution is a bell-shaped curve that represents the distribution of a set of data. It is symmetrical and centered around the mean, with most values falling close to the mean and fewer values at the extremes. Many natural phenomena, such as human height and IQ, follow a normal distribution. This makes it a useful tool in statistics for analyzing and making predictions about data.

How do you interpret a p-value?

A p-value is a measure of the strength of evidence against the null hypothesis in a statistical test. It represents the probability of obtaining results as extreme or more extreme than the observed results, assuming the null hypothesis is true. A p-value of 0.05 or less is typically considered statistically significant, meaning there is a 5% or less chance that the results occurred by chance alone. A larger p-value indicates weaker evidence against the null hypothesis.

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