# Difference between revisions of "Home"

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* [[Predicting Precision]]: Valid deductions that can be made from measured precision | * [[Predicting Precision]]: Valid deductions that can be made from measured precision | ||

* [[Prior Art]]: Reviews of papers and past efforts to address this question | * [[Prior Art]]: Reviews of papers and past efforts to address this question | ||

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+ | = Synopsis = | ||

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+ | When testing a gun to estimate its precision the most useful data are the (''x'', ''y'') coordinates of each impact on sample targets. These allow for [[Measuring Precision|closed-form estimates and confidence intervals on the standard deviation of dispersion]] along each axis, and [[Predicting Precision|from the standard deviation we can deduce any standard precision measure]] for the gun. | ||

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+ | If we assume that the inherent dispersion along each axis is roughly identical then we can use the average of the standard deviations, a single parameter ''σ'', to characterize precision. |

## Revision as of 18:46, 29 November 2013

This site explains and demonstrates statistics for analyzing the precision of guns.

In particular:

- What is Precision?
- Describing Precision: Units, terms, and relationships
- Measuring Precision: What statistical inference from sample targets can tell you about precision
- Predicting Precision: Valid deductions that can be made from measured precision
- Prior Art: Reviews of papers and past efforts to address this question

# Synopsis

When testing a gun to estimate its precision the most useful data are the (*x*, *y*) coordinates of each impact on sample targets. These allow for closed-form estimates and confidence intervals on the standard deviation of dispersion along each axis, and from the standard deviation we can deduce any standard precision measure for the gun.

If we assume that the inherent dispersion along each axis is roughly identical then we can use the average of the standard deviations, a single parameter *σ*, to characterize precision.