Lesson plan
Precision (statistics)
In statistics, the precision matrix or concentration matrix is the matrix inverse of the covariance matrix or dispersion matrix, P = \Sigma^{-1} . For univariate distributions, the precision matrix degenerates into a scalar precision, defined as the reciprocal of the variance, p = \frac{1}{\sigma^2} . Other summary statistics of statistical dispersion also called precision (or imprecision ) include the reciprocal of the standard deviation, p = \frac{1}{\sigma} ; the standard deviation itself and the relative standard deviation; as well as the standard error and the confidence interval (or its half-width, the margin of error).
Article summary
In statistics, the precision matrix or concentration matrix is the matrix inverse of the covariance matrix or dispersion matrix, P = \Sigma^{-1} . For univariate distributions, the precision matrix degenerates into a scalar precision, defined as the reciprocal of the variance, p = \frac{1}{\sigma^2} . Other summary statistics of statistical dispersion also called precision (or imprecision ) include the reciprocal of the standard deviation, p = \frac{1}{\sigma} ; the standard deviation itself and the relative standard deviation; as well as the standard error and the confidence interval (or its half-width, the margin of error).
Key claims
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Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
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Key claims and bibliography are generated from the same tracked claims and sources as the article page.