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In statistics, a **prediction interval** bears the same relationship to a future observation that a confidence interval bears to an unobservable population parameter.

## Example[edit | edit source]

Suppose one has drawn a sample from a normally distributed population. The mean and standard deviation of the population are unknown except insofar as they can be estimated based on the sample. It is desired to predict the next observation. Let *n* be the sample size; let μ and σ be respectively the unobservable mean and standard deviation of the population. Let *X*_{1}, ..., *X*_{n}, be the sample; let *X*_{n+1} be the future observation to be predicted. Let

and

Then it is fairly routine to show that

has a Student's t-distribution with *n* − 1 degrees of freedom. Consequently we have

where *A* is the 100(1 − (p/2))^{th} percentile of Student's t-distribution with *n* − 1 degrees of freedom. Therefore the numbers

are the endpoints of a 100p% **prediction interval** for *X*_{n+1}.

## See also[edit | edit source]

## References[edit | edit source]

- Chatfield, C. (1993) "Calculating Interval Forecasts,"
*Journal of Business and Economic Statistics,***11**121-135. - Meade, N. and T. Islam (1995) "Prediction Intervals for Growth Curve Forecasts,"
*Journal of Forecasting,***14**413-430.

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