Prediction interval
In statistics (particularly in predictive inference), a prediction interval, or PI for short,[1] is an interval estimate in which future observations will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis and forecasting.[2][3]
Prediction Interval Media
Prediction interval (on the y-axis) given from z (the quantile of the standard score, on the x-axis). The y-axis is logarithmically compressed (but the values on it are not modified).
Diagram showing the cumulative distribution function for the normal distribution with mean (µ) 0 and variance (σ2) 1. In addition to the quantile function, the prediction interval for any standard score can be calculated by (1 − (1 − Φµ,σ2(standard score))·2).
Related pages
References
- ↑ "List of Probability and Statistics Symbols". Math Vault. 2020-04-26. Retrieved 2020-10-14.
- ↑ 3.5 Prediction intervals | Forecasting: Principles and Practice.
- ↑ "3.3 - Prediction Interval for a New Response | STAT 501". PennState: Statistics Online Courses. Retrieved 2020-10-14.