ADM 2304 Lecture Notes - Lecture 20: Prediction Interval, Point Estimation, Analysis Of Variance

• School: University of Ottawa
• Department: Administration
• Course: ADM 2304
• Professor: Tony Quon

Document Summary

1. c. i. for the mean of y at a given value of x: prediction interval (p. i. ) for a future observation of y at a given value of x. P. i. since the mean varies less than a given observation. Same point estimate for both: y x b. What we are really interested in is estimating y so we look at: c. i. for the mean of y for a given x, p. i. for a future observed y at a given x* Two key intervals: c. i. for the mean of y at a given value of x, prediction interval (p. i. ) for a future observation of y at a given value of x. P. i. since the mean varies less than a given observation y x b. If the extrapolation penalty is small and n is reasonably large, then an approximate 95% Why do both the c. i. and the p. i. increase in size as you move away from the mean of.