Is a 1-sided 90% prediction interval equivalent to a 2-sided 95% prediction interval? I have located references that indicate that a 1-sided confidence band is equivalent to the 95% 2-sided confidence band on a linear regression.  Does this equivalence hold true for prediction intervals over a linear regression model?
Example:
Given a set of data with a random sampling of age vs strength values, and using a simple linear regression on the above.  If I state: "I want to be 95% sure that future values of strength where age=39 are >= XX", to find XX, can I plot a typical 90% prediction interval, and use it's lower band as the equivalent of 95% 1-sided prediction interval to answer that question?  Any references available for such an equivalence?
 A: [Edited: This answer has been improved since original posting based on comments.]
You have this backwards.  If you have a 95% prediction interval, then you expect that 2.5% of future observations will have values larger than the upper limit, and 2.5% of future observations will have values smaller than the lower limit.  If you don't care about values that are smaller than the lower limit, then you can just use the upper limit as the top bound of a 97.5% one-sided prediction interval, as the same percentage will exceed it whether you care about the lower bound or not.  On the other hand, if you want to be able (later) to say that 95% of future observations will be below some limit, then you need to get a 90% prediction interval (and subsequently ignore the lower limit).  
Note: standard equations for calculating a prediction interval following an OLS regression fit assume that the data (specifically the residuals) follow a normal distribution with mean = 0, and variance = $\sigma^2$.  (This means, among other things, that the distribution is symmetrical.)  This answer is assuming that as well.  If your residuals are not normal, a bootstrapping approach may be appropriate.  For example, you could simulate a bootstrapped distribution, sort it, and take the 95th percentile as the upper limit of a 95% prediction interval.  
A: For a regression analysis of a normally distributed variable X it is true because the  distribution of X and the distribution of the residuals are both symmetric (X is normal and residuals have a t distribution).
Because it is symmetric the 90% two sided prediction interval will have 5% in each tail. A 95% one sided prediction interval will have 5% in one tail so this works out as having the same limit as the two sided 90% prediction interval
