Functions called generalized means are of interest in statistics because they are simple to compute, have intuitive appeal, and can serve as reasonable parameter estimates. The well-known arithmetic, geometric, and harmonic means are all examples of generalized means. We show how generalized means can be derived in a unified way, as least squares estimates for a transformed data set. We also investigate models that have generalized means as their maximum likelihood estimates.