Estimation and Testing of Learning Curves

This article describes a new approach to learning curve estimation. Our approach is to formulate statistical procedures that conform to alternative learning curve theories. This leads to the development of nonlinear statistical models of the learning curves. For the three data sets analyzed, autocorrelation seems to be an important problem. Parameter estimates were derived using the maximum likelihood principle in the presence of first-order autocorrelation. Nonnested tests were used to select the appropriate formulation of the learning curve. Research conclusions are to use unit data when estimating a learning curve and to be prepared to treat autocorrelation if present.