  In this paper, we consider the problem of estimating parameters in a linear
regression model. We propose a sequential learning procedure to determine the
sample size for achieving a given small estimation risk, under the widely used
Gauss-Markov setup with independent normal errors. The procedure is proven to
enjoy the second-order efficiency and risk-efficiency properties, which are
validated through Monte Carlo simulation studies. Using e-commerce data, we
implement the procedure to examine the influential factors of online sales.
