Stochastic iterative algorithms have gained recent interest for solving large-scale systems of equations, Ax=y. One such example is the Randomized Kaczmarz (RK) algorithm, which acts only on single rows of the matrix A at a time. While RK randomly selects a row, Motzkin's algorithm employs a greedy row selection; meanwhile the Sampling Kaczmarz-Motzkin (SKM) algorithm combines these two strategies. In this talk, we present an improved convergence analysis for SKM which interpolates between RK and Motzkin's algorithm.