This paper introduces some relatively simple computational tools for estimating poverty measures from the sort of data that are typically available from published sources. All that is required for using these tools is an elementary regression package. The methodology also easily lends itself to a number of poverty simulations that are discussed. The paper addresses the central question: How do we construct poverty measures from grouped data? Two broad approaches are examined: simple interpolation methods and methods based on parameterized Lorenz curves. The second method is examined in detail.