In connection with our proposed methodology to create a black-box, automated, easy-to-interpret, sample-based, robust technique called jackknife regression, to be used in small and big data environments by non-statisticians, We offer an award and massive promotion to the successful candidate who

1. Provide the exact formulas for the solution of the 2x2, 3x3 and 4x4 linear systems of equations described in section 3.2 in my recent article (this is straightforward)
2. Perform more tests on simulated data (say 10 data sets, each with 10,000 observations) to compare my methodology (with one and two M's computed on the first 100 observations) with full classical regression. The test must include data with strong correlation structure, and data with up to n=20 independent variables. Comparison should be about (i) accuracy and (ii) sensitivity to little changes in the data set (measured e.g. via confidence intervals for regression coefficients, both for classical regression and my methodology)

This project must be completed by August 31, 2014. You will be authorized to publish a paper featuring your research results (with your name as main or only author), and your results will also be published on Data Science Central, and seen by dozens of thousands of practitioners. Your article must meet professional quality standards similar to those required by leading peer-reviewed statistical journals. Payment will be sent after completion of the project. Depending on the success of this initiative, and the quality of participants, we might offer more than one award.

Read details here  (see section 5)