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The Multiple Regression Test is a hypothesis test that determines whether there is a correlation between two or more values of X and the output, Y, of continuous data. It is useful for determining the level to which changes in Y can be attributable to one or more Xs. Multiple Regression produces a “prediction equation” that estimates the value of Y that can be expected for given values of one or more X values within the range of the data set.

An example would be to test if crop yield were correlated to both rainfall and fertilizer amount, and then to calculate approximately how much water and fertilizer are required to achieve the desired yield.

For a better understanding of the Multiple Regression Test, check out our Black Belt Training & Certification Course!

Elisabeth Swan

Elisabeth is a Managing Partner at For over 25 years, she's helped leading organizations like Amazon, Charles Schwab and Starwood Hotels & Resorts build problem-solving muscles with Lean Six Sigma to achieve their goals.

Tracy O'Rourke

Tracy is a Managing Partner at She is also a Lean Six Sigma Green Belt Instructor at UC San Diego and teaches in San Diego State University’s Lean Enterprise Program. For almost 20 years, she has helped leading organizations like Washington State, Charles Schwab and GE build problem-solving muscles.