I analyzed the data using the same ordinary least squares regression (OLS) technique described in Part 1, but this time the resulting model was highly predictive of mash efficiency. OLS results are shown in Table 2, where it's seen that the most significant variable by far is Experience on the new system, as the magnitude of its t-stat is much larger than any of the others. Grain Crush is also significant, and Average Mash Temperature is approaching statistical significance. (As a rule of thumb, a t-stat whose magnitude is 1.96 or greater indicates statistical significance.)

Using the coefficients from Table 2, I built a mathematical model for mash efficiency,

and the model's predicted mash efficiencies are plotted against the actual mash efficiencies in Figure 1. In a perfect model all points would lie directly on the diagonal line, and this model's points, shown by blue diamonds, are very close to that line, providing visual support that the model is very predictive. In fact, the adjusted R-squared of the model is 0.89, which is outstanding and essentially means that the model accounts for 89% of the real-world variation.

My conclusions from this experiment are as follows:

1. It took a lot more batches than expected to start achieving consistent mash efficiencies. With the exception of the sixth batch on my new system, where I presumably got lucky, it took a surprising ten batches before results started looking repeatable. I don't know exactly what caused my learning curve, but I presume it has to do with sparging method, transfer times, stirring technique, and other "brewing moves."

2. When starting to brew on a new system, use the same mash thickness, mash temperature, and mash duration every time until you start to achieve consistent results. Until then, the effects of these variables will be imperceptible within the "noise" anyway.

3. Much of the brewing science and theory homebrewers consume is applicable only after you're able to achieve consistent mash efficiencies. On my last three batches I've finally averaged about 87% efficiency with an overall variation of less than 5%. If I can keep this the same for the next three or four batches, I may start to experiment with mash thickness, temperature, and duration again.

Overall, many homebrewers like myself enjoy the science aspect of this hobby, and can sometimes focus a bit too much on recording numbers out to third decimal place. But until your process is controlled enough to achieve consistent mash efficiencies, none of this really matters. Such control is harder to achieve than expected when using an "igloo-style" homebrew system, but keep aiming for it!