Mixture models (also known as "Scheffé," after the inventor) differ from standard polynomials by their lack of intercept and squared terms. For example, most of us learned about quadratic models in high school and/or college math classes, such as this one for two factors:
These models are extremely useful for optimizing processes via response surface methods (RSM) such as central composite designs (CCDs).
Mixture models look different. For example, consider this non-linear blending model for the melting point (Y) of copper (X₁) and gold (X₂) derived from a statistically designed mixture experiment*:
As you can see, this equation, set up to work with components coded on a 0 to 1 scale, does not include an intercept (ß₀) or squared terms (X₁², X₂²). However, it works quite well for predicting the behavior of a two-component mixture. The first-order coefficients, 1043 and 1072, are quite simple to interpret—these fitted values quantify the measured** melting points in degrees C for copper and gold, respectively. The difference of 29 characterizes the main-component effect (copper 29 degrees higher than gold).
The second-order coefficient of 536 is a bit trickier to interpret. It being negative characterizes the counterintuitive (other than for metallurgists) nonlinear depression of the melting point at a 50/50 composition of the metals. But be careful when quantifying the reduction in the melting: It is far less than you might think. Figure 1 tells the story.

Figure 1: Response surface for melting point of copper versus gold
First off, notice that the left side—100% copper—is higher than the right side—100% gold. This is caused by the main-component effect. Then observe the big dip in the middle created by a significant, second-order impact from non-linear blending. Because of this, the melting point reaches a minimum of 923 degrees C at and just beyond the 50/50 blend point. This falls 134 degrees below the average melting point of 1057 degrees. Given the coefficient of -536 on the X₁X₂ term, you probably expected a much bigger reduction. It turns out 541 divided by 4 equals 134. This is not coincidental—at the 50/50 blend point the product of the coded values reaches a maximum of 0.25 (0.5 x 0.5), and thus the maximum deflection is one-fourth (1/4) of the coefficient.
If your head is spinning at this point, I advise you not to attempt to interpret coefficients of the mixture model beyond the main component effects and, if significant, only the sign of the second-order, non-linear blending term, that is, whether it is positive or negative. Then after validating your model via Stat-Ease software diagnostics, visualize the model performance via our program’s wonderful model graphics—trace plot, 2D contour, and 3D surface. Follow up by doing a numeric optimization to pinpoint an optimum blend that meets all your requirements.
However, if you would like to truly master mixture modeling, come to our next Fundamentals of Mixture DOE workshop.
* For the raw data, see Table 1-1 of A Primer on Mixture Design: What’s in it for Formulators. Due to a more precise fitting, the model coefficients shown in this blog differ slightly from those presented in the Primer.
** Keep in mind these are results from an experiment and thus subject to the accuracy and precision of the testing and the purity of the metals—the theoretical melting points for pure gold and copper are 1064 and 1085 degrees C, respectively.
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A few years ago, while evaluating our training facility in Minneapolis, I came up with a fun experiment that demonstrates a great application of RSM for process optimization. It involves how sound travels to our students as a function of where they sit. The inspiration for this experiment came from a presentation by Tom Burns of Starkey Labs to our 5th European DOE User Meeting. As I reported in our September 2014 Stat-Teaser, Tom put RSM to good use for optimizing hearing aids.
Classroom acoustics affect speech intelligibility and thus the quality of education. The sound intensity from a point source decays rapidly by distance according to the inverse square law. However, reflections and reverberations create variations by location for each student—some good (e.g., the Whispering Gallery at Chicago Museum of Science and Industry—a very delightful place to visit, preferably with young people in tow), but for others bad (e.g., echoing). Furthermore, it can be expected to change quite a bit from being empty versus fully occupied. (Our then-IT guy Mike, who moonlights as a sound-system tech, called these—the audience, that is—“meat baffles”.)
Sound is measured on a logarithmic scale called “decibels” (dB). The dBA adjusts for varying sensitivities of the human ear.
Frequency is another aspect of sound that must be taken into account for acoustics. According to Wikipedia, the typical adult male speaks at a fundamental frequency from 85 to 180 Hz. The range for a typical adult female is from 165 to 255 Hz.

Stat-Ease training room at one of our old headquarters—sound test points spotted by yellow cups.
This experiment sampled sound on a 3x3 grid from left to right (L-R, coded -1 to +1) and front to back (F-B, -1 to +1)—see a picture of the training room above for location—according to a randomized RSM test plan. A quadratic model was fitted to the data, with its predictions then mapped to provide a picture of how sound travels in the classroom. The goal was to provide acoustics that deliver just enough loudness to those at the back without blasting the students sitting up front.
Using sticky notes as markers (labeled by coordinates), I laid out the grid in the Stat-Ease training room across the first 3 double-wide-table rows (4th row excluded) in two blocks:
I generated sound from the Online Tone Generator at 170 hertz—a frequency chosen to simulate voice at the overlap of male (lower) vs female ranges. Other settings were left at their defaults: mid-volume, sine wave. The sound was amplified by twin Dell 6-watt Harman-Kardon multimedia speakers, circa 1990s. They do not build them like this anymore 😉 These speakers reside on a counter up front—spaced about a foot apart. I measured sound intensity on the dBA scale with a GoerTek Digital Mini Sound Pressure Level Meter (~$18 via Amazon).
I generated my experiment via the Response Surface tab in Design-Expert® software (this 3³ design shows up under "Miscellaneous" as Type "3-level factorial"). Via various manipulations of the layout (not too difficult), I divided the runs into the two blocks, within which I re-randomized the order. See the results tabulated below.
| Block | Run | Space Type | Coordinate (A: L-R) | Coordinate (B: F-B) | Sound (dBA) |
|---|---|---|---|---|---|
| 1 | 1 | Factorial | -1 | 1 | 70 |
| 1 | 2 | Center | 0 | 0 | 58 |
| 1 | 3 | Factorial | 1 | -1 | 73.3 |
| 1 | 4 | Factorial | 1 | 1 | 62 |
| 1 | 5 | Center | 0 | 0 | 58.3 |
| 1 | 6 | Factorial | -1 | -1 | 71.4 |
| 1 | 7 | Center | 0 | 0 | 58 |
| 2 | 8 | CentEdge | -1 | 0 | 64.5 |
| 2 | 9 | Center | 0 | 0 | 58.2 |
| 2 | 10 | CentEdge | 0 | 1 | 61.8 |
| 2 | 11 | CentEdge | 0 | -1 | 69.6 |
| 2 | 12 | Center | 0 | 0 | 57.5 |
| 2 | 13 | CentEdge | 1 | 0 | 60.5 |
Notice that the readings at the center are consistently lower than around the edge of the three-table space. So, not surprisingly, the factorial model based on block 1 exhibits significant curvature (p<0.0001). That leads to making use of the second block of runs to fill out the RSM design in order to fit the quadratic model. I was hoping things would play out like this to provide a teaching point in our DOESH class—the value of an iterative strategy of experimentation.
The 3D surface graph shown below illustrates the unexpected dampening (cancelling?) of sound at the middle of our Stat-Ease training room.

3D surface graph of sound by classroom coordinate.
Perhaps this sound ‘map’ is typical of most classrooms. I suppose that it could be counteracted by putting acoustic reflectors overhead. However, the minimum loudness of 57.4 (found via numeric optimization and flagged over the surface pictured) is very audible by my reckoning (having sat in that position when measuring the dBA). It falls within the green zone for OSHA’s decibel scale, as does the maximum of 73.6 dBA, so all is good.
The results documented here came from an empty classroom. I would like to do it again with students (aka meat baffles) present. I wonder how that will affect the sound map. Of course, many other factors could be tested. For example, Rachel from our Front Office team suggested I try elevating the speakers. Another issue is the frequency of sound emitted. Furthermore, the oscillation can be varied—sine, square, triangle and sawtooth waves could be tried. Other types of speakers would surely make a big difference.
What else can you think of to experiment on for sound measurement? Let me know.
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Here's the latest Publication Roundup! In these monthly posts, we'll feature recent papers that cited Design-Expert® or Stat-Ease® 360 software. Please submit your paper to us if you haven't seen it featured yet!
Green extraction of poplar type propolis: ultrasonic extraction parameters and optimization via response surface methodology
BMC Chemistry, 19, Article number: 266 (2025)
Authors: Milena Popova, Boryana Trusheva, Ralitsa Chimshirova, Hristo Petkov, Vassya Bankova
Mark's comments: A worthy application of response surface methods for optimizing an environmentally friendly process producing valuable bioactive compounds. I see they used Box-Behnken designs appropriately - good work!
Be sure to check out this important study, and the other research listed below!
Here's the latest Publication Roundup! In these monthly posts, we'll feature recent papers that cited Design-Expert® or Stat-Ease® 360 software. Please submit your paper to us if you haven't seen it featured yet!
When choosing a featured article for each month, we try to make sure it's available for everyone to read. Unfortunately, none of this month's publications that met our standards are available to the public, so there's no featured article this month. We still recommend checking out the incredible research done by these teams, and congratulations to everyone for publishing!
Here's the latest Publication Roundup! In these monthly posts, we'll feature recent papers that cited Design-Expert® or Stat-Ease® 360 software. Please submit your paper to us if you haven't seen it featured yet!
Design and optimization of imageable microspheres for locoregional cancer therapy
Scientific Reports volume 15, Article number: 27487 (2025)
Authors: Brenna Kettlewell, Andrea Armstrong, Kirill Levin, Riad Salem, Edward Kim, Robert J. Lewandowski, Alexander Loizides, Robert J. Abraham, Daniel Boyd
Mark's comments: This is a great application of mixture design for optimal formulation of a medical-grade glass. The researchers used Stat-Ease software tools to improve the properties of microspheres to an extent that their use can be extended to cancers beyond the current application to those located in the liver. Well done!
Be sure to check out this important study, and the other research listed below!