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.
Most people who have been exposed to design of experiment (DOE) concepts have probably heard of factorial designs—designs that target the discovery of factor and interaction effects on their process. But factorial designs are hardly the only tool in the shed. And oftentimes to properly optimize our system a more advanced response surface design (RSM) will prove to be beneficial, or even essential.
This is the case when there is “curvature” within the design space, suggesting that quadratic (or higher) order terms are needed to make valid predictions between the extreme high/low process factor settings. This gives us the opportunity to find optimal solutions that reside in the interior of the design space. If you include center points in a factorial design, you can check for non-linear behavior within the design space to see if an RSM design would be useful (1). But which RSM options should you pick?
Let’s start by introducing the Stat-Ease® software menu options for RSM designs. Once we understand the alternatives we can better understand when which might be most useful for any given situation and why optimal designs are great—when needed.

Stat-Ease software design selection options
The natural question that often pops up is this. Since optimal designs are third on our list, are we defaulting to suboptimal designs? Let’s dig in a bit deeper.
The central composite design (“CCD”) has traditionally been the workhorse of response surface methods. It has a predictable structure (5 levels for each factor). It is robust to some variations in the actual factor settings, meaning that you will still get decent quadratic model fits even if the axial runs have to be tweaked to achieve some practical values, including the extreme case when the axial points are placed at the face of the factorial “cube” making the design a 3-level study. A CCD is the design of choice when it fits the problem and generally creates predictive models that are effective throughout the design space--the factorial region of the design. Note that the quadratic predictive models generally improve when the axial points reside outside the face of the factorial cube.
When a 5-level study is not practical, for example, if we are looking at catalyst levels and the lower axial point would be zero or a negative number, we may be forced to bring the axial points to the face of the factorial cube. When this happens, Box-Behnken designs would be another standard design to consider. It is a 3-level design that is laid out slightly differently than a CCD. In general, the Box-Behnken results in a design with marginally fewer runs and is generally capable of creating very useful quadratic predictive models.
These standard designs are very effective when our experiments can be performed precisely as scripted by the design template. But this is not always the case, and when it is not we will need to apply a more novel approach to create a customized DOE.
Optimal designs are “custom” creations that come in a variety of alphabet-soup flavors—I, D, A, G, etc. The idea with optimal designs is that given your design needs and run-budget, the optimization algorithm will seek out the best choice of runs to provide you with a useful predictive model that is as effective as possible. Use of the system defaults when creating optimal designs is highly advised. Custom optimal designs often have fewer runs than the central composite option. Because they are generated by a computer algorithm, the number of levels per factor and the positioning of the points in the design space may be unique each time the design is built. This may make newcomers to optimal designs a bit uneasy. But, optimal designs fill the gap when:
The classic designs provide simple and robust solutions and should always be considered first when planning an experiment. However, when these designs don’t work well because of budget or practical design space constraints, don’t be afraid to go “outside the box” and explore your other options. The goal is to choose a design that fits the problem!
Acknowledgement: This post is an update of an article by Shari Kraber on “Modern Alternatives to Traditional Designs Modern Alternatives to Traditional Designs" published in the April 2011 STATeaser.
(1) See Shari Kraber’s blog post, “"Energize Two-Level Factorials - Add Center Points!” from August. 23, 2018 for additional insights.
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!
Quantification of Menthol by Chromatography Applying Analytical Quality by Design
Chromatographia, Published: 17 January 2026
Authors: Santiago Puentes, Julian Puentes, Ronald Andrés Jiménez & Emerson León Ávila
Mark's comments: Well done deploying the Ishikawa (fishbone) diagram to consider all the factors that might affect the analytical method. This is a great application of a multifactor approach to achieve quality by design.
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!
Sustainable protection for mild steel (S235): Anti-corrosion effectiveness of natural Paraberlinia bifoliolata barks extracts in HCl 1 M
Hybrid Advances, Volume 11, December 2025, 100526
Authors: Liliane Nga, Benoit Ndiwe, Jean Jalin Eyinga Biwolé, Armand Zébazé Ndongmo, Achille Desiré Omgba Béténé, Yvan Sandy Nké Ayinda, Joseph Zobo Mfomo, Cesar Segovia, Antonio Pizzi, Achille Bernard Biwolé
Mark's comments: I like this for its application of response surface methods to thoroughly explore three process factors for optimization of a 'green' corrosion inhibitor. They then provide a scientific explanation of its "remarkable effectiveness."
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!
Lipid nanocapsule-chitosan and iota-carrageenan hydrogel composite for sustained hydrophobic drug delivery
Scientific Reports volume 15, Article number: 42349 (November 27, 2025)
Authors: Grady K. Mukubwa, Justin B. Safari, Zikhona N. Tetana, Caroline N. Jones, Roderick B. Walker, Rui W. M. Krause
Mark's comments: This is an outstanding application of mixture design for optimal formulation of a novel composite system for enhancing oral delivery of hydrophilic antiviral drugs--a very worthy cause.
Be sure to check out this important study, and the other research listed below!