Stat-Ease Blog

Stat-Ease software provides powerful tools for design of experiments (DOE) with a great deal of intelligence baked in. Here are 10 “smart” features that make DOE easy for our users. From bottom to top (ordered by DOE phase: design, modeling, optimization, and confirmation), every one of them provides great value.

Here we go—the countdown begins!

Finally, one bonus feature in Stat-Ease software that will make you more intelligent: screen tips via the lightbulb icon (click the >> chevron if showing) next to the Help bubble. This will show interesting information about each feature on the screen for you to understand the underlying statistics.

Email me your favorite “they thought of everything” quality aspect of Stat-Ease software, and I will add it to my list for my next ‘shout out’ on intelligent features.

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!

Featured Article

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!

More new publications from August

1. Use of experimental design for screening and optimization of variables influencing photocatalytic degradation of pollutants in aqueous media: A review of chemometrics tools
   Chemical Engineering Research and Design, Volume 220, August 2025, Pages 270-291
   Authors: Pedro César Quero–Jiménez, Aracely Hernández–Ramírez, Jorge Luis Guzmán–Mar, Jorge Basilio de la Torre–López, Matheus Silva–Gigante, Laura Hinojosa–Reyes
2. Analytical Quality by Design-Based Stability-Indicating UHPLC Method for Determination of Inavolisib in Bulk and Formulation
   Separation Science Plus, no. 8 (2025): 8, e70110
   Authors: Ashwinkumar Matta, Raja Sundararajan
3. Enhanced anti-infective activities of sinapic acid through nebulization of lyophilized protransferosomes
   Frontiers in Nanotechnology | Biomedical Nanotechnology, Volume 7 - 2025
   Authors: Hani A. Alhadrami, Amr Gamal, Ngozi Amaeze, Ahmed M. Sayed, Mostafa E. Rateb, and Demiana M. Naguib
4. Optimizing Anti-Corrosive Properties of Polyester Powder Coatings Through Montmorillonite-Based Nanoclay Additive and Film Thickness
   Corrosion and Materials Degradation, 2025, 6(3), 39
   Authors: Marshall Shuai Yang, Chengqian Xian, Jian Chen, Yolanda Susanne Hedberg, James Joseph Noël
5. Regulatory mechanism and multi-index coordinated optimization of pipeline transportation performance of coarse-grained gangue slurry: Experimental and simulation investigation
   Physics of Fluids 37, 073343 (2025)
   Authors: Jianfei Xu (许健飞); Jixiong Zhang (张吉雄); Nan Zhou (周楠); Hao Yan (闫浩); Wenfu Zhou (周文福); Qian Chen (陈乾); Jiarun Chen (陈嘉润)
6. Optimization of clayey soil parameters with aeolian sand through response surface methodology and a desirability function
   Scientific Reports volume 15, Article number: 30831 (2025)
   Authors: Ghania Boukhatem, Messaouda Bencheikh, Mohammed Benzerara, Mehmet Serkan Kırgız, N. Nagaprasad, Krishnaraj Ramaswamy, Souhila Rehab-Bekkouche, R. Shanmugam
7. Development of electromagnetic drop weight release mechanism for human occupied vehicle
   Scientific Reports volume 15, Article number: 30663 (2025)
   Authors: Sathia Narayanan Dharmaraj, Karthikeyan Shanmugam, Jothi Chithiravel, Ramesh Sethuraman
8. Operating parameter optimization and experiment of spiral outer grooved wheel seed metering device based on discrete element method
   Scientific Reports volume 15, Article number: 30762 (2025)
   Authors: Tao Zhang, Xinglong Tang, Cong Dai, Guiying Ren
9. Parameter optimization of key components in seed-metering device for pre-cut seed stems of Pennisetum hydridum
   Scientific Reports volume 15, Article number: 31318 (2025)
   Authors: Chong Liu, Xiongfei Chen, Qiang Xiong, Muhua Liu, Junan Liu, Jiajia Yu, Peng Fang, Yihan Zhou, Chuanhong Zhan, Yao Xiao
10. Optimization of new and thermally aged natural monoesters blends for a sustainable management of power transformers
    Industrial Crops and Products, Volume 235, 1 November 2025, 121741
    Authors: Gerard Ombick Boyekong, Gabriel Ekemb, Emeric Tchamdjio Nkouetcha, Ghislain Mengata Mengounou, Adolphe Moukengue Imano

Observing process improvement teams at Imperial Chemical Industries in the late 1940s George Box, the prime mover for response surface methods (RSM), realized that as a practical matter, statistical plans for experimentation must be very flexible and allow for a series of iterations. Box and other industrial statisticians continued to hone the strategy of experimentation to the point where it became standard practice for stats-savvy industrial researchers.

Via their Management and Technology Center (sadly, now defunct), Du Pont then trained legions of engineers, scientists, and quality professionals on a “Strategy of Experimentation” called “SCO” for its sequence of screening, characterization and optimization. This now-proven SCO strategy of experimentation, illustrated in the flow chart below, begins with fractional two-level designs to screen for previous unknown factors. During this initial phase, experimenters seek to discover the vital few factors that create statistically significant effects of practical importance for the goal of process improvement.

The ideal DOE for screening resolves main effects free of any two-factor interactions (2FI’s) in broad and shallow two-level factorial design. I recommend the “resolution IV” choices color-coded yellow on our “Regular Two-Level” builder (shown below). To get a handy (pun intended) primer on resolution, watch at least the first part of this Institute of Quality and Reliability YouTube video on Fractional Factorial Designs, Confounding and Resolution Codes.

If you would like to screen more than 8 factors, choose one of our unique “Min-Run Screen” designs. However, I advise you accept the program default to add 2 runs and make the experiment less susceptible to botched runs.

After throwing the trivial many factors off to the side (preferably by holding them fixed or blocking them out), the experimental program enters the characterization phase (the “C”) where interactions become evident. This requires a higher-resolution of V or better (green Regular Two-Level or Min-Run Characterization), or possibly full (white) two-level factorial designs. Also, add center points at this stage so curvature can be detected.

If you encounter significant curvature (per the very informative test provided in our software), use our design tools to augment your factorial design into a central composite for response surface methods (RSM). You then enter the optimization phase (the “O”).

However, if curvature is of no concern, skip to ruggedness (the “R” that finalizes the “SCOR”) and, hopefully, confirm with a low resolution (red) two-level design or a Plackett-Burman design (found under “Miscellaneous” in the “Factorial” section). Ideally you then find that your improved process can withstand field conditions. If not, then you will need to go back up to the beginning for a do-over.

The SCOR strategy, with some modification due to the nature of mixture DOE, works equally well for developing product formulations as it does for process improvement. For background, see my October 2022 blog on Strategy of Experiments for Formulations: Try Screening First!

Stat-Ease provides all the tools and training needed to deploy the SCOR strategy of experiments. For more details, watch my January webinar on YouTube. Then to master it, attend our Modern DOE for Process Optimization workshop.

Know the SCOR for a winning strategy of experiments!

I am often asked if the results from one-factor-at-a-time (OFAT) studies can be used as a basis for a designed experiment. They can! This augmentation starts by picturing how the current data is laid out, and then adding runs to fill out either a factorial or response surface design space.

One way of testing multiple factors is to choose a starting point and then change the factor level in the direction of interest (Figure 1 – green dots). This is often done one variable at a time “to keep things simple”. This data can confirm an improvement in the response when any of the factors are changed individually. However, it does not tell you if making changes to multiple factors at the same time will improve the response due to synergistic interactions. With today’s complex processes, the one-factor-at-a-time experiment is likely to provide insufficient information.

The experimenter can augment the existing data by extending a factorial box/cube from the OFAT runs and completing the design by running the corner combinations of the factor levels (Figure 2 – blue dots). When analyzing this data together, the interactions become clear, and the design space is more fully explored.

In other cases, OFAT studies may be done by taking a standard process condition as a starting point and then testing factors at new levels both lower and higher than the standard condition (see Figure 3). This data can estimate linear and nonlinear effects of changing each factor individually. Again, it cannot estimate any interactions between the factors. This means that if the process optimum is anywhere other than exactly on the lines, it cannot be predicted. Data that more fully covers the design space is required.

A face-centered central composite design (CCD)—a response surface method (RSM)—has factorial (corner) points that define the region of interest (see Figure 4 – added blue dots). These points are used to estimate the linear and the interaction effects for the factors. The center point and mid points of the edges are used to estimate nonlinear (squared) terms.

If an experimenter has completed the OFAT portion of the design, they can augment the existing data by adding the corner points and then analyzing as a full response surface design. This set of data can now estimate up to the full quadratic polynomial. There will likely be extra points from the original OFAT runs, which although not needed for model estimation, do help reduce the standard error of the predictions.

Running a statistically designed experiment from the start will reduce the overall experimental resources. But it is good to recognize that existing data can be augmented to gain valuable insights!

Learn more about design augmentation at the January webinar: The Art of Augmentation – Adding Runs to Existing Designs.

Thank you to our presenters and all the attendees who showed up to our 2022 Online DOE Summit! We're proud to host this annual, premier DOE conference to help connect practitioners of design of experiments and spread best practices & tips throughout the global research community. Nearly 300 scientists from around the world were able to make it to the live sessions, and many more will be able to view the recordings on the Stat-Ease YouTube channel in the coming months.

Due to a scheduling conflict, we had to move Martin Bezener's talk on "The Latest and Greatest in Design-Expert and Stat-Ease 360." This presentation will provide a briefing on the major innovations now available with our advanced software product, Stat-Ease 360, and a bit of what's in store for the future. Attend the whole talk to be entered into a drawing for a free copy of the book DOE Simplified: Practical Tools for Effective Experimentation, 3rd Edition. New date and time: Wednesday, October 12, 2022 at 10 am US Central time.

Even if you registered for the Summit already, you'll need to register for the new time on October 12. Click this link to head to the registration page. If you are not able to attend the live session, go to the Stat-Ease YouTube channel for the recording.

Want to be notified about our upcoming live webinars throughout the year, or about other educational opportunities? Think you'll be ready to speak on your own DOE experiences next year? Sign up for our mailing list! We send emails every month to let you know what's happening at Stat-Ease. If you just want the highlights, sign up for the DOE FAQ Alert to receive a newsletter from Engineering Consultant Mark Anderson every other month.

Thank you again for helping to make the 2022 Online DOE Summit a huge success, and we'll see you again in 2023!