The DOI matches its Crossref record.
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
Abstract
From the Publisher: Using a practical approach, it discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniques, focusing on response surface methodology, mixture experiments and robust design techniques. Features numerous authentic application examples and problems. Illustrates how computers can be a useful aid in problem solving. Includes a disk containing computer programs for a response surface methodology simulation exercise and concerning mixtures.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Khuri, A. I., & Mukhopadhyay, S. (2010) · WIREs Computational Statistics · doi:10.1002/wics.73 · Cited by 2,113
Identification and Review of Sensitivity Analysis Methods
Christopher Frey, H., & Patil, S. R. (2002) · Risk Analysis · doi:10.1111/0272-4332.00039 · Cited by 1,036
Optimization of extraction of phenolic compounds from wheat using response surface methodology
LIYANAPATHIRANA, C., & SHAHIDI, F. (2005) · Food Chemistry · doi:10.1016/j.foodchem.2004.08.050 · Cited by 809
Hou, R., & Xia, Y. (2021) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2020.115741 · Cited by 674
Response-Surface Methods in R, Using rsm
Lenth, R. V. (2009) · Journal of Statistical Software · doi:10.18637/jss.v032.i07 · Cited by 668
Metadata
- Authors
- Gunst, R. F., Myers, R. H., & Montgomery, D. C.
- Venue
- Technometrics
- Publisher
- JSTOR
- Published
- 1996-08
- Volume
- 38
- Issue
- 3
- Pages
- 285
- ISSN
- 0040-1706 (ISSN-L)
- OpenAlex
- W2044771513
- Cited by
- 9,530
- References
- 0
- FWCI
- 86.48
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Response Surface Methodology: Process and Product Optimization Using Designed Experiments. https://app.cochat.ai/papers/10.2307/1270613
- The paper
- Gunst, R. F., Myers, R. H., & Montgomery, D. C. (1996). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Technometrics. https://doi.org/10.2307/1270613
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
