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About 10,000 papers for “Design of experiments”

Design of experiments.

Antoine Abrieux (454208), Amel Mhamdi (3370766), Kaouther K. Rabhi (675942) et al. · Figshare · 2016

<p>Treatments were performed on 1-day-, 4-day-, and 5-day-old males, and behavioral tests in wind tunnel were performed either the same day of treatment or 24 h later. Test: behavioral test in wind tunnel.</p>

Cited by 0Open access

Design of experiments

Wishart, John · NCSU Libraries Repository (North Carolina State University Libraries) · 2009

Cited by 0

Design of Experiments

Richard J. Rossi · 2022

This chapter discusses the principles and techniques that are used in designing an experiment for comparing two or more populations. Regardless of the reason for performing an experiment, the goal of an experiment is to obtain data that are representative of the populations being studied for making statistical inferences about the differences between the populations. The two types of statistical studies are experiments that are studies where the researcher controls the assignment of the units to the treatment groups and observational studies that are studies where the units come to the researcher already assigned to the treatment groups. The factors in a factorial experiment can be quantitative or qualitative factors and each factor must have at least 2 levels. Linear models are often used to model the quantitative observations on a response variable that result from a designed experiment. The chapter also discusses the models for a quantitative response variable.

Cited by 1

Design of Experiments

ASM International eBooks · 2009

Abstract After the failure analysis team hypothesizes failure causes, prepares a failure mode assessment and assignment, and evaluates all potential failure causes, it may find in some cases that several causes are still suspect but cannot be confirmed. In this situation, an experiment is necessary to confirm or rule out suspected causes. This chapter discusses two predominant methods for doing this, namely analysis of variance (ANOVA) and Taguchi methods (a more powerful technique based on ANOVA).

Cited by 1

Design of Experiments

Mark J. Anderson, Martin A. Bezener · Kirk‐Othmer Encyclopedia of Chemical Technology · 2024

Abstract The statistical design of experiments (DOEs) is an essential ingredient of successful product development and improvement. DOE provides an efficient and scientific approach to obtaining meaningful information. In contrast to traditional one‐factor‐at‐a‐time (OFAT) experimentation, DOE changes factors together, permitting evaluation of interactions. Standard texts give details about the construction of specific experiment designs, such as incomplete block, full and fractional factorial, response surface designs, and mixture designs; and the analysis of the resulting data. This chapter focuses on the fundamental elements of DOE: defining the purpose and scope of the experiment, differentiating between alternative types of experimental variables, understanding the underlying environment and constraints, and conducting stage‐wise experimentation. It covers the basic elements of the design and analysis of experiments based on a multifactor approach that is far more effective and efficient than OFAT for chemical research and development.

Cited by 2

Design of experiments.

Michaela Kotkova (401872), Bohumil Sak (219955), Dana Kvetonova (5654602) et al. · Figshare · 2015

<p><b><sup>a</sup></b>inoculation with 200 ul sterilized deionised water; <b><sup>b</sup></b>infection with 10<sup>7 </sup><i>E. cuniculi</i> spores in 0.2 ml of sterilized deionised water; INF – infection; REINF – reinfection (black column); IMSUP – dexamethasone immunosuppression (highlighted in dark grey); TREAT – albendazole treatment (highlighted in light grey); <b>n1–</b> number of used animals; <b>n2</b>– number of dissected animals; <b>NS</b> – not shown; <b>x</b> – not observed due to mouse death; <b>DPI</b> – day post infection;</p>

Cited by 0Open access

Design of experiments

Sorana D. Bolboacă, Lorentz Jäntschi · 2015

Abstract: A methodology for the design of an experiment is proposed in order to find as many schemes as possible with the maximum number of factors with different levels for the smallest number of experimental runs. An algorithm was developed and homemade software was implemented. The abilities in generation of the largest groups of orthogonal

Cited by 0

Design of experiments.

Marta Kinga Lemieszek (10810662), Katarzyna Sawa-Wejksza (10810665), Marcin Golec (39014) et al. · Figshare · 2021

Cited by 0Open access

Design of experiments.

Ilshat Khasanshin (11767012), Aleksey Osipov (11767015) · Figshare · 2021

Cited by 0Open access