GRAPHICAL METHODS FOR THE DESIGN OF EXPERIMENTS
Par :Formats :
- Nombre de pages193
- PrésentationBroché
- Poids0.315 kg
- Dimensions15,5 cm × 23,5 cm × 1,3 cm
- ISBN0-387-94750-7
- EAN9780387947501
- Date de parution27/10/1999
- Collectionlecture notes in statistics
- ÉditeurSpringer
Résumé
The design of experiments is recognized as a critical activity in scientific investigations and industrial quality management programs, but many texts on the design of experiments focus on the analysis of experimental data, not the creation of the design. Graphical Methods for the Design of Experiments presents a strategic view of the planning of experiments, and provides a number of graphical tools that are useful for justifying the effort required for experimentation, for identifying variables and candidate statistical models, for selecting the set of run conditions, and for assessing the quality of the design. In addition, the graphical framework for creating fractional factorial designs is used to present experimental results in a fashion that can be easier to understand than a set of model coefficients. The text assumes a basic knowledge of statistics and matrices. Many of the graphical techniques are accessible without any knowledge of statistical models, and require only some familiarity with the plotting of functions and with the concept of projection from elementary mechanical drawing.
The design of experiments is recognized as a critical activity in scientific investigations and industrial quality management programs, but many texts on the design of experiments focus on the analysis of experimental data, not the creation of the design. Graphical Methods for the Design of Experiments presents a strategic view of the planning of experiments, and provides a number of graphical tools that are useful for justifying the effort required for experimentation, for identifying variables and candidate statistical models, for selecting the set of run conditions, and for assessing the quality of the design. In addition, the graphical framework for creating fractional factorial designs is used to present experimental results in a fashion that can be easier to understand than a set of model coefficients. The text assumes a basic knowledge of statistics and matrices. Many of the graphical techniques are accessible without any knowledge of statistical models, and require only some familiarity with the plotting of functions and with the concept of projection from elementary mechanical drawing.