Designing Quantitative Experiments [recurso electrónico] : Prediction Analysis / by John Wolberg.
Tipo de material: TextoEditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Descripción: XII, 208p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642115899Tema(s): Physics | Engineering | Physics | Measurement Science and Instrumentation | Engineering, general | Statistics for Engineering, Physics, Computer Science, Chemistry and Earth SciencesFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 530.8 Clasificación LoC:T50Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
---|---|---|---|---|---|---|---|
Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | T50 (Browse shelf(Abre debajo)) | 1 | No para préstamo | 374051-2001 |
Statistical Background -- The Method of Least Squares -- Prediction Analysis -- Separation Experiments -- Initial Value Experiments -- Random Distributions.
The method of Prediction Analysis is applicable for anyone interested in designing a quantitative experiment. The design phase of an experiment can be broken down into problem dependent design questions (like the type of equipment to use and the experimental setup) and generic questions (like the number of data points required, range of values for the independent variables and measurement accuracy). This book is directed towards the generic design phase of the process. The methodology for this phase of the design process is problem independent and can be applied to experiments performed in most branches of science and technology. The purpose of the prediction analysis is to predict the accuracy of the results that one can expect from a proposed experiment. Prediction analyses can be performed using the REGRESS program which was developed by the author and can be obtained free-of-charge through the author's website. Many examples of prediction analyses are included in the book ranging from very simple experiments based upon a linear relationship between the dependent and independent variables to experiments in which the mathematical models are highly non-linear.
19