Description
Book SynopsisSensory evaluation is the perception science of the food industry. Sensory data can be costly to obtain and so gleaning the most information possible from the data is key. Increasingly, value is added to sensory evaluation by the use of statistics, especially to improve the quality of product development and to make the most of market research.
Nonparametrics for Sensory Science is written to complement existing parametric methodology. Nonparametric methods are appropriate when facts are only available in nominal or ordinal form, and when the model assumptions necessary for parametric procedures do not hold.
Author Rayner and his colleagues consider problems including the most commonly occurring and important experimental designs: the one-sample, k-sample, blocked samples, samples with factorial structure and samples with correlation structure. Innovative new techniques are outlined and complemented with real examples. Techniques described may be applied to data where the tra
Table of Contents
Preface.
1. Introduction.
2. The Completely Randomized Design.
3. The Randomized Block Design.
4. Balanced Incomplete Block Designs.
5. Correlation Effects.
6. Categorical Data for Randomized Block Designs.
7. Goodness of Fit.
8. Concluding Remarks.
References.
Subject Index.
Author Index.
Examples Index
