Analyzing sensory data with R
著者
書誌事項
Analyzing sensory data with R
（The R series）
CRC Press, c2015
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Choose the Proper Statistical Method for Your Sensory Data Issue
Analyzing Sensory Data with R gives you the foundation to analyze and interpret sensory data. The book helps you find the most appropriate statistical method to tackle your sensory data issue.
Covering quantitative, qualitative, and affective approaches, the book presents the big picture of sensory evaluation. Through an integrated approach that connects the different dimensions of sensory evaluation, you'll understand:
The reasons why sensory data are collected
The ways in which the data are collected and analyzed
The intrinsic meaning of the data
The interpretation of the data analysis results
Each chapter corresponds to one main sensory topic. The chapters start with presenting the nature of the sensory evaluation and its objectives, the sensory particularities related to the sensory evaluation, details about the data set obtained, and the statistical analyses required. Using real examples, the authors then illustrate step by step how the analyses are performed in R. The chapters conclude with variants and extensions of the methods that are related to the sensory task itself, the statistical methodology, or both.
目次
Quantitative Descriptive Approaches: When panelists rate products according to one single list of attributes. When products are rated according to one single list of attributes. When products are rated according to several lists of attributes. Qualitative Descriptive Approaches: When products are depicted by comments. When two different products are put in various experimental contexts. When products are grouped into homogeneous clusters. When products are positioned onto a projective map. Affective Descriptive Approaches: When products are solely assessed by liking. When products are described by both liking and external information "independently." When products are described by a mix of liking and external information. Appendices. Index.
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