Easy Statistics For Food Science With R

Easy Statistics for Food Science with R presents the application of statistical techniques to assist students and researchers who work in food science and food engineering in choosing the appropriate statistical technique. The book focuses on the use of univariate and multivariate statistical methods in the field of food science. The techniques are presented in a simplified form without relying on complex mathematical proofs. This book was written to help researchers from different fields to analyze their data and make valid decisions. The development of modern statistical packages makes the analysis of data easier than before. The book focuses on the application of statistics and correct methods for the analysis and interpretation of data. R statistical software is used throughout the book to analyze the data. Contains numerous step-by-step tutorials help the reader to learn quickly Covers the theory and application of the statistical techniques Shows how to analyze data using R software Provides R scripts for all examples and figures

Many statistical innovations are linked to applications in food science. For example, the student t-test (a statistical method) was developed to monitor the quality of stout at the Guinness Brewery and multivariate statistical methods are applied widely in the spectroscopic analysis of foods. Nevertheless, statistical methods are most often associated with engineering, mathematics, and the medical sciences, and are rarely thought to be driven by food science. Consequently, there is a dearth of statistical methods aimed specifically at food science, forcing researchers to utilize methods intended for other disciplines. The objective of this Brief will be to highlight the most needed and relevant statistical methods in food science and thus eliminate the need to learn about these methods from other fields. All methods and their applications will be illustrated with examples from research literature. ​

Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day-to-day research problems. Includes step-by-step tutorials to aid in understanding the process and implementation of unique data Presents statistical theory in a simple way without complex mathematical proofs Shows how to analyze data using R software and provides R scripts for all examples and figures

The recording and analysis of food data are becoming increasingly sophisticated. Consequently, the food scientist in industry or at study faces the task of using and understanding statistical methods. Statistics is often viewed as a difficult subject and is often avoided because of its complexity and a lack of specific application to the requirements of food science. This situation is changing – there is now much material on multivariate applications for the more advanced reader, but a case exists for a univariate approach aimed at the non-statistician. This second edition of Statistical Methods for Food Science provides a source text on accessible statistical procedures for the food scientist, and is aimed at professionals and students in food laboratories where analytical, instrumental and sensory data are gathered and require some form of summary and analysis before interpretation. It is suitable for the food analyst, the sensory scientist and the product developer, and others who work in food-related disciplines involving consumer survey investigations will also find many sections of use. There is an emphasis on a ‘hands-on’ approach, and worked examples using computer software packages and the minimum of mathematical formulae are included. The book is based on the experience and practice of a scientist engaged for many years in research and teaching of analytical and sensory food science at undergraduate and post-graduate level. This revised and updated second edition is accompanied by a new companion website giving the reader access to the datasets and Excel spreadsheets featured in the book. Check it out now by visiting www.wiley.com/go/bower/statistical or by scanning the QR code below.

Applications of Hypothesis Testing for Environmental Science presents the theory and application of hypothesis testing in environmental science, allowing researchers to carry out suitable tests for decision-making on a variety of issues. This book works as a step-by-step resource to provide understanding of the concepts and applications of hypothesis testing in the field of environmental science. The tests are presented in simplified form without relying on complex mathematical proofs to allow researchers to easily locate the most appropriate test and apply it to real-world situations. Each example is accompanied by a case study showing the application of the method to realistic data. This book provides step-by-step guidance in analyzing and testing various environmental data for researchers, postgraduates and graduates of environmental sciences, as well as academics looking for a book that includes case studies of the applications of hypothesis testing. It will also be a valuable resource for researchers in other related fields and those who are not familiar with the use of statistics who may need to analyze data or perform hypothesis tests in their research. Includes step-by-step tutorials to aid in the understanding of procedures and allowing implementation of suitable tests Presents the theory of hypothesis testing in a simple yet thorough manner without complex mathematical proofs Describes how to implement hypothesis testing in analyzing and interpretation environmental science data

Abstract: An applied statistics text is offered for college students, teachers, research scientists, and those engaged in agriculture and the food industries. The sequence of topic presentation proceeds from data organization and tabulation, through graphing (to obtain the data distribution pattern), to analysis of the pattern (to determine location and dispersion parameters); interpretation of the results and report-writing are the final phases. A comprehensive range of direct statistical tests is covered, along with numerous examples and problems. Mathematical complexities and formula derivations are avoided; a knowledge of high school mathematics is sufficient to underastand and apply the statistical methods covered. (wz).

Mathematical and Statistical Approaches in Food Science and Technology offers an accessible guide to applying statistical and mathematical technologies in the food science field whilst also addressing the theoretical foundations. Using clear examples and case-studies by way of practical illustration, the book is more than just a theoretical guide for non-statisticians, and may therefore be used by scientists, students and food industry professionals at different levels and with varying degrees of statistical skill.

Statistical Methods in Food and Consumer Research, Second Edition, continues to be the only book to focus solely on the statistical techniques used in sensory testing of foods, pharmaceuticals, cosmetics, and other consumer products. This new edition includes the most recent applications of statistical methods, and features significant updates as well as two new chapters. Covering the application of techniques including R-index, the Bayesian approach for sensory differences tests, and preference mapping in addition to several other methodologies, this is the comprehensive reference needed by those studying sensory evaluation and applied statistics in agriculture and biological sciences. Research professionals working with food, beverages, healthcare, cosmetics, and other related areas will find the book a valuable guide to the variety of statistical methods available. Provides comprehensive coverage of statistical techniques in sensory testing Includes data compiled from real-world experiments Covers the latest in data interpretation and analysis Addresses key methods such as R-index, Thursonian Discriminal Distances, group sequential tests, beta-binomial tests, sensory difference and similarity tests, just-about-right data, signal-to-noise ratio, analysis of cosmetic data, Descriptive Analysis, claims substantiation and preference mapping