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 presents the use of univariate and multivariate statistical tools for the analysis of data such as hypothesis testing, regression analysis, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), principal components analysis (PCA), factor analysis (FA), discriminant analysis (DA), cluster analysis (CA), and multivariate multiple regression. The techniques are presented in a simplified form without relying on complex mathematical proofs.

Using the open-source, R statistical software, Easy Statistics for Food Science with R offers examples based on actual research data over a ten year span of research in Food Science. The book provides guidance in modelling food science data and how to extract as much information as possible from the data. Analysis and interpretation are presented step-by-step to facilitate easy understanding.

This book is sure to be a useful resource for undergraduate students, postgraduate students, and researchers in food science.

Contains numerous step-by-step tutorials help the reader to learn quicklyPresents theory in a simple way without complex mathematical proofsCovers the theory and application of the statistical techniquesDescribes, step-by-step, how to implement statistical techniques in analyzing dataShows how to analyze data using R softwareProvides R scripts for all examples and figures

1. Introduction 2. Introduction to R 3. Statistical Concepts 4. Measures of Location and Dispersion 5. Hypothesis Testing 6. Comparing Several Population Means 7. Regression Models 8. Principal Component Analysis 9. Factor Analysis 10. Discriminant Analysis and Classification 11. Cluster Analysis