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 quickly
- Presents theory in a simple way without complex mathematical proofs
- Covers the theory and application of the statistical techniques
- Describes, step-by-step, how to implement statistical techniques in analyzing data
- Shows how to analyze data using R software
- Provides R scripts for all examples and figures
Undergraduate students, postgraduate students, and researchers in food science
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
- No. of pages:
- © Academic Press 2019
- 1st October 2018
- Academic Press
- Paperback ISBN:
Abbas F. M. Alkarkhi received his Ph.D in applied statistics from University of Science, Malaysia (2002). He received his BSc and MSc in statistics from Baghdad University in 1985 and 1992 respectively. Dr. Alkarkhi spent fourteen years as a faculty member in the school of Industrial Technology at University of Science Malaysia (2002-2016), then moved to Kuala Lumpur University- (MICET campus) in the technical foundation section. Prior to joining a Ph.D study, he worked in Iraq for two years and in Libya for five years as a lecturer. He have been published more than 80 papers in national and international journals, a text book, titled “Elementary statistics for Technologist,” and chapters in other publications. Dr. Alkarkhi’s research focuses on the applications of experimental design and multivariate analysis.
University of Kuala Lumpur, Malaysian Institute of Chemical and Bioengineering Technology, Malacca, Malaysia
Dr. Alqaraghuli received his BSc and MSc in statistics from Al-Mustansirya University in 1987 and 1995 respectively. He worked at a specialized institute for engineering industries in Iraq from 1988 to 1992,and during this time, he conducted training in statistical methods. He worked also at the University level in Iraq, Jordan and then Libya after receiving his MSc in statistics. In 2014, Dr. Alqaraghuli received his Ph.D from the school of mathematical sciences at the University of Science, Malaysia. He has taught a number of statistical courses, including elementary statistics, mathematical statistics, biostatistics and the design and analysis of experiments as well as courses related to different statistical software, including SPSS and R. Dr. Alqaraghuli’s research is focused on the application of experimental design, modelling, and multivariate. He is currently self-employed and conducts workshops for non-statisticians while also conducting research.
Self-employed, Kajang, Malaysia