Statistics for Food Technologists – Computational Statistics and Data Analysis

General

• Code: 276-190304
• Semester: 3rd
• Study Level: Undergraduate
• Course type: Special Background
• Teaching and exams language: Greek
• Teaching Methods (Hours/Week): Lectures (2) / Laboratory (2)
• ECTS Units: 5
• Course homepage: https://exams-geo.the.ihu.gr/course/view.php?id=193
• Instructors: Karageorgiou Vassilis, Voultsios Dimitrios

Course Contents

A) Statistics for food technologists

Adaptation of terms and examples of statistical analysis to food technology, because this science is a mosaic of scientific fields, where biology appears inextricably linked to physics and chemistry. Embedding the fundamental knowledge of basic statistics and, at the same time, the knowledge required to deal with practical and research needs for food technologists.

Summary of contents

• Examination and processing of data: accuracy and reliability of the sample, reference scales, categories of variables, descriptive statistics (mean, standard deviation, median, quartiles, Box plot), basic distributions.
• Rating scales ofcharacteristics of a product. Types of sampling: random, systematic, aggregate, longitudinal, stratified.
• Parametric statistical tests: tests of the distribution and homogeneity of the variance, tests of the distribution t, one-way analysis of variance, random and fixed factors, tests of multiple comparisons of means. Simple linear regression and correlation. Comparison of linear regressions and correlations.
• Non-parametric tests of rank variables (Mann-Witney, Wilcoxon, Kruskal-Walis and Mood tests, Spearman’s and Kendall’s tests), tests of comparison of proportions. Tests of categorical variables (x2and G-test), correlation indices.

B) Computational statistics and data analysis

Application of statistical methods of data analysis using statistical software. Learning the commands of descriptive statistics, descriptive statistics, two-sample hypothesis (t test,) analysis of variance (F test), regression, correlation and frequencies (categorical variables). Interpretation of the results of statistical analyses based on examples from food science.

Summary of contents

• Computational statistics: Impact of computers on statistical methodology (bioinformatics, computational graphics), exploration and modeling of data. Methods of product optimization, evaluation and validity of results via statistical process.
• Strategies for data analysis: application of methods to selected sciences (biostatistics, industrial statistics), data classification, population estimation, experimental design, parametric and non-parametric methods.
• Specific applications: Comparison of statistical methods, application of statistics to real-life.
• Comparison of statistics with real data (case studies).

Educational Goals

• Acquiring knowledge in the basic principles of statistical analysis specially designed for the needs of the Food Industry.
• Understanding of statistical terms and methodology in order to consolidate a different philosophy of thought and perception of experimental data and processes.
• Ability to design simple experimental designs and ensure successful completion during their implementation.
• Gain experience in managing statistical and graphical programs using a PC.
• Facilitation in the recognition of problematic situations and faster understanding, interpretation and finding solutions by testing statistical knowledge.

General Skills

• Promoting creative initiative and transmission of thinking.
• Encouraging participation in group assignment projects in the same workplace or interdisciplinary.
• Projecting internal knowledge for decision making.
• Searching and analyzing data using the acquired skills.
• Flexibility in dealing with adverse conditions.
• Respect in the work environment.

Teaching Methods

Face to face:

• Lectures (theory and problems) in the classroom.
• Practical exercises (practice in the statistical software MINITAB by solving educational problems).

Use of ICT means

• Presentation with PowerPoint slides using PC and projector.
• Posting course material and communicating with students on the Moodle online platform.
• Use of electronic devices for recording data and solving practical exercises.

Teaching Organization

Students Evaluation

Evaluation methods:

• Written final exams in the theoretical part of the course (Statistics for food technologists) (50% of the final grade).
• Compulsory attendance at (at least) 80% of the practical exercises.
• Written final exams in the practical part of the course (Computational statistics and data analysis) involving problem solving using the statistical software MINITAB (50% of the final grade).
• Optional exams in the practical part of the course using computers as previously stated (20% of the previous grade).

The course material and the evaluation criteria are presented and analyzed at the beginning of the semester in the classroom but also permanently online.

Recommended Bibliography

Στατιστική για Τεχνολόγους Τροφίμων

1. Agarwal B.L., (1988), Basic Statistics, 2^nd Ed., Wiley Eastern Ltd., New Delhi, pp. 758.
2. Everitt B.S., (1994), The Analysis of Contigency Tables, 2^nd Ed., Chapman & Hall, London, pp. 164.
3. Κάτος Α.Β., (1986), Στατιστική, Παρατηρητής, Θεσ/νίκη, σελ. 708.
4. Κιόχος Π.Α., (1993), Περιγραφική Στατιστική, Εκδόσεις Interbooks, Αθήνα, σελ. 340.
5. Κίτσος Χ.Π., (1991), Εισαγωγή στην Εφαρμοσμένη Στατιστική, Εκδόσεις Νέων Τεχνολογιών, Αθήνα, σελ. 290.
6. Κίτσος Χ.Π., (1994), Στατιστική Ανάλυση Πειραματικών Δεδομένων, Εκδόσεις Νέων Τεχνολογιών, Αθήνα, σελ. 228.
7. Κολυβά-Μαχαίρα Φ. & Μπόρα-Σέντα Ε., (1996), Στατιστική, Θεωρία και Εφαρμογές, Εκδόσεις Ζήτη, σελ. 495.
8. Πετρίδης Δ. (2013). Εφαρμοσμένη Στατιστική με έμφαση στην επιστήμη τροφίμων. Εκδόσεις Δίβατον, σελ 520.

Υπολογιστική Στατιστική και Ανάλυση Δεδομένων

1. Βλαχάβας Γ (2011)., Εφαρμοσμένη Στατιστική με χρήση του πακέτου Minitab. Εκδόσεις Τζιόλα.
2. Casella, G. and R. Berger, Statistical Inference. Duxbury Press, 1990.
3. Draper, N.R. and H. Smith, Applied Regression Analysis, Second Edition. John Wiley & Sons, Inc, 1981.
4. Levene, H., Contributions to Probability and Statistics. Stanford University Press, 1960.
5. Little, T.M., Interpretation and presentation of result. HortScience, 19:637–640, 1981.
6. Piggott, J.R., Statistical procedures in food research. Elsevier Publishers, London, 1987.
7. Minitab-specific: Joiner B., Cryer J., Ryan B., Minitab Handbook. Brooks/Cole Publishing, 2003.
8. Mathews P.G., Design of Experiments with Minitab. Amer Society for Quality. 2004.
9. Montgomery D., Design and Analysis of Experiments: Minitab Companion. John Wiley & Sons, Inc, 2011.
10. Sincich T., Business Statistics By Example, SAS,SPSS, Minitab, ASP. Prentice-Hall, 1995.
11. Sleeper A., Minitab Demystified. McGraw-Hill books, 2011.

Related Research Journals

1. Journal of Applied Statistics.
2. Journal of Statistics Education.
3. Biometrika.
4. Teaching Statistics.