Statistical Methods for Food and Agriculture
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Statistical Methods for Food and Agriculture

Filmore E Bender

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๐Ÿ“– eBook - ePub

Statistical Methods for Food and Agriculture

Filmore E Bender

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About This Book

This classic book will meet the needs of food and agricultural industries in both their research and business needs. Learn the fundamentals of applying statistics to the business and research needs in the food and agricultural industries. Statistical Methods for Food and Agriculture is a practical, hands-on resource that explores how statistics, a relatively recent development for science and business, facilitates the decision-making process. The range of techniques and applications explained and demonstrated in each of the four major sections of this volume provides a substantial course of study for those in business, government, and universities dealing with food, agriculture, and economics.

  • Part I provides an introduction to the uses of statistics today, including basic concepts and definitions.
  • Part II examines the statistical needs of the food researcher. The emphasis is on design of planned experiments, the analysis of data generated by planned experiments, and decision making in a research environment.
  • Part III deals with statistical procedures that have a wide range of uses for the researcher and business analyst in both business and research situations.
  • Part IV focuses on those statistical methods that have primarily a business application. This important volume is sufficiently detailed to enable the reader to learn and develop without outside assistance. References lead to more detailed presentations for those desiring additional specialized information, and helpful exercises at the end of each chapter permit the book?s use as a textbook as well.

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CRC Press


The Use of Statistics

Throughout this text our primary task has been to strive for clarity of exposition. The tools and techniques presented in this book have demonstrated their utility in a wide range of disciplines, but we have attempted to pull together into a single volume those procedures which have the greatest application to individuals working in food science or the food industry.


Statistics as a discipline encompasses all of those techniques which utilize numerical values to describe, analyze, or interpret experiments and experiences. Tables and charts are valid, albeit simple, statistical tools. Various descriptive values (means, standard deviations, index numbers, etc.) represent somewhat more complex tools. Statistical techniques which assist decision making when facing uncertainty (regression analysis, the analysis of variance, the Chi-square test, etc.) represent the culmination of considerable mathematical theory and applied experience. It is this last body of ideas that normally comes to mind when a scientist speaks of using statistics. This book presents all three aspects of statistics. Primary emphasis is on the last because it is the most difficult to master and also because it yields the greatest payoff as a result of the acquired knowledge.


Descriptive statistics are normally one of the first steps toward analyzing a problem or making a decision. No individual can keep track of the movement of the prices associated with the more than 1000 stocks traded on the New York Stock Exchange. The Dow Jones Index of 30 industrial stocks provides a summary number which is designed to provide in a single number a description of the overall behavior of the market. Similarly, if 50 trials of a specific cooking time-temperature experiment are run, it may be sufficient to report only the average tenderness (i.e., the arithmetic mean of the 50 trials). In other words a single value is reported that summarizes the results in a way that can be comprehended more readily than reporting all 50 individual results. In general, this is the nature of descriptive statistics. They provide a means of reducing a large number of complex data points into a smaller number of summary values. Admittedly, information is lost in this process but the gain in understanding is substantial. Chapters 2, 17, and 18 deal with those descriptive statistics commonly encountered in the food industry.


One of the more important contributions that statistics has made to our thinking is the development of statistical inference. It would not be possible to test every can of food produced by a plant to determine whether or not net weight specifications are being met since the test procedure destroys the product. Inferential statistics provide a technique for examining a sample of the total production and utilizing that information to infer something about the characteristics of the total population from which the sample was drawn. Virtually everyone has drawn samples of grain or other products to determine grade or other characteristics. Few people realize the limitations that exist in the use of inferential statistics. One of the goals of this book (in addition to presenting the techniques and their uses) is to help the reader develop a healthy respect for the potential abuses that exist when dealing with statistical inference. Chapter 3 provides the basic concepts associated with the probability theory that underlies all statistical inference. Chapter 4 examines the use of the normal distribution and presents a number of simple tests. Together, these two chapters provide the nucleus of statistical thought for decision making.


There are probably as many definitions of the scientific method as there are scientists. Furthermore, scientists do not have any unique claim to the scientific method since nearly any decision maker uses some variant of the scientific method when examining problems. For our purposes, the scientific method can be considered to contain the following elements:
(1)Statement of the problem
(2)Organization of ideas and theories
(3)Statement of a hypothesis
(4)Testing the stated hypothesis
(5)Confirmation or denial of the stated hypothesis leading to a decision. The decision may result in action or may lead to the refinement or restatement of initial ideas and theories and the construction of a subsequent hypothesis.
The individualโ€™s discipline or job provides the basis for steps (1), (2), and (3). Statistics plays a role in step (3) by assisting in formulating a testable hypothesis. Statistics also carries the bulk of the load in step (4). In particular, the principles of experimental design (presented in Chapter 6 and reexamined in Chapters 7, 8, 9, and 10) provide an important tool which greatly facilitates organized thinking and problem approach. The final step (confirming or denying the hypothesis) rests on the concepts of probability presented in Chapters 3 and 4 and illustrated throughout the book with a wide variety of examples.
Since the scientific method has been presented in general terms, a concrete example may assist in clarifying the bas...

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Citation styles for Statistical Methods for Food and AgricultureHow to cite Statistical Methods for Food and Agriculture for your reference list or bibliography: select your referencing style from the list below and hit 'copy' to generate a citation. If your style isn't in the list, you can start a free trial to access over 20 additional styles from the Perlego eReader.
APA 6 Citation
Bender, F. (2020). Statistical Methods for Food and Agriculture (1st ed.). CRC Press. Retrieved from (Original work published 2020)
Chicago Citation
Bender, Filmore. (2020) 2020. Statistical Methods for Food and Agriculture. 1st ed. CRC Press.
Harvard Citation
Bender, F. (2020) Statistical Methods for Food and Agriculture. 1st edn. CRC Press. Available at: (Accessed: 14 October 2022).
MLA 7 Citation
Bender, Filmore. Statistical Methods for Food and Agriculture. 1st ed. CRC Press, 2020. Web. 14 Oct. 2022.