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.
Table of Contents
PREFACE -- PART I INTRODUCTION -- 1 The Use of Statistics -- 2 Observations -- 3 Probability -- 4 Sampling from a Normal Distribution -- PART II STATISTICS IN RESEARCH -- 5 Comparisons Involving 2 Samples -- 6 Concepts of Experimental Design -- 7 Analysis of Variance I: The 1-Way ANOVA -- 8 Analysis of Variance II: The Multiway ANOVA -- 9 Factorial Experiments -- 10 Analysis of Covariance -- 11 Chi-square -- PART III STATISTICS IN BUSINESS AND RESEARCH -- 12 Linear Regression -- 13 Multiple Regression -- 14 Correlation Analysis -- 15 Nonparametric Tests -- PARTIV STATISTICS IN BUSINESS -- 16 Evolutionary Operations -- 17 Index Numbers -- 18 Time Series -- 19 Control Charts -- 20 Using Computers in Statistical Analysis -- APPENDIX -- INDEX.
Professor of Agricultural Economics, University of Maryland, College Park, Maryland. Associate Professor of Dairy Science, University of Maryland, College Park, Maryland. Professor Emeritus, Food Science, University of Maryland, College Park, Maryland.