Overview

An understanding of the basics, logic, and theory of statistics is essential for agricultural researchers for dealing with the interpretation of data. This volume presents some of the basic and necessary concepts of statistical tools, specifically as applied to the statistics of agriculture and allied fields. It covers basic statistics, design of experiments, sampling techniques, time series, inference outlines, forecasting models, data handling, and statistical software in an easy-to-understand manner that is aimed at students and researchers with little or no mathematical background. In the agriculture scenario, students and researchers face problems that can be addressed with statistical tools, planning of field experiments, collection of data, analysis, interpretation of the data, etc. In this book, statistical theories are discussed with the help of examples from real-life situations in agriculture and allied fields, followed by worked-out examples. Each chapter is followed by a number of problems and questions that will help readers gain confidence in solving those problems. The volume also provides an analysis of how data is important and introduces the reader to using statistical software such as MS Excel, SAS (Statistical Analysis System), JMP, Minitab, and R (from the R Foundation for Statistical Computing).

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9781774634448

Table of Contents

Reviews

A very useful and well-conceived book . . . The book aims at a varied range of readers. Mainly, the book has two parts: the basic statistics part and the specialized chapters. In the first chapter, the authors have tried to cover almost all the basic needs of users of statistics in the field of agriculture. Topics like central tendency, dispersion, correlation-regression, testing of hypothesis, and widely used probability distributions, like normal, binomial, etc., have been covered in this chapter. For a user having no or limited knowledge of statistics, this chapter will be of very much help. For an inquisitive and interested reader, the specialized chapters on design of experiments, statistical inference, sampling techniques, time series, and index number analysis will definitely draw attention. The authors have intended to provide in-depth of coverage each of these topics . . . The most important part of the book is most probably the last two chapters. Statistical software has made it possible for wide applications of statistical theories in everyday life and science. In these two chapters, the authors provide overviews of some of the statistical software programs and their uses in solving statistical problems. Numerical problems solved with the help of the software in the form of demonstration will encourage users to use more and more sophisticated statistical tools in unearthing the so long hidden information from their respective fields. By and large, the book will be appreciated by the readers in agriculture. - Prof. P. K. Sahu, Professor, Department of Agricultural Statistics, Bidhan Chandra Agricultural University, West Bengal, India

Author Information

Pradeep Mishra, PhD, is an Assistant Professor of Statistics at the College of Agriculture at the Jawaharlal Nehru Agricultural University, Jabalpur, Madhya Pradesh, India. Dr. Mishra previously served as a data management specialist at a private multinational company for several years. He specializes in the field of time series, experiment design, and agricultural statistics. He has published more than 45 research articles in international and national journals and has won several awards. Fozia Homa, PhD, is an Assistant Professor and Scientist in the Department of Statistics, Mathematics and Computer Application, Bihar Agricultural University, Sabour, India, and author or co-author of several journal articles. Dr. Fozia has received numerous awards in recognition of her research and teaching achievements from several national and international organizations. She has received several grants from various funding agencies to carry out her research projects. Her areas of specialization include sample surveys, population studies, and mathematical modeling.

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