Papers presented at a mini-meeting-cum-workshop held at Visva-Bharati University with support from Institute of Mathematical Statistics.

Providing practical training supported by a sound theoretical basis, this textbook introduces students to the principles of investigation by experiment and the role of statistics in analysis. It draws on the author's extensive teaching experience and is illustrated with fully worked, contextualized examples throughout, helping readers to correctly design their own experiments and identify the most appropriate technique for analysis. Subjects include sampling and determining sample reliability, hypothesis testing, relationships between variables, the role and use of computer packages such as Microsoft Excel spreadsheet software and GenStat, and more complex experimental designs, such as randomized blocks and split plots. This book is an essential text for students of agriculture, horticulture and related disciplines

This book is the outcome of more than 20 years of experience of the author in teaching and research field. The wider scope and coverage of the book will help not only the students/ researchers/professionals in the field of agriculture and allied disciplines, but also the researchers and practitioners in other fields. Written in simple and lucid language, the book would appeal to all those who are meant to be benefitted out of it. All efforts have been made to present "RESEARCH", its meaning, intention and usefulness. The book reflects current methodological techniques used in interdisciplinary research, as illustrated with many relevant worked out examples. Designing of research programme, selection of variables, collection of data and their analysis to interpret the data are discussed extensively. Statistical tools are complemented with real-life examples, making the otherwise complicated subject like statistics seem simpler. Attempts have been made to demonstrate how a user can solve the problems using simple computer-oriented programme. Emphasis is placed not only on solving the problems in various fields but also on drawing inferences from the problems. The importance of instruments and computers in research processes and statistical analyses along with their misuse/incorrect use is also discussed to make the user aware about the correct use of specific technique. In all the chapters, theories are combined with examples, and steps are enumerated to follow the correct use of the available packages like MSEXCELL, SPSS, SPAR1, SAS etc. Utmost care has been taken to present varied range of research problems along with their solutions in agriculture and allied fields which would be of immense use to readers.

The first edition of this book, popular around the world, is surpassed only by this new Second Edition. Improvements such as new and revised exercises, a broad range of practical and relevant case studies, and expanded theoretical concepts make this even better for users of statistics. The book emphasizes the practical application of statistics and provides examples in various fields of environmental and agriculture sciences. Because it uses simple, non-mathematical language to present statistical techniques, the reader requires only a familiarity with elementary algebra and mathematical notations to understand and apply the concepts described. This logically organized book covers the following topics: Part 1 introduces statistical concepts as they apply to different fields of environmental and agriculture sciences and provides descriptive measures of central tendency and variability; Part 2 covers probability and sampling concepts used in inferential statistics; Part 3 presents parametric methods in hypothesis testing, which include research designs; Part 4 discusses a number of nonparametric techniques; Part 5 explains tests of association and prediction; and lastly, analysis of change over time is detailed in Part 6. The appendices contain statistical tables for reference purposes.

"Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--

This book provides a sound understanding for creating new knowledge, which takes three main forms: Exploratory research, which structures and identifies new problems; constructive research, which develops solutions to a problem; and empirical research, which tests the feasibility of a solution using empirical evidence. This book encompasses both qualitative and quantitative research and analysis. The reader should gain an understanding of the skills needed to design and undertake a research project, including legal and ethical requirements in planning research projects, choosing the best experimental design and analytical methods, and how to present data for extension to the wider community and establish the knowledge. Hands-on exercises are provided to improve reasoning skills, emphasising agricultural problems and issues to solve and interpret the experimental data to knowledge. The book covers research methods within these three forms with basic knowledge of research methodology. Design of experiments and significant results are interpreted through the scientific organisation and information in each of the chapters. The inherent discussion should help interdisciplinary graduate students and researchers accomplish their scientific experiments and write research articles. The cognitive writing style to interpret the observed data from experiments and surveys is emphasised in this book. The cognitive summary for each of the chapters is provided in the form of wording and graphics to focus on the chapter highlights as well as the use of analytical tools in the research. The utmost care is taken to present a varied range of research problems along with their solutions in agriculture and its allied fields, which should be of immense use to the readers interested in this topic.

Provides a coherent and comprehensive account of the theory and practice of real-time human disease outbreak detection, explicitly recognizing the revolution in practices of infection control and public health surveillance. - Reviews the current mathematical, statistical, and computer science systems for early detection of disease outbreaks - Provides extensive coverage of existing surveillance data - Discusses experimental methods for data measurement and evaluation - Addresses engineering and practical implementation of effective early detection systems - Includes real case studies