This book focuses on the meaning of statistical inference and estimation. Statistical inference is concerned with the problems of estimation of population parameters and testing hypotheses. Primarily aimed at undergraduate and postgraduate students of statistics, the book is also useful to professionals and researchers in statistical, medical, social and other disciplines. It discusses current methodological techniques used in statistics and related interdisciplinary areas. Every concept is supported with relevant research examples to help readers to find the most suitable application. Statistical tools have been presented by using real-life examples, removing the fear factor usually associated with this complex subject. The book will help readers to discover diverse perspectives of statistical theory followed by relevant worked-out examples. Keeping in mind the needs of readers, as well as constantly changing scenarios, the material is presented in an easy-to-understand form.

Discusses all major topics in statistical inference Explains the applications of statistical inference in everyday life Helps draw inference about the population under varied situations Appends suitable examples with theories to help readers find the most suitable applications Will be useful to students of statistics Includes supplementary material: sn.pub/extras

Autorentext

P.K. Sahu, PhD in agricultural statistics, is associate professor and head of the Department of Agricultural Statistics, Bidhan Chandra Krishi Viswavidyalaya (a state agriculture university), West Bengal. With over 20 years of teaching experience, Dr. Sahu has published over 70 research papers in several international journals of repute and has guided several postgraduate students and research scholars. He has authored four books: Agriculture and Applied Statistics, Vol. 1, and Agriculture and Applied Statistics, Vol. 2 (both published by Kalyani Publishers), Gender, Education, Empowerment: Stands of Women (published by Agrotech Publishing House) and Research Methodology: A Guide for ResearchersIn Agricultural Science, Social Science and Other Related Fields(published by Springer) as well as contributed a chapter to the book Modelling, Forecasting, Artificial Neural Network and Expert System in Fisheries and Aquaculture, edited by Ajit Kumar Roy and Niranjan Sarangi (Daya Publishing House). Dr. Sahu has presented his research papers in several international conferences. He also visited the USA, Bangladesh, Sri Lanka and Vietnam to attend international conferences. S.R. Pal, PhD in agricultural statistics, an eminent professor, had been teaching at the Department of Agricultural Statistics at R.K. Mission Residential College and Bidhan Chandra Krishi Viswavidyalaya (a state agriculture university). An expert in agricultural statistics, Prof. Pal has over 35 years of teaching experience and has guided several postgraduate students and research scholars. He has several research papers published in statistics and related fields in several international journals of repute. With his vast experience in teaching, research and industrial advisory role, Prof. Pal has tried to incorporate the problems faced by the users, students and researchers in this field. A.K. Das, PhD in agricultural statistics, is professorat the Department of Agricultural Statistics, Bidhan Chandra Krishi Viswavidyalaya (a state agriculture university). With over 30 years of teaching experience, Prof. Das has a number of good research articles to his credit published in several international journal of repute and has guided several postgraduate students and research scholars. He has coauthored a book, Agriculture and Applied Statistics, Vol. 2, and contributed a chapter to the book, Modelling, Forecasting, Artificial Neural Network and Expert System in Fisheries and Aquaculture, edited by Ajit Kumar Roy and Niranjan Sarangi.

Inhalt

1. Theory of Point Estimation.- 2. Methods of Estimation.- 3. Theory of testing of hypothesis.- 4. Likelihood Ratio Test.- 5. Interval Estimation.- 6. Non-parametric test.- 7. Statistical Decision Theory.