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Written in a reader-friendly style, this thoroughly revised text teaches the students how to handle data and get the desired output through commonly available software like Microsoft Office 2007 and Excel using a step-by-step approach. Real-life data have been analyzed and illustrated through graphs, tables and screenshots. An entire chapter is devoted to Crystal Reports (CRP) software, which is currently used for rendering custom-designed reports from databases. This book will also benefit all those professionals who are not aware of the use of computer for data handling and statistical analysis.
This book explains the advanced but essential concepts of Multivariate Statistics in a practical way while touching the mathematical logic in a befitting manner. The illustrations are based on real case studies from a super specialty hospital where active research is going on.
Statistics is vital to decision making in business and our everyday lives. This book on statistics, in its Second Edition, continues to cover both the theoretical and the practical aspects of statistics which facilitate easy understanding of the fundamentals. The book contains twenty-two chapters. It begins with an introduction of statistics and describes statistical survey and sampling methods. It then discusses collection, classification, tabulation, as well as diagrammatic and graphical presentation of data very lucidly. The book then goes on to explain measures of central tendency or averages, measures of dispersion, measures of skewness, kurtosis and moments, and correlation and regression analysis. Finally, index numbers, time series analysis, probability and theoretical distributions, along with vital and population statistics, are discussed in a clear way. This book is primarily designed for the undergraduate and the postgraduate students of economics, commerce and management. In addition, it will be of great benefit to the students of demography and mathematics. NEW TO THIS EDITION • Chapter-end Multiple Choice Questions and Answers. • Sections on “Population Census of 2011”, in Chapter 22. KEY FEATURES • Includes numerous illustrative examples with solutions throughout the text to illustrate the application of the concepts. • Incorporates a large number of tables, diagrams and graphs to help students understand the concepts clearly. • Provides chapter-end exercises to enable students to test their comprehension of the topics discussed.
It is not necessary to know how to do a statistical analysis to critically appraise a paper. However, it is necessary to have a grasp of the basics, of whether the right test has been used and how to interpret the resulting figures. Short, readable, and useful, this book provides the essential, basic information without becoming bogged down in the
Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy! gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
The high-level language of R is recognized as one of the mostpowerful and flexible statistical software environments, and israpidly becoming the standard setting for quantitative analysis,statistics and graphics. R provides free access to unrivalledcoverage and cutting-edge applications, enabling the user to applynumerous statistical methods ranging from simple regression to timeseries or multivariate analysis. Building on the success of the author’s bestsellingStatistics: An Introduction using R, The R Book ispacked with worked examples, providing an all inclusive guide to R,ideal for novice and more accomplished users alike. The bookassumes no background in statistics or computing and introduces theadvantages of the R environment, detailing its applications in awide range of disciplines. Provides the first comprehensive reference manual for the Rlanguage, including practical guidance and full coverage of thegraphics facilities. Introduces all the statistical models covered by R, beginningwith simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression andanalysis of variance, through to generalized linear models,generalized mixed models, time series, spatial statistics,multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates andprofessionals in science, engineering and medicine. It is alsoideal for students and professionals in statistics, economics,geography and the social sciences.