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A Review of Fundamentals of Applied Statistics by S. C. Gupta and V. K. Kapoor


A Review of Fundamentals of Applied Statistics by S. C. Gupta and V. K. Kapoor

Fundamentals of Applied Statistics is a textbook written by S. C. Gupta and V. K. Kapoor, two eminent statisticians from India. The book covers various topics in applied statistics, such as probability theory, sampling theory, estimation, testing of hypotheses, analysis of variance, regression and correlation, design of experiments, non-parametric methods, and multivariate analysis.

Fundamentals Of Applied Statistics By Gupta And Kapoor Pdf Free 13

The book is intended for undergraduate and postgraduate students of statistics, management, engineering, and other disciplines that require the use of statistical methods. The book is also useful for candidates preparing for Indian civil service examinations and other competitive examinations.

The book has been revised and updated several times since its first edition in 1994. The latest edition, published in 2007, has 708 pages and contains more than 2000 solved examples and exercises. The book also provides tables of statistical distributions and formulas for easy reference.

The book has received positive reviews from readers and teachers for its clarity, comprehensiveness, and practical orientation. The book explains the concepts and techniques of applied statistics with the help of real-life examples and data sets. The book also provides step-by-step solutions to the problems and exercises, along with hints and notes for further reading.

Fundamentals of Applied Statistics by S. C. Gupta and V. K. Kapoor is available in PDF format for free download from various online sources[^1^] [^2^] [^3^]. However, readers are advised to purchase the original book from the publisher or a reputed bookstore to support the authors and ensure the quality of the content.



Some of the topics covered in the book are:

  • Probability theory: This chapter introduces the basic concepts and rules of probability, such as events, sample space, conditional probability, Bayes' theorem, independence, and random variables. The chapter also discusses various discrete and continuous probability distributions, such as binomial, Poisson, normal, exponential, and chi-square.

  • Sampling theory: This chapter deals with the methods and techniques of selecting a sample from a population and drawing inferences about the population parameters. The chapter covers topics such as sampling methods, sampling distributions, central limit theorem, standard error, confidence intervals, and sample size determination.

  • Estimation: This chapter explains the concepts and methods of estimating the unknown population parameters from the sample statistics. The chapter covers topics such as point estimation, interval estimation, properties of estimators, methods of moments, maximum likelihood estimation, and Bayesian estimation.

  • Testing of hypotheses: This chapter describes the procedure and criteria of testing the validity of a claim or a statement about the population parameters based on the sample evidence. The chapter covers topics such as null and alternative hypotheses, types of errors, level of significance, power of a test, p-value, one-tailed and two-tailed tests, and various parametric and non-parametric tests.

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