Rajan Chattamvelli – författare

This book provides the theoretical framework needed to build, analyze and interpret various statistical models. It helps readers choose the correct model, distinguish among various choices that best captures the data, or solve the problem at hand.This is an introductory textbook on probability and statistics. The authors explain theoretical concepts in a step-by-step manner and provide practical examples. The introductory chapter in this book presents the basic concepts. Next, the authors discuss the measures of location, popular measures of spread, and measures of skewness and kurtosis. Probability theory, discrete distributions, and important continuous distributions that are often encountered in practical applications are analyzed. Mathematical Expectation is covered, along with Generating Functions and Functions of Random Variables. It discusses joint distributions, and novel methods to find the mean deviation of discrete and continuous statistical distributions. Provides insight on coding complex algorithms using the 'loop unrolling technique'Covers illuminating discussions on Poisson limit theorem, central limit theorem, mean deviation generating functions, CDF generating function and extensive summary tablesContains extensive exercises at the end of each chapter and examples from interdisciplinary fieldsStatistics for Scientists and Engineers is a great resource for students in engineering, physical sciences, and management, and also practicing engineers who require skill sets to model practical problems in a statistical setting.

A short chapter on logarithmic series distribution follows it, in which a theorem to find the kth moment of logarithmic distribution using (k-1)th moment of zero-truncated geometric distribution is presented.

This is an introductory book on continuous statistical distributions and its applications. Chapter 1 discusses important concepts on continuous distributions like location-and-scale distributions, truncated, size-biased, and transmuted distributions.

The Birnbaum-Saunders distribution and its extensions along with applications in actuarial sciences is discussed in Chapter 11. The Laplace distribution and its applications in navigational errors is discussed in the next chapter. Chapter 15 discusses Student's ???? distribution, its extensions and applications in statistical inference.

Specifically, the book discusses probability GFs (PGF), moment and cumulant GFs (MGF, CGF), mean deviation GFs (MDGF), survival function GFs (SFGF), rising and falling factorial GFs, factorial moment, and inverse factorial moment GFs.

Specifically, the book discusses probability GFs (PGF), moment and cumulant GFs (MGF, CGF), mean deviation GFs (MDGF), survival function GFs (SFGF), rising and falling factorial GFs, factorial moment, and inverse factorial moment GFs.

This book introduces descriptive statistics and covers a broad range of topics of interest to students and researchers in various applied science disciplines. and classification of spread, skewness, and kurtosis measures, L-moment based measures, van Zwet ordering of kurtosis, and multivariate kurtosis.

This book introduces descriptive statistics and covers a broad range of topics of interest to students and researchers in various applied science disciplines. and classification of spread, skewness, and kurtosis measures, L-moment based measures, van Zwet ordering of kurtosis, and multivariate kurtosis.

The book presents novel topics such as (i) a quick analytical method to approximate Pearson's correlation, (ii) single-variable correlation, (iii) fractional co-skewness and co-kurtosis, and (iv) the fallacy on correlation between the sample mean and sample variance.

The book presents novel topics such as (i) a quick analytical method to approximate Pearson's correlation, (ii) single-variable correlation, (iii) fractional co-skewness and co-kurtosis, and (iv) the fallacy on correlation between the sample mean and sample variance.

This book provides an introductory overview of random variables and their transformations. The authors have updated and expanded upon their writing on these topics, which they originally covered in their previous book, Statistics for Scientists and Engineers.

This book provides an introductory overview of random variables and their transformations. The authors approach the topic with statistics students in mind, along with researchers in various fields who are interested in data analysis. The book begins with by defining and explaining mathematical expectation. The authors then discuss transformations of random variables, including distribution functions and special functions. The book also covers joint probability distribution and its applications. The authors have updated and expanded upon their writing on these topics, which they originally covered in their previous book, Statistics for Scientists and Engineers.

This book presents a broad range of regression models including count regression models, constrained and penalised regression models such as ridge, LASSO, and elasticnet regression that are used in various applied science fields.