Book Description

Welcome to "Foundations of Probability and Statistics," a vital part of our series designed for aspiring scholars preparing for the CSIR NET (JRF) and various mathematics competitive exams. This book aims to provide a comprehensive introduction to essential statistical concepts and probability theory, crucial for understanding the foundational aspects of advanced mathematical studies. In the first chapter, we delve into descriptive statistics and exploratory data analysis, focusing on measures of central tendency and variability, as well as graphical methods for effective data visualization. Understanding outliers and managing missing data is also emphasized, ensuring that students can analyze real-world datasets accurately. Moving forward, we explore probability theory, beginning with fundamental concepts such as sample spaces and events, before examining discrete probability, independent events, and conditional probabilities through Bayes’ Theorem. The subsequent chapters introduce random variables and their distribution functions, detailing both univariate and joint distributions, alongside marginal and conditional distributions. We also cover the crucial topics of expectation and moments, discussing expected values, variance, and the relationships between random variables. Finally, we investigate characteristic functions and key inequalities, including those of Markov, Chebyshev, and Jensen, which serve as powerful tools in statistical analysis. This book serves as both a foundation and a reference for students, guiding them through the intricate landscape of probability and statistics. We hope it inspires a deeper understanding and appreciation of these essential mathematical fields.