Titlebook: Introduction to Probability, Statistics & R; Foundations for Data Sujit K. Sahu Textbook 2024 The Editor(s) (if applicable) and The Author(

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Conditional Probability and Independencerem. The famous Monty Python problem is discussed and illustrated using a simulation tool in R. The concept of independence is discussed and illustrated with many examples such system reliability and randomised response methods.

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Introduction to Statistical Inferencetions and sample statistics (quantities). The concepts of estimators and their sampling (probability) distributions are also introduced. The properties of bias and mean square errors of estimators and defined.

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Generating Functionsrating function and probability generating function for discrete random variables. The uniqueness theorem for the moment generating function is also stated here to facilitate many proofs in statistical distribution theory.

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Multivariate Distributionsiated with bivariate and multivariate normal distributions. It also discusses the joint moment generating function for the multivariate normal distribution. In the discrete case it introduces the multinomial distribution as a generalisation of the binomial distribution.

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The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Getting Started with Chapter 2: This chapter introduces the R software package and discusses how to get started with many examples. It revisits some of the data sets already mentioned in Chap. . by drawing simple graphs and obtaining summary statistics.