Discrete Random Variables and Probability Distributions

Abstract

AbstractWhether an experiment yields qualitative or quantitative outcomes, methods of statistical analysis require that we focus on certain numerical aspects of the data (such as a sample proportion x/n, mean \( \bar{x} \), or standard deviation s). The concept of a random variable allows us to pass from the experimental outcomes themselves to a numerical function of the outcomes. There are two fundamentally different types of random variables—discrete random variables and continuous random variables. In this chapter, we examine the basic properties and discuss the most important examples of discrete variables. Chapter 4 focuses on continuous random variables.KeywordsNegative BinomialSingle SpikeletBinomial ExperimentUnacceptable VoltageAcceptance Sampling PlansThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.