4. Discrete Random Variables

This chapter explores the foundational concepts of Discrete Random Variables, essential to probability and statistics.

Chapter Outline:

1. Probability Distribution Function (PDF) for a Discrete Random Variable: We begin by introducing the probability distribution function, which defines the likelihood of each outcome for a discrete random variable.

2. Mean or Expected Value and Standard Deviation: This section discusses measures of central tendency and variability, focusing on calculating and interpreting the mean (expected value) and standard deviation for discrete random variables.

3. Binomial Distribution: We examine the binomial distribution, a widely used discrete probability distribution, discussing its properties, applications, and methods for computing probabilities using the binomial formula.

4. Poisson Distribution: This section concludes with the Poisson distribution, another important discrete probability distribution, highlighting its characteristics and practical applications across various fields.

Table of contents:

• 4.1. Probability Distribution Function (PDF) for a Discrete Random Variable
• 4.2. Mean or Expected Value and Standard Deviation
• 4.3. Binomial Distribution
• 4.4. Poisson Distribution