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5: Continuous Random Variables

Last updated

Mar 17, 2025
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- 4.13: Solutions
- 5.0: Introduction to Continuous Random Variables

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5.0: Introduction to Continuous Random Variables
This page explains the differences between continuous and discrete random variables. Continuous random variables are applicable in areas such as baseball batting averages and IQ scores, measuring quantities like distance, while discrete random variables involve countable values, such as the number of miles. Understanding the definitions and classifications of these variables is essential.
5.1: Properties of Continuous Probability Density Functions
This page discusses continuous probability distributions, highlighting the probability density function (pdf) and the cumulative distribution function (cdf) for evaluating probabilities as areas. It notes that probabilities for specific values are zero and emphasizes intervals, with the total pdf area equaling one. The text covers various continuous distributions, including uniform, exponential, and normal, and offers examples for calculating probabilities within specified ranges.
5.2: The Uniform Distribution
This page explains the uniform distribution, a continuous probability distribution with equally likely outcomes defined by endpoints $a$ and $b$ . It provides formulas for mean $\mu$ and standard deviation $\sigma$ , includes examples like bus wait times and baseball game durations, and stresses the significance of inclusive vs. exclusive endpoints, along with the calculation of probabilities for specific intervals.
5.3: The Exponential Distribution **
This page covers the exponential distribution, which models time until specific events, characterized by shorter waiting times. It discusses its applications in product reliability and customer service scenarios, emphasizing its memoryless property and relationship with the Poisson distribution, which counts events.
5.4: Key Terms
This page provides definitions of key statistical terms related to probability distributions, covering conditional probability, exponential distributions' decay parameter, and the characteristics of exponential, Poisson, and uniform distributions.
5.5: Chapter Review
This page offers an overview of continuous probability density functions (pdfs) and cumulative distribution functions (cdf). It highlights that pdfs represent probabilities for continuous random variables, with total area equaling one. The text covers uniform and exponential distributions, explaining their definitions, key statistical measures (mean and standard deviation), and their probability density functions.
5.6: Formula Review
This page covers properties of continuous probability density functions (pdf) and cumulative distribution functions (cdf), focusing on uniform and exponential distributions. It details their characteristics, including mean, standard deviation, and probability calculations. The page also introduces the Poisson distribution and its probability calculation formula based on the mean.
5.7: Practice
This page covers properties of continuous probability density functions, questions about distributions like uniform and exponential, and the interpretation of probability graphs. It includes mean, standard deviation calculations, and practical examples like home sizes and car ages. The text also discusses carbon-14 decay, differentiating between continuous and discrete data, and provides exercises to reinforce the concepts.
5.8: Homework
This page covers problems related to probability density functions, focusing on continuous and uniform distributions, including real-life applications such as nurse qualifications, waiting times, and weight loss programs. It emphasizes identifying data types, defining random variables, and calculating probabilities.
5.9: References
This page references various sources on uniform and exponential distributions, including a book by John A. McDougall on weight loss, U.S. Census Bureau data, information about world earthquakes, and a baseball reference. It notes lecture slides by Rick Zhou covering the exponential distribution, all accessed on June 11, 2013.
5.10: Solutions

Curated and edited by Kristin Kuter | Saint Mary's College, Notre Dame, IN

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