Search or ask a question

What are some common examples of random variables?

Stochastic process

Algebra of random variables

Normal distribution

Probability density function

Best insight from top research papers

Common examples of random variables include discrete random variables and continuous random variables. Discrete random variables have a countable set of possible values, such as the number of heads obtained when flipping a coin multiple times . Continuous random variables, on the other hand, have an uncountable set of possible values, such as the height of a person or the time it takes for a car to travel a certain distance . Examples of specific distributions that are commonly used to model random variables include the binomial distribution, which is used for discrete random variables, and the normal distribution, which is used for continuous random variables . These distributions provide a way to describe the pattern of probabilities associated with a random variable .

Answers from top 4 papers

PDF

Open Access

More filters

Papers (4)	Insight
Book Chapter•DOI Chapter 7 – Random Variables: Working with Uncertain Numbers Andrew F. Siegel 01 Jan 2016 1 Citations	Some common examples of random variables include next quarter's sales, number of heads in a coin toss, and height of a randomly selected person.
Book Chapter•DOI Distributions of Random Variables Milan Holický 01 Jan 2013	Some common examples of random variables include the binomial, normal, exponential, and uniform distributions.
Open access Of random variables George W. Bohrnstedt, Arthur S. Goldberger - Show less +1 more 01 Jan 2016 54 Citations	Some common examples of random variables include discrete random variables and continuous random variables.
Book Chapter•DOI Random Variables Andrew F. Siegel, Michael R. Wagner - Show less +1 more 01 Jan 2022	Some common examples of random variables include the number of heads in a series of coin flips and the time it takes for a bus to arrive.

My columns

Related Questions

What is random sampling?5 answersRandom sampling is a method used to select a subset of items from a larger population in a way that each item has an equal chance of being chosen. It is commonly used in various fields, including data analysis, research, and quantum measurements. The goal of random sampling is to ensure that the selected sample is representative of the population and provides reliable information about it. Random sampling can be applied in different scenarios, such as when the size of the input sequence is known or unknown, or when the sequence length is smaller or larger than the computer's memory capacity. It is a powerful tool that allows for efficient and accurate analysis of data and has applications in areas like high-resolution spectroscopy, quantum sensing, and circuit quantum electrodynamics.

Does randomness really exist?5 answersRandomness is a concept that is widely debated in various disciplines. While some argue that randomness is a mathematical model for uncertain outcomes with underlying patterns, others claim that there is no known random sequence and that the concept of randomness disappears upon testing. Quantum technologies offer new methods for generating certified randomness, independent of device modeling, through the violation of Bell inequalities. Philosophical debates have also questioned the existence of randomness in nature, with some arguing that every event has a cause or determining condition, suggesting that randomness does not exist. However, the compatibility of randomness and Christian belief has been explored, with arguments made for their coexistence based on the interpretation of scientific data and the understanding of God's nature. Ultimately, the existence of randomness remains a topic of ongoing discussion and interpretation.

What is sample random sampling?5 answersA simple random sample is a method of selecting a subset of units from a population, where each unit has an equal probability of being selected. It is a basic form of probability sampling, where the selection is randomized and not based on the researcher's discretion or self-selection processes by respondents. Simple random sampling is useful when the population is homogeneous and is often used in data analysis tasks to enhance scalability. It can be applied directly or integrated as a component in the data analysis process. The design and applications of weighted and coordinated sampling schemes have been explored, with an emphasis on algorithmic simplicity and practicality in the context of streaming or distributed data. The stability of random sampling procedures has been studied, establishing sampling inequalities that hold with high probability for large sampling sizes.

What are the example of random variable?3 answersRandom variables are numerical results or descriptions of outcomes from random experiments. Examples of random variables include the exchange rate Euro/USD next New Year and the number of car accidents in a certain month and county. Another example is "next quarter's sales," which specifies and describes the number that will be produced by the random experiment of waiting until next quarter's numbers are in and computing the sales. Random variables can be discrete, where a list of all possible values can be made, or continuous, such as the normal distribution. Random variables are also functions from the probability space to some other measurable space, and their transformation of distributions can be analyzed.

What is definition of random sampling?3 answersRandom sampling is a method of selecting a sample from a population where each unit in the population has an equal chance of being chosen. It is used to ensure that the sample is representative of the target population and to provide reliable information about the population. Random sampling can be done with or without replacement, and it is commonly used in various fields such as statistics, research, and computer science. The central limit theorem states that the average of the sample values follows a distribution that becomes more normal-shaped as the sample size increases. Simple random sampling is a specific type of random sampling where units are drawn one by one with equal probability of selection.

What is random sampling method?4 answersRandom sampling is a method used to select a subset of individuals or items from a larger population in a way that each member of the population has an equal chance of being included in the sample. It is a technique commonly used in various fields such as statistics, data analysis, and quality control. The goal of random sampling is to obtain a representative sample that accurately reflects the characteristics of the population. Several papers discuss different applications of random sampling. Chen and Dou propose a random number sampling method for electronic meters to improve metering precision and reduce power consumption. Yao, Su, and Ye present a random sampling method based on an information distribution mode to achieve signal acquisition with compressed data volume. Zhang describes a random sampling and analysis method for soil quality to improve the efficiency of soil analysis and ecological production. Xiaojuan, Minkai, Qingyang, and Xiaojuan introduce the principle of random equivalent sampling for high-speed digital-to-analog converter chips. Gao, Zhang, Tong, Wang, Jiao, Lyu, Yang, and Li propose a random time-interval sampling method for metallurgy quality inspection.

See what other people are reading

What is the definition of latent function?

What are continuous variables and discrete variables?

What are continuous variables and discrete variables?

What are the real examples of infinite servers in queuing theory?

In queuing theory, real examples of infinite servers include models with highly fluctuating arrival rates, systems with an unlimited number of servers processing heterogeneous data, and simulations of infinite server queues to study busy period parameters and probability distributions. Additionally, an example involves an inventory system associated with an $$MAP/M/\infty $$ queue, where customers are served as long as there is inventory available. Furthermore, time-dependent infinite-server queueing systems are analyzed to study batch departures induced by job interactions, such as in models simulating communication in the Bitcoin network. These examples showcase the diverse applications of infinite servers in queuing theory, ranging from fluctuating arrival rates to inventory management and communication systems.How important is arriving in class on time?

Arriving in class on time is crucial for various reasons. Studies show that punctuality is a significant social behavior that positively impacts classroom behavior. Tardiness among students is a prevalent issue, with reasons ranging from distance to lack of interest in the lesson. Professors play a vital role in setting the atmosphere and engaging students before and after class, emphasizing the importance of timeliness. Implementing strategies like positive reinforcement and quizzes have been proven effective in reducing tardiness and improving class performance. Furthermore, in stochastic transportation networks, maximizing the probability of arriving on time is a key objective, highlighting the broader importance of timeliness in various contexts. Overall, being punctual not only demonstrates responsibility but also contributes to a conducive learning environment and academic success.How many guests could a 19th-century inn accept?

During the 19th century, American hotels and inns played a crucial role in accommodating travelers and facilitating economic growth. These establishments evolved to meet the needs of a growing population engaged in long-distance commerce, providing reliable and safe accommodations for merchants and travelers. While specific numbers regarding the capacity of 19th-century inns are not provided in the abstracts, it is evident that hotels and inns proliferated to cater to the increasing mobility of the American population and the demands of a travel-dependent economy. The development of specialized buildings like hotels and resorts reflected the rise of capitalism and modernity in the United States, highlighting the importance of these establishments in facilitating human mobility and economic activities during that era.What is the probability of randomly selecting a chính phẩm from the first box?

The probability of randomly selecting a chính phẩm from the first box depends on the specific random trajectories considered in the study by Clement. The study explores two types of random trajectories passing through a box of arbitrary relative dimension. These trajectories are defined either by a uniform random selection of a spatial location paired with a directional vector or by a uniformly distributed position vector on a surface. By formulating joint probability distributions for trajectory length as a function of box position, the study provides insights into the physical representations of different size boxes and their associated probabilities. This analysis sheds light on the likelihood of selecting a chính phẩm, a term that likely represents a specific object or characteristic within the context of the study.What does AR and MA terms fix and what are the ebenfits of including these?

AR (autoregressive) and MA (moving average) terms in models help address different aspects of the error structure. AR terms capture the relationship between a variable's current value and its past values, while MA terms capture the relationship between the current error and previous errors. Including these terms can simplify inference by transforming models with moving average errors into models with autoregressive errors, making the analysis more straightforward. Additionally, the inclusion of interaction terms in models can help prevent omitted variable bias, enhancing the accuracy of estimates and predictive power. However, it is crucial to strike a balance, as including too many interaction terms can overly complicate the model without significant benefits.What is independent sample t test?

An independent samples t-test is a statistical method used to compare the means of two distinct groups. This test is typically applied when the groups being compared are unrelated or independent of each other. The key assumption in an independent samples t-test is that the two groups have equal variances. While normal distribution is not a strict requirement, having equal standard deviations is crucial for accurate results, especially when the sample sizes are unequal. The independent samples t-test is a parametric test that relies on the t-distribution for inference, and it is essential to ensure that the underlying assumptions of normality and equal variances are met for the test to be valid.Randomize data for machine learning?

Randomizing data in machine learning is a technique that can enhance the robustness and reliability of models. Entropy randomization, a novel approach, involves introducing randomness into machine learning procedures by considering models with random parameters and estimating probability density functions under balance conditions with measured data. This method aims to optimize solutions in the presence of uncertainty, such as uncertain data and models. Randomized machine learning can be implemented through "hard" and "soft" randomization, where the former precisely replicates empirical balances and the latter approximates them within an acceptable criterion. By assuming model parameters and data errors as random, randomized machine learning maximizes generalized information entropy based on empirical balances, offering a unique perspective compared to traditional machine learning approaches.How are stellar density determined in nebula?

Stellar density in nebulae is determined through various methods outlined in the research papers. One approach involves analyzing linear polarization maps to derive density distributions in disks around protostellar objects. Another method utilizes the electron density calculations from different diagnostics like [S II], [O II], [Cl III], and [Ar IV] to determine density structures in planetary nebulae, showcasing the impact of atomic data on these results. Additionally, the fundamental integral equation of stellar statistics offers a model-independent way to calculate stellar densities, with techniques like total least squares system with Tikhanov regularization providing advantages in solving ill-posed problems and deriving continuous and positive densities. These diverse methodologies contribute to understanding and quantifying stellar density in various astronomical environments.