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1. Probability Distribution | Formula, Types, & Examples - Scribbr
Link: https://www.scribbr.com/statistics/probability-distributions/
Description: WEBJun 9, 2022 · Common probability distributions include the binomial distribution, Poisson distribution, and uniform distribution. Certain types of probability distributions are used in hypothesis testing, including the standard normal distribution, the F distribution, and Student’s t distribution.
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2. What is a Distribution in Statistics? | 365 Data Science
Link: https://365datascience.com/tutorials/statistics-tutorials/distribution-in-statistics/
Description: WEBApr 26, 2023 · A distribution in statistics is a function that shows the possible values for a variable and how often they occur. Think about a die. It has six sides, numbered from 1 to 6.
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3. Probability distribution - Wikipedia
Link: https://en.wikipedia.org/wiki/Probability_distribution
Description: WEBIn probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space).
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4. Probability Distribution: Definition & Calculations - Statistics by Jim
Link: https://statisticsbyjim.com/basics/probability-distributions/
Description: WEBApr 23, 2018 · Statistical hypothesis testing uses particular types of probability distribution functions to determine whether the results are statistically significant. Specifically, they use sampling distributions and the distributions of test statistics. Use these probability distributions to calculate p-values. Sampling distributions
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5. Normal Distribution | Examples, Formulas, & Uses - Scribbr
Link: https://www.scribbr.com/statistics/normal-distribution/
Description: WEBPublished on October 23, 2020 by Pritha Bhandari . Revised on June 21, 2023. In a normal distribution, data is symmetrically distributed with no skew. When plotted on a graph, the data follows a bell shape, with most values clustering around a central region and tapering off as they go further away from the center.
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6. Normal distributions review (article) | Khan Academy
Link: https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data/normal-distributions-library/a/normal-distributions-review
Description: WEBNormal distributions review. Google Classroom. Normal distributions come up time and time again in statistics. A normal distribution has some interesting properties: it has a bell shape, the mean and median are equal, and 68% of the data falls within 1 …
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7. Normal Distribution in Statistics - Statistics By Jim
Link: https://statisticsbyjim.com/basics/normal-distribution/
Description: WEBApr 30, 2018 · Normal Distribution in Statistics. By Jim Frost 181 Comments. The normal distribution, also known as the Gaussian distribution, is the most important probability distribution in statistics for independent, random variables. Most people recognize its familiar bell-shaped curve in statistical reports.
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8. Modeling data distributions | Statistics and probability | Khan Academy
Link: https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data
Description: WEBWe'll measure the position of data within a distribution using percentiles and z-scores, we'll learn what happens when we transform data, we'll study how to model distributions with density curves, and we'll look at one of the most important families of distributions called Normal distributions.
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9. Statistics and Probability | Khan Academy
Link: https://www.khanacademy.org/math/statistics-probability
Description: WEBLearn statistics and probability—everything you'd want to know about descriptive and inferential statistics.
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10. 3: Distributions - Statistics LibreTexts
Link: https://stats.libretexts.org/Bookshelves/Probability_Theory/Probability_Mathematical_Statistics_and_Stochastic_Processes_(Siegrist)/03%3A_Distributions
Description: WEBIn this chapter we explore the basic types of probability distributions (discrete, continuous, mixed), and the ways that distributions can be defined using density functions, distribution functions, and quantile functions.
