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R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

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Sampling from distributions

Observing the outcome of trials that involve a random variable, a variable whose value changes due to chance, can be thought of as sampling from a probability distribution—one that describes the likelihood of each member of the sample space occurring.

That sentence probably sounds much scarier than it needs to be. Take a die roll for example.

Sampling from distributions

Figure 4.1: Probability distribution of outcomes of a die roll

Each roll of a die is like sampling from a discrete probability distribution for which each outcome in the sample space has a probability of 0.167 or 1/6. This is an example of a uniform distribution, because all the outcomes ...

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