A random variable is a numerical description of the outcome of a statistical experiment. A random variable that may assume only a finite number or an infinite sequence of values is said to be discrete; one that may assume any value in some interval on the real number line is.

# Random variable in statistics slideshare

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The quantitative **variable** definition in **statistics** is one that can be measured and assigned a numerical value. It describes a quantity instead of a quality. Quantitative **variables** are also called. A probability distribution can be a table, with a column for the values of the **random variable** and another column for the corresponding probability, or a graph, like a histogram with the values of the **random variable** on the horizontal axis and the probabilities on the vertical axis. In a probability distribution, each probability is between 0. A **random** **variable** is a rule that assigns a numerical value to each outcome in a sample space. **Random** **variables** may be either discrete or continuous. A **random** **variable** is said to be discrete if it assumes only specified values in an interval. Otherwise, it is continuous. We generally denote the **random** **variables** with capital letters such as X and Y.

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Mathematically, **random variable** is a function with Sample Space as the domain. It’s range is the set of Real Numbers. **Random Variables** are represented by English Uppercase letters. Their. Therefore, we define a **random variable** as a function which associates a unique numerical value with every outcome of a **random** experiment. For example, in the case of the tossing of an unbiased coin, if there are 3 trials, then the number of times a ‘head’ appears can be a **random variable**. This has values 0, 1, 2, or 3 since, in 3 trials. The post-class version of the **slides** contains the solutions to the board problems, clicker questions, and discussion questions that were posed to the students during class. It was not always the case that the end of the planned set of **slides** was reached in each class, so the last **slides** in one deck may be repeated in the next deck. Fiveable has free study resources like AP **Statistics** Probability Review: **Random Variables**, Binomial/Geometric Distributions - **Slides**. Plus, join AP exam season live streams & Discord. A random variable is a real valued function whose domain is the sample space of a random experiment To make this more intuitive, let us consider the experiment of tossing a coin two times in succession. The sample space of the experiment is S = {HH, HT, TH, TT}.

Indicator function. by Marco Taboga, PhD. The indicator function of an event is a **random variable** that takes: value 1 when the event happens; value 0 when the event does not happen. **Indicator functions** are also called indicator **random variables**. Indicator **random variables** explained in 3 minutes. Watch on. step 1 - y ~ n(69.1 , 2.6) step 2 - want to determine 95th percentile (p = .95) step 3 - since 100p > 50, a = 1-p = 0.05 zp = za = z.05 = 1.645 step 4 - y.95 = 69.1 + (1.645)(2.6) = 73.4 statistical models when making statistical inference it is useful to write **random** **variables** **in** terms of model parameters and **random** errors. II Sub-Gaussian **random variables** 1 Deﬁnitions 2 Examples 3 Hoe↵ding inequalities III Sub-exponential **random variables** 1 Deﬁnitions 2 Examples 3 Cherno↵/Bernstein bounds Prof. John Duchi. Motivation I Often in this class, goal is to argue that sequence of **random** ... concentration-**slides** Author: John Duchi.

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**Random Variables Slides** developed by Mine Çetinkaya-Rundel of OpenIntro The **slides** may be copied, edited, and/or shared via the CC BY-SA license Some images may be included under fair use guidelines (educational purposes).

Properties of moments of **random** **variables**∗ Jean-Marie Dufour† McGill University First version: May 1995 Revised: January 2015 This version: January 13, 2015 Compiled: January 13, 2015, 17:30 ∗This work was supported by the William Dow Chair in Political Economy (McGill University), the Bank of Canada.

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