- published: 22 Oct 2012
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In statistics, the standard deviation (SD, also represented by the Greek letter sigma σ or s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. A standard deviation close to 0 indicates that the data points tend to be very close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values.
The standard deviation of a random variable, statistical population, data set, or probability distribution is the square root of its variance. It is algebraically simpler, though in practice less robust, than the average absolute deviation. A useful property of the standard deviation is that, unlike the variance, it is expressed in the same units as the data. There are also other measures of deviation from the norm, including mean absolute deviation, which provide different mathematical properties from standard deviation.
In probability and statistics, a random variable, random quantity, aleatory variable or stochastic variable is a variable whose value is subject to variations due to chance (i.e. randomness, in a mathematical sense). A random variable can take on a set of possible different values (similarly to other mathematical variables), each with an associated probability, in contrast to other mathematical variables.
A random variable's possible values might represent the possible outcomes of a yet-to-be-performed experiment, or the possible outcomes of a past experiment whose already-existing value is uncertain (for example, due to imprecise measurements or quantum uncertainty). They may also conceptually represent either the results of an "objectively" random process (such as rolling a die) or the "subjective" randomness that results from incomplete knowledge of a quantity. The meaning of the probabilities assigned to the potential values of a random variable is not part of probability theory itself but is instead related to philosophical arguments over the interpretation of probability. The mathematics works the same regardless of the particular interpretation in use.
Deviation may refer to:
Randomness is the lack of pattern or predictability in events. A random sequence of events, symbols or steps has no order and does not follow an intelligible pattern or combination. Individual random events are by definition unpredictable, but in many cases the frequency of different outcomes over a large number of events (or "trials") is predictable. For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will occur twice as often as 4. In this view, randomness is a measure of uncertainty of an outcome, rather than haphazardness, and applies to concepts of chance, probability, and information entropy.
The fields of mathematics, probability, and statistics use formal definitions of randomness. In statistics, a random variable is an assignment of a numerical value to each possible outcome of an event space. This association facilitates the identification and the calculation of probabilities of the events. Random variables can appear in random sequences. A random process is a sequence of random variables whose outcomes do not follow a deterministic pattern, but follow an evolution described by probability distributions. These and other constructs are extremely useful in probability theory and the various applications of randomness.
Standard may refer to:
Any norm, convention or requirement
Demonstrates how to find the mean and standard deviation of discrete (countable) random variables BY HAND. It's long and tedious, and involves lots of steps. But, with time, and attention to detail, it can be done!
statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
Excel 2010: Mean, Standard Deviation, and Variance of a Discrete Random Variable. See www.mathheals.com for more videos
An introduction to measures of variability. I discuss the range, mean absolute deviation, variance, and standard deviation, and work through a simple example of calculating these quantities. I then discuss interpreting the standard deviation, including a brief discussion of the empirical rule. In this video it is assumed that we are dealing with sample data, and not data representing the entire population. This will be the case the vast majority of the time in practice. The birth weight data is from random sample of 1000 males drawn from Table 7-2 (Live births, by birth weight and geography -- Males) of the Statistics Canada publication 84F0210X, available at \url{http://www.statcan.gc.ca/pub/84f0210x/2009000/t011-eng.htm}.
We introduce the idea of a random variable X: a function on a probability space. Associated to such a function is something called a probability distribution, which assigns probabilities, say p_1,p_2,...,p_n to the various possible values of X, say x_1,x_2,...,x_n. The probabilities p_i have to be in the interval [0,1] and have to sum to 1, while the values x_i can be completely arbitrary. Associated to such a probability distribution, and hence associated to the random variable X, are three important numbers: the mean E(X), the variance Var(X) and the standard deviation SD(X). These are key concepts in statistics. My research papers can be found at my Research Gate page, at https://www.researchgate.net/profile/.... I also have a blog at http://njwildberger.com/, where I will discuss lo...
Random Numbers, Mean and Standard Deviation in MATLAB: In probability theory, the normal distribution is a very commonly occurring probability distribution — a function that tells the probability that any real observation will fall between any two real limits or real numbers, as the curve approaches zero on either side. Normal distributions are extremely important in statistics and are often used in the natural and social sciences for real-valued random variables whose distributions are not known. Matlab can construct an array of uniformly distributed random numbers with the rand command. The command randn constructs an array with normally distributed random numbers. Use X = randn(1,n) and Y = rand(1,n) to generate a one-dimensional array of random numbers. a) Construct an array with 1000...
statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
How to find the mean and standard deviation when combining two DISCRETE random variables.
Mean, Standard Deviation and probability of Continuous Random Variable
Demonstrates how to find the Mean (Expected Value) and Standard Deviation of discrete random variables using MS Excel (version 2007).
Demonstrates how to find the mean and standard deviation of discrete (countable) random variables BY HAND. It's long and tedious, and involves lots of steps. But, with time, and attention to detail, it can be done!
statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
Excel 2010: Mean, Standard Deviation, and Variance of a Discrete Random Variable. See www.mathheals.com for more videos
An introduction to measures of variability. I discuss the range, mean absolute deviation, variance, and standard deviation, and work through a simple example of calculating these quantities. I then discuss interpreting the standard deviation, including a brief discussion of the empirical rule. In this video it is assumed that we are dealing with sample data, and not data representing the entire population. This will be the case the vast majority of the time in practice. The birth weight data is from random sample of 1000 males drawn from Table 7-2 (Live births, by birth weight and geography -- Males) of the Statistics Canada publication 84F0210X, available at \url{http://www.statcan.gc.ca/pub/84f0210x/2009000/t011-eng.htm}.
We introduce the idea of a random variable X: a function on a probability space. Associated to such a function is something called a probability distribution, which assigns probabilities, say p_1,p_2,...,p_n to the various possible values of X, say x_1,x_2,...,x_n. The probabilities p_i have to be in the interval [0,1] and have to sum to 1, while the values x_i can be completely arbitrary. Associated to such a probability distribution, and hence associated to the random variable X, are three important numbers: the mean E(X), the variance Var(X) and the standard deviation SD(X). These are key concepts in statistics. My research papers can be found at my Research Gate page, at https://www.researchgate.net/profile/.... I also have a blog at http://njwildberger.com/, where I will discuss lo...
Random Numbers, Mean and Standard Deviation in MATLAB: In probability theory, the normal distribution is a very commonly occurring probability distribution — a function that tells the probability that any real observation will fall between any two real limits or real numbers, as the curve approaches zero on either side. Normal distributions are extremely important in statistics and are often used in the natural and social sciences for real-valued random variables whose distributions are not known. Matlab can construct an array of uniformly distributed random numbers with the rand command. The command randn constructs an array with normally distributed random numbers. Use X = randn(1,n) and Y = rand(1,n) to generate a one-dimensional array of random numbers. a) Construct an array with 1000...
statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
How to find the mean and standard deviation when combining two DISCRETE random variables.
Mean, Standard Deviation and probability of Continuous Random Variable
Demonstrates how to find the Mean (Expected Value) and Standard Deviation of discrete random variables using MS Excel (version 2007).
Mean, Standard Deviation and probability of Continuous Random Variable
Statistical Videos, linear combinations, non linear functions, expected value, variance, standard deviation, combination random variables, trick changing variables cdf
Description coming soon! This video talks about discrete random variables, probabilities and new ways of counting, like permutations and combinations, BTW :)
We'll look at random variables, probability models for random variables, expected value of a random variable, and standard deviation (and variance) of a random variable.
Week-1 plan 0:57 Preliminary 1: Rate of return 2:06 Preliminary 2: Short-selling 6:43 Preliminary 3: Mean, Variance, Covariance, Standard deviation, and Correlation coefficient of random variables 9:27 Mean and variance when there are more than 2 random variables 22:59 Expectation of a linear combination 28:05 Variance formulas 33:32 Variance formula in the matrix form 41:48
eLearning and Online Education by Virtual University, STA301, Statistics and Probability, Bayes’ Theorem, Discrete Random Variable, Discrete Probability Distribution, Graphical Representation of a Discrete Probability Distribution, Mean, Standard Deviation and Coefficient of Variation of a Discrete Probability Distribution, Distribution Function of a Discrete Random Variable
Learning Objectives: 1. Identify the characteristics of a probability distribution. 2. Distinguish between discrete and continuous random variables. 3. Compute the mean, variance, and standard deviation of a discrete probability distribution.
5.5 Counting Techniques - combinations, permutations, fundamental counting principle, factorial 6.1 Discrete Random Variables - discrete, continuous, probability distribution, standard deviation, mean, expected value
Fundamental Concepts in Discrete-event Simulation: Review of Probability & Statistics for Simulation Randomness in Simulation Process-interaction Based Simulation (discrete and continuous random variables; expectation, variance, standard deviation, covariance, correlation; sample estimates of population parameters and their variance)
The Chernoff Bounds. Flipping n fair coins and counting Heads minus Tails one naturally gets a limiting Gaussian distribution. But here we are concerned with extremely small probabilities, e.g., that there are n2/3 more heads than tails. This general method gives good upper bounds on the probability of a random variable X being more than α standard deviations off the mean, when α is “large” and X is the sum of mutually independent random variables. A variety of applications are given. One example: there exist tournaments T on n players so that no matter how the players are orders nearly half the games will be upsets.