Karteikarten
PT&MS
Karten in diesem Stapel (34)
- 1—cumulative distribution function
- 20 <= F(x) <= 1.The distribution function of a random variable is a non-negative function taking on values between 0 and 1:
- 3if x1 < x2 then F(x1) <= F(x2)The distribution function of a random variable is a non-decreasing function for the entire numerical axis
- 4—Functions of infinity
- 5—The probability of hit of a random variable in an interval [x1, x2) (including x1) is equal to the increment of its distribution function on this interval
- 6—The Probability density
- 7—the required probability
of appearance of the event A k times for n trials
- 8—The most probable number k0 of occurrences of an event in independent trials
- 9—Local theorem of Laplace: If the probability p of appearance of an event A for each trial is constant and differs from 0 and 1 then the probability Pn(k) that the event A will appear for n trials exactly k times is approximately equal to
- 10—If probability p of appearance of an event A for each trial is constant and differs from 0 and 1 then the probability Pn(k1, k2) that the event A will appear for n trials from k1 up to k2 times is approximately equal to the definite integral
- 11—If probability p of appearance of an event A for each trial is constant and differs from 0 and 1 then the probability Pn(k1, k2) that the event A will appear for n trials from k1 up to k2 times is approximately equal to
- 12—the series of distribution of a discrete random variable
- 13—the sum of probabilities for every discrete random variable
- 14—The prooperties of discrete random variables
- 15—The prooperties of discrete random variables
- 16—the mathematical expectation (the expected value or the mean)
- 17—The mathematical expectation of a constant is equal to the constant
- 18—A constant multiplier can be taken out for a sign of mathematical expectation
- 19—The mathematical expectation of the algebraic sum of finitely many random variables is equal to the sum of their mathematical expectations
- 20—The mathematical expectation of the product of finitely many mutually independent random variables is equal to the product of their mathematical expectations
- 21—The mathematical expectation of deviation of a random variable from its mathematical expectation is equal to zero
- 22—the dispersion of X
- 23—The mean square deviation (the standard deviation) of a random variable X
- 24—the dispersion of X properties
- 25—the dispersion of X properties
- 26—the dispersion of X properties
- 27—the dispersion of X properties
- 28—The probability of hit of a continuous random variable in an interval [a, b] is equal to the definite integral of its density in limits from a to b
![The probability of hit of a continuous random variable in an interval [a, b] is equal to the definite integral of its density in limits f...](https://examtimeassets.s3.amazonaws.com/uploads/media/image/9301976/desktop_53c0980f-38c4-4941-8917-8efa5aae2d91.JPG)
- 29—The distribution function of a continuous random variable can be expressed by the probability density
- 30—The improper integral in infinite limits of the probability density of a continuous random variable is equal to 1
- 31—A discrete random variable X has a binomial law of distribution with parameters n and p if it takes on values 0, 1, 2, ..., m, ..., n with probabilities
- 32—A discrete random variable X has the law of Poisson distribution with parameter > 0 if it takes on values 0, 1, 2, ..., m, ...(infinite countable set of values) with probabilities
- 33—A discrete random variable X has a geometric distribution with the parameter p if it takes on values
1, 2, ..., m, ... (infinite countable set of values) with probabilities
- 34—A discrete random variable X has the hypergeometric distribution with parameters n, M, N if it
takes on values 0, 1, 2, ..., m, ..., min(n, M) with the probabilities
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- Karteikarten am PT&MS, erstellt von Madina Umirbekova am 11/04/2017.
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- Madina Umirbekova
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