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PT&MS

Madina Umirbekova Inglés 34 fichas

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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
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