“Units of measurement of physical quantities” - The absolute error is equal to half the division value of the measuring device. Micrometer. The result is obtained directly using the measuring device. Box length: 4 cm with a deficit, 5 cm with an excess. For each physical quantity there are corresponding units of measurement. Watch. Relative error.
“Values of length” - 2. What quantities can be compared with each other: 2. Explain why the following problem is solved using addition: 2. Justify the choice of action when solving the problem. How many packages did you get? How many pens are in three of these boxes? Dresses were made from 12 m of fabric, using 4 m for each. How many dresses were made?
“Physical quantities” - Boundaries separating physics and others natural sciences, historically conditional. The result of any measurement always contains some error. New topic. Speed. Interaction of bodies. Physical laws are presented in the form of quantitative relationships expressed in the language of mathematics. Measurement error.
“Number as a result of measuring a quantity” - “Number as a result of measuring a quantity” mathematics lesson in 1st grade. Measuring the length of a segment using a measuring stick.
“Numbers and quantities” - Introduction to the concept of mass. Comparison of masses without measurements. Roman written numbering. Capacity. The student will learn: Numbers and quantities (30 hours) Coordinate ray The concept of a coordinate ray. Planned subject results for the section “Numbers and Quantities” in 2nd grade. General principle formation of cardinal numbers within the limits of the studied numbers.
“Amount of demand” - Reasons for changes in demand. The DD curve obtained on the graph (from the English demand - “demand”) is called the demand curve. Elastic demand (Epd>1). Quantity of demand. Factors influencing demand. The dependence of the quantity demanded on the price level is called the demand scale. Absolutely inelastic demand (Epd=0).
Mathematical expectation. Mathematical expectation discrete random variable X, taking a finite number of values Xi with probabilities ri, the amount is called:
Mathematical expectation continuous random variable X is called the integral of the product of its values X on the probability distribution density f(x):
(6b)
Improper integral (6 b) is assumed to be absolutely convergent (otherwise they say that the mathematical expectation M(X) does not exist). The mathematical expectation characterizes average value random variable X. Its dimension coincides with the dimension of the random variable.
Properties of mathematical expectation:
Dispersion. Variance random variable X the number is called:
The variance is scattering characteristic random variable values X relative to its average value M(X). The dimension of variance is equal to the dimension of the random variable squared. Based on the definitions of variance (8) and mathematical expectation (5) for a discrete random variable and (6) for a continuous random variable, we obtain similar expressions for the variance:
(9)
Here m = M(X).
Dispersion properties:
Standard deviation:
(11)
Since the standard deviation has the same dimension as a random variable, it is more often used as a measure of dispersion than variance.
Moments of distribution. The concepts of mathematical expectation and dispersion are special cases of more general concept for numerical characteristics random variables – distribution moments. The moments of distribution of a random variable are introduced as mathematical expectations of some simple functions of a random variable. So, moment of order k relative to the point X 0 is called the mathematical expectation M(X–X 0 )k. Moments about the origin X= 0 are called initial moments and are designated:
(12)
The initial moment of the first order is the center of the distribution of the random variable under consideration:
(13)
Moments about the center of distribution X= m are called central points and are designated:
(14)
From (7) it follows that the first-order central moment is always equal to zero:
The central moments do not depend on the origin of the values of the random variable, since when shifted by a constant value WITH its distribution center shifts by the same value WITH, and the deviation from the center does not change: X – m = (X – WITH) – (m – WITH).
Now it's obvious that dispersion- This second order central moment:
Asymmetry. Third order central moment:
(17)
serves for evaluation distribution asymmetries. If the distribution is symmetrical about the point X= m, then the third-order central moment will be equal to zero (like all central moments of odd orders). Therefore, if the third-order central moment is different from zero, then the distribution cannot be symmetric. The magnitude of asymmetry is assessed using a dimensionless asymmetry coefficient:
(18)
The sign of the asymmetry coefficient (18) indicates right-sided or left-sided asymmetry (Fig. 2).
Rice. 2. Types of distribution asymmetry.
Excess. Fourth order central moment:
(19)
serves to evaluate the so-called excess, which determines the degree of steepness (pointedness) of the distribution curve near the center of the distribution in relation to the curve normal distribution. Since for a normal distribution, the value taken as kurtosis is:
(20)
In Fig. Figure 3 shows examples of distribution curves with different kurtosis values. For normal distribution E= 0. Curves that are more peaked than normal have a positive kurtosis, those that are more flat-topped have a negative kurtosis.
Rice. 3. Distribution curves with varying degrees of steepness (kurtosis).
Higher order moments are not usually used in engineering applications of mathematical statistics.
Fashion
discrete a random variable is its most probable value. Fashion continuous a random variable is its value at which the probability density is maximum (Fig. 2). If the distribution curve has one maximum, then the distribution is called unimodal. If a distribution curve has more than one maximum, then the distribution is called multimodal. Sometimes there are distributions whose curves have a minimum rather than a maximum. Such distributions are called anti-modal. In the general case, the mode and mathematical expectation of a random variable do not coincide. In the special case, for modal, i.e. having a mode, symmetrical distribution and provided that there is a mathematical expectation, the latter coincides with the mode and center of symmetry of the distribution.
Median random variable X- this is its meaning Meh, for which equality holds: i.e. it is equally probable that the random variable X will be less or more Meh. Geometrically median is the abscissa of the point at which the area under the distribution curve is divided in half (Fig. 2). In the case of a symmetric modal distribution, the median, mode and mathematical expectation are the same.
When solving many practical problems, it is not always necessary to characterize a random variable completely, i.e., to determine the laws of distribution. In addition, constructing a function or a series of distributions for a discrete random variable and density for a continuous random variable is cumbersome and unnecessary.
Sometimes it is enough to indicate individual numerical parameters that partially characterize the features of the distribution. It is necessary to know some average value of each random variable around which its possible value is grouped, or the degree of scattering of these values relative to the average, etc.
The characteristics of the most significant features of the distribution are called numerical characteristics random variable. With their help, it is easier to solve many probabilistic problems without defining distribution laws for them.
The most important characteristic of the position of a random variable on the number axis is mathematical expectation M[X]= a, which is sometimes called the mean of the random variable. For discrete random variable X with possible values x 1 , x 2 , … , x n and probabilities p 1 , p 2 ,… , p n it is determined by the formula
Considering that =1, we can write
Thus, mathematical expectation A discrete random variable is the sum of the products of its possible values and their probabilities. With a large number of experiments, the arithmetic mean of the observed values of a random variable approaches its mathematical expectation.
For continuous random variable X mathematical expectation is determined not by the sum, but integral
Where f(x) - quantity distribution density X.
The mathematical expectation does not exist for all random variables. For some of them, the sum, or integral, diverges, and therefore there is no mathematical expectation. In these cases, for reasons of accuracy, the range of possible changes in the random variable should be limited X, for which the sum, or integral, will converge.
In practice, such characteristics of the position of a random variable as mode and median are also used.
Random variable modeits most probable value is called. In general, the mode and the mathematical expectation do not coincide.
Median of a random variableX is its value relative to which it is equally probable that a larger or smaller value of the random variable will be obtained, i.e. this is the abscissa of the point at which the area limited by the distribution curve is divided in half. For a symmetric distribution, all three characteristics are the same.
In addition to the mathematical expectation, mode and median, other characteristics are used in probability theory, each of which describes a specific property of the distribution. For example, numerical characteristics that characterize the dispersion of a random variable, i.e., showing how closely its possible values are grouped around the mathematical expectation, are dispersion and standard deviation. They significantly complement the random variable, since in practice there are often random variables with equal mathematical expectations, but different distributions. When determining the dispersion characteristics, use the difference between the random variable X and its mathematical expectation, i.e.
Where A = M[X] - mathematical expectation.
This difference is called centered random variable, corresponding value X, and is designated :
Variance of a random variable is the mathematical expectation of the squared deviation of a value from its mathematical expectation, i.e.:
D[ X]=M[( X-a) 2 ], or
D[ X]=M[ 2 ].
The dispersion of a random variable is a convenient characteristic of the dispersion and scattering of the values of a random variable around its mathematical expectation. However, it is not clear, since it has the dimension of a square of a random variable.
To visually characterize dispersion, it is more convenient to use a value whose dimension coincides with the dimension of the random variable. This quantity is standard deviation random variable, which is the positive square root of its variance.
Expectation, mode, median, variance, standard deviation - the most commonly used numerical characteristics of random variables. When solving practical problems, when it is impossible to determine the distribution law, an approximate description of a random variable is its numerical characteristics, expressing some property of the distribution.
In addition to the main characteristics of the distribution of the center (mathematical expectation) and dispersion (dispersion), it is often necessary to describe other important characteristics of the distribution - symmetry And pointedness, which can be represented using distribution moments.
The distribution of a random variable is completely specified if all its moments are known. However, many distributions can be completely described using the first four moments, which are not only parameters describing the distributions, but are also important in the selection of empirical distributions, i.e. by calculating the numerical values of the moments for a given statistical series and, using special graphs, you can determine the distribution law.
In probability theory, moments of two types are distinguished: initial and central.
Initial moment of kth order random variable T is called the mathematical expectation of a quantity Xk, i.e.
Consequently, for a discrete random variable it is expressed by the sum
and for continuous – by the integral
Among the initial moments of the random variable special meaning has a first-order moment, which is the mathematical expectation. Higher order initial moments are used primarily to calculate central moments.
Central moment of kth order random variable is the mathematical expectation of the value ( X - M [X])k
Where A = M[X].
For a discrete random variable it is expressed by the sum
A for continuous – by integral
Among the central moments of a random variable, of particular importance is second order central moment, which represents the variance of the random variable.
The first order central moment is always zero.
Third starting moment characterizes the asymmetry (skewness) of the distribution and, based on the results of observations for discrete and continuous random variables, is determined by the corresponding expressions:
Since it has the dimension of a cube of a random variable, to obtain a dimensionless characteristic, m 3 divided by the standard deviation to the third power
The resulting value is called the asymmetry coefficient and, depending on the sign, characterizes the positive ( As> 0) or negative ( As< 0) skewness of distribution (Fig. 2.3).
71, Numerical characteristics of random variables widely used in practice for calculating reliability indicators. In many practical issues there is no need to fully, exhaustively characterize a random variable. Often it is enough to indicate only numerical parameters that to some extent characterize the essential features of the distribution of a random variable, for example: average value , around which possible values of the random variable are grouped; a number characterizing the scattering of a random variable relative to the average value, etc. Numerical parameters that allow expressing in a compressed form the most significant features of a random variable are called numerical characteristics of a random variable.
A) b)
Rice. 11 Definition of mathematical expectation
The numerical characteristics of random variables used in reliability theory are given in Table. 1.
72,Mathematical expectation(average value) of a continuous random variable whose possible values belong to the interval 
, represents definite integral(Fig., 11, b)
. (26)
The mathematical expectation can be expressed through the complement of the integral function. To do this, we substitute (11) into (26) and integrate the resulting expression by parts
, (27)
because 
And 
, That
. (28)
For non-negative random variables whose possible values belong to the interval , formula (28) takes the form
. (29)
i.e. the mathematical expectation of a non-negative random variable whose possible values belong to the interval , is numerically equal to the area under the graph of the complement of the integral function (Fig., 11, A).
73, Average time to first failure according to statistical information determined by the formula
, (30)
where is time to first failure i-th object; N- number of tested objects.
The average resource, average service life, average recovery time, and average shelf life are determined similarly.
74, Dispersion of a random variable around its mathematical expectation assessed using standard deviation variance(RMS) and coefficient of variation.
The variance of a continuous random variable X is the mathematical expectation of the squared deviation of the random variable from its mathematical expectation and is calculated by the formula
. (31)
Dispersion has the dimension of a squared random variable, which is not always convenient.
75, Standard deviation random variable is square root from variance and has the dimension of a random variable
. (32)
76,Coefficient of variation is a relative indicator of the dispersion of a random variable and is defined as the ratio of the standard deviation to mathematical expectation
. (33)
77, Gamma - percentage value of a random variable- value of a random variable corresponding to a given probability 
that the random variable will take a value greater than ,
. (34)
78. Gamma - the percentage value of a random variable can be determined by the integral function, its complement and differential function (Fig. 12). The gamma percentage value of a random variable is a quantile of probability (Fig. 12, A)
. (35)
Reliability theory uses gamma percentage value of resource, service life and shelf life(Table 1). Gamma percentage is the resource, service life, shelf life, which has (and exceeds) percent of objects of a given type.
A) b)
Fig. 12 Determination of the gamma percentage value of a random variable
Gamma percentage resource characterizes durability at the selected level 
probability of non-destruction. The gamma percentage resource is assigned taking into account the responsibility of the objects. For example, for rolling bearings, a 90 percent service life is most often used; for the bearings of the most critical objects, a 95 percent service life and higher is chosen, bringing it closer to 100 percent if the failure is dangerous to human life.
79,Median of random variable is its gamma percentage value at 
. For median 
it is equally likely that the random variable will be T more or less than it, i.e. .
Geometrically, the median is the abscissa of the intersection point of the integral distribution function and its complement (Fig. 12, b). The median can be interpreted as the abscissa of the point at which the ordinate of the differential function bisects the area limited by the distribution curve (Fig. 12, V).
The median of a random variable is used in reliability theory as a numerical characteristic of resource, service life, and shelf life (Table 1).
There is a functional connection between the reliability indicators of objects. Knowledge of one of the functions 
allows you to determine other reliability indicators. A summary of the relationships between reliability indicators is given in Table. 2.
Table 2. Functional relationship between reliability indicators