Static modeling is a representation or description of a certain phenomenon or system of relationships between phenomena through a set of variables (indicators, characteristics) and statistical relationships between them. The purpose of static modeling (like any other modeling) is to present the most essential features of the phenomenon being studied in a visual and accessible form for study. All statistical models are ultimately designed to measure the strength and direction of relationships between two or more variables. The most complex models also allow one to judge the structure of relationships between several variables. Most statistical models can be broadly divided into correlational, structural and causal. Correlation models are used to measure pairwise “non-directional” relationships between variables, i.e. such connections in which the causal component is absent or ignored. Examples of such models are Pearson's pairwise linear correlation coefficient, rank coefficients of pairwise and multiple correlations, and most measures of association developed for contingency tables (with the exception of information-theoretic coefficients and log-linear analysis).

Structural models in static modeling are designed to study the structure of a certain set of variables or objects. The initial data for studying the structure of relationships between several variables is the matrix of correlations between them. Analysis of the correlation matrix can be carried out manually or using multidimensional statistical analysis methods - factor, cluster, multidimensional scaling method. In many cases, studying the structure of relationships between variables is a preliminary step in solving a more complex problem - reducing the dimension of the feature space.

To study the structure of a set of objects, methods of cluster analysis and multidimensional scaling are used. The matrix of distances between them is used as the initial data. The distance between objects is smaller, the more the objects are “similar” to each other in the sense of the values ​​of the variables measured on them; if the values ​​of all variables for two objects are the same, the distance between them is zero. Depending on the goals of the study, structural models can be presented in the form of matrices (correlations, distances), factor structure, or visually. The results of cluster analysis are most often presented in the form of a dendrogram; the results of factor analysis and multidimensional scaling are presented in the form of a scatterplot. The structure of the correlation matrix can also be presented in the form of a graph reflecting the most significant relationships between variables. Causal models are designed to explore causal relationships between two or more variables. Variables that measure causal phenomena are called independent variables or predictors in statistics; Variables that measure consequence phenomena are called dependent. Most statistical causal models assume one dependent variable and one or more predictors. The exception is linear-structural models, in which several dependent variables can be used simultaneously, and some variables can at the same time act as dependent in relation to some indicators and as predictors in relation to others.

There are two areas of application of the statistical modeling method: static simulation modeling planning

• - for studying stochastic systems;
• - for solving deterministic problems.

The main idea that is used to solve deterministic problems using the statistical modeling method is to replace the deterministic problem with an equivalent circuit of some stochastic system, the output characteristics of the latter coincide with the result of solving the deterministic problem. With such a replacement, the error decreases with increasing number of tests (implementation of the modeling algorithm) N.

As a result of statistical modeling of the system S a series of partial values ​​of the desired quantities or functions is obtained, the statistical processing of which makes it possible to obtain information about the behavior of a real object or process at arbitrary moments in time. If the sales quantity N is sufficiently large, then the obtained system modeling results acquire statistical stability and can be accepted with sufficient accuracy as estimates of the required characteristics of the system functioning process S.

Statistical modeling is a numerical method for solving mathematical problems, in which the desired quantities are represented by the probabilistic characteristics of some random phenomenon. This phenomenon is modeled, after which the required characteristics are approximately determined by statistical processing of the “observations” of the model.

The development of such models consists of choosing a method of statistical analysis, planning the process of obtaining data, assembling data about the ecological system, algorithmizing and computer calculation of statistical relationships. Changing the patterns of development of the ecological situation requires repeating the described procedure, but in a new capacity.

Statistical finding of a mathematical model includes choosing the type of model and determining its parameters. Moreover, the desired function can be either a function of one independent variable (single-factor) or many variables (multi-factor). The task of choosing the type of model is an informal task, since the same dependence can be described with the same error by a variety of analytical expressions (regression equations). A rational choice of the type of model can be justified by taking into account a number of criteria: compactness (for example, described by a monomial or polynomial), interpretability (the ability to give meaningful meaning to the coefficient of the model), etc. The task of calculating the parameters of the selected model is often purely formal and is carried out on a computer.

When forming a statistical hypothesis about a certain ecological system, it is necessary to have an array of diverse data (database), which can be unreasonably large. An adequate understanding of the system is associated in this case with the separation of unimportant information. Both the list (type) of data and the amount of data can be reduced. One of the methods for carrying out such a compression of environmental information (without a priori assumptions about the structure and dynamics of the observed ecosystem) can be factor analysis. Data reduction is carried out by the method of least squares, principal components and other multivariate statistical methods, using in the future, for example, cluster analysis.

Note that primary environmental information has more-less the following features:

– multidimensionality of data;

– nonlinearity and ambiguity of relationships in the system under study;

– measurement error;

– the influence of unaccounted factors;

– spatiotemporal dynamics.

When solving the first problem of choosing the type of model, it is assumed that m input (x 1, x 2, ..., x m and n output (y 1, y 2, ..., y) data are known. In this case, it is possible, in in particular, the following two models in matrix notation:

where X and Y are known input (output) and output (input) parameters of an environmental object ("black box") in vector notation form; A and B are the desired matrices of constant model coefficients (model parameters).

Along with the indicated models a more general form of statistical modeling is considered:

where F is the vector of hidden influencing factors; C and D are the required coefficient matrices.

When solving environmental problems It is advisable to use both linear and nonlinear mathematical models, since many environmental patterns have been little studied. As a result, the multidimensionality and nonlinearity of the modeled relationships will be taken into account.

Based on a generalized model it is possible to identify internal hidden factors of the environmental processes being studied that are not known to the environmental engineer, but their manifestation is reflected in the components of the vectors X and Y. This procedure is most appropriate in the case where there is no strict cause-and-effect relationship between the values ​​of X and Y. A generalized model taking into account the influence of hidden factors eliminates a certain contradiction between two models with matrices A and B, when in fact two different models could be used to describe the same environmental process. This contradiction is caused by the opposite meaning of the cause-and-effect relationship between quantities A and Y (in one case, X is the input, and Y is the output, and in the other, vice versa). A generalized model, taking into account the value F, describes a more complex system from which both values ​​X and Y are output, and hidden factors F act on the input.

It is important in statistical modeling to use a priori data, when during the decision process some regularities of the models can be established and their potential number can be narrowed.

Suppose it is necessary to create a model with the help of which the fertility of a certain type of soil can be numerically determined in 24 hours, taking into account its temperature T and humidity W. Neither wheat nor an apple tree can produce a crop in 24 hours. But for test sowing, you can use bacteria with a short life cycle, and use the amount of P released CO 2 per unit time as a quantitative criterion for the intensity of their vital activity. Then the mathematical model of the process under study is the expression

where P 0 is a numerical indicator of soil quality.

It seems that we do not have any data on the form of the function f(T, W) because the systems engineer does not have the necessary agronomic knowledge. But it is not so. Who doesn’t know that at T≈0°C water freezes and, therefore, CO 2 cannot be released, and at 80°C pasteurization occurs, i.e. most bacteria die. A priori data are already sufficient to state that the desired function is quasi-parabolic in nature, close to zero at T = 0 and 80°C and has an extremum within this temperature range. Similar reasoning regarding humidity leads to the fact that the maximum extremum of the desired function is recorded at W=20% and its approach to zero at W=0 and 40%. Thus, the form of the approximate mathematical model has been determined a priori, and the task of the experiment is only to clarify the nature of the function f(T, W) at T = 20 ... 30 and 50 ... 60 ° C, as well as for W = 10 ... 15 and 25 ... 30% and more accurate determination of the coordinates of the extremum (which reduces the volume of experimental work, i.e., the volume of statistical data).

Statistical Modeling

a numerical method for solving mathematical problems, in which the required quantities are represented by the probabilistic characteristics of some random phenomenon, this phenomenon is modeled, after which the necessary characteristics are approximately determined by statistical processing of the “observations” of the model. For example, it is necessary to calculate the heat flows in a heated thin metal plate, the edges of which are maintained at zero temperature. Heat distribution is described by the same equation as the spreading of a spot of paint in a layer of liquid (see Thermal conductivity, Diffusion). Therefore, they simulate the plane Brownian motion of “paint” particles on the plate, monitoring their positions at moments kτ, k= 0, 1, 2,... It is approximately assumed that over a small interval τ the particle moves one step h equally likely in all directions. Each time the direction is chosen randomly, regardless of everything previous. The relationship between τ and h determined by the thermal conductivity coefficient. The movement begins at the heat source and ends when the edge is first reached (sticking of “paint” to the edge is observed). The heat flow Q (C) through section C of the boundary is measured by the amount of paint adhering. With total quantity N particles according to the law of large numbers such an estimate gives a random relative error of order h due to the discreteness of the chosen model).

The desired value is represented by the mathematical expectation (See Mathematical expectation) of a numerical function f from the random outcome ω of the phenomenon: , i.e., an integral over the probability measure P (see Measure of a set). For evaluation , where ω 1 ,..., ω N -simulated outcomes can be viewed as a quadrature formula for the indicated integral with random nodes ω k and random error R N is usually accepted , considering a large error to be negligible; Dispersion Df can be assessed through observations (see Error theory).

In the example above f(ω)= 1 , when the trajectory ends at C; otherwise f(ω) = 0. Variance

The conduct of each “experiment” is divided into two parts: the “draw” of a random outcome ω and the subsequent calculation of the function f(ω). When the space of all outcomes and the probability measure P are too complex, the drawing is carried out sequentially in several stages (see example). Random selection at each stage is carried out using random numbers, for example generated by some physical sensor; Their arithmetic imitation is also used - pseudorandom numbers (see Random and pseudorandom numbers). Similar random selection procedures are used in mathematical statistics and game theory.

SM is widely used for solving integral equations on a computer, for example, in the study of large systems (see Large system). They are convenient due to their versatility; as a rule, they do not require a large amount of memory. The disadvantage is large random errors, which decrease too slowly as the number of experiments increases. Therefore, methods for transforming models have been developed that make it possible to reduce the scatter of observed values ​​and the volume of the model experiment.

Lit.: Method of statistical tests (Monte Carlo method), M., 1962; Ermakov S. M., Monte Carlo method and related issues, M., 1971.

N. N. Chentsov.

Great Soviet Encyclopedia. - M.: Soviet Encyclopedia. 1969-1978 .

See what “Statistical Modeling” is in other dictionaries:

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A method of applied and computational mathematics, consisting in the implementation on a computer of specially developed stochastics. models of the phenomena or objects being studied. The expansion of the scope of application of S. m. is associated with the rapid development of technology and especially... ... Mathematical Encyclopedia

Modeling situations using statistical patterns inherent in the phenomenon under consideration. Dictionary of business terms. Akademik.ru. 2001... Dictionary of business terms

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SIMULATION MODELING in sociology- a type of mathematical modeling that consists of reproducing a social process or the functioning of a social system on a computer. Almost always involves the reproduction of random factors influencing the phenomenon being studied, and, as a consequence,... ... Sociology: Encyclopedia

MODELING, STATISTICAL- development of various models that reflect the statistical patterns of the described object, phenomenon. A common specific feature of these models is the consideration of random disturbances or deviations. Objects S.m. are different... ... Large economic dictionary

STATISTICAL MODELING- representation or description of a certain phenomenon or system of relationships between phenomena through a set of variables (indicators, characteristics) and statistical relationships between them. The goal of M.S. (like any other modeling) imagine... ... Sociology: Encyclopedia

To improve this article, it is advisable?: Correct the article according to the stylistic rules of Wikipedia. Simulation modeling (situational... Wikipedia

SIMULATION MODELING- (...from the French modele sample) a method of studying any phenomena and processes using statistical tests (Monte Carlo method) using a computer. The method is based on drawing (simulating) the influence of random factors on the phenomenon being studied or... ... Encyclopedic Dictionary of Psychology and Pedagogy

Books

• Statistical modeling. Monte Carlo methods. Textbook for bachelor's and master's degrees, Mikhailov G.A. The textbook is devoted to the features of modeling random variables, processes and fields. Particular attention is paid to numerical integration, in particular the Monte Carlo method. A solution is given...

The idea of ​​random selection. Before we begin describing statistical hypotheses, let us once again discuss the concept of random selection.

Leaving aside the details and some (albeit important) exceptions, it can be said that all statistical analysis is based on the idea of ​​random choice. We accept the thesis that the available data appeared as a result of a random selection from a certain general population, often imaginary. We usually assume that this random choice is produced by nature. However, in many problems this general population is quite real, and the choice from it is made by an active observer.

For brevity, we will say that all the data that we are going to study as a whole is one observation. The nature of this collective observation can be very diverse. It can be a single number, a sequence of numbers, a sequence of characters, a number table, etc. Let us denote for the moment this collective observation by X. Once we count X result of a random selection, we must indicate the general population from which X was chosen. This means that we must indicate those values ​​that could appear instead of the real one X. Let us denote this collection by X. A bunch of X also called sample space, or sample space.

We further assume that the specified choice occurred in accordance with a certain probability distribution on the set X, according to which each element of X has a certain chance of being selected. If X - is a finite set, then each of its elements x; there is a positive probability R(X) to be chosen. Random selection according to such a probabilistic law is easy to understand literally. For more complex infinite sets X it is necessary to determine the probability not for its individual points, but for subsets. Random selection of one of the infinite number of possibilities is more difficult to imagine, it is similar to choosing a point X from a segment or spatial region X.

Relationship between observation X and sample space X, between the elements of which probability is distributed - exactly the same as between elementary outcomes and the space of elementary outcomes with which probability theory deals. Thanks to this, probability theory becomes the basis of mathematical statistics, and therefore, in particular, we can apply probabilistic considerations to the problem of testing statistical hypotheses.

Pragmatic rule. It is clear that once we have adopted a probabilistic point of view on the origin of our data (i.e., we believe that they were obtained by random selection), then all further judgments based on these data will be probabilistic in nature. Any statement will be true only with some probability, and with some also positive probability it may turn out to be false. Will such conclusions be useful, and is it even possible to obtain reliable results along this path?

Both of these questions should be answered positively. Firstly, knowledge of the probabilities of events is useful, since the researcher quickly develops probabilistic intuition, allowing him to operate with probabilities, distributions, mathematical expectations, etc., benefiting from it. Secondly, purely probabilistic results can be quite convincing: a conclusion can be considered practically reliable if its probability is close to one.

The following can be said pragmatic rule which guides people and which connects the theory of probability with our activities.

We consider as practically certain an event whose probability is close to 1;

We consider it practically impossible for an event whose probability is close to 0.

And we not only think so, but also act in accordance with it!

The pragmatic rule set forth is, in the strict sense, of course, incorrect, since it does not completely protect against errors. But errors when using it will be rare. The rule is useful because it makes it possible to practically apply probabilistic conclusions.

Sometimes the same rule is expressed slightly differently: in a single trial the unlikely event does not occur(and vice versa - an event necessarily occurs, the probability of which is close to 1). The word “single” is inserted for the sake of clarification, because in a sufficiently long sequence of independent repetitions of the experiment, the mentioned unlikely (in one experiment!) event will almost certainly occur. But this is a completely different situation.

It remains unclear what probability should be considered low. There is no quantitative answer to this question that is applicable in all cases. The answer depends on what danger the error poses to us. Quite often - when testing statistical hypotheses, for example, see below - probabilities are assumed to be small, starting from 0.01 ¸ 0.05. Another thing is the reliability of technical devices, for example, car brakes. Here the probability of failure will be unacceptably high, say 0.001, since failure of the brakes once per thousand braking events will lead to a large number of accidents. Therefore, when calculating reliability, it is often required that the probability of failure-free operation be of the order of 1-10 -6. We will not discuss here how realistic such requirements are: whether an inevitably approximate mathematical model can provide such accuracy in calculating probability and how to then compare the calculated and real results.

Warnings 1. Some advice should be given on how to build statistical models, often in problems that do not have an obvious statistical nature. To do this, it is necessary to express the inherent features of the problem under discussion in terms related to the sample space and probability distribution. Unfortunately, this process cannot be described in general terms. Moreover, this process is creative and cannot be memorize like, say, a multiplication table. But he can learn to, by studying patterns and examples and following their spirit. We will look at several such examples. In the future, we will also pay special attention to this stage of statistical research.

2. When formalizing real problems, very diverse statistical models can arise. However, mathematical theory has prepared the means for studying only a limited number of models. For a number of standard models, the theory has been developed in great detail, and there one can get answers to the main questions of interest to the researcher. We will discuss some of these standard models, which we deal with most often in practice, in this book. Others can be found in more specialized and detailed guides and reference books.

3. It is worth remembering the limitations of mathematical tools when mathematically formalizing an experiment. If possible, we should reduce the matter to a standard statistical problem. These considerations are especially important when planning experiment or research; when collecting information, if we are talking about a statistical survey; when setting up experiments, if we are talking about an active experiment.

Statistical observation.

The essence of statistical observation.

The initial stage of any statistical research is the systematic, scientifically organized collection of data on the phenomena and processes of social life, called statistical observation. The significance of this stage of the study is determined by the fact that the use of only completely objective and sufficiently complete data obtained as a result of statistical observation at subsequent stages is able to provide scientifically based conclusions about the nature and patterns of development of the object being studied. Statistical observation is carried out by assessing and recording the characteristics of units of the population being studied in the relevant accounting documents. The data obtained in this way represent facts that in one way or another characterize the phenomena of social life. The use of evidence-based argumentation does not contradict the use of theoretical analysis, since any theory is ultimately based on factual material. The evidentiary power of facts increases even more as a result of statistical processing, which ensures their systematization and presentation in a compressed form. Statistical observation must be distinguished from other forms of observation carried out in everyday life, based on sensory perception. Only such observation can be called statistical that ensures the registration of established facts in accounting documents for their subsequent generalization. Specific examples of statistical observation are the systematic collection of information, for example, at machine-building enterprises about the number of machines and components produced, production costs, profits, etc. Statistical observation must satisfy fairly stringent requirements: 1. The observed phenomena must have a certain national economic significance, scientific or practical value, express certain socio-economic types of phenomena. 2. Statistical observation should ensure the collection of mass data, which reflects the entire set of facts related to the issue under consideration, since social phenomena are in constant change, development, and have different qualitative states.

Incomplete data that does not sufficiently comprehensively characterize the process leads to erroneous conclusions being drawn from their analysis. 3. The variety of causes and factors that determine the development of social and economic phenomena predetermines the orientation of statistical observation, along with the collection of data that directly characterizes the object being studied, to take into account the facts and events under the influence of which changes in its states occur. 4. To ensure the reliability of statistical data at the stage of statistical observation, a thorough check of the quality of the collected facts is necessary. The strict reliability of its data is one of the most important characteristics of statistical observation. Defects in statistical information, expressed in its unreliability, cannot be eliminated in the process of further processing, so their appearance makes it difficult to make scientifically based decisions and balance the economy. 5. Statistical observation should be carried out on a scientific basis according to a pre-developed system, plan and rules (program), providing a strictly scientific solution to all programmatic, methodological and organizational issues.

Software and methodological support for statistical observation.

Preparation for statistical observation, ensuring the success of the case, presupposes the need to timely resolve a number of methodological issues related to the definition of tasks, goals, objects, units of observation, development of programs and tools, and determination of the method of collecting statistical data. The tasks of statistical observation directly follow from the tasks of statistical research and consist, in particular, of obtaining mass data directly on the state of the object being studied, taking into account the state of phenomena that influence the object, and studying data on the process of development of phenomena. The goals of surveillance are determined, first of all, by the needs of information support for the economic and social development of society. The goals set for state statistics are clarified and specified by its governing bodies, as a result of which the directions and scope of work are determined. Depending on the purpose, the question of the object of statistical observation is decided, i.e. what exactly should be observed. An object is understood as a set of material objects, enterprises, work collectives, individuals, etc., through which phenomena and processes that are subject to statistical research are carried out. The objects of observation, depending on the goals, can be, in particular, masses of units of production equipment, products, inventory, settlements, regions, enterprises, organizations and institutions of various sectors of the national economy, the population and its individual categories, etc. The establishment of an object of statistical observation is associated with the determination of its boundaries on the basis of an appropriate criterion, expressed by some characteristic restrictive feature called qualification. The choice of qualification has a significant impact on the formation of homogeneous populations and ensures the impossibility of mixing different objects or undercounting some part of the object. The essence of the object of statistical observation is more fully understood when considering the units of which it consists: The units of observation are the primary elements of the object of statistical observation, which are carriers of the registered characteristics.

A reporting unit must be distinguished from an observation unit. The reporting unit is the unit of statistical observation from which information subject to registration is received in the prescribed manner. In some cases, both concepts coincide, but often they have a completely independent meaning. It turns out to be impossible and impractical to take into account the entire set of features characterizing the object of observation, therefore, when developing a statistical observation plan, the issue of the composition of features to be registered in accordance with the goal should be carefully and skillfully decided. A list of characteristics formulated in the form of questions addressed to units of the population, to which a statistical study must answer, represents a program of statistical observation.

In order to obtain a comprehensive description of the phenomenon being studied, the program must take into account the entire range of its essential features. However, the problematic nature of the practical implementation of this principle necessitates the inclusion in the program of only the most essential features that express the socio-economic types of the phenomenon, its most important features, properties and relationships. The scope of the program is regulated by the amount of resources available to statistical authorities, the timing of obtaining results, requirements for the degree of detail of developments, etc. The content of the program is determined by the nature and properties of the object being studied, the goals and objectives of the study. Among the general requirements for drawing up a program is the inadmissibility of including questions to which it is difficult to obtain accurate, completely reliable answers that give an objective picture of a particular situation. When considering some of the most important characteristics, it is customary to include control questions in the program to ensure consistency of the information received. To enhance the mutual verification of questions and the analytical nature of the observation program, interrelated questions are arranged in a certain sequence, sometimes in blocks of interrelated characteristics.

The questions of the statistical observation program must be formulated clearly, concisely, and concisely, without allowing for the possibility of different interpretations. The program often provides a list of possible answer options, through which the semantic content of the questions is clarified. Methodological support for statistical observation assumes that simultaneously with the observation program, a program for its development is drawn up. The research objectives are formulated in a list of generalizing statistical indicators. These indicators should be obtained as a result of processing the collected material, the characteristics with which each indicator corresponds, and the layouts of statistical tables, which present the results of processing the primary information. The development program, by identifying missing information, allows you to clarify the statistical observation program. Conducting statistical observation requires the preparation of appropriate tools: forms and instructions for filling them out. A statistical form is a primary document that records the answers to the program questions for each of the population units. The form, therefore, is a carrier of primary information. All forms are characterized by certain mandatory elements: a content part, including a list of program questions, a free column or several columns for recording answers and response codes, title and address printing. In order to ensure uniformity of interpretation of their content, statistical forms are usually accompanied by instructions, i.e. written instructions and explanations for filling out statistical observation forms. The instructions explain the purpose of statistical observation, characterize its object and unit, the time and duration of observation, the procedure for preparing documentation, and the deadline for presenting results. However, the main purpose of the instructions is to explain the content of the program questions, how to answer them and fill out the form.

Types and methods of statistical observation.

The success of collecting high-quality and complete initial data, taking into account the requirement for economical use of material, labor and financial resources, is largely determined by the decision on the choice of the type, method and organizational form of statistical observation.

Types of statistical observation.

The need to choose one or another option for collecting statistical data that best suits the conditions of the problem being solved is determined by the presence of several types of observation, differing primarily in the nature of recording facts over time. Systematic observation, carried out continuously and necessarily as signs of a phenomenon arise, is called current. Current observation is carried out on the basis of primary documents containing information necessary for a fairly complete description of the phenomenon being studied. Statistical observation carried out at certain equal intervals of time is called periodic. An example is the population census. Observation carried out from time to time, without observing strict frequency or on a one-time basis, is called one-time. Types of statistical observation are differentiated taking into account differences in information based on the completeness of coverage of the population. In this regard, a distinction is made between continuous and non-continuous observations. A continuous observation is one that takes into account all units of the population under study without exception. Non-continuous observation is obviously oriented towards taking into account a certain, usually quite massive part of the units of observation, which nevertheless makes it possible to obtain stable generalizing characteristics of the entire statistical population. In statistical practice, various types of non-continuous observation are used: selective, bulk method, questionnaire and monographic. The quality of non-continuous observation is inferior to the results of continuous observation, however, in a number of cases, statistical observation in general turns out to be possible only as non-continuous. To obtain a representative characteristic of the entire statistical population for some part of its units, sample observation is used, based on the scientific principles of forming a sample population. The random nature of the selection of population units guarantees the impartiality of the sampling results and prevents their bias. Using the main array method, the largest, most significant units of the population are selected, predominant in their total mass according to the characteristic being studied. A specific type of statistical observation is a monographic description, which is a detailed examination of a separate, but very typical object, which is of interest from the point of view of studying the entire population.

Methods of statistical observation.

Differentiation of types of statistical observation is also possible depending on the sources and methods of obtaining primary information. In this regard, a distinction is made between direct observation, survey and documentary observation. Direct observation is carried out by counting, measuring the values ​​of signs, taking instrument readings by special persons carrying out observations, in other words, recorders. Quite often, due to the impossibility of using other methods, statistical observation is carried out through a survey on a certain list of questions. The answers are recorded in a special form. Depending on the methods of receiving responses, a distinction is made between forwarding and correspondent methods, as well as the method of self-registration. The forwarding survey method is carried out orally by a special person (counter, forwarder), who simultaneously fills out a form or survey form.

The correspondent survey method is organized by statistical bodies distributing survey forms to a certain appropriately prepared circle of people called correspondents. The latter are obliged, according to the agreement, to fill out the form and return it to the statistical organization. Checking the correctness of filling out the forms takes place during the survey using self-registration. Questionnaires are filled out, as in the correspondent method, by the respondents themselves, but they are distributed and collected, as well as instructed and monitored for correct completion, by enumerators.

Basic organizational forms of statistical observation.

All the variety of types and methods of observation is carried out in practice through two main organizational forms: reporting and specially organized observation. Statistical reporting is the main form of statistical observation in social society, covering all enterprises, organizations and institutions of the production and non-production spheres. Reporting is the systematic presentation of accounting and statistical documentation in a timely manner in the form of reports that comprehensively characterize the results of the work of enterprises and institutions during reporting periods. Reporting is directly related to primary and accounting documents, is based on them and represents their systematization, i.e. the result of processing and generalization. Reporting is carried out in a strictly established form, approved by the State Statistics Committee of Russia. The list of all forms indicating their details (accessories) is called a reporting sheet. Each reporting form must contain the following information: name; approval number and date; name of the enterprise, its address and subordination; addresses to which reporting is submitted; frequency, date of presentation, method of transmission; content in the form of a table; the official composition of persons responsible for the development and reliability of reporting data, i.e. required to sign the report. The variety of conditions of the production process in various sectors of material production, the specificity of the reproduction process in local conditions, taking into account the significance of certain indicators determine the difference in types of reporting. There are, first of all, standard and specialized reporting. Standard reporting has the same form and content for all enterprises or institutions in the national economy. Specialized reporting expresses aspects specific to individual enterprises in the industry. Based on the principle of frequency, reporting is divided into annual and current: quarterly, monthly, biweekly, weekly. Depending on the method of transmitting information, postal and telegraph reporting are distinguished. Statistical censuses serve as the second most important organizational form of statistical observation. A census is a specially organized statistical observation aimed at recording the number and composition of certain objects (phenomena), as well as establishing the qualitative characteristics of their aggregates at a certain point in time. Censuses provide statistical information not provided for in reporting, and in some cases significantly clarify current accounting data.

To ensure high quality of statistical census results, a complex of preparatory work is carried out. The content of organizational measures for the preparation of censuses, carried out in accordance with the requirements and rules of statistical science, is set out in a specially developed document called an organizational plan for statistical observation. In the organizational plan, the issues of the subject (executor) of statistical observation, the place, time, timing and procedure of the conduct, the organization of census areas, the selection and training of counting workers, providing them with the necessary accounting documentation, a number of other preparatory work and etc. The subject of observation is the organization (institution) or its division responsible for the observation, organizing its implementation, as well as directly performing the functions of collecting and processing statistical data. The question of the place of observation (the place where facts are recorded) arises primarily when conducting statistical and sociological research and is resolved depending on the purpose of the study.

The observation time is the period of time during which the work of recording and verifying the data obtained must be started and completed. The observation time is selected based on the criterion of minimal spatial mobility of the object being studied. The critical moment to which the collected data is dated should be distinguished from the time of observation.

The concept of statistical observation is quite an interesting topic to consider. Statistical observations are used almost everywhere where their application can be determined. At the same time, despite the wide scope of application, statistical observations are a rather complex subject and errors are not uncommon. However, in general, statistical observations as a subject for consideration are of great interest.