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THEME 1

The subject of statistics as a science is...

statistics method

statistics

Quantitative patterns of mass varying social phenomena

groupings and classifications

Statistical science began to take shape ...

In the 17th century

in the 7th century

in the 19th century

before the beginning of modern reckoning

Indicate which of the following is not a characteristic feature of statistics as a science

the study of the quantitative side of mass social phenomena in close connection with the qualitative side

The study of all social and natural phenomena

study of phenomena in specific conditions of place and time

identification of trends and patterns in mass socio-economic phenomena and processes

The main branch of statistical science is ...

General theory statistics

industrial statistics

probability theory

math statistics

An element of which branch of statistical science is construction statistics

economic statistics

general theory of statistics

social statistics

mathematical statistics

"Social physics" was called statistics by a representative of the "statistical-mathematical" school (who?) ...

Graunt, Halley (England, 17th century)

Achenwal (Germany, 18th century)

M.Lomonosov (Russia, 18th century)

Quetelet (Belgium, 19th century)

Point out the wrong sentence. State statistics in Russia…

Publishes 1 monthly, 1 quarterly journal, 1 statistical yearbook

develops and approves forms of statistics. reporting, mandatory for execution by legal entities in the Russian Federation

It has territorial bodies in the regions of the country

presented by the State Statistics Committee of the Russian Federation

An employee for whom the collection of statistical data is a professional activity is called…

data collector

an extra

census taker

Statistician

Choose the most precise definition: "A statistical aggregate is..."

set of elements that have common features

a set of elements that have one common and one varying feature

any subject set of natural and social phenomena

Really existing set of homogeneous elements with common features and internal connection

What is the carrier of information in the statistical population?

Population unit

attributive variable

population attribute

A statistical population is different from a mathematical one...

what is taught in statistics

the existence of elements in the abstract representation

number of elements

The existence of elements in material reality

An element of a statistical population is...

element of the periodic table

mathematical set element

population attribute

Information carrier

The sign "land area for individual construction" is ...

attributive

quantitative

quality

quantitative-qualitative

The sign of "social stratum in society" is ...

attributive

quality

quantitative

quantitative-qualitative

Variation is:

population change

change of mass phenomena in time

change in the structure of the statistical population in space

Change in the values ​​of a feature within the observed population

Which of the following features is variable?

the speed at which a body falls in a vacuum

dollar exchange rate

The price of one kilogram of grapes

boiling point of water

Theme 2

What is the difference between statistical observation and the observation of a writer, artist

observation time difference

Different Purpose of Surveillance

Scientific organization and planning

the difference in the object of observation

The list of signs (or questions) to be recorded during the observation process is called

monitoring program

monitoring tools

statistical form

instruction

The question in the statistical survey form “How much time do you devote to watching TV programs (hours per week)?”, is in the form of compilation ...

closed

open

open-closed

mixed

The question in the statistical survey form “How often do you go to the cinema?”: “Almost every week”, “Once a month”, “Less than once a month” - is in the form of compilation ...

indirect

open-closed

Closed

open

unit of observation

population unit

reporting unit

Population

An object statistical observation- this is…

unit of observation

population unit

reporting unit

accounting unit

The unit of observation and the reporting unit are concepts that…

never match

always match

same

Sometimes they can match

The period (period) of observation is

critical moment (date) of observation

The time during which the statistical forms are filled out

specific day of the year, hour of the day, as of which the registration of signs for each unit of the population should be carried out

Continuous statistical observation of long-term processes that have a fixed beginning, a stage of development and a fixed end is ...

specially organized statistical observation

Register form of observation

selective observation of the main array

a form of statistical observation, the census of the population of Russia refers to ...

one-time, specially organized observation

periodic, register, selective observation

Periodic, specially organized, continuous observation

Russia's population census is being conducted...

once every 25 years

once every 5 years

Once every 10 years

once every 15 years

The population census of the Russian Federation was carried out from October 9 to October 16, 2002. The critical moment was 00 am from October 08 to 09. The counter came to the family on October 16th. A child was born in the family on October 14. What should the counter do about this child?

consult management

mark in notepad

put on the register

Do not write in the census form

The population census of the Russian Federation was carried out in the period from October 9 to October 16, 2002. The critical moment was 00 am from October 08 to 09. The counter came to the family on October 11th. A family member died on October 10. How should the counter

mark in notepad

Submit with a note of death

do not enter information about the deceased in the census form

enter without a note of death

The population census of the Russian Federation was carried out from October 9 to October 16, 2002. The critical moment was 0 am from October 08 to 09. The counter came to the family on October 15 and got to the wedding. Two hours ago, the newlyweds returned from the registry office after registering their marriage (they had not been married before). What should the enumerator write on the census form in the question: "Are you currently married?" about each spouse?

Not married

is married

put a dash, because difficult to determine

Why are censuses usually held in winter?

lower transport costs

savings in personnel training resources are achieved

no need for a critical moment of observation

The least mobility of respondents

Does the Russian Federation have such a form of statistical observation as a population register?

Only being designed

Not

1. The subject of statistics.

statistics called systematic and systematic accounting carried out throughout the country by state statistics bodies headed by the State Committee of the Russian Federation on Statistics.

Statistics - digital data published in special reference books and mass media.

Statistics - a special scientific discipline.

The subject and content of statistical science.

The subject and content of statistical science have been debatable for a long time. In order to resolve these issues in 1954 and 1968. special meetings were held with the involvement of a wide range of scientists and practitioners, not only statisticians, but also specialists related to science. In addition, until the mid-1970s there was a discussion about the subject of statistics in the specialized literature. During the discussions, it emerged 3 main points of view on the subject of statistics:

1. Statistics - a universal science that studies the mass phenomenon of nature and society.

2. Statistics - a methodological science that does not have its own subject of knowledge, but is a doctrine of the method used by the social sciences.

3. Statistics - social science, which has its own subject, methodology and explores the quantitative patterns of social development.

As a result of the meetings and discussions held in statistical science, the first two points of view were rejected by the majority of scientists and practitioners, and the third one was basically accepted, supplemented and clarified.

The subject of statistics is the quantitative side of mass socio-economic phenomena, inextricable links with their qualitative side, specific conditions, place and time. From this definition it follows main features of the subject of statistical science:

1. Statistics - social science.

2. Unlike other social sciences statistics studies the quantitative side of social phenomena.

3. Statistics studies a mass phenomenon.

4. Statistics studies the quantitative side of phenomena in inseparable connection with the quantitative side, and this is embodied in the existence of a system of statistical indicators.

5. Statistics studies the quantitative side of phenomena in specific conditions of place and time.

2. Features of statistical methodology. statistics method.

Statistical methodology is understood as a system of principles and methods for their implementation aimed at studying quantitative patterns that manifest themselves in the structure of relationships and the dynamics of socio-economic phenomena. The most important constituent elements methods of statistics and statistical methodology are mass statistical observation, summary and grouping, as well as the use of generalizing statistical indicators and their analysis.

Essence of the first element of statistical methodology is the collection of primary data about the object under study. For example: in the course of the country's population census, data is collected on each person living in its territory, which is entered in a special form.

Second element: summary and grouping is the division of the totality of data obtained at the observation stage into homogeneous groups according to one or more characteristics. For example as a result of the grouping of census materials, the population is divided into groups (by sex, age, population, education, etc.).

The essence of the third element of statistical methodology lies in the calculation and socio-economic interpretation of generalizing statistical indicators:

1. Absolute

2. Relative

3. Medium

4. Indicators of variation

5. Speakers

The three main elements of statistical methodology also constitute the three stages of any statistical study.

3. Theoretical basis statistics.

The theoretical basis of statistics is formed by concepts and categories, in the aggregate of which the basic principles of this science are expressed. In statistics, the most important categories and concepts include: totality, variation, sign, regularity.

Population - this is a set (mass) of the same quality (homogeneous) at least one of any sign of phenomena, the existence of which is limited in space and time. For example, the population of Russia as of January 1, 1997, the total of farms in the Rostov region in 1997 can be considered as a statistical aggregate. However, the statistical population (set) does not necessarily represent a large number of units, in principle it can be very small; for example, the population size of a small sample may sometimes be 8-10 units.

The most important property of the statistical population is her indecomposability. This means that further fragmentation of individual phenomena does not cause the loss of their qualitative basis. The disappearance or elimination of one or a number of phenomena does not destroy the qualitative basis of the statistical population as a whole. Thus, the population of a country or a city will remain a population, despite the ongoing processes of mechanical and natural population movement.

Quantitative changes in the value of a feature in the transition from one unit of the population to another are called variation. Variation occurs under the influence of random, primarily external causes.

Statistical aggregates have certain properties, the carriers of which are units (individual elements) of the population (phenomena) that have certain characteristics. According to the form of external expression, signs are divided into:

Attributive (descriptive)

quantitative

Attributive (qualitative) signs are not amenable to direct quantitative (numerical) expression.

quantitative signs are divided into discrete (discontinuous) and continuous.

The most important category of statistics is a statistical regularity. Under pattern in general, it is customary to call the repetition, sequence and order of changes in phenomena.

Statistical same regularity in statistics it is considered as a quantitative pattern of changes in space and time of mass phenomena and processes of social life, consisting of many elements (units of the totality). It is characteristic not of individual units of the population, but of their entire mass, or of the population as a whole.

statistical regularity is a form of expression causation, expressed in the sequence, regularity, recurrence of events with a sufficiently high degree of probability, if the causes (conditions) that generate the events. Do not change or change slightly. Statistical patterns are established based on the analysis of mass data.

4. Basic concepts and categories of statistical science in general.

6. General theory of statistics as a branch of statistical science.

Population - a set of elements of the same type similar to each other in some respects and differing in others. For example: this is a set of sectors of the economy, a set of universities, a set of cooperation between design bureaus, etc.

Individual elements of a statistical population are called its units. In the examples discussed above, the units of the population are, respectively, the industry, the university (one) and the employee.

Units of the population usually have many characteristics.

sign - property of units of the population, expressing their essence and having the ability to vary, i.e. change. Signs that take a single value in individual units of the population are called varying, and the values ​​themselves are options.

Variable signs are subdivided into attributive or qualitative ones. An attribute is called attributive or qualitative if its separate value (variants) are expressed as a state or properties inherent in the phenomenon. Variants of attributive features are expressed in verbal form. Examples of such signs can serve - economic.

An attribute is called quantitative if its individual value is expressed in the form of numbers. For example: wage, scholarship, age, size of OF.

According to the nature of variation, quantitative signs are divided into discrete and continuous.

Discrete - such quantitative signs that can only take on a well-defined, as a rule, integer value.

Continuous - are such signs that, within certain limits, can take on the value of both integer and fractional. For example: GNP of the country, etc.

There are also primary and secondary features.

Main signs characterize the main content and essence of the phenomenon or process being studied.

Minor signs give Additional information and are directly related to the inner content of the phenomenon.

Depending on the goals of a particular study, the same signs in the same cases may be primary, and in others secondary.

statistic - this is a category that reflects the dimensions and quantitative ratios of the signs of socio-economic phenomena and their qualitative certainty in specific conditions of place and time. It is necessary to distinguish between the content of a statistical indicator and its specific numerical expression. Content, i.e. qualitative certainty lies in the fact that indicators always characterize socio-economic categories (population, economy, financial institutions, etc.). Quantitative dimensions of statistical indicators, i.e. their numerical values ​​depend primarily on the time and place of the object that is subjected to statistical research.

Socio-economic phenomena, as a rule, cannot be characterized by any one indicator, For example: the standard of living of the population. A scientifically substantiated system of statistical indicators is necessary for a comprehensive comprehensive characterization of the phenomena under study. Such a system is not permanent. It is constantly being improved based on the needs of social development.

5. Branches of statistical science.

In the process of historical development, as part of statistics as a single science, the following branches emerged and gained a certain independence:

1. The general theory of statistics, which develops the concept of categories and methods for measuring the quantitative patterns of social life.

2. economic statistics studying the quantitative patterns of reproduction processes at various levels.

3. Social statistics, which studies the quantitative side of the development of the social infrastructure of society (statistics of health care, education, culture, moral, judicial, etc.).

4. Industry statistics (statistics of industry, agro-industrial complex, transport, communications, etc.).

All branches of statistics, developing and improving their methodology, contribute to the development of statistical science as a whole.

7. Main tasks and principles of organization of state statistics in Russia.

The main tasks of statistics in the context of the development of market relations in Russia are the following:

1. Improving accounting and reporting and reducing the document flow on this basis.

2. Strengthening work to control the reliability of statistical information provided to enterprises, institutions and organizations of all sectors of the economy and forms of ownership.

3. Improving the timeliness of statistical information both to the incoming statistical agency and the structures provided by them state power and management.

4. Deepening the analytical functions of the developed statistical data, the formation of the topics of the conducted statistical data in accordance with the current tasks of the socio-economic development of the country.

5. Further development and improvement of statistical methodology based on the increasingly widespread introduction of PC practice and ... statistical analysis was not predicted.

8. The concept of statistical observation, the stages of its implementation.

9. Basic forms and types of statistical observation.

Statistical observation - this is a mass, systematic, scientifically organized observation of the phenomena of social and economic life, which consists in registering selected features for each unit of the population.

An example of statistical observation is public opinion polls, which have become especially popular in Russia in recent years. Such observation is taken for the purpose of revealing people's attitudes towards some issue of interest or controversial events. The study of public opinion forms the basis of the general system of market research and is an important part of it. Such observation requires interviewing a number of individuals according to a predetermined program.

Statistical observation can be carried out by state statistics bodies, research institutes, economic services of banks, stock exchanges, firms.

The process of statistical observation includes the following stages:

Observation preparation

Conducting mass data collection

Preparing data for automated processing

Development of proposals for improving statistical observation

Any statistical observation requires careful, thoughtful preparation. The reliability and reliability of information, the timeliness of its receipt will largely depend on it.

The preparation of a statistical observation is a process that includes various types of work. First, it is necessary to solve methodological issues, the most important of which are the definition of the purpose and object of observation, the composition of signs to be registered; development of documents for data collection; the choice of the reporting unit and the unit to be observed, as well as the methods and means of obtaining data.

In addition to methodological problems, it is necessary to solve problems organizational nature, for example, to determine the composition of the bodies conducting the observation; select personnel for observation; draw up a calendar plan of work for the preparation, conduct and processing of observation materials; replicate documents for data collection.

Carrying out mass data collection includes work directly related to filling in statistical forms. It begins with the distribution of census questionnaires, questionnaires, forms, statistical reporting forms and ends with their delivery after filling in to the bodies conducting the observation.

The collected data at the stage of their preparation for automated processing are subject to arithmetic and logical control. Both of these controls are based on knowledge of the relationship between indicators and qualitative features.

At the final stage of the observation, the reasons that led to the incorrect completion of statistical forms are analyzed, and proposals are developed to improve the observation. This is very important for organizing future surveys.

Obtaining information in the course of statistical observation requires considerable financial and labor resources, as well as time.

10. Program and methodological issues of statistical observation.

Purpose of observation. Statistical observations most often pursue a practical goal - obtaining reliable information to identify patterns in the development of phenomena and processes. For example, the purpose of the micro-census of the population of Russia in 1994. There was a receipt of data on the size, composition of the population, living conditions.

Observation task predetermines its program and forms of organization. An unclear goal can lead to the fact that during the observation process unnecessary data will be collected or, conversely, the information necessary for analysis will not be obtained.

Object and unit of observation. Reporting unit. When preparing an observation, in addition to the goal, it is necessary to determine exactly what exactly is to be examined, i.e. set the object of observation.

Under object of observation refers to a certain statistical totality in which the studied socio-economic phenomena and processes take place. For example, the set individuals, individuals, legal entities.

Unit of observation called constituent element an object that is a carrier of features subject to registration.

reporting unit is the object from which the data about the unit of observation are received.

Statistical Surveillance Program. Every phenomenon has many different features. Collecting information on all grounds is impractical, and often impossible. Therefore, it is necessary to select those features that are essential for characterizing the object based on the purpose of the study. To determine the composition of the registered features, an observation program is developed.

Observation Program- this is a list of signs (or questions) to be recorded in the process of observation. The quality of the collected information largely depends on how well the program of statistical observation is developed.

Place and time of observation. The choice of the survey site depends mainly on the purpose of the survey.

11. Organizational matters statistical observation.

The success of any statistical observation depends not only on the thoroughness of methodological preparation, but also on the correct and timely solution of a wide range of organizational issues.

The most important place in organizational work is occupied by personnel training, during which various kinds of briefings are held with employees of statistical bodies, with organizations that provide data on filling in statistical documents, preparing observation materials for automated processing, etc.

If the observation is associated with large labor costs, then persons from among the unemployed (including the unemployed) and certain categories of students are involved in registering information during the period of the survey. When conducting a census, such persons are called enumerators. Training of temporary staff is usually organized. It is carried out to develop skills for the correct filling of statistical forms by enumerators.

Reproduction of the documentation of the survey itself, documentation for briefings and distribution of them to the republican, regional, regional committees and departments of statistics also relate to organizational issues.

During preparation big role is given to mass explanatory work: lectures, conversations, organization of speeches in the press, on radio and television about the meaning, goals and objectives of the upcoming survey.

To harmonize the activities of all services involved in the preparation and conduct of observations, it is advisable to draw up a calendar plan, which is a list (name) of work and the timing of their execution separately for each organization involved in the survey.

12. Types of non-continuous statistical observation.

discontinuous observation is deliberately oriented towards taking into account some, as a rule, a fairly large part of observation units, which nevertheless allows obtaining stable generalizing characteristics of the entire statistical population. In statistical practice, different kinds random observation: selective, the method of the main array, questionnaire and monographic. The quality of non-continuous observation is inferior to the results of continuous observation, however, some advantages of the first one are quite obvious: gain in time for making an operational decision, as well as compliance with the resource saving mode. In a number of cases, statistical observation generally turns out to be possible only as a non-continuous one.

For is used to obtain a representative characteristic of the entire population for some part of its units, selective observation is used. In industry, selective observation is used in the statistical control of product quality, the study of the use of production equipment, the workplace of machine operators, etc.

According to the method of the main array, the largest, most significant units of the population are selected, which prevail in their total mass according to the trait under study. According to the principle of the main array in Russia, the statistics of urban market trade is carried out. The number of cities covered by it is less than 5% of all cities, but they are home to more than half of the total urban population of the country.

A monographic description is a detailed examination of a separate, but very typical object, which also determines interest from the point of view of studying the entire population. A monographic study can be aimed at studying best practices or, on the contrary, shortcomings in the work of enterprises. The object of a monographic description can also be a family, a production team, a school, a higher educational institution, medical institution, city, region and other objects.

13. Accuracy of statistical observation. Control of statistical observation materials.

Statistical Observation Accuracy- the degree of conformity of the value of any indicator (the value of some attribute), determined by the materials of statistical observation, to its actual value.

The discrepancy between the calculated and actual values ​​of the studied quantities is called the observation error.

Data Accuracy This is the main requirement for statistical observation. To avoid observation errors, to prevent, identify and correct them, it is necessary to:

Ensure quality training of personnel who will conduct surveillance

Organize special partial or complete control checks on the correctness of filling in statistical forms

Carry out logical and arithmetic control of the received data of the end of information collection

Depending on the causes of occurrence, registration errors and representativeness errors are distinguished.

Registration errors- these are deviations between the value of the indicator obtained in the course of statistical observation and its actual, actual value. This type of error can occur in both continuous and non-continuous observations.

Random bugs- this is the result of the action of various random factors (for example, numbers are rearranged, adjacent lines or columns are mixed up when filling out a statistical form). Such errors have different directions: they can both increase and decrease the values ​​of indicators. With a sufficiently large surveyed population, as a result of the law of large numbers, these errors cancel each other out.

Systematic registration errors always have the same tendency to either increase or decrease the value of indicators for each unit of observation, and therefore the value of the indicator for the population as a whole will include the accumulated error. For example, when conducting sociological surveys of the population, rounding off the age of the population, as a rule, on numbers ending in 5 and 0, can serve.

14. The essence and significance of statistical groupings, their types.

Groupings are still a method of researching socio-economic phenomena, in which the statistical population is divided into homogeneous groups that reveal the state and development of the entire population.

Grouping is the most important stage of statistical research, combining the collection of primary information about the scope of the study and the analysis of this information on the basis of generalizing statistical indicators.

Grouping methods are varied. This diversity is due, on the one hand, to a huge variety of features subjected to statistical research, and, on the other hand, to a variety of tasks that are solved on the basis of groupings.

Depending on the tasks solved with the help of groupings, the following types are distinguished:

typological groups.

For example:

structural groupings.

A grouping is called structural, in which a homogeneous population is divided into groups that characterize its structure according to some varying feature. For example: composition of the population by sex, age, place of residence; the composition of enterprises by the number of employees, the cost of fixed assets; the structure of deposits by the term of their attraction, etc.

analytical groupings.

The phenomena of social life and the features that reflect them are closely interrelated. Analytical- grouping, revealing the relationship between the studied phenomena and their features. Analytical groupings allow you to study the variety of relationships and dependencies between varying features.

The whole set of signs can be divided into two groups: factorial and effective. Factor signs are called signs, under the influence of which other signs change - they form a group of effective signs. The relationship is manifested in the fact that with an increase in the value of the factor attribute, the average value of the resultant attribute systematically increases or decreases. For example: labor productivity depends on the technical level of the enterprise: the higher it is, the other equal conditions higher productivity of employees in the enterprise.

A grouping in which groups are formed on the same basis is called simple. To characterize a phenomenon, it is not enough to divide the population into groups according to some homogeneous feature. In this case, complex groupings are built.

complex called grouping, in which the division of the population into groups is carried out according to two or more features taken in combination (combination).

15. The main problems arising in the construction of groups.

The most important problem in building a grouping is the choice of a grouped feature or the basis of the grouping.

Grouping sign - a variable sign by which the units of the population are combined into groups.

According to the nature of variation, signs are divided, as is known, into: attributive and quantitative. This division determines the features of solving the second problem of groupings, namely, the determination of the number of allocated groups. When choosing some attributive features as grouping features, only a strictly defined number of groups can be distinguished. In particular, when grouping the population by sex, it can be distinguished ...

When grouping enterprises by profit, 3 groups can be distinguished.

For many attributive features, stable groupings are developed, called classifications. For example: classification of sectors of the economy, classification of occupations of the population, etc.

When grouping on a quantitative basis, the question of the number of group boundaries should be decided based on the essence of the socio-economic phenomenon under study. In this case, one should take into account such an indicator as the range of variations. The greater the range of variation, the more groups are formed and vice versa. It is also necessary to take into account the number of units of the population on which the grouping is built. With a small volume of the population, it is not advisable to form a large number of groups, because in this case, the groups will not have a sufficient number of units to identify statistical patterns.

An essential issue in grouping by quantitative characteristics is the definition of intervals. The indicators of the number of groups and the size of the intervals are inversely related. The larger the intervals, the fewer groups are required and vice versa.

An interval is the difference between its upper and lower bounds.

According to the size of the grouping attribute, the intervals are divided into equal and unequal. Equal intervals are used in cases where the change in the grouping attribute within the population occurs evenly. The calculation of the value of an equal interval is carried out according to the formula:

k - number of groups

Xmax, Xmin - respectively, the largest and smallest value of the attribute to the quality of the groups.

If the distribution of the grouping attribute within the population is uneven, then unequal intervals are used. Unequal intervals can be progressively increasing and progressively decreasing. often when grouping, so-called specialized intervals are used, i.e. those that are determined based on the purpose of the study and the essence of the phenomenon. For example: when grouping with the aim of characterizing the able-bodied population of the country, five-year intervals of the age of people are used.

The third problem of constructing groupings is the designation of interval boundaries. When selecting intervals according to discrete quantitative characteristics, their boundaries should be designated so that the lower limit of the next interval differs from the upper limit of the previous one by one.

When grouping on a continuous quantitative basis, the boundaries are marked so that the groups are clearly separated from one another. This is achieved by adding to the numerical boundaries of the intervals indications of where to refer the unit that has a grouping feature in sizes that exactly coincide with the boundaries of the intervals. Usually, additional explanations for the numerical boundaries of intervals formed according to continuous quantitative principles are expressed in the words: “more”, “less”, “over”, etc.

16. Typological groupings.

17. Structural groupings.

18. Analytical groupings.

typological groups.

The main task of the typological one is to classify socio-economic phenomena by identifying groups that are homogeneous in terms of qualitative relations. For example: grouping enterprises of economic sectors.

Typological groupings are widely used in the study of socio-economic phenomena and processes. They allow us to trace the origin, development and death of various types of phenomena.

When conducting a typological grouping, the main attention should be paid to identifying the types of socio-economic phenomena. It is based on a deep theoretical analysis of the phenomenon under study.

structural groupings.

A grouping is called structural, in which a homogeneous population is divided into groups that characterize its structure according to some varying feature. For example: composition of the population by sex, age, place of residence; the composition of enterprises by the number of employees, the cost of fixed assets; the structure of deposits by the term of their attraction, etc.

Changes in the structure of social phenomena reflect the most important patterns of their development.

analytical groupings.

The phenomena of social life and the features that reflect them are closely interrelated. Analytical - a grouping that reveals the relationship between the studied phenomena and their features. Analytical groupings allow you to study the variety of relationships and dependencies between varying features.

The whole set of signs can be divided into two groups: factorial and effective. Factor signs are called signs, under the influence of which other signs change - they form a group of effective signs. The relationship is manifested in the fact that with an increase in the value of the factor attribute, the average value of the indicator of the effective one systematically increases or decreases. For example: labor productivity depends on the technical level of the enterprise: the higher it is, the higher, other things being equal, the labor productivity of those employed in the enterprise.

A grouping in which groups are formed according to one attribute is called simple. To characterize a phenomenon, it is not enough to divide the population into groups according to some homogeneous feature. In this case, complex groupings are built.

A grouping is called complex, in which the population is divided into groups according to two or more features taken in combination (combination).

19. Building groupings on a quantitative basis.

The question of the number of groups, and, consequently, of the grouping intervals, is solved differently in typological groupings and in the selection of groups within types. Studying the quantitative side of mass social phenomena, statistics, relying on specific provisions economic theory, must, in the process of grouping, outline the points of transition from quantity to a new quality; based on the analysis of quantitative changes in grouping characteristics, identify points of transition from one quality to another. In typological grouping, the intervals should be marked in such a way that they delimit the social economic types established on the basis of economic theory.

The theoretical and economic analysis of the phenomenon under study should be a prerequisite for scientific statistical grouping, but at the same time, the use of the apparatus of modern statistical methods makes it possible to assess the degree of homogeneity of the selected groups, to select essential grouping features, and to improve the methodology for determining the size of the grouping intervals. The number of selected groups may also depend on the nature of the variation of the indicator under study. If a discrete feature is used as a grouping feature, i.e. a feature that can take only some specific values ​​(for example, integers), then the number of distinguished groups corresponds to the number of options for the value of the feature, if their number is not very large. For example, distribution of workers of the enterprise by wage categories, grouping of families by size, etc.

20. Secondary grouping.

When analyzing heterogeneous data, for example, when analyzing material collected in different periods of time related to various industries national economy, there is a need to use a secondary grouping. In addition, the secondary grouping method is also used to show the intensity of processes and phenomena under various conditions, for example, when it is necessary to show the degree of enlargement of collective farms in different regions, and the initial data are presented by different groupings.

As grouping characteristics, the size of the sown area, the number of livestock, the number of horses, etc. can be used.

21. Rows of distribution. Their graphic representation.

Statistical distribution series - this is an ordered distribution of population units into groups according to a certain varying attribute.

Depending on the signs underlying the formation of a distribution series, attributive and variation distribution series are distinguished.

Attributive called distribution series, built on qualitative grounds. It is customary to arrange the distribution series in the form of tables. For example, an attribute series of the distribution of assistance to citizens by lawyers. Lawyers can be distributed, for example, according to types and forms of legal assistance.

Attribute distribution series characterize the composition of the population according to one or another essential feature. Taken over several periods, these data will allow us to study the change in the structure.

Variational distribution series are called, built on a quantitative basis. Any variational series consists of elements: variants and frequencies.

Variants are individual values ​​of a feature that it takes in a variation series, i.e. specific value of the variable attribute.

Frequencies are the numbers of individual variants or each group of the variation series, i.e. these are numbers showing how often certain options occur in a distribution series. The sum of all frequencies determines the size of the entire population, its volume.

22. Absolute and relative values.

24. Types of statistical relative values.

An absolute value is an indicator that expresses the dimensions of a socio-economic phenomenon.

A relative value in statistics is an indicator that expresses the quantitative relationship between phenomena. It is obtained by dividing one absolute value by another absolute value. The value with which we compare is called basis or base of comparison .

Absolute values are always named values.

Relative values ​​are expressed in coefficients, percentages, ppm, etc.

Relative value shows how many times, or how many percent the compared value is more or less than the comparison base.

In statistics, there are 8 types of relative values:

1. The relative value of the implementation of the plan (OVVP) shows how many times or by how many percent this task has been completed.

OVDP = actual data of the reporting period

planned data of the reporting period

2. The relative value of the planned target (OVPZ) shows how many times or how many percent the planned target of the reporting period is more or less than the level of the base period.

OVPV = planned number of the reporting period

3. The relative value of the dynamics (AR) shows how many times or how many percent the level of the reporting period is more or less than the level of the base period.

ATS = actual reporting period data

actual base period data

4. The relative value of comparison (RBC) shows how many times or how many percent the phenomenon in territory A is greater or less than the phenomenon in territory B.

EVav.= actual the level of the phenomenon in territory A for a certain period of time

actual the level of the same phenomenon for the same period of time in territory B

5. Relative intensity value (RVI). Fertility rate, etc., the number of people born in a certain area in a certain period of time.

JVI = actual the level of the phenomenon for opred. period of time

the size of the environment in which this phenomenon developed

6. Relative Coordination Value (RVR) is calculated only for grouped data and shows the relationship between parts of the population.

HVAC = number of units of a particular group

the number of units of the group taken as the base of comparison

7. Relative value of the structure (RBC).

OVst.= part of the population

the totality

8. Relative value of the level of economic development (ERWER)

OVWER= annual output

average annual population population

23. Units of measurement of absolute and relative indicators.

Absolute indicators.

In international practice, such natural units of measurement like tons, kilograms, ounces, square, cubic and simple meters, miles, kilometers, gallons, liters, pieces, etc.

In a market economy, the greatest importance and application are value units, giving a monetary assessment of socio-economic phenomena and processes, such as GNP.

To labor units, allowing to take into account both the total labor costs at the enterprise, and the labor intensity of individual operations technological process, include man-days and man-hours.

Relative indicators.

Relative indicators can be expressed in coefficients, percentages, ppm, decimilles, or named numbers. If the comparison base is taken as 100, 1000 or 10,000, then the relative indicator is expressed as a percentage (%), ppm (‰) and decimille (‰).

26. Statistical tables. Their types.

In the practice of economic and statistical analysis, various types of statistical tables are used, which differ in the number and nature of the aggregates, in the different structure of the subject and predicate, in the structure and ratio of the features that form them.

Depending on the structure of the subject and the grouping of units in it, simple and complex statistical tables are distinguished, and the latter, in turn, are divided into group and combination tables.

In a simple table, the subject gives a simple list of any objects or territorial units, i.e. there is no grouping of aggregate units in the subject. Simple tables are monographic and list. Monographic tables do not characterize the entire set of units of the object under study, but only one group from it, singled out according to a certain, pre-formed feature.

Thus, simple list tables are called tables, the subject of which contains a list of units of the object under study.

Group tables are called statistical tables, the subject of which contains a grouping of population units according to one quantitative or attributive attribute. The predicate in group tables consists of the number of indicators necessary to characterize the subject.

The simplest type of group tables are attribute and variation distribution series. The group table can be more complex if the predicate contains not only the number of units in each group, but also a number of other important indicators that quantitatively and qualitatively characterize the subject groups. Such tables are often used to summarize indicators for groups, which allows us to draw certain practical conclusions.

Thus, group tables make it possible to identify and characterize the socio-economic types of phenomena, their structure, depending on only one attribute.

Combination tables are called statistical tables, the subject of which contains a grouping of population units simultaneously according to two or more characteristics: each of the groups, built on one basis, is divided, in turn, into subgroups according to some other attribute, etc.

Combination tables make it possible to characterize typical groups identified according to several characteristics, and the relationship between the latter. The sequence of splitting units of the population into homogeneous groups according to features is determined either by the importance of one of them in their combination, or by the order of their emission.

28. Development of a predicate statistical table.

In the predicate of the statistical table, indicators are given that are a characteristic of the object under study.

According to the structural structure of the predicate, statistical tables are distinguished with a simple and complex development.

With a simple development of the predicate, the indicator that defines it is not divided into subgroups, and the final values ​​are obtained by simply summing the values ​​for each feature separately, independently of each other.

After filling in this fragment of the table, a detailed description of the privatized enterprises is obtained according to the structure of their owners. For each company, you can get information on the number and price conditions for the sale of shares.

Complex predicate development involves the division of the feature that forms it into subgroups.

With a complex development of the predicate, a more complete and detailed description of the object is obtained.

The combined development of indicators on the conditions for the sale of shares and their types makes it possible to deepen the economic and statistical analysis of the share market and its structure by privatized enterprises.

Here, both predicates (price and species) are closely related to each other. It is possible to analyze not only the number of purchased shares by types and conditions, but also to determine the number of preferred and ordinary shares purchased under different price conditions. So, with a complex development of the predicate, each group of enterprises or each enterprise individually can be characterized by a different combination of features that form the predicate.

30. Tables of contingency.

A contingency table is a table that contains a summary numerical characteristic of the studied population for two or more attributive (qualitative) features or a combination of quantitative and attributive features.

Contingency tables are most widely used in the study of social phenomena and processes: public opinion, the level and way of life, the socio-political system, etc.

Most simple view The contingency table is a 2x2 frequency table.

The construction of this table is based on the assumption that the answers of the respondents or the analyzed attributes will take only two values ​​A1 and A2, B1 and B2. The internal digital content of the table is represented by frequencies (fij) that simultaneously have the i-th (i=1.2) value of one (Ai) and the j-th (j=1.2) value (Bj) of another quality attribute.

The final column and line contain information about the quantitative distribution of the population, respectively, according to A and B attributes.

For a more complete description and analysis of phenomena and processes characterized by attributive features, contingency tables of a higher dimension are used: ixj, where i=1,2,...,k is the number of value options (for example, answers of respondents, etc.) of one feature (for example, feature A); j=1,2,...,n - the number of options for the values ​​of another feature (B).

The principle of mutual contingency is most effective in identifying and evaluating the relationships and interdependencies between social phenomena and processes.

31. Reading and analysis of the statistical table.

The analysis of statistical tables is preceded by the stage of familiarization - their reading.

Reading and analysis of the table should not be carried out randomly, but in a certain sequence.

Reading assumes that the researcher, having read the words and numbers of the table, assimilated its content, formulated the first judgments about the object, understood the purpose of the table, understood its content as a whole, and assessed the phenomenon or process described in the table.

Table analysis as a method of scientific research by dividing the subject of study into parts is divided into structural and meaningful.

Structural analysis involves the analysis of the structure of the table, the characteristics presented in the table:

The totality and units of observation that form it

Signs and their combinations that form the subject and predicate of the table

Signs: quantitative and attributive

Correlations of the signs of the subject with the indicators of the predicate

Table type: simple or complex, and the latter - group or combination

Solved problems - analysis of the structure, types of phenomena or their relationships

Content analysis involves the study of the internal content of the table: analysis of individual groups of the subject according to the corresponding features of the predicate; identification of correlations and proportions between groups of phenomena according to one and different characteristics; comparative analysis and formulation of conclusions for individual groups and for the entire population as a whole; establishing patterns and determining the reserves for the development of the object under study.

A logical test consists in the possibility of determining specific features by certain numerical values ​​(for example, it is absurd if the number of employees in the company was 106.7 people).

A counting check involves the selective calculation of individual characteristic values ​​for a group, or the total values ​​of rows or columns, etc.

Analysis of group and combination tables to characterize the types of socio-economic phenomena, the structure of the population, relationships and proportions between individual groups and units of observation.

32. Statistical chart. Its elements and rules of construction.

Statistical Graph - this is a drawing in which statistical aggregates characterized by certain indicators are described using conditional geometric images or signs. The presentation of table data in the form of a graph makes a stronger impression than numbers, allows you to better understand the results of statistical observation, interpret them correctly, greatly facilitates the understanding of statistical material, makes it visual and accessible. This, however, does not at all mean that the graphs have only an illustrative meaning. They provide new knowledge about the subject of research, being a method of generalizing the initial information.

When constructing a graphic image, a number of requirements must be observed. First of all, the graph should be quite visual, since the whole point of the graphic image is to visually depict statistical indicators. In addition, the schedule should be expressive, intelligible and understandable. To fulfill the above requirements, each graph must include a number of basic elements: a graphic image; graph field; spatial landmarks; scale landmarks; chart explication.

Graphic image are geometric signs, i.e. a set of points, lines, figures, with the help of which statistical indicators are depicted.

Chart field - this is the part of the plane where the graphic images are located. The graph field has certain dimensions, which depend on its purpose.

Spatial landmarks of the graph are set in the form of a system of coordinate grids. The coordinate system is necessary for placing geometric symbols in the graph field.

Scale landmarks of the statistical graph are determined by the scale and scale system. Statistical Graph Scale is a measure of converting a numerical value into a graphic one.

Scale bar a line is called, the individual points of which can be read as certain numbers. The scale is of great importance in graphics and includes three elements: a line (or scale carrier), a certain number of dots marked with dashes, which are located on the scale carrier in a certain order, a digital designation of numbers corresponding to individual marked points.

33. Classification of types of charts.

There are many types graphic images. Their classification is based on a number of features: a) a method of constructing a graphic image; b) geometric signs depicting statistical indicators; c) tasks solved with the help of a graphic image.

According to the method of construction, statistical graphs are divided into diagrams and statistical maps.

Diagrams - the most common way of graphic images. These are graphs of quantitative relations. The types and methods of their construction are varied. Diagrams are used for visual comparison in various aspects (spatial, temporal, etc.) of independent quantities: territories, population, etc. In this case, the comparison of the aggregates is carried out according to some significant varying attribute.

Statistical maps - plots of quantitative distribution over the surface. In their main purpose, they closely adjoin diagrams and are specific only in the sense that they are conditional representations of statistical data on a contour map, i.e. show the spatial distribution or spatial distribution of the statistical data. Geometric signs are either points, or lines or planes, or geometric bodies.

Averages are one of the most common summary statistics. They aim to characterize by one number a statistical population consisting of a minority of units. Averages are closely related to the law of large numbers. The essence of this dependence lies in the fact that, with a large number of observations, random deviations from the general statistics cancel each other out and, on average, a statistical regularity is more clearly manifested.

Using the method of averages, the following main tasks are solved:

1. Characteristics of the level of development of phenomena.

2. Comparison of two or more levels.

3. The study of the relationship of socio-economic phenomena.

1. 4. Analysis of the distribution of socio-economic phenomena in space.

To solve these problems, statistical methodology has developed various types of averages.

37. Types of averages.

The harmonic mean is the primitive form of the arithmetic mean. It is calculated in those cases when the weights fi are not given directly, but are included as a factor in one of the available indicators. As well as the arithmetic mean, the harmonic mean can be simple and weighted.

Average harmonic simple:

Average harmonic mixed:

Wi - product of variants by frequencies

When calculating average values, it must be remembered that any intermediate calculations should result both in the numerator and in the denominator and have economic sense indicators.

38. Arithmetic mean and its properties.

To clarify the methodology for calculating the arithmetic mean, we use the following notation:

X - arithmetic sign

X (X1, X2, ... X3) - variants of a certain feature

n - number of population units

The average value of the feature

Depending on the initial data, the arithmetic mean can be calculated in two ways:

1. If the statistical observation data are not grouped, or the grouped variants have the same frequencies, then the simple arithmetic mean is calculated:

2. If the frequencies are grouped in the data are different, then the weighted arithmetic mean is calculated:

Number (frequencies) of variants

Sum of frequencies

The arithmetic mean is calculated differently in discrete and interval variation series.

In discrete series, feature variants are multiplied by frequencies, these products are summed up, and the resulting sum of products is divided by the sum of frequencies.

In interval series, the value of a feature is given, as is known, in the form of intervals, therefore, before calculating the arithmetic mean, it is necessary to switch from an interval series to a discrete one.

As options for Xi, the middle of the corresponding intervals is used. They are defined as half the sum of the lower and upper bounds.

If the interval has no lower limit, then its middle is defined as the difference between the upper limit and half the value of the following intervals. In the absence of upper bounds, the middle of the interval is defined as the sum of the lower bound and half the value of the previous interval. After the transition to a discrete series, further calculations are carried out according to the method discussed above.

If a weight fi are given not in absolute terms, but in relative ones, then the formula for calculating the arithmetic mean will be as follows:

pi - relative values ​​of the structure, showing what percentage is the frequency of variants in the sum of all frequencies.

If the relative values ​​of the structure are given not in percentages, but in shares, then the arithmetic mean will be calculated by the formula:

39. Structural average.

40. Mode and median, their definition in variational series.

The structural average characterizes the composition of the statistical population according to one of the varying features. These means are the mode and the median.

Fashion is the value of the variable that has the highest frequency in the given distribution series.

In discrete series of distributions, the mode is determined visually. First, the highest frequency is determined, and the modal value of the feature is determined from it. In interval series, the following formula is used to calculate the mode:

Xmo - the lower limit of the modality (interval of the series with the highest frequency)

Mo - interval value

fMo - modal interval frequency

fMo-1 - frequency of interval preceding modal

fMo+1 - frequency of the interval following the modal

median such value of a variable sign is called, which divides the distribution series into two equal parts according to the volume of frequencies. The median is calculated differently in discrete and interval series.

1. If the distribution series is discrete and consists of an even number of members, then the median is defined as the average of the two median values ​​of the ranked series of features.

2. If there is an odd number of levels in the discrete distribution series, then the median will be the middle value of the ranked series of features.

In interval series, the median is determined by the formula:

The lower bound of the median interval (the interval for which the accumulated frequency exceeds half the sum of frequencies for the first time)

Me - interval value

The sum of the frequencies of the series

The sum of the accumulated frequencies preceding the median interval

Median Interval Frequency

41. General concept about variation.

variation called the difference in the values ​​of the attribute in individual units of the population.

The variation arises due to the fact that the individual values ​​of the attribute are formed by the influence of a large number of interrelated factors. These factors often act in opposite directions, and their joint action forms the value of features in a particular unit of the population. The need to study variations is due to the fact that the average value, summarizing the data of statistical observation, does not show how the individual value of the trait fluctuates around it. Variations are inherent in the phenomena of nature and society. At the same time, the revolution in society is happening faster than similar changes in nature. Objectively, there are also variations in space and time.

Variations in space show the difference in statistical indicators related to various administrative-territorial units.

Variations in time show the difference in indicators depending on the period or point in time to which they refer.

42. The essence and significance of the indicators of variation.

43. Absolute indicators of variation (=42, no coefficient).

Examples of variations include the following indicators:

1. range of variations

2. average linear deviation

3. standard deviation

4. dispersion

5. ratio

1. Range of Variations is its simplest indicator. It is defined as the difference between the maximum and minimum value of the attribute. The disadvantage of this indicator is that it depends only on the two extreme values ​​of the attribute (min, max) and does not characterize the fluctuation within the population. R=Xmax-Xmin.

2. Average linear deviation is the average value of the absolute values ​​of the deviations from the arithmetic mean. Deviations are taken modulo, because otherwise, due to mathematical properties medium size, they would always be zero.

3. Standard deviation is defined as the root of the variance.

4. Dispersion(mean square of deviations) has the greatest use in statistics as an indicator of the measure of volatility.

The variance is a named indicator. It is measured in units corresponding to the square of the units of measurement of the trait under study.

5. Variation coefficient is defined as the ratio of the standard deviation to the average value of the feature, expressed as a percentage:

It characterizes the quantitative homogeneity of the statistical population. If this coefficient< 50%, то это говорит об однородности статистической совокупности. Если же совокупность не однородна, то любые статистические исследования можно проводить только внутри выделенных однородных групп.

44. Dispersion and its properties.

Dispersion - the average square of the deviations of the individual values ​​of the trait from their average value.

Dispersion properties:

1. The dispersion of a constant value is zero.

2. Reducing all values ​​of the attribute by the same value A does not change the value of the variance. This means that the mean square of deviations can be calculated not from the given values ​​of the attribute, but from their deviations from some constant number.

3. Reducing all values ​​of the attribute by k times reduces the variance by k2 times, and the standard deviation - by k times. This means that all the values ​​of the attribute can be divided by some constant number (say, by the interval of the series), calculate the standard deviation, and then multiply it by a constant number.

4. If we calculate the average square of deviations from any value A, then to some extent different from the arithmetic mean (X~), then it will always be greater than the average square of deviations calculated from the arithmetic mean. In this case, the average square of deviations will be larger by a well-defined value - by the square of the difference between the average and this conditionally taken value.

45. Intragroup and intergroup dispersion.

Dispersion is divided into total, intergroup and intragroup. The total variance s2 measures the variation of a trait in the entire population under the influence of all the factors that caused this variation.

Intergroup variance (s2x) characterizes systematic variation, i.e. differences in the magnitude of the trait under study, arising under the influence of the trait-factor underlying the grouping.

The within-group variance (s2i) reflects random variation, i.e. part of the variation that occurs under the influence of unaccounted for factors and does not depend on the trait-factor underlying the grouping.

46. ​​The rule of adding variances.

There is a law relating the three types of dispersion. The total variance is equal to the sum of the average of the intragroup and intergroup variances:

This relation is called the rule of addition of variances. According to this rule, the total variance arising under the influence of all factors is equal to the sum of the variance arising due to the grouping attribute.

Knowing any two types of dispersions, one can determine or check the correctness of the calculation of the third type.

The rule for adding variances is widely used in calculating the indicators of the closeness of relationships, in analysis of variance, in assessing the accuracy of a typical sample, and in a number of other cases.

47. Relationships of social phenomena, their types, forms.

The variety of relationships in which there are socio-economic phenomena, give rise to the need for their classification.

According to the types, functional and correlation dependence is distinguished.

functional such a dependence is called, in which one value of the factor attribute X corresponds to one strictly defined value of the resultant attribute Y.

Unlike functional dependency, correlation expresses such a relationship between socio-economic phenomena, in which one value of the factor attribute X can correspond to several values ​​of the effective attribute Y.

There are direct and inverse relationships according to direction.

Straight they call such a dependence in which the value of the factor attribute X and the resultant attribute Y change in the same direction. That. as X increases, Y values ​​increase on average, and as X decreases, Y decreases.

Reverse the relationship between factor and resultant features, if they change in opposite directions.

50. Analysis of the relationship of qualitative features.

associations and contingents

If the signs have 3 or more gradations, then the Pearsen and Chuprov coefficients are used to study the relationships. They are calculated according to the formulas:

C - Pearsen coefficient

K - Chuprov coefficient

j - indicator of mutual contingency

mi - table columns

nj - strings

n - number of observations

51. Statistical methods for studying relationships.

An important place in the statistical study of relationships is occupied by the following methods:

1. Method of reduction of parallel data.

2. Method of analytical groupings.

3. Graphical method.

4. Balance method.

6. Correlation-regression.

1. Essence parallel data reduction method is as follows:

The initial data on the basis of X are arranged in ascending or descending order, and on the basis of Y, the corresponding indicators are recorded. By comparing the values ​​of X and Y, a conclusion is made about the presence and direction of dependence.

3. Essence graphic method is a visual representation of the presence and direction of relationships between features. To do this, the value of the factor attribute X is located along the abscissa axis, and the value of the resulting attribute along the ordinate axis. According to the joint arrangement of points on the graph, a conclusion is made about the direction and the presence of dependence. In this case, the following options are possible:

a \, b / (up), c \ (down).

If the points on the graph are arranged randomly (a), then there is no relationship between the studied features.

If the points on the graph are concentrated around the straight line (b) /, the relationship between the features is direct.

If the points are concentrated around the straight line (c) \, then this indicates the presence of an inverse relationship.

Based on the method of parallel data and the graphical method, indicators can be calculated that characterize the degree of closeness of the correlation dependence.

The most multiple of them is the Fechner sign coefficient. It is calculated by the formula:

C - the sum of the coinciding signs of the deviations of the individual values ​​of the attribute from the average.

H - sum of mismatches

This coefficient varies within (-1;1).

The value of KF=0 indicates the absence of dependence between the studied features.

If KF=±1, then this indicates the presence of a functional direct (+) and inverse (-) dependence. With a value of KF>? 0.6? it is concluded that there is a strong direct (inverse) relationship between the features.

Rank difference squares

(R2-R1), n ​​- number of pairs of ranks

This coefficient, like the previous one, varies within the same limits and has the same economic interpretation as KF.

52. Non-parametric indicators of the tightness of the relationship. Spearman. Kendall.

54. The concept of the rank of dynamics. Types of dynamic series.

In the analysis of socio-economic phenomena, one often has to resort to various conditional estimates, for example, ranks, and the relationship between individual features is measured using non-parametric correlation coefficients. These coefficients are calculated under the condition that the characteristics under study obey different distribution laws.

Ranging is a procedure for ordering objects of study, which is performed on the basis of preference.

Rank - this is the serial number of the attribute values, arranged in ascending or descending order of their values. If the attribute values ​​have the same quantitative assessment, then the rank of all these values ​​is taken equal to the arithmetic mean of the corresponding numbers of places that are determined. These ranks are called liaison.

The principle of numbering the values ​​of the characteristics under study is the basis of non-parametric methods for studying the relationship between socio-economic phenomena and processes.

Among the nonparametric methods for estimating the tightness of the connection, the rank coefficients of Spearman (r) and Kendall (t) are of the greatest importance. These coefficients can be used to determine the frequency of relationships between both quantitative and qualitative features, provided that their values ​​are sorted or ranked according to the degree of decrease or increase of the attribute.

The correlation coefficient of ranks (Spearman coefficient) is calculated by the formula (for the case when there are no connected ranks). Spearman's coefficient takes any value in the range [-1;1].

Kendall's rank correlation coefficient (t) can also be used to measure the relationship between qualitative features that characterize homogeneous objects, ranked according to the same principle.

The calculation of this coefficient is performed in the following sequence:

1. X values ​​are ranked in ascending or descending order

2. Y values ​​are arranged in order corresponding to X values

3. For each rank Y, the number of following values ​​of ranks that exceed its value is determined

4. For rank Y, the number of ranks following it, less than its value, is determined. The total value is denoted by Q and is fixed with a sign (-)

5. The sum of points for all members of the series is determined.

To determine the tightness of the relationship between an arbitrary number of ranked features, multiple rank correlation coefficient(concordance factor) (W).

53. Indicators of mutual contingency.

To study the relationship of qualitative alternative features that take only 2 mutually exclusive values, the coefficient is used associations and contingents. When calculating these coefficients, the so-called. table of 4 stones, and the coefficients themselves are calculated by the formula:

If the association coefficient? 0.5, and the contingency coefficient? 0.3, then we can conclude that there is a significant relationship between the studied features.

If the signs have 3 or more gradations, then to study the relationships are used conjugacy coefficients Piersen and Chuprov. They are calculated according to the formulas:

C - Pearsen coefficient

K - Chuprov coefficient

j- mutual contingency index

K - number of values ​​(groups) of the first feature

K1 - number of values ​​(groups) of the second feature

fij - frequencies of the corresponding cells of the table

mi - table columns

nj - strings

To calculate the Pearsen and Chuprov coefficients, an auxiliary table is compiled:

When ranking qualitative features in order to study their relationship, the Kendall correlation coefficient is used.

n - number of observations

S is the sum of the differences between the number of sequences and the number of inversions by the second feature.

P is the sum of rank values ​​following the data and exceeding its value

Q - the sum of the rank values ​​following the data and less than its value (taken into account with the "-" sign).

In the presence of related ranks, the formula for the Kendall coefficient will be as follows:

Vx and Vy are determined separately for ranks X and Y by the formula:

55. Comparability of levels and closure of series of dynamics.

The most important condition for the correct construction of a series of dynamics is the comparability of all its constituent levels. This condition is solved either in the process of collecting and processing data, or by recalculating them.

The incompatibility of the levels of the series may arise due to a change units of measurement or units of account.

The comparability of the levels of a series of dynamics is directly affected by accounting methodology or calculation of indicators. For example, if in some years the average yield was calculated from the sown area, and in others - from the harvested area, then such levels will not be comparable.

The condition for comparability of the levels of a series of dynamics is periodization of dynamics. In the process of development in time, first of all, quantitative changes in phenomena occur, and then, at certain stages, qualitative leaps are made, leading to a change in the regularity of the phenomenon. Therefore, the scientific approach to the study of the series of dynamics is to divide the series covering large periods of time into those that would unite only periods of the same quality in the development of an aggregate characterized by one pattern of development.

The process of identifying homogeneous stages of development is called periodization of dynamics.

It is also important that in the series of dynamics intervals or moments, according to which the levels are determined, had the same economic sense. For example, when studying the growth of the livestock population, it is meaningless to compare the numbers of the livestock as of October 1 with January 1, since the first figure includes not only the cattle left for the winter, but also intended for slaughter, and the second figure includes only the cattle left for the winter.

The levels of the dynamics series may turn out to be incomparable in terms of covered objects due to the transition of a number of objects from one subordination to another.

The incompatibility of the levels of the series may arise due to changes in the territorial boundaries of regions, districts, etc. At the same time, speaking of a change in the territory to which the levels of the series pertain different time, it should be borne in mind that the question of comparability or incompatibility when changing the territory is solved in different ways, depending on the purpose of the study.

In order to bring the levels of a series of dynamics to a comparable form, sometimes one has to resort to a technique called “closing the series of dynamics”. Closing is understood as the union in one series (longer) of two or more series of dynamics, the levels of which are calculated according to different methodology or different territorial boundaries. To implement the closure, it is necessary that for one of the periods (transitional) there are data calculated according to different methodology (or within different boundaries).

60. Components of a series of dynamics.

A number of dynamics can be influenced by factors of an evolutionary and oscillatory nature, as well as be influenced by factors of various influences.

Influences of an evolutionary nature are changes that determine a certain general direction of development, as it were, a multi-year evolution that makes its way through other systematic and random fluctuations. Such changes in the dynamic series are called development trend, or trend.

Oscillatory influences are cyclical (opportunistic) and seasonal fluctuations. Cyclic (or periodic) consist in the fact that the value of the trait under study increases for some time, reaches a certain maximum, then decreases, reaches a certain minimum, increases again to the previous value, etc. Cyclical fluctuations in economic processes roughly correspond to the so-called conjuncture cycles. seasonal fluctuations- these are fluctuations that periodically repeat at some specific time of each year, days of the month or hours of the day. These changes are clearly observed on the graphs of many time series containing data for a period of at least one year.

Irregular fluctuations for socio-economic phenomena can be divided into two groups: a) sporadically occurring changes caused, for example, by war or environmental catastrophe; b) random fluctuations resulting from the action a large number relatively weak secondary factors.

61. Methods for identifying trends in time series.

62. Determination of the main trend of dynamics based on the enlargement of intervals and the moving average.

The levels of a series of dynamics are formed under the attention of 3 groups of factors:

1. Factors determining the main direction, i.e. development trend of the phenomenon under study.

2. Factors acting periodically, i.e. directional fluctuations by weeks of the month, months of the year, etc.

3. Factors acting in different, sometimes in opposite directions and not having a significant impact on the level of a given series of dynamics.

The main task of the statistical study of dynamics is to identify trends.

The main methods for identifying trends in time series are:

Interval coarsening method

moving average method

Analytical alignment method

1. Essence interval enlargement method is as follows:

The original series of dynamics is transformed and replaced by others consisting of other levels related to enlarged periods or points in time.

For example: a series of dynamics of the profit of a small enterprise for 1997 by quarters of the same year. At the same time, the levels of the series for enlarged periods or points in time can be either total or average indicators. However, in any case, the levels of the series calculated in this way more clearly reveal trends, since seasonal and random fluctuations cancel out and balance out when summing up or determining averages.

2. moving average method, like the previous one, involves the transformation of the original series of dynamics. To identify a trend, an interval consisting of the same number of levels is formed. In this case, each subsequent interval is obtained by shifting by 1 level from the initial one. According to the intervals thus formed, the sum is determined at the beginning, and then the averages. It is technically more convenient to define moving averages for an odd interval. In this case, the calculated average value will refer to a specific level of the time series, i.e. to the middle of the slip interval.

When determining the moving average over an even interval, the calculated value of the average refers to the interval between two levels, and thus loses economic meaning. This necessitates additional calculations related to centering according to the arithmetic simple formula from two adjacent non-centered averages.

64. The role of the index method in statistical research.

Individual For example, change in the production of cars of a certain brand. The individual index is denoted by i. Consolidated the index reflects the change in the entire set of elements of a complex phenomenon. If the indices do not cover all the elements of a complex phenomenon, but only a part, then they are called group or sub-indices. For example, product indices for individual industries.

65. Aggregate indices, their relationships.

Aggregate index - a complex relative indicator that characterizes the average change in a socio-economic phenomenon, consisting of incommensurable elements.

The Latin word "aggregate" means "added, summed up." The peculiarity of this form of the index is that in the aggregate form two sums of indicators of the same name are directly compared. At present, this is the most common form of indices used in practical statistics in many countries of the world.

The numerator and denominator of the aggregate index are the sum of two values, one of which changes (indexed value), and the other remains unchanged in the numerator and denominator (index weight).

An indexed value is a sign, the change of which is being studied (the price of goods, the stock price, the cost of working time for the production of products, the number of goods sold, etc.). Index weight - this is the value that serves for the purposes of comparing the indexed values.

Behind each economic index are certain economic categories. The economic content of the index predetermines the method of its calculation.

The methodology for constructing an aggregate index provides for the solution of three questions:

1. What value will be indexed

2. According to what composition of heterogeneous elements of the phenomenon is it necessary to calculate the index

3. What will serve as a weight when calculating the index.

When choosing the weight of the index, it is customary to be guided by the following rule: if the index of a quantitative indicator is being built, then the weights are taken for the base period; when constructing the index of a qualitative indicator, the weights of the reporting period are used.

66. Individual and composite aggregate indices.

The index is relative value, obtained as a result of comparing the levels of socio-economic phenomena in time, in space or with a plan.

As a measure of comparison of heterogeneous products, you can use the price, cost or labor intensity of a unit of production.

In the development of index theory in our country, two directions have developed: generalizing, or synthetic, and analytical.

The difference between these directions is due to two possibilities for interpreting indices in their application.

The generalizing or so-called synthetic direction interprets the index as an indicator of the average change in the level of the phenomenon under study. In analytical theory, indices are perceived as indicators of a change in the level of the resulting value under the influence of a change in the indexed value.

The development of the second direction was due to the use of the index method in economic analysis.

Methods for constructing indices depend on the content of the phenomena under study, the methodology for calculating the initial statistical indicators, and the objectives of the study.

Individual are called indices that characterize the change in only one element of the population. For example, change in the production of cars of a certain brand. Individual index denoted i. Consolidated the index reflects the change in the entire set of elements of a complex phenomenon. If the indices do not cover all the elements of a complex phenomenon, but only a part, then they are called group or sub-indices. For example, product indices for individual industries.

Depending on the content and nature of the indexed value, there are indices of quantitative (volume) indicators (for example, the index of the physical volume of production) and indices of qualitative indicators (for example, price indices, cost prices).

When calculating indices, there is a difference between the compared level and the level with which the comparison is made, called the base level. In this case, two methods of calculating indices are possible - chain and basic. Chained indices are obtained by comparing the current levels with the previous one. Basic indices are obtained by comparison with the level of any one specific period, taken as the basis of comparison.

Depending on the calculation methodology, aggregate indices and averages of individual indices are distinguished. Individual indices are divided into arithmetic mean and harmonic mean indices. Aggregate indices of qualitative indicators can be calculated as indices of variable composition and indices of fixed (constant) composition. In indices of variable composition, indicators calculated on the basis of changing structures of phenomena are compared, and in indices of fixed composition, on the basis of an unchanged structure of phenomena.

67. Essential economic indices and their relationship.

There are interrelations between the most important indices, which make it possible to obtain others on the basis of some indices. Knowing, for example, the value of chain indices for any period of time, it is possible to calculate the basic indices. And vice versa, if the basic indices are known, then by dividing one of them by the other one can obtain chain indices.

The existing relationships between the most important indices make it possible to identify the influence of various factors on the change in the phenomenon under study, for example, the relationship between the index of the cost of production, the physical volume of production and prices. Other indexes are also related. So, production cost index is the product of the production cost index and the volume index of production.

The index of time spent on production can be obtained as a result of multiplying the index of the physical volume of production and the value, the reciprocal of the index of labor intensity, i.e. labor productivity index.

There is an important relationship between indices of the physical volume of production and indices of labor productivity.

Labor productivity index represents the ratio of the average production output (in comparable prices) per unit of time (or per employee) in the current and base periods. For example, the index of the physical volume of production is equal to the product of the labor productivity index and the index of working hours (or the number of employees).

The relationship between individual indices can be used to identify individual factors that affect the phenomenon under study.

The labor productivity index for production costs shows how many times the labor productivity increased (decreased), or how many percent was the decrease (growth) in labor productivity in the current period compared to the base one.

The index value reduced by 100% shows the percentage change in labor productivity in the current period compared to the base period.

The difference between the numerator and denominator shows the absolute amount of savings (overspending) of the cost of living labor in connection with the growth (decrease) of its productivity.

A change in the structure of a phenomenon is understood as a change in the proportion of individual groups of population units in their total number. Thus, the average salary at the enterprise may increase as a result of an increase in the wages of employees or an increase in the proportion of highly paid employees.

The variable composition index is an index that expresses the ratio of the average levels of the phenomenon under study, relating to different periods of time.

The variable composition index reflects a change not only in the indexed value (in this case, the cost), but also in the structure of the population (weights).

An index of constant (fixed) composition is an index calculated with weights fixed at the level of one of any period, and showing a change in only the indexed value.

The index of structural shifts is understood as an index that characterizes the effect of a change in the structure of the phenomenon under study on the dynamics of the average level of this phenomenon.

In statistical practice, there is often a need to compare the levels of an economic phenomenon in space: by countries, economic regions, regions, i.e. in the calculation of territorial indices. When constructing territorial indices, one has to decide what weights were used in their calculation.

In the theory and practice of statistics, various methods for constructing territorial indices are proposed, including standard weight method. This method lies in the fact that the values ​​of the indexed value are weighted not by the weights of any one region, but by the weights of the region, economic region, republic in which the compared regions are located.

P. S.: The answers to the tickets were taken from the lectures and from the textbook by Shmoylova R.A. "Theory of Statistics"

? SHVACHKIN MAXIM DE-103

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KNOW RUSSIAN NEW UNIVERSITY (RosNOU)

INSTITUTE OF DISTANCE AND CORRESPONDENCE LEARNING

STUPINSKY BRANCH

FACULTY OF LAW

Topic: "Objects of study of customs statistics."

DISCIPLINE WORKSHOP ON THE APPLICATION OF MATHEMATICAL METHODS AND MODELS IN CUSTOMS STATISTICS

Student Konyashina Irina Sergeevna

Head: Demyanova N.S.

STUPINO 2010

1. Basis of foreign trade customs statistics

2. Sources of customs statistics

Bibliography

1. BASIS OF FOREIGN TRADE CUSTOMS STATISTICS

The basis of customs statistics includes legal, methodological, documentary, structural, technological foundations.

The legal basis for maintaining customs statistics of Russia's foreign trade is the Customs Code of the Russian Federation.

The purpose of maintaining customs statistics of foreign trade is to provide the highest state authorities and other state bodies determined by the legislation of the Russian Federation with information on the state of foreign trade of the Russian Federation, on receipts to the federal budget customs duties, fees and certain taxes, the implementation of currency control, analysis of the state and development of the foreign trade of the Russian Federation, its trade and payment balances and the economy as a whole.

Documents and information for the above purposes are submitted in accordance with the provisions of the Customs Code of the Russian Federation on the procedure for customs clearance and customs control, and the information provided for statistical purposes is confidential. This provision is supported by the provisions of the Customs Code of the Russian Federation, according to which information submitted to the customs authorities of the Russian Federation by state bodies, enterprises, organizations and citizens can be used exclusively for customs purposes and must not be disclosed or passed on to third parties, and government bodies, except for the cases stipulated by the legislative acts of the Russian Federation.

The methodological basis of the customs statistics of foreign trade is the provisions of a special document "Methodology of customs statistics of foreign trade of the Russian Federation".

The methodological basis determines the objects of statistical observation and accounting in the customs statistics of foreign trade and the procedure for their accounting, including goods transported pipeline transport and over power lines.

The basic principle of accounting for all imported and exported goods is determined by the customs regimes given in the Customs Code of the Russian Federation. Customs statistics include goods placed under 12 customs regimes.

The customs statistics of foreign trade of the Russian Federation takes into account the import and export of goods on the basis of the so-called General Accounting System for Foreign Trade and includes all goods (including valuables, with the exception of currency valuables in circulation), the import and export of which, respectively, increases or reduces the material resources of the country .

The methodology of customs statistics of foreign trade also performs the following functions:

* gives a uniform interpretation of the materials used in it, which is ensured with the help of terms and definitions given taking into account world practice and the provisions of the Customs Code of the Russian Federation;

* reproduces the definitions of the main customs regimes, as well as the current regulations regarding the determination of the country of origin and customs value;

¾ contains, in connection with customs regimes, clear lists of goods that are taken into account by customs statistics of foreign trade when exporting and importing, as well as goods that are not taken into account when exporting and importing;

* determines the threshold of statistical observation, as well as the list of mandatory indicators of customs foreign trade statistics and the procedure for maintaining the confidentiality of statistical information on the country's foreign trade.

documentary basis. characteristic feature customs statistics of foreign trade is that it is based on the content of primary documents (representing its documentary basis):

ѕ presented by the participants of foreign economic relations to a controlling agency independent of them;

According to the Labor Code of the Russian Federation, from the moment of registration of acceptance of the cargo customs declaration(GTD) from the declarant to the customs authority, it becomes a document evidencing facts of legal significance.

Being certified by the customs authority that accepted the customs declaration for registration, the data contained in the latter, after the actual documentary verification, associated (at the discretion official customs authority) with a physical examination of the presented customs clearance goods can form an objective and reliable picture of a country's foreign trade. This picture is a reflection of statistical accounting, namely:

Leaving the goods within the customs territory of Russia - when exporting;

The entry of goods into the customs territory of Russia - when importing.

In development of the provisions of the Labor Code of the Russian Federation, there is an "Instruction on the procedure filling in the customs declaration". It contains instructions on the procedure for filling in, including the column of the customs declaration, which should form the customs statistics of foreign trade.

The list of such indicators is given in the Methodology of Customs Statistics of Foreign Trade of the Russian Federation. These include the following indicators:

1. reporting period;

3. country of origin (when importing);

4. country of destination (when exporting);

5. statistical value;

6. code and name of goods according to the FEACN of the CIS;

7. net weight;

8. code and name of additional units of measurement;

9. Quantity in additional units of measure;

10. type of customs regime;

12. region (republic, territory, region, cities of Moscow or St. Petersburg, autonomous region, autonomous district)

There is no departmental influence in the formation and release of data on foreign trade statistics of the Russian Federation.

Such an impact on the customs authorities of Russia is excluded, since:

Control over the movement of goods across the customs border of the country is carried out by a nationwide control agency, independent of participants in foreign economic relations;

The CCD is filled in by the participants in foreign economic relations themselves or by authorized and customs brokers acting on a contractual basis;

The CCD has a universal status, since it is not only a basic document that serves to form customs statistics of foreign trade, but also a legal document that is the basis for customs control, as well as a financial document that determines the relationship of participants in foreign economic relations with the federal budget.

The structural basis of the customs statistics of foreign trade of the Russian Federation are the forms of official publications. We are talking about the output forms of official publications in the form of quarterly bulletins and annual collections, which is accepted in international practice.

The output forms of official publications have passed international examination in the GATT (WTO) and in the Statistical Bureau of the European Union (Eurostat), which ensures the comparability of data on mutual trade between the Russian Federation and its foreign trade partners.

After passing the international examination, the mentioned output forms were interdepartmentally coordinated by more than a dozen federal ministries and departments involved in foreign economic relations, and received approval from the Government of the Russian Federation.

In accordance with the above-mentioned output forms of official quarterly and annual publications, the distribution of foreign trade of the Russian Federation in the context of groups of countries is carried out according to the following scheme:

Including:

CIS countries; OECD countries; EU countries; APEC countries; OPEC countries.

At the same time, trade with individual countries is presented in the distribution by continent.

The quarterly bulletins publish data on the foreign trade of the Russian Federation (in terms of exports and imports): by commodity groups (the first two characters of the HS); by commodity items in the context of "country-commodity" (the first four characters of the HS); by commodity items in the context of "goods-country" (the first four characters of the HS).

At the same time, data are published on the main products of Russian exports and imports, which still account for more than 80% of the value of exports and about 60% of imports.

Data on trade of the Russian Federation in the context of "country-commodity" are published in quarterly bulletins for 55 partner countries, the total value of mutual trade with which is about 90% of the total foreign trade turnover of the Russian Federation. In the annual collections, in addition to the publication of data on foreign trade (on exports and imports): by commodity groups (the first two characters of the HS), the publication of statistical data in the sections "commodity-country" and "country-commodity" is carried out by commodity subitems (the first six HS signs).

In connection with the joint decision of the Central Bank of the Russian Federation and other departments on accounting for export-import operations in US dollars, since 1992, the statistical value in the GTD is presented in US dollars.

In order to make the official quarterly publications on the customs statistics of the foreign trade of the Russian Federation more analytical, four appendices have been added to the latter containing the general results of Russia's trade with each of the CIS states, as well as data on trade with each of these states in the context of "goods-country" by major goods of Russian import and export.

These totals are given in Russian rubles.

Official publications of the data of customs statistics of foreign trade of the Russian Federation according to the above-described output forms are carried out in the form of quarterly bulletins that go out of print on the 60th day after the end of the reporting quarter, as well as in the form of annual collections that go out of print approximately on the 120th day after the end of the reporting quarter. of the year. Such deadlines are generally accepted in the world practice of issuing such publications.

Technological basis. Automation of the collection and processing of data on export-import transactions contained in the customs declaration is one of the defining conditions that make it possible to implement the possibility of switching to customs statistics of foreign trade.

The Customs Service of Russia has developed an automated subsystem for generating initial data in the interests of customs statistics of foreign trade. This subsystem is an integral part of the Unified Automated information system of the Russian customs department (UAIS State Customs Committee), designed to provide comprehensive computerization of the activities of the customs authorities of the Russian Federation.

The procedure for collecting and processing information contained in the customs declaration, in particular, in the interests of generating the initial data of the customs statistics of the Russian Federation, is strictly regulated and approved by the order of the State Customs Committee of Russia, brought to the management and execution by all customs authorities. This regulation is specified in without fail by issuing the next order of the State Customs Committee of Russia at the end of each year, and, if necessary, by issuing separate orders during the year.

In the context of the entire customs service, a hierarchical system for collecting and transmitting information has been adopted, including the following levels:

I. customs posts;

II. customs;

III. regional customs departments;

IV. State Customs Committee of Russia and GNIVTs.

The collection and processing of information contained in the CCD, coming directly from customs or through regional information collection points, is entrusted to the Main Scientific and Information Computing Center (GNIVTS) of the State Customs Committee of Russia. This allows:

Ensure the efficiency of information processing;

Implement the possibility of generating the initial data of customs statistics of foreign trade.

13 RTUs have been identified as regional GTE collection points throughout the country. GTEs are transferred to the regional departments of the GNIVTS at the above RTU from about 150 customs offices, which, in turn, receive information from customs posts (more than 500).

By the end of 1993 the receipt for processing at the GNIVTs GTD on paper has practically ceased.

Improving the quality of information on customs statistics of foreign trade is determined by two main factors, namely: the reliability of information and the timeliness of receipt of customs declarations by the State Research and Development Center of the State Customs Committee of Russia.

The increase in reliability is largely ensured by special software tools that allow to control the correctness in the system of the State Customs Committee of Russia and implemented in the customs authorities of all levels. In general, to ensure the formation, maintenance and publication of foreign trade customs statistics, it was necessary to create about 20 computer programs for all participants in the technological process.

The issue of quarterly bulletins and annual collections "Customs Statistics of Foreign Trade of the Russian Federation" is based on the generalization of computer-processed CCD data. These data contain information on foreign trade operations that took place in the relevant reporting periods and are reflected in at least 96-98% of the total number of customs declarations, for which participants in foreign economic relations presented goods at customs that were transported across the customs border of the Russian Federation during import or export. Such a level of timeliness in processing the declaration array and including it in the officially published data of the customs statistics of the country's foreign trade is in line with international practice, and subsequent amendments fit into the context international practice release of official statistical publications on foreign trade.

customs statistics value trading

2. SOURCES FOR CUSTOMS STATISTICS

The GTD is the main source of foreign trade statistics and customs payments statistics. It allows you to get operational information on goods transported across the customs border of the Russian Federation.

Customs declaration - one of the ways state regulation foreign economic activity. Declaration is made by participants of foreign economic relations, and customs institutions carry out state control both for the process of declaring, and for the reliability of the type of goods declared in the customs declaration. The information contained in the GTD is the official source data for maintaining foreign trade turnover statistics.

The basis of statistics of foreign trade turnover is the product, the code of which is determined in accordance with the FEACN of the CIS.

In gr. 31 of the customs declaration, the name of the product and, if necessary, additional characteristics of the product, including the assortment, dimensions, models, completeness and other data necessary for the unambiguous classification of the product in accordance with the principles of the TN VED of the CIS, are indicated.

In gr. 33 of the customs declaration, a 9-digit code of goods according to the FEACN of the CIS is indicated.

Quantitative accounting of goods, the net weight of which is indicated in the form of the main unit of measurement (in kg), is reflected in gr. 38, this weight is rounded to integer values ​​according to the rounding rules.

When using other units of measurement, which take into account specific goods, in gr. 31 of the GTD, the short name of this additional unit is indicated and, in accordance with the classifier of units of measurement, the corresponding code "kg" is affixed in gr. 38, and the code of the additional unit in gr. 41.

The valuation of exports and imports is reflected in gr. 46 GTD. Since September 1992, Russia's foreign trade turnover has been valued in US dollars. The cost of the goods is converted into US dollars on the basis of the invoice value indicated in column 42, calculated in FOB prices or the French-border of the Russian Federation - when exporting the goods, and CIF prices or the French-border of the importing country - when importing the goods.

Conversion into US dollars is made on the day the declaration is accepted for customs clearance. At the same time, the value of the invoice value in the currency of the contract (column 42 of the customs declaration) is multiplied by the ruble exchange rate (column 23 of the customs declaration) in relation to the currency of the contract on the day the customs declaration is accepted for clearance and divided by the ruble against the US dollar: gr. 17 of the export customs declaration, which indicates the short name of the country of destination (consumption) of the goods, and in gr. 17a, which indicates the numeric code of the country of destination in accordance with the Classifier of the countries of the world.

The name of the country from which the goods were sent to the Russian Federation is indicated in gr. 15 imported gas turbine engines; in gr. 15a of this declaration is the numerical code of this country in accordance with the Classifier of the countries of the world.

BIBLIOGRAPHY

1. V.G. Draganov "Fundamentals of Customs" 1998

2. Customs statistics RIO RTA 1997

3. B.T. Ryabushkin "Reflection of data of customs statistics of foreign trade" RIO RTA 1997

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Main goals:

History of development of customs statistics in Russia

The study of statistical accounting of foreign trade and customs activities allows us to distinguish 8 stages in the development of customs statistics.

The first stage is the second half of the 16th century to the beginning of the 19th century. On the borders Russian state customs posts are established and duties are imposed on imported and exported goods. Accounting for the import and export of goods and the payment of duties began to be carried out.

The second stage - the beginning of the 19th century until 1930. During this period, the publication of statistical data on foreign trade began. The first report was published in 1802 and was called "State trade in its various forms." Since then, the publication of statistical data has become annual. All customs statistics were maintained by customs agencies. They filled out special statistical sheets and sent them to the Main customs administration, where the data were summarized according to certain generalizing indicators.

The third stage - from 1930 to the beginning of the Great Patriotic War. In 1930, a department of statistics was organized at the Main Customs Directorate and all statistical reporting customs authorities going to them. Publications on foreign trade become monthly. During this period, in parallel with the Main Customs Administration, the collection of data on foreign trade is carried out by the People's Commissariat for Foreign Trade. The Commissariat collected data from economic organizations involved in foreign trade and from trade missions in foreign countries.

The fourth stage is the years of the Great Patriotic War. During this period, all accounting of foreign trade is carried out by the accounting and economic department of the People's Commissariat for Foreign Trade. During the war years, the customs administration did not conduct accounting.

The fifth stage is the years after the Great Patriotic War until 1959. During this period, the pre-war situation is restored. Statistical records of foreign trade are maintained by the Main Customs Administration and the People's Commissariat for Foreign Trade.

The sixth stage - from 1959 to 1988. During this period, in accordance with the order of the Ministry of Trade, all foreign trade statistics were assigned to the planning and economic department of the Ministry of Trade (the former People's Commissariat for Foreign Trade). During this period, the Main Customs Administration kept records of transit, smuggling, passengers and luggage, Vehicle and international mail and postal parcels. On the basis of this information, the customs administration prepared statistical surveys on exports and imports of goods.

The seventh stage - from 1988 to 1995. In 1988, by a decree of the Council of Ministers of the USSR, a decision was made to revive customs statistics. The main reasons for this decision were the liberalization of the economy and the signing by the USSR of a number of intergovernmental and international agreements. At this stage, the collection of data on foreign trade is carried out by:

State Customs Committee (SCC) according to the declaration file;

State Committee according to statistics (Goskomstat) according to the reporting forms of enterprises engaged in foreign economic activity;

Ministry of Foreign Economic Relations according to trade missions and other sources.

The eighth stage - from 1995 to the present. In 1995, an agreement on information cooperation was signed between the State Customs Committee of the Russian Federation and the State Statistics Committee of the Russian Federation (Decree of the State Customs Committee of the Russian Federation No. 01-12/1522). In accordance with this agreement, the function of collecting information on the country's foreign trade was transferred to the State Customs Committee. The State Statistics Committee of the Russian Federation reserved the collection of data on some commodity groups.

Eighth stage - In September 2004, the State Customs Committee was renamed the Federal Customs Service

On May 11, 2006, by decree of the President of the Russian Federation, the Federal Customs Service was placed under the control of the Government of the Russian Federation. Previously FCS of Russia was subordinated to the Ministry of Economic Development and Trade.

The FCS maintains customs statistics on foreign trade and special customs statistics.

The ninth stage is the creation of the Customs Union. On July 1, 2010, the Customs Code began to be applied on the territory of the Customs Union. Maintenance of customs statistics of mutual trade.

Structure of customs statistics

Customs statistics includes two major sections:

Customs statistics of foreign trade,

Special customs statistics. Customs statistics of foreign trade studies:

a) export statistics of goods in kind and value terms;

b) statistics of imports of goods in physical and value terms;

c) geographical distribution of exports and imports;

d) statistics of the country's foreign trade turnover;

e) statistics of the country's trade balance.
Special customs statistics studies:

a) declaration statistics;

b) statistics of customs payments;

c) customs value control statistics;

d) currency control statistics;

e) statistics of customs offenses;

f) statistics of international traffic;

g) statistics of international postal items;

h) international passenger traffic statistics, etc.

Of all the sections of customs statistics, the most developed is the first section - foreign trade statistics.

Year/Eq/IM/WTO/Balance

2) The second grouping represents the results of the foreign trade of the Russian Federation by groups of countries. In this grouping, the name of groups of countries is used as a grouping feature. The difference of this grouping is that in it the selected elements of the grouping intersect with each other, because different countries of the world may belong to different groups of countries. In this regard, the final data for this grouping do not reflect the results of the country's foreign trade and are not given.

Country group/EC/IM

This grouping gives a clear idea of ​​which groups of countries Russia mainly conducts trade relations with.

3) One of the most important in foreign trade statistics is grouping by commodity groups. The grouping attribute in this case is the "name of the product group".

Such a grouping makes it possible to visualize the distribution of exports, imports and foreign trade turnover by enlarged commodity groups, to identify those that prevail in the country's exports or imports. On the basis of this grouping, it is possible to build and study the structure of the foreign trade of the Russian Federation.

Product Groups/EC/Im

All the groupings discussed above are typological, that is, they allow us to study various economic types of units as part of the total population. As a rule, these groupings are built according to attributive features.

In addition to typological, variational (structural) groupings are often used in foreign trade statistics. Unlike typological variation groupings can be built only on quantitative grounds. In addition to studying the structure of the population according to the grouping feature, variational groupings allow you to study the feature that is the basis of the grouping. An example of a variational (structural) grouping is:

Requirements for the information base for studying the dynamics of foreign trade.

When studying dynamics, certain requirements are imposed on the initial information.

The first requirement is the comparability of time series levels. Comparability of levels is achieved as a result of the same approach to observation at different stages of the formation of the time series: the category under study must be defined in the same way, taken into account or calculated using the same methodology, expressed in the same units of measurement, covering the same territory.

The second requirement is the completeness of the levels that make up the time series. They should not have gaps.

The third requirement is the equality of the intervals (segments) of time for which the levels of the series are given.

The fourth requirement is that the number of observations (levels) must be large enough. It is believed that it is possible to predict using a time series containing at least 6 levels. The longer the period covered by the time series, the more accurately it is possible to identify the development trend of the process and obtain its forecast.

The subject and objectives of customs statistics

The subject of the study of the Customs Union are the processes of implementation by the customs authorities of accounting, control, fiscal, law enforcement and analytical functions carried out when moving goods, vehicles and individuals across the border of the country.

The purpose of the TC is economic evaluation and forecasts of the conditions and results of all customs activities.

All R. In the 90s of the last century, an order was issued by the State Customs Committee, where TS was declared a science.

At the present stage, in reality, TS should be understood as:

1) A specific type of accounting for there information

2) The customs information itself, i.e. data

3) some areas of analysis of this information

Analytical functions are also included in the tasks of the TS, they are defined in the following documents:

Unified methodology for maintaining the CU of foreign trade and statistics of mutual trade of the CU member states (Order No. 525, Commission of the CU on January 28, 2011)

Methodology for analyzing and evaluating the activities of the local authorities of the Russian Federation

Main goals:

1. Ensuring a complete objective accounting of information on the country's foreign trade and special customs information.

2. Studying the volumes, structure and dynamics of foreign trade flows.

3. Analysis of the completeness of receipt of customs payments to the federal budget.

4. The study of the dynamics and structure of the commission of offenses in trade and non-trade turnover.

5. Evaluation of the results of carrying out measures to suppress and disclose offenses.

6. Analysis of the results of the implementation of currency control and customs value control.

7. Study of the domestic market conditions.

8. Provision of customs information to the government of the Russian Federation, federal government bodies, etc. org.

9. Transfer of information on Russia's foreign trade to the CU Commission.