Using statistical methods of analysis in assessment economic activity enterprise coursework
INTRODUCTION 3
CHAPTER I. STATISTICAL ANALYSIS OF THE EFFICIENCY OF USE OF THE BASIC RESOURCES OF THE ENTERPRISE
1.1. CALCULATION AND EVALUATION OF FIXED ASSETS FLOW INDICATORS 6
1.2. CALCULATION AND EVALUATION OF INDICATORS FOR THE USE OF FIXED CAPITAL 9
1.3. CALCULATION AND EVALUATION OF THE EFFICIENCY OF THE ENTERPRISE USING INDEX INDICATORS 12
CHAPTER II. ANALYSIS OF PROFITABILITY INDICATORS 14
CHAPTER III. STATISTICAL ANALYSIS OF THE FINANCIAL ACTIVITY OF THE ENTERPRISE 22
CONCLUSION 30
LIST OF REFERENCES USED 31
LIST OF REFERENCES USED
1. Ayvazyan S.A., Mkhitaryan V.S. Applied Statistics and fundamentals of econometrics: Textbook for universities. – M.: UNITY, 1998.
2. Bakanov M.I., Sheremet A.D. Theory economic analysis: Textbook. – M.: Finance and Statistics, 1997.
3. Civil Code Russian Federation Part 1. – M.: Prospekt, 1997.
4. Gusarov V.M. Theory of statistics: Textbook for universities. – M.: Audit, UNITY, 1998.
5. Eliseeva I.I. General theory of statistics: Textbook for universities. – M.: Finance and Statistics, 1999.
6. Efimova M.R. Workshop on general theory Statistics: Textbook. – M.: Finance and Statistics, 1999.
7. Kotler F. Fundamentals of Marketing: Transl. from English – M.: Progress, 1990.
8. Maksimov O. B. Analysis of the financial state of the enterprise. Basic provisions of the methodology. – St. Petersburg: IKF “ALT”, 1994.
9. Mkhitaryan V.S. Statistics. – M.: Economist, 2005.
10. Decree of the Government of the Russian Federation of May 20, 1994 No. 498 “On some measures to implement legislation on the insolvency (bankruptcy) of enterprises.”
11. Russian statistical yearbook: Stat. Sat./Goskomstat of Russia. – M.:, 2001.
12. Ryabushkin B.T. Fundamentals of financial statistics: Textbook. – M.: Finstatinform, 1997.
13. Financial statistics: Textbook / Ed. Prof. V.N. Salina. – M.: Finance and Statistics, 2000.
15. Financial Economics/ Ed. Yu.M. Osipova, V.G. Belolipetsky, E.S. Zotova. – M.: Yurist, 2001.
Program of the discipline “Methods of applied statistics and econometrics” (adaptation course) for direction 080100. 68 “Economics” of the master’s program
Discipline programIn his research work Economists have to analyze a variety of data. Properly used statistical methods of data analysis significantly expand the possibilities of scientific research.
The program of the discipline "Econometrics-2" for the direction
Discipline programSample program name of discipline Econometrics Recommended for training direction 080200 “Management”
Sample programThe purpose of the discipline “Econometrics” is to train students in the methodology and technique of constructing and applying econometric models to analyze the state and assess the prospects for the development of economic and social systems in an interconnected environment
Program of the discipline “Computer methods for analyzing sociological data (introduction to mathematical statistics and data analysis)” for direction 040100. 68 “Sociology” for master’s preparation Government of the Russian Federation
Discipline program
Ayvazyan S.A., Mkhitaryan V.S., Zekhin V.A. Workshop on multidimensional statistical methods(textbook)/ Moscow State University of Economics, Statistics and Informatics. M., 2012 (2nd edition) – 77 pages.
The textbook was prepared to support practical classes in the discipline "Multivariate Statistical Methods", taught at MESI and at the Faculty of Economics of Moscow State University. M.V. Lomonosov. The manual is focused on the textbook by S.A. Ayvazyan, V.S. Mkhitaryan "Applied statistics and fundamentals of econometrics", M., UNITI, 1998; (2nd edition) 2001.
The textbook contains a fairly large set of tasks and exercises for each topic, as well as a test.
Students will benefit from the final test when preparing for exams and tests.
The textbook is intended for students of economic specialties and may be of interest to teachers, graduate students and specialists involved in the application of statistical methods in socio-economic research.
The work uses materials from scientific research of the Department of Mathematical Statistics and Econometrics of MESI.
Il. - 7, table. - 47, bibliography - 11 titles.
Reviewers: Ph.D., prof. Kalinina V.N.,
Ph.D., Associate Professor Gorchakova N.F.
Ayvazyan S.A., 2012
Mkhitaryan V.S., 2012
Zekhin V.A., 2012
Moscow State University of Economics, Statistics and Informatics 2012
S.A. Ayvazyan
V.S.Mkhitaryan
V.A.Zekhin
PRACTICUM
by multivariate statistical methods
Moscow, 2012
Preface 4
Chapter 1. Correlation analysis 5
1.1. Correlation analysis of performance indicators
sand pits 5
1.2. Problems and exercises 8
1.3. Test 11
Chapter 2. Regression analysis (classical model) 13
2.1. Regression model of labor productivity 13
2.2. Regression model of grain yield
crops 16
2.3.Tasks and exercises 20
2.4. Test 23
Chapter 3. Component Analysis 25
3.1. Analysis of the activities of construction organizations 25
3.2. Component activity analysis
agricultural areas 28
3.3. Problems and exercises 31
3.4. Test 34
Chapter 4. Cluster and discriminant analysis 36
4.1. Classification of families according to the analyzed structure
expenses 36
4.2. Classification of countries by standard of living 43
4.3. Classification of household motor-compressors
refrigerators by quality level 45
4.4. Discriminant analysis of the activities of JSC 48
4.5. Problems and exercises 49
4.6. Test 56
Chapter 5. Final test 58
Literature 61
Applications 62
P 1. Options for tasks for independent computer
research. Initial statistics 63
P 2. Mathematical and statistical tables 65
PREFACE
The textbook has been prepared to support practical classes in the discipline "Multivariate statistical methods".
The authors proceeded from the fact that in this discipline, half of the practical classes are conducted in regular classrooms, and the other half in computer classes. Problems and exercises solved in classrooms are aimed at studying the statistical methods and algorithms themselves. The purpose of computer classes is to conduct independent socio-economic research using statistical software packages for PCs. Research includes the formulation of the problem, carrying out calculations on a PC, meaningful interpretation of the results obtained and conclusions.
In this regard, in textbook The algorithms of the considered methods of applied statistics are analyzed in detail using economic examples, some of which are solved using a microcalculator (targeted at the audience), and some using a PC. The authors sought to show the possibilities of obtaining multivariate solutions and the technique of choosing the final model and result, based not only on statistical techniques, but largely on a priori (preliminary) information that a researcher in economics usually has.
The tasks proposed for independent research are quite transparent in economic terms, which allows the student to act as a specialist who, when conducting computer statistical research, always has a general idea of what he can expect from a computer when implementing a particular method.
The authors express their deep gratitude to the head of the Laboratory of Applied Statistics and Econometric Modeling at the Department of Mathematical Statistics and Econometrics N.Ya. Bambaeva. for assistance in carrying out computer calculations and preparing the original layout of the manuscript.
Year of manufacture: 1998
Genre: Econometrics, statistics
Publisher: Unity
Format: DjVu
Quality: Scanned pages
Number of pages: 1000
Description: The textbook covers all issues of probabilistic-statistical modeling and data analysis in economics - from elementary courses probability theory and mathematical statistics to advanced methods of multivariate statistics, time series analysis and econometrics itself. The combination and interconnected presentation of all these basic econometric disciplines in one textbook make it unique in its own way not only in the domestic, but also in the world. educational literature of this profile, make it possible to structure the educational process in such a way as to achieve an integral systemic perception of the entire block of these disciplines.
The proposed textbook reflects an understanding of the content of the mathematical and statistical tools of econometrics, which is somewhat different from the generally accepted one. In our opinion, modern achievements mathematical and statistical science (especially in multivariate statistical analysis), on the one hand, and a significant expansion of the circle economic tasks, requiring econometric methods of solution, on the other hand, necessitated a broader view of the mathematical and statistical tools of econometrics and, in particular, the inclusion in it, in addition to the traditional sections on regression models, time series analysis and systems of simultaneous equations, such sections of multivariate statistical analysis , How Markov chains, classification of multidimensional observations and reduction of the dimension of the analyzed factor space. Speaking about a wide range of economic problems that require solutions that go beyond the traditional framework of econometric methods, we meant, in particular, the statistical study of the dynamics structural changes(in demography, in the stratification structure of society, etc.), identifying hidden (latent) factors that determine the course of a particular socio-economic process, constructing integral indicators of the quality or efficiency of the functioning of the socio-economic system, typology of socio-economic objects and etc.
Secondly, during many years of experience in teaching various disciplines of probabilistic and statistical profile in economic universities and on economic faculties universities, we have come to the conviction that it is necessary to structure the educational process in such a way as to achieve a holistic, systematic perception of the entire block of these disciplines. It's about, in particular, about courses on elementary methods statistical processing data science (or descriptive statistics), probability theory, mathematical statistics, multivariate statistical analysis (or multivariate statistical methods), time series analysis, and finally econometrics. Obviously, the realization of this goal should be facilitated by a textbook that simultaneously contains an interconnected presentation of all these courses.
In other words, we tried to write the kind of book that we would want to have at hand during our teaching activities. Unfortunately, among the many excellent foreign books on econometrics, there was no book that has the two above features.
Note that despite the presence of a number of illustrative examples and exercises, the proposed textbook does not solve the problem of the econometrics problem book. Therefore, to carry out a full educational process it should be supplemented by a set of econometric problems and exercises (for example, in the spirit of the book).
The textbook material and responsibility are distributed among the authors as follows. V. S. Mkhitaryan took part in writing chapters 6, 7, 8 and 13, and also suggested most of problems included in the chapters of the textbook. The rest of the material (including the chapters mentioned) was written by S.A. Ayvazyan. He also carried out general scientific editing of the textbook.
We continue to engage in quality management and related areas.
This time it is offered reference book on applied statistics in 3 volumes.
The author of this magnificent publication, Professor Sergei Artemyevich Ayvazyan, together with A.I. Orlov, for the first time in the USSR introduced the concept of “applied statistics,” which caused a real storm of indignation among party bosses and the top of the State Statistics Committee: statistics has always been a political matter. During perestroika, the controversy spilled onto the pages of specialty journals.
Volume 1. Fundamentals of modeling and primary data processing
The book is devoted to methods of preliminary statistical data analysis and model building real phenomenon characterized by these data. Information on probability theory and mathematical statistics is provided, and issues of software implementation are covered.
the methods presented.
Volume 2: Dependency Research
The book discusses the methods of correlation, regression and analysis of variance. Their algorithms and an overview of the software are given.
Volume 3. Classification and dimensionality reduction
The problems of object classification and dimension reduction are considered. Big
attention is paid to exploratory statistical analysis.
NATA: Books are premium, no backup needed
Topic tags:
Statistics
Publisher: Finance and Statistics
Year of publication: 1983
Pages: 472
Language: Russian
Quality: good
Econometric methods. Ayvazyan S.A.
M.: 2010 - 512 p.
The content of the textbook complies with current educational standards and curriculum higher educational institutions of economic profile in the discipline “Econometrics”. The peculiarity of this publication is that in its description traditional methods solutions to econometric problems are organically integrated for the first time (where this allows for increased accuracy and depth of analysis) modern methods multivariate statistical analysis, which were not previously included in the econometrics tools (in particular, discriminant and cluster analyses, principal component analysis, etc.). The methods and models of regression analysis, binary and multiple choice, time series analysis may form the content of one or two core semester courses in econometrics as part of an undergraduate curriculum. For undergraduates, graduate students, teachers, as well as specialists in applied economics and econometrics.
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Size: 5.8 MB
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TABLE OF CONTENTS
Preface 9
Chapter 1. Introduction 13
1.1. Econometrics: Evolution of Definition and Reality 13
1.2. Impoverishment of the mathematical apparatus of econometrics 16
1.3. The place of econometrics among mathematical, statistical and economic disciplines 19
1.4. Econometric model and problems of econometric modeling 22
Conclusions 30
Chapter 2. Introduction to regression analysis 33
2.1. General formulation of the problem statistical research dependencies 33
2.2. What is the ultimate applied goal of statistical dependency research? 42
2.3. Some typical tasks econometric modeling practices 45
2.4. Main types of dependencies between quantitative variables 50
2.5. About the choice general view regression functions 55
Conclusions 65
Chapter 3. Introduction to Correlation Analysis 67
3.1. Purpose and place of correlation analysis in statistical research 67
3.2. Correlation analysis of quantitative characteristics 69
3.3. Correlation analysis of rank (ordinal) variables: rank correlation 96
3.4. Correlation analysis of categorized variables: contingency tables 111
Conclusions 117
Chapter 4. Classical linear model multiple regression(KLMMR) 121
4.1. Description of KLMMR. Basic assumptions of the model 121
4.2. Assessment unknown parameters KLMMR: method least squares and maximum likelihood method 126
4.3. Analysis of the variation of the resulting indicator y and the sample determination coefficient Shx 140
4.4. Multicollinearity and selection of the most significant explanatory variables in KLMMR 145
4.5. KLMMR with linear restrictions on parameters 162
4.6. General approach to statistical testing of hypotheses about the presence of linear relationships between CLMMR parameters 167
Conclusions 176
Chapter 5. Generalized Linear Multiple Regression Model 179
5.1. Description of the generalized linear multiple regression model (GLMMR) 179
5.2. Estimates of GLMMR parameters using the generalized least squares method (GLS-estimates) 183
5.3. GLMMR with heteroscedastic residuals 188
5.4. GLMMR with autocorrelated residuals 198
5.5. Practical implementation of OMC ( general approach) 207
Conclusions 210
Chapter 6. Forecasting Based on Linear Multiple Regression Models 213
6.1. Analysis of the accuracy of the estimated LMMR (theoretical basis for solving forecast problems) 214
6.2. Best point forecast y(X) and f(X) = E(y|X) based on OLMMR 216
6.3. Interval forecast y(X) and f(X) = E(y|X), based on OLMMR 220
6.4. Analysis of the accuracy of the regression model and forecasting in a realistic situation 226
Conclusions 230
Chapter 7. Linear regression models with stochastic explanatory variables 233
7.1. The random residuals e are independent of the predictors X and the estimated regression coefficients in 235
7.2. General case: stochastic predictors X are correlated with regression residuals e. Instrumental variable method 238
7.3. Random errors in measuring the values of explanatory variables 243
Conclusions 249
Chapter 8. Linear regression models with variable structure 251
8.1. The problem of heterogeneous (in the regression sense) data 251
8.2. Introducing “dummies” (dummy variables) into a linear regression model 254
8.3. Checking the regression homogeneity of two groups of observations (G. Chow test) 263
8.4. Construction of KLMMR from heterogeneous data in conditions where the values of associated variables are unknown 265
Conclusions 269
Chapter 9. Models with Discrete and Discrete-Continuous Dependent Variables 271
9.1. Binary choice models 273
9.2. Multiple Choice Models 282
9.3. Relationship between binary and multiple choice models and discriminant analysis 285
9.4. Model with a discrete-continuous dependent variable (Tobit model) 287
Conclusions 291
Chapter 10. Univariate Time Series Analysis (Models and Forecasting) 293
10.1. Time series: definitions, examples, formulation of main tasks 295
10.2. Stationary time series and their main characteristics 302
10.3. Non-random component of a time series and methods for smoothing it 314
10.4. Models of stationary time series and their identification 336
10.5. Models of non-stationary time series and their identification 378
10.6. Forecasting economic indicators, based on the use of time series models 395
Conclusions 409
Appendix 1. Tables of mathematical statistics 413
Appendix 2. Required information from matrix algebra.. 433
Appendix 3. Multidimensional statistical analysis 455
Literature 493
Alphabetical subject index 497
