1. Application of regression analysis Economic structure and air pollution in a transition economy: The case of the Czech republic Gabriela Jandová Michaela Krčílková

2. Structure of presentation I.Definition of regression model II.Compilation of regression model III.Analysis of the results

3. represents reality by using the system of equations. explains relationship between variables. enables quantification of these relationships. I. Regression model

4. II. Compilation of regression model Conceptual model Hypotheses Equations Data collection Calculation Verification Errors of the model

5. is a graphical scheme. serves for specification of sought- after mutual relations. is a tool for defining of investigation matter. should clarify our minds and help during determination of researching methods. Compilation of regression model Conceptual model

6. Our conceptual model Agriculture IndustryServices Individual people Economic system Air Soil Water Organisms Ecological system Political system INPUTS: production resources OUTPUTS: products, waste

7. Hypotheses are formulated expectations and suppositions. Theirs confirmation or rejection is the goal of the regression analysis. Compilation of regression model

8. Our hypotheses There is a relationship between economic structure and air pollution. Industry is the biggest polluter of air. There is a significant improvement of air quality during the 90th. Decrease of functioning of economy is not a cause of this fact.

9. Equations should involve mainly essential relations between examined phenomenons, which have permanent character. consist from explained and explanatory variables. Partial correlation coefficients measure the effect of given explanatory variable on explained variable. Compilation of regression model 1.

10. Requests on the variables: Measurability Accessibility Conclusiveness Testify ability Standardized methods of attaining Compilation of regression model Equations 2. Comparability Time series Inter-independence Uniqueness Convenience

11. Our equations Where:Y n = dependent variables (NO,CO, Dust) X 1 = Gross value added in agriculture X 2 = Gross value added in industry X 3 = Gross value added in services  = Random error term  1  2  3 = partial correlation coefficients Y n =  n1 X 1 +  n2 X 2 +  n3 X 3 +  n

12. Data collection Statistical office´s reports Library Internet Journal Interview Compilation of regression model Sources:

13. Our data Underlying data necessary for compilation of basic matrixes have been acquired from regional branches of Czech statistical office and from Czech Hydrometeorological office. 1. Form of indicators of one region.

14. To acquire underlying data was necessary to contact all 14 regions. Our data 2.

15. Underlying data were adjusted and used for compilation of basic mattrixes. Our data 3. Example of basic mattrixes for NO x

16. Calculation Ordinary least square method (OLS) Two stage least square method Instrumental variables Maximum likelyhood method General least square Non-linear least square Compilation of regression model

17. Our calculation Method of callculation: OLS Results:

18. Verification statistical verification  R-squared R2 should be equal at least 0,66  t-statistic Every attained t-value should be higher than critical t-values mentioned in statistical tables  F-statistic Every attained F-value should be higher than critical F-values mentioned in statistical tables  confidence interval Estimated intervals have not include zero. logical verification Compilation of regression model

19. Verification of our model Statistical verification 1.

20. Verification of our model Statistical verification 2. Confidence interval:

21. Verification of our model Logical verification Coefficients of industry have a positive slope. Coefficients for services and agriculture have negative slope.

22. Errors of the model Indicators of the errors  Low value of R-squared  Coefficients are not significant  Zero lies in the confidence intervals Reasons of the errors:  Bad choice of variables  Omission of important factors  Equations are not identificated  Errors in data collection  Low number of executed observation Compilation of regression model

23. Experiments with our model Calculation with additive constant

24. III. Analysis of the results is an important step for correct interpretation of the model. is crowned and concluded by confirmation or rejection of hypothesis.

25. Analysis of results The significance of coefficients comfirms our first hypothesis. First hypothesis 1. There is a relationship between economic structure and air pollution.

26. The coefficients for industry have the biggest value and positive slope. This fact confirms our second hypothesis. Analysis of results Second hypothesis 2. Industry is the biggest polluter of air.

27. All coefficients decrease during the time, that confirms our third hypothesis. Analysis of results Third hypothesis 3. There is a significant improvement of air quality during the 90th. Decrease of functioning of economy is not a cause of this fact.

28. Conclusion All hypotheses are confirmed. Our recommendation is: to use the model in the conditions of non-transition economy. to use the model in a country with higher number of regions.

29. END