F o o t p r i n t test and R egression model : how to deal with data?

1. Footprint test and Regression model:how to deal with data? Co-finanziato Dal Programma LLP dell’Unione Europea L’autore è il solo responsabile di questa comunicazione. L’Unione europea declina ogni responsabilità sull’uso che potrà essere fatto delle informazioni in essa contenute.

2. Footprint

3. The ecological footprint is a measure of human demand on the Earth's ecosystems. It is a standardized measure of demand for natural capital that may be contrasted with the planet's ecological capacity to regenerate

4. Ecological footprint F is calculated by this equation:

5. Ei = ecological footprint coming from the wasteCi = product i-thqi = (hectare/kg) reciprocal of the average productivity per hectare produced the i-th.

6. The ecological footprint per capita f is calculated by dividing for the population N residing in the region considered:

7. Studies carried out on a global scale and in some countries shows that the global footprint is larger than the capacity bioproductive world. According to Mathis Wackernagel, in 1961 humanity used 70% of the overall capacity of the biosphere, but in 1999 had increased to 120%.

8. Ecological footprint in the world

9. Evidence through observation • To find out whether and how our actions and lifestyle affect our environment • We selected an appropriate • Ecological footprint calculation quiz - as amethods for collecting relevant information related to our environment and lifestyle

10. We adopted strategies for planning, organizing and most efficientlymanage a Footprint quiz • Pointing out criteria for making right questions in order to ensure accuracy, significance, and fairness about collected data • Administering the “ Footprint Test” to a quantitative significant sample of people of Pisa area

11. Footprint TEST I travel mostly by 1- car ( average user ) 2- car ( heavy user ) 3- car (light user ) 4- bus/train 5- walking/cycling 6- motorbike

12. usually holiday 1- close to home 2- a short flight away 3- a long flight away

13. I live in a 1 – large house 2 – medium-sized house 3 – small house 4 – flat/apartment 5 – zero emission development

14. that I share with 1 – no other person 2 – one other person 3 – two other person 4 – three other person 5 – four other person 6 – five other person 7 – six other person 8 – more than six others

15. My heating/cooling bills are relatively 1 – normal 2 – high 3 – low

16. I buy my electricity from 1 – non-renewable sources 2 – renewable sources

17. I tend 1 – not to conserve energy 2 – to conserve energy

18. I am 1 –a regular meat-eater 2 – an occasional meat-eater 3 – a heavy meat-eater 4 – a vegetarian 5 – a vegan

19. usually eat 1 – a mix of fresh and convenience foods 2 – mostly fresh, locally grown produce 3 – mostly convenience foods

20. I produce 1 – an average 2 – a below average 3 – an above average 4 – half the average amount of domestic waste

21. most of which is 1 – not recycled 2 – recycled

22. Aim of our research: to study the impact of specific characteristics of the respondents about their ecological footprint.

23. How we made it: For data processing we used methods provided by a branch of statistics known as econometrics.

24. Econometrics may be defined as a branch of statistics that deals with the analysis of economic phenomena, or alternatively, can be considered a sector of the economy devoted to the empirical verification of theoretical models formulated in scope.

25. In our survey, • we applied several statistic methods and techniques to collect data • we focused on the Regression Model for managing and analyzing quantitative data

26. The Linear Regression Model A Math /Stats Model • Often Describe Relationship between Variables • Types • Deterministic Models (no randomness) • Probabilistic Models (with randomness) EPI 809/Spring 2008

27. Deterministic Models • Hypothesize Exact Relationships • Suitable When Prediction Error is Negligible EPI 809/Spring 2008

28. Probabilistic Models • Hypothesize 2 Components • Deterministic • Random Error EPI 809/Spring 2008

29. Types of Probabilistic Models EPI 809/Spring 2008

30. Regression Models • Relationship between one dependentvariable and explanatory variable(s) • Use equation to set up relationship • Numerical Dependent (Response) Variable • 1 or More Numerical or Categorical Independent (Explanatory) Variables • Used Mainly for Prediction & Estimation EPI 809/Spring 2008

31. Regression Modeling Steps • 1. Hypothesize Deterministic Component • Estimate Unknown Parameters • 2. Evaluate the fitted Model • 3. Use Model for Prediction & Estimation EPI 809/Spring 2008

32. Specifying the deterministic component • 1. Define the dependent variable and independent variable • 2. Hypothesize Nature of Relationship • Expected Effects (i.e., Coefficients’ Signs) EPI 809/Spring 2008

33. Linear Regression Model Population Y-Intercept Population Slope Random Error Y     X   i 0 1 i i Dependent (Response) Variable(e.g., CD+ c.) Independent (Explanatory) Variable (e.g., Years s. serocon.) 1. Relationship Between Variables Is a Linear Function

34. Population Linear Regression Model Observedvalue i= Random error Observed value EPI 809/Spring 2008

35. Population & Sample Regression Models Random Sample Population Unknown Relationship        EPI 809/Spring 2008

36. Model Specification Is Based on Theory 1. Theory of Field (e.g., Epidemiology) 2. Mathematical Theory 3. Previous Research 4. ‘Common Sense’ EPI 809/Spring 2008

37. Sample Linear Regression Model i= Random error ^ Unsampled observation Observed value EPI 809/Spring 2008

38. Our data:the application of this methodology of statistical analysis requires the identification of a dependent variable and multiple independent variables. The independent variables will be the ones through which will be explained the variance of the dependent variable. These are the variables identified for this project:

39. • dependent variable: Through a test on footprint, administered to a large and significant sample , we will obtain a value that expresses the footprint of a subject;

40. • independent variables: - age - usually - Number of people in household - distance home-school/work (categorical variable: 1 = 5km, 10km = 2, 3 = 15km)

41. sensitization (categorical variable: 1 = "I never discussed the issue of energy and pollution in school or personally," 2 = "I did a course of primary awareness on energy and pollution", 3 = "I have dealt with in depth and more than once the subject of energy and pollution") where for a categorical variable we mean a variable measured at different levels (categories).

42. Estimation model: starting from the estimation equation: Y = a + bX+ c Z + e (where e is the error of our estimate a,b,cconstant, and together account for the variation in Y not explained by our dependent variables) we obtain the following equation that represents our model to estimate

43. Footprinta + b age + c sort + d sensation +…. + e

44. From this equation we will get different values ​​for the coefficients b, c, d ... that will allow us to see how the footprint vary with age, gender, and so on.

45. Specifically:- the absolute value of the coefficient indicates the strength of the effect of the independent on the dependent variable.

46. What next?Next year the above model will be estimated using a specific statistics program : Stata.

47. The following slides report our collected data from Footprint quizzes