Co-finanziato
Dal Programma LLP dell’Unione Europea
F o o t p r i n t test and
Regression model : how to deal with data?
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.

F o o t p r i n t

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

Ecological footprint F is calculated by this equation:

E i
= ecological footprint coming from the waste q i
C i
= product i-th
= (hectare/kg) reciprocal of the average productivity per hectare produced the i-th.

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

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%.

Ecological footprint in the world

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 a methods for collecting relevant information related to our environment and lifestyle

We adopted strategies for planning, organizing and most efficiently manage 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

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

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

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

• 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

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

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

• I tend 1 – not to conserve energy
2 – to conserve energy

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

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

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

• most of which is 1 – not recycled
2 – recycled

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.

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

A Math /Stats Model
1.
Often Describe Relationship between
Variables
2.
Types
-
Deterministic Models (no randomness)
-
Probabilistic Models (with randomness)
EPI 809/Spring 2008 26

1.
2.
Hypothesize Exact Relationships
Suitable When Prediction Error is
Negligible
EPI 809/Spring 2008 27

1.
Hypothesize 2 Components

Deterministic

Random Error
EPI 809/Spring 2008 28

Probabilistic
Models
Regression
Models
Correlation
Models
Other
Models
EPI 809/Spring 2008 29




Relationship between one dependent variable 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 30

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

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

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

Y
Y i
 
0
 
1
X i
  i
Observed value

i = Random error
E
  
0

1
X i
Observed value
EPI 809/Spring 2008 34

Population
Y i
Unknown
Relationship
 
0
 
1
X i


 i
 


EPI 809/Spring 2008
Random Sample
Y i
  
0
  
1
X i
   i


35

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

Y
Y i
  
0
  
1
X i
   i
 ^
i = Random error
Y
 i
  
0
   X
1
X i
Unsampled observation
Observed value
EPI 809/Spring 2008 37

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:

Through a test on footprint, administered to a large and significant sample , we will obtain a value that expresses the footprint of a subject;

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).

Estimation model: starting from the estimation equation:
Y = a + bX + c Z + e
(where e is the error of our estimate a,b,c constant, 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

a + b age + c sort + d sensation
+…. + e

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.

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

The following slides report our collected data from Footprint quizzes

M
M
M
M
M
M
M
F
F
F
F
F
Sesso Età Dist. Casa scuola
M 14 6
24
51
47
65
56
5
10
15
10
3
16
17
17
16
16
13
16
5
10
5
5
15
1,5
10
2
1
2
2
1
1
1
1
3
2
2
3
Sensibil ità
2
8,7
6,9
6,5
9,3
7,9
6
6,8
CO2 (t) Ettari globali
7,1 4,4
8,8
10,3
8,9
8,2
7,5
4,8
6,2
5,2
4,4
4,4
4,4
4,1
3,1
5,3
3,8
4,4
4,3 pianeti
2,7
2,7
2,3
2,7
2,6
2.7
3
3,8
3,2
2,7
2,5
1,9
3,2

M
F
F
F
M
F
M
F
M
M
M
M
F m
18
42
20
19
19
16
16
9
18
17
18
19
54
16
2
1
2
1
2
1
1
2
2
1
1
2
2
2
10
5
13
12
5
10
20
3
3,6
7
20
5
1,5
15
9,9
6,3
7,1
7,5
6,3
6,3
6,2
9,3
7,6
6,4
5,1
4
8,2 4,6
11,3 5,9
9,5 4,6
10,4 6,1
4,1
3,3
5
5,4
3,7
4,3
4,5
3,3
3,3
2,3
2,7
2,8
2
3,1
2,4
2,8
3,6
2,8
3,7
2,5
2
3,1

M
M
M
F
F
F
F
F
M
F
M
F
F
M
18
1
17
16
17
17
14
18
17
14
14
14
15
15
5
0,3
10
30
20
0,2
2
15
0,1
10 2
0,45 1
10
10
2
2
3
1
1
1
2
2
1
2
2
2
7,8
8,4
6,9
6
6,6
6,9
9,4
7,7
7 4,2
10,4 5,9
7,1
7,4
7,8
9,1
4,8
3,8
4,6
4,5
4
4,3
3,9
3,9
4,7
3,7
3,7
3,5
2,4
2,9
2,3
2,3
2,2
2,5
3,6
2,9
2,3
2,8
2,7
2,5
2,6
2,4

F
F
M
M
M
F
F
F
F
M
F
F
M
M
15
19
44
15
16
16
17
55
15
15
15
17
16
16
2
1
1
2
1
2
2
2
2
2
2
3
1
1
15
10
10
15
5
10
10
5
10
20
15
15
0,5
1
6,5
6,9
8,6
7,1
9,3
6,1
6,2
3,9
5,4
3,8
3,8
10,1 5,2
6,8 3,8
7,9
8,3
4,6
4,7
12,3 5,7
6,1
7
4,4
4,2
3,9
4
4,8
2,8
2,8
3,5
2,7
2,6
2,4
2,3
2,3
2,3
3,2
2,3
2,4
2,4
2,9

f
F
M
M
F
F
M
F
F
M
F
M
M
16
16
18
16
46
17
17
16
17
16
16
15
15
2
1
2
3
1
2
2
2
1
2
1
2
3
10
5
5
25
27
0,8
2
5
5
10
10
10
5
9,5
6,8
5,3
4,2
10,2 5,1
8,2 4,6
7,1
9,1
4,9
4,9
10,4 5
9,2 4,8
10,6 4,3
7,1
7,6
4,4
4,1
6,8
7,4
3,4
4,5
3,1
3
2,6
2,7
2,5
2,1
2,7
3
3
3,2
2,6
3,1
2,8

M
M
M
M
M
F
F
F
M
M
M
M
M
F
15
16
19
17
17
53
54
17
50
17
17
17
17
15
2
2
2
2
2
2
2
1
2
2
3
3
2
2
10
5
15
10
5
10
10
10
0,3
10
5
5
10
5
6,1
7,5
8
6,9
7,9
7,1
6,6
6,5
7
8,9
4,2
5,2
11,8 5,8
6,7 3,7
15,4 6,8
6,8 4,1
4,8
3,9
3,9
3,8
4,5
4,5
4,7
4,4
2,3
2,8
2,8
2,9
2,7
2,6
3,2
3,5
2,2
4,2
2,5
2,9
2,4
2,4

References
Ecological footprint analysis http://www.bestfootforward.com/resources/ecologicalfootprint/ http://www.epa.vic.gov.au/Ecologicalfootprint/calculator s/default.asp
http://footprint.wwf.org.uk/ http://myfootprint.org/en/about_the_quiz/what_it_measu res// http://www.bestfootforward.com/resources/ecologicalfootprint/
Scientific American Paper http://sams.scientificamerican.com/article/humans-notusing-more-than-one-planet/ http://www.statsoft.com/Textbook/Multiple-
Regression
Chap. 11: Simple Linear Regression