Research Question:
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Effect of Carbon Dioxide Levels on Life Expectancy on Countries with Different Income Levels
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To what extent do carbon dioxide emission levels affect the life expectancy in countries with different income levels?
Introduction
“In fact, the whole climate crisis, as they call it, is not only fake news, it’s fake science. There is no climate crisis. There is weather and climate all around the world. And, in fact, carbon dioxide is the main building block of all life.” – Patrick Moore
This controversial statement made by Patrick Albert Moore, a Canadian industry consultant, former activist, and past president of Greenpeace Canada was recently re tweeted by Donald Trump. This retweet by The President of the United States was understandably met with outrage and backlash from the twitter community. Personally, when I came across the statement I was taken aback by Moore’s candidness considering, he was a past president of a non-governmental environment organisation. As a student learning about global warming and its effect on the environment and society I disagree with Moore’s views and feel that this is one of the most ubiquitous and alarming issues of the 21st century. This sparked my interest in further analysing the validity behind the claim.
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I started my process by searching for articles or research studies done on carbon dioxide emission levels and the climate, considering Moore directly addressed carbon dioxide as a “building block of life”. I was aiming to find a foundation on which I could start off and do a more in depth study on. I came across a research paper titled, ‘Pathways of human development and carbon emissions embodied in trade’ written by J Timmons Roberts, Julia K. Steinberger, Glen P. Peters and Giovanni Baiocchi. This provided me great insight on the issue and helped me in selecting and operationalizing my variables.
Luckily, I have opted for Economics HL as a part of my IB Curriculum which made this process much easier for me and equipped me with the necessary tools of analysis and evaluative skills required to study the data.
I decided to examine the relationship between human development and a country’s economic activity to see whether they are dependent on one another, mutually exclusive or independent of one another. I have chosen life expectancy as an indicator for human development and CO2 emission levels as an indicator for economic activity. For the sake of simplicity, I have chosen only two variables and therefore will not be addressing the other variables and indicators that play a role.
The independent variable here is carbon dioxide emission levels and the dependent variable is life expectancy of the sexes combined. The reason I chose CO2 levels as my independent variable was because I wanted to if the emission levels, which is interlinked with the economic growth of a country, has any effect on the human development of countries in varying income brackets.
My null hypothesis is that in low income countries, especially where there are not many technological advancements and there is a high reliability on outdated energy sources, the CO2 emissions will be higher than the average global average level, and the life expectancy will also be lower than the global average due to high pollution and CO2 levels present in the air. For the middle-income countries, I hypothesized that the CO2 levels will be less than those of the low-income countries since these counties mostly consist of developing nations where technological advancements are taking place and the governments of these countries are moving to more green energy sources. In high income countries I hypothesize that the life expectancy levels will be high while the emission levels will be relatively lower than the global average due other factors such as better development and healthcare however, the emission levels will still be high due to policies aimed at economic growth.
This investigation involves finding the carbon emission levels and life expectancy levels of 138 countries in total. These countries have been divided into four sub categories based on the Gross National Income (GNI) levels – high, upper middle, lower middle- and low-income countries. I have used the Pearson’s coefficient correlation to find both the strength and the direction of the association, the chi squared test to test if the variables are independent or dependent on one another and linear regression to form an equation in order to graph it.
My aim of this investigation is – ‘To find a co-relation between carbon dioxide levels and life expectancy levels of countries in varying income brackets.’
Pearson’s coefficient correlation
Understanding the concept
The Pearson product-moment correlation coefficient is a measure of the strength of the linear relationship between two variables.
It is the test statistics that measures the statistical relationship between two continuous variables. Since it is based on the method of covariance, it is a good method to test the association between the variables. It provides information on both the correlation as well as direction of the association.
Application
Interpretation of values
The Pearson correlation coefficient, r, can take a range of values from +1 to -1. A value of 0 indicates that there is no relationship between the two variables. A positive association is indicated by a value greater than 0; this shows that the value of both variables move in the same direction. A negative association is indicated by a value less than 0; this shows that the values of the two variables as the value of one variable move in different directions.
The closer the value of r is to either +1 or -1 the stronger the association between the two variables are.
The table below shows the interpretation of the values of r
Table (a) : Interpretation for the values of r
We have yet not covered this in the IB syllabus, so I had to study and do background research in order to calculate the value of r.
Understanding the formula
x is the first set of data points – here it refers to the CO2 emission levels
x̅refers to mean of the first set of data points
y is the second set of data points – here it refers to the life expectancy values.
ȳ refers to the mean of the second set of data points.
Calculation
In order to calculate the co relation between the two variables I found out the CO2 emission levels as well as the life expectancies of 138 countries which were chosen by a random country generator. The table below lists out all the calculations required to be plugged into the formula in order to calculate the value of r.
The countries have been categorized into 4 groups.
High Income – more than $12,615
Upper Middle Income – $4,086 and $12,615
Lower middle income – between $1,036 and $4,085
Low Income – less than $1,035 GNI per capita
GNI per capita in dollar terms is estimated using the World Bank Atlas method.
Referring to Table (b): Calculations and values showing how the value of r was calculated in the appendix
r = 3445.42 11111111111111
√ (3062.285 x 10994.039)
r = 0.594 [ using GDC ]
As seen from table (a) the value 0.593 is moderately strong and shows a moderate association between the two variables. Next an equation will be modelled using the above data sets
Referring to Table (c): Calculations and values showing how equation for linear regression was calculated in the appendix.
Linear regression
Understanding the concept
It is a form of statistical analysis that models the relationship between two continuous variables, in this case it will be CO2 emission levels will be the independent variable and life expectancy the dependent.
It finds a statistical relationship which refers to the fact that one variable cannot be expressed by the other. Linear regression aims to find the line of best fir- which is the line for which total prediction error is as small as possible. Error is the distance between a data point and the regression line.
Formula
Y = mx +b
m refers to the slope
b refers to the y- intercept
In order to calculate linear regression, we require the value of both the slope and the y intercept
Application and Calculation
Slope
Slope (m) =
= 138 (47782.594970) – (635.97) (9620.76)
138 (5993.1427) – (635.97)2
= (6593998.106) – (6118514.737)
(827053.6926) – (404457.8409)
= 475483.369 6
422595.8517
Slope = 1.125 [ using GDC ]
Understanding the formula
n is the total number of data points in this investigation which is –
sig xy refers to the sum of the multiplication of the two sets of data points, which in this case is life expectancy values with CO2 emission levels
sig x refers to the first set of data points
sig x2 refers to the sum of the squared values of the first set of data points
sig y refers to the second set of data points
sig y2refers to the sum of the squared values of the second set of data points
The formula below has been derived using the above symbols, this in turn allows us to calculate the y-intercept.
Y- Intercept
Interception (b) =
= (9620.76)( 5993.1427) – (635.97) (47782.59493)
138 (5993.1427) – (635.97)2
= (57658587.56) – (30388296.88)
(827053.6926) – (404457.8409)
= 27270290.68
422595.8517
= 64.530 (Rounded off value) [ using GDC ]
We can obtain the linear regression equation by plugging the obtained values into the formula.
y = mx + b
= 1.125x + 64.530
Calculation on GDC Calculator
Graph
Using the linear regression equation and after modelling a formula we can input the data to form a scatter plot graph. Using the equation, we can input the values of one variable to calculate the value of another.
y = mx + b
= 1.125x + 64.530
Hence by inputting an x value we can find out the value of y.
However, the data calculated is not completely accurate due to the presence of outliers and all the data is not centered along the regression line.
Image from GDC Calculator
Chi squared test for independence of two variables
Understanding the concept
It is a statistical method assessing the goodness of fit between a set of observed values and those expected theoretically and It is used to check if two variables are independent of one another or not.
Formula
Degree of freedom =
Understanding the formula
Oi refers to the observed count
r is number of rows
c is the number of columns
Ei is the expected count
Application
Null hypothesis : There is no association between the two variables being analyzed.
OBSERVED
Below average life expectancy
Close to average LE
64.29± 5
Above average LE
Total
Below average CO2 emission
35
23
6
64
Close to average CO2 emission 4.18 + 2
1
11
19
31
Above average CO2 emission
2
8
33
43
Total
38
42
58
138
EXPECTED
Below average life expectancy
Close to average LE
64.29± 5
Above average LE
Below average CO2 emission
17.623
19.478
26.898
Close to average CO2 emission 4.18 + 2
8.5362
9.4347
13.028
Above average CO2 emission
11.84
13.086
18.072
Calculation
X2 = (35 – 17.623)2 + (23 – 19.478)2 + …….. + (33 – 18.072)2
17.623 19.478 18.072
X2 = 66.1426 [ using GDC ]
Degree of freedom = (R – 1) (C – 1 )
= (3 – 1) (3 – 1) = 4
X2 critical value ( DF = 4 ) = 18. 467 [ for 0.001 accuracy ]
Since,
66.1426 > 18. 467
X2 calculated > X2 critical
The null hypothesis has been disproved.
Evaluation
One of the advantages of using a correlational study is that the researcher is able to assimilate and collect a large sample space of data and this in turn increases the reliability of the results calculated. One of the biggest advantages for me and the main reason I chose this technique is that I will be able to easily and effectively interpret and analyse the data using a scatter plot graph. This study can also act as a foundation for further research on other factors, apart from CO2 levels and life expectancy, which affect economic growth and human development.
However knowing the strengths we must also keep in mind the drawback of this analysis technique
Correlational studies do not imply causality between the variables. Even in cases where there is a high co efficient value (+/- 0.8 and above) it does not allow for a cause and effect relationship to be established between the two variables being analysed. Outliers in the graph also skew the data calculated.
Pearson’s coefficient of correlation also assumes the existence of a linear relationship between the variables, this may not always be the case. Even if a linear relationship is established, a high degree of correlation does not necessarily always mean a high degree of a linear relationship.
By looking at not just the global averages and correlations allows us to analyse the effects and causes of outlier nations, and the qualitative differences in the growth and development programmes adopted by nations in different income brackets.
Referring to the table below, the outliers in the graph are the high-income countries like which have developed socioeconomically and maintained human development standards having an above average life expectancy with either a below average or close to average carbon emission level. The upper middle-income countries also have close to or above average life expectancy with a below average carbon emission level. This may due to the fact that the government of these countries have progressed economically by using greener energy sources. There are a few low-income countries which have below or close to average life expectancy with an above average carbon emission level. This may be due to the fact that in order to develop and progress economically the governments of these countries focus more on their economic goals as compared to healthcare, environment control and sanitation leading to a low life expectancy.
On further analysis, the table shows that most of the low-income countries had a below average life expectancy as well as below average carbon emission level. The lower middle-income countries had a below average life expectancy with either a below average or close to average carbon emission level. The upper middle-income countries had a close to average life expectancy with either a close to or above average carbon emission level.
The high-income countries had an above average life expectancy as well as an above average carbon emission level.
OBSERVED
Below average life expectancy
Close to average LE
64.29± 5
Above average LE
Below average CO2 emission
H = 0
UM = 3
LM = 13
L = 19
H = 0
UM = 1
LM = 0
L = 0
Close to average CO2 emission 4.18 + 2
H = 0
UM = 4
LM = 15
L = 4
H = 2
UM = 7
LM = 2
L = 0
H = 2
UM = 5
LM = 1
L = 0
Above average CO2 emission
H = 4
UM = 14
LM = 1
L = 0
H = 31
UM = 2
LM = 0
L = 0
Conclusion
My aim of this investigation was to find a co-relation between carbon dioxide levels and life expectancy levels of countries in varying income brackets. A moderately strong positive correlation was established between the two variables.
One of the major challenges I faced during my investigation was operationalizing and defining human development and economic growth in terms of an independent and dependent variable. It took me multiple tries to finally get a moderately strong coefficient value, I had to choose between various indicator options before I found two appropriate variables. However, it was quite an informative and interesting process and it equipped me with the skills of easily and quickly interpreting data on an excel sheet, which will be quite useful for me in any future projects. This investigation taught me to self-reflect at each and every stage to make sure my methodology was logical and efficient and to see if I had not deviated from the aim of my study.
After I had decided upon my variables my next hurdle was accurately curating data; I had to ensure that it was from the same year to make sure time was not a confounding variable in this investigation. This process was definitely time consuming but I wanted to ensure that all my data was reliable so that I could conduct a precise analysis
This investigation is extremely relevant, given that we are living in a world where climate change is a major crisis which is neither being accurately addressed or dealt with. Studies such as this should be done to establish empirical evidence and to make sure a change will take place.
At the end of my investigation my claim and opinion of the existence of a climate crisis has not changed in actuality it makes me disagree with the statements made by Moore to a stronger degree.
Citations
“Chegg.com.” Definition of Chi-Square Test | Chegg.com, www.chegg.com/homework-help/definitions/chi-square-test-14.
“PEARSON Function in Excel – Find PEARSON CORRELATION in Excel.” DataScience Made Simple, www.datasciencemadesimple.com/pearson-function-in-excel/.
“Region and Country Classification.” Asian-Pacific Economic Literature, vol. 25, no. 2, 2011, pp. 195–195., doi:10.1111/j.1467-8411.2011.01315.x.
“Star Trek.” Chi-Square Test for Independence: Definition, stattrek.com/statistics/dictionary.aspx?definition=chi-square test for independence.
CO2 Emissions per capita (tonnes)
Life expectancy at birth for both sexes 2005 – 2010
Country
X
Y
X – x̅
Y-ȳ
(X – x̅)2
(Y-ȳ )2
(X – x̅))(Y-ȳ )
Albania
1.35
75.64
-3.258
5.924
10.618
35.098
-19.304
Algeria
4.14
73.89
-0.468
4.174
0.219
17.425
-1.956
Angola
1.41
55.59
-3.198
-14.126
10.23
199.534
45.181
Argentina
4.65
75.18
0.042
5.464
0.002
29.859
0.227
Armenia
1.65
72.74
-2.958
3.024
8.753
9.147
-8.947
Australia
19
81.48
14.392
11.764
207.116
138.4
169.307
Austria
8.93
80.13
4.322
10.414
18.676
108.459
45.006
Azerbaijan
3.68
70.10
-0.928
0.384
0.862
0.148
-0.357
Bangladesh
0.28
69.05
-4.328
-0.666
18.736
0.443
2.881
Belarus
5.82
69.26
1.212
-0.456
1.468
0.208
-0.552
Belgium
10.88
79.57
6.272
9.854
39.332
97.108
61.802
Benin
0.46
58.56
-4.148
-11.156
17.21
124.449
46.279
Bolivia
1.38
64.95
-3.228
-4.766
10.423
22.711
15.386
Bosnia and Herzegovina
7.68
75.53
3.072
5.814
9.434
33.807
17.859
Botswana
2.64
56.53
-1.968
-13.186
3.875
173.861
25.956
Brazil
1.94
72.91
-2.668
3.194
7.121
10.204
-8.524
Brunei Darussalam
19.8
76.70
15.192
6.984
230.782
48.781
106.103
Bulgaria
7.71
73.14
3.102
3.424
9.619
11.726
10.621
Burkina Faso
0.12
55.27
-4.488
-14.446
20.146
208.677
64.839
Cameroon
0.33
54.39
-4.278
-15.326
18.305
234.876
65.57
Canada
17.91
80.76
13.302
11.044
176.93
121.978
146.907
Chile
4.31
78.13
-0.298
8.414
0.089
70.801
-2.511
China
4.92
74.68
0.312
4.964
0.097
24.645
1.547
Colombia
1.43
72.86
-3.178
3.144
10.103
9.887
-9.994
Comoros
0.19
60.89
-4.418
-8.826
19.523
77.892
38.996
Congo
0.45
57.95
-4.158
-11.766
17.293
138.431
48.927
Costa Rica
1.82
78.41
-2.788
8.694
7.776
75.592
-24.244
Cote d’Ivoire
0.32
49.19
-4.288
-20.526
18.391
421.302
88.024
Croatia
5.61
76.09
1.002
6.374
1.003
40.632
6.384
Cuba
2.41
78.66
-2.198
8.944
4.833
80.001
-19.664
Cyprus
9.6
78.96
4.992
9.244
24.915
85.458
46.143
Czech Republic
12.66
76.98
8.052
7.264
64.827
52.771
58.489
Denmark
9.83
78.58
5.222
8.864
27.264
78.577
46.285
Djibouti
0.58
59.05
-4.028
-10.666
16.229
113.756
42.966
Dominican Republic
2.12
72.19
-2.488
2.474
6.193
6.122
-6.157
Ecuador
2.25
74.57
-2.358
4.854
5.562
23.565
-11.449
Egypt
2.31
69.88
-2.298
0.164
5.283
0.027
-0.378
El Salvador
1.1
71.13
-3.508
1.414
12.309
2
-4.962
Equatorial Guinea
7.47
54.94
2.862
-14.776
8.188
218.32
-42.281
Eritrea
0.12
60.70
-4.488
-9.016
20.146
81.282
40.467
Estonia
14.22
73.78
9.612
4.064
92.381
16.519
39.065
Ethiopia
0.08
59.08
-4.528
-10.636
20.507
113.117
48.163
Finland
12.51
79.54
7.902
9.824
62.434
96.518
77.627
France
6.5
80.82
1.892
11.104
3.578
123.307
21.004
Gabon
1.43
61.33
-3.178
-8.386
10.103
70.319
26.654
Gambia
0.25
58.83
-4.358
-10.886
18.996
118.497
47.445
Georgia
1.38
72.65
-3.228
2.934
10.423
8.61
-9.473
Germany
10.22
79.73
5.612
10.014
31.489
100.287
56.196
Ghana
0.43
60.02
-4.178
-9.696
17.46
94.006
40.513
Greece
10.22
80.01
5.612
10.294
31.489
105.974
57.767
Guatemala
0.97
70.50
-3.638
0.784
13.239
0.615
-2.854
Guinea
0.14
55.45
-4.468
-14.266
19.967
203.509
63.746
Guinea-Bissau
0.19
54.18
-4.418
-15.536
19.523
241.356
68.644
Haiti
0.25
60.23
-4.358
-9.486
18.996
89.978
41.343
Honduras
1.23
72.01
-3.378
2.294
11.414
5.264
-7.751
Hungary
5.76
73.74
1.152
4.024
1.326
16.195
4.634
Iceland
10.67
81.39
6.062
11.674
36.742
136.29
70.764
India
1.38
65.57
-3.228
-4.146
10.423
17.186
13.384
Indonesia
1.77
67.68
-2.838
-2.036
8.057
4.144
5.778
Iran (Islamic Republic of)
6.85
72.73
2.242
3.014
5.024
9.086
6.757
Iraq
3.4
68.01
-1.208
-1.706
1.46
2.909
2.061
Ireland
10.91
79.68
6.302
9.964
39.709
99.288
62.791
Israel
9.63
80.94
5.022
11.224
25.216
125.986
56.363
Italy
8.01
81.50
3.402
11.784
11.57
138.871
40.085
Jamaica
5.18
74.20
0.572
4.484
0.327
20.109
2.563
Japan
10.23
82.65
5.622
12.934
31.602
167.297
72.711
Jordan
3.61
73.00
-0.998
3.284
0.997
10.787
-3.279
Kazakhstan
14.76
66.08
10.152
-3.636
103.053
13.218
-36.907
Kenya
0.3
59.72
-4.308
-9.996
18.563
99.913
43.066
Kyrgyzstan
1.14
67.47
-3.468
-2.246
12.03
5.043
7.789
Latvia
3.79
71.55
-0.818
1.834
0.67
3.365
-1.501
Lebanon
3.21
77.74
-1.398
8.024
1.956
64.39
-11.222
Liberia
0.19
58.11
-4.418
-11.606
19.523
134.691
51.279
Libyan Arab Jamahiriya
9.29
71.79
4.682
2.074
21.917
4.303
9.711
Lithuania
4.74
71.87
0.132
2.154
0.017
4.641
0.283
Madagascar
0.12
62.23
-4.488
-7.486
20.146
56.035
33.599
Malaysia
7.32
73.72
2.712
4.004
7.352
16.035
10.858
Malta
6.71
79.40
2.102
9.684
4.416
93.787
20.352
Mauritania
0.62
61.32
-3.988
-8.396
15.908
70.487
33.486
Mauritius
3.06
72.76
-1.548
3.044
2.398
9.268
-4.714
Mexico
4.39
75.72
-0.218
6.004
0.048
36.052
-1.312
Morocco
1.49
72.89
-3.118
3.174
9.725
10.076
-9.899
Mozambique
0.12
53.24
-4.488
-16.476
20.146
271.447
73.951
Myanmar
0.27
64.26
-4.338
-5.456
18.822
29.764
23.669
Namibia
1.45
54.98
-3.158
-14.736
9.976
217.139
46.542
Nepal
0.12
66.79
-4.488
-2.926
20.146
8.559
13.132
Netherlands
10.49
80.18
5.882
10.464
34.592
109.503
61.546
New Zealand
8.4
80.32
3.792
10.604
14.376
112.452
40.207
Nicaragua
0.82
72.83
-3.788
3.114
14.353
9.699
-11.799
Nigeria
0.64
49.74
-3.968
-19.976
15.749
399.027
79.273
Norway
9.53
80.60
4.922
10.884
24.221
118.469
53.568
Oman
13.69
75.06
9.082
5.344
82.474
28.562
48.535
Pakistan
0.9
64.37
-3.708
-5.346
13.753
28.576
19.824
Panama
2.17
76.36
-2.438
6.644
5.946
44.147
-16.202
Papua New Guinea
0.52
64.18
-4.088
-5.536
16.716
30.643
22.632
Paraguay
0.67
71.75
-3.938
2.034
15.512
4.139
-8.012
Peru
1.51
73.15
-3.098
3.434
9.601
11.795
-10.641
Philippines
0.8
68.05
-3.808
-1.666
14.505
2.774
6.344
Poland
8.61
75.56
4.002
5.844
16.012
34.156
23.386
Portugal
5.9
79.28
1.292
9.564
1.668
91.477
12.353
Republic of Moldova
1.28
68.27
-3.328
-1.446
11.079
2.09
4.812
Romania
5.17
73.08
0.562
3.364
0.315
11.319
1.889
Russian Federation
11.13
67.14
6.522
-2.576
42.53
6.634
-16.797
Rwanda
0.08
60.05
-4.528
-9.666
20.507
93.425
43.771
Sao Tome and Principe
0.81
65.47
-3.798
-4.246
14.428
18.026
16.127
Saudi Arabia
16.31
73.22
11.702
3.504
136.926
12.28
41.006
Senegal
0.46
62.41
-4.148
-7.306
17.21
53.373
30.307
Serbia and Montenegro
5.13
73.33
0.522
3.614
0.272
13.064
1.885
Sierra Leone
0.24
45.88
-4.368
-23.836
19.084
568.138
104.126
Singapore
12.08
81.21
7.472
11.494
55.824
132.12
85.88
Slovakia
7.07
74.77
2.462
5.054
6.059
25.546
12.441
Slovenia
8.45
78.55
3.842
8.834
14.757
78.046
33.937
South Africa
8.82
53.07
4.212
-16.646
17.737
277.078
-70.104
Spain
8.32
81.21
3.712
11.494
13.775
132.12
42.662
Sri Lanka
0.62
74.07
-3.988
4.354
15.908
18.96
-17.367
Sudan
0.28
61.50
-4.328
-8.216
18.736
67.497
35.561
Sweden
5.64
81.06
1.032
11.344
1.064
128.694
11.702
Switzerland
5.81
81.78
1.202
12.064
1.444
145.548
14.496
Syrian Arab Republic
3.41
74.45
-1.198
4.734
1.436
22.414
-5.674
Tajikistan
1.07
68.71
-3.538
-1.006
12.521
1.011
3.558
Thailand
4.14
74.17
-0.468
4.454
0.219
19.841
-2.087
The Former Yugoslav Rep. of Macedonia
5.53
73.15
0.922
3.434
0.849
11.795
3.165
Togo
0.21
55.80
-4.398
-13.916
19.347
193.645
61.208
Tunisia
2.37
74.56
-2.238
4.844
5.011
23.468
-10.844
Turkey
4.17
73.37
-0.438
3.654
0.192
13.354
-1.602
Turkmenistan
9.2
65.87
4.592
-3.846
21.082
14.789
-17.657
Uganda
0.1
55.15
-4.508
-14.566
20.326
212.158
65.669
Ukraine
7.35
67.89
2.742
-1.826
7.516
3.333
-5.005
United Kingdom
8.97
79.69
4.362
9.974
19.023
99.488
43.503
United Rep. of Tanzania
0.15
58.82
-4.458
-10.896
19.878
118.715
48.578
United States
19.74
78.16
15.132
8.444
228.963
71.307
127.776
Uruguay
1.86
76.20
-2.748
6.484
7.554
42.047
-17.822
Uzbekistan
4.32
69.10
-0.288
-0.616
0.083
0.379
0.178
Venezuela (Bolivarian Republic of)
5.99
73.35
1.382
3.634
1.909
13.208
5.021
Viet Nam
1.29
74.69
-3.318
4.974
11.012
24.744
-16.507
Yemen
0.99
62.75
-3.618
-6.966
13.093
48.52
25.205
Zambia
0.22
52.93
-4.388
-16.786
19.259
281.758
73.663
Zimbabwe
0.77
48.35
-3.838
-21.366
14.734
456.491
82.012
Total
635.97
9620.76
Mx: 4.608
My: 69.716
3062.285
10994.039
3445.421
Average
4.184013158
63.29448684
72.32920395
22.66724342
Table b : Calculations and values showing how the value of r was calculated.
NAME
CO2 Emissions per capita (tonnes)
Life expectancy at birth for both sexes 2005 – 2010
Country
X
Y
x2
y2
XY
Albania
1.35
75.64
1.8225
5721.561
102.11535
Algeria
4.14
73.89
17.1396
5458.993
305.8839
Angola
1.41
55.59
1.9881
3089.803
78.37626
Argentina
4.65
75.18
21.6225
5651.431
349.5684
Armenia
1.65
72.74
2.7225
5290.817
120.0177
Australia
19
81.48
361
6638.176
1548.025
Austria
8.93
80.13
79.7449
6420.817
715.5609
Azerbaijan
3.68
70.10
13.5424
4913.309
257.9496
Bangladesh
0.28
69.05
0.0784
4767.488
19.33316
Belarus
5.82
69.26
33.8724
4796.809
403.08738
Belgium
10.88
79.57
118.3744
6331.385
865.7216
Benin
0.46
58.56
0.2116
3429.274
26.9376
Bolivia
1.38
64.95
1.9044
4218.113
89.62686
Bosnia and Herzegovina
7.68
75.53
58.9824
5704.781
580.0704
Botswana
2.64
56.53
6.9696
3195.189
149.22864
Brazil
1.94
72.91
3.7636
5315.868
141.4454
Brunei Darussalam
19.8
76.70
392.04
5882.737
1518.6402
Bulgaria
7.71
73.14
59.4441
5348.728
563.87085
Burkina Faso
0.12
55.27
0.0144
3054.994
6.63264
Cameroon
0.33
54.39
0.1089
2958.381
17.94903
Canada
17.91
80.76
320.7681
6521.855
1446.37578
Chile
4.31
78.13
18.5761
6103.516
336.71875
China
4.92
74.68
24.2064
5577.401
367.43544
Colombia
1.43
72.86
2.0449
5308.725
104.19123
Comoros
0.19
60.89
0.0361
3707.105
11.56834
Congo
0.45
57.95
0.2025
3358.203
26.0775
Costa Rica
1.82
78.41
3.3124
6148.599
142.71166
Cote d’Ivoire
0.32
49.19
0.1024
2419.459
15.74016
Croatia
5.61
76.09
31.4721
5790.297
426.88734
Cuba
2.41
78.66
5.8081
6188.025
189.58024
Cyprus
9.6
78.96
92.16
6235.313
758.0544
Czech Republic
12.66
76.98
160.2756
5926.536
974.61744
Denmark
9.83
78.58
96.6289
6175.131
772.46106
Djibouti
0.58
59.05
0.3364
3486.903
34.249
Dominican Republic
2.12
72.19
4.4944
5211.252
153.04068
Ecuador
2.25
74.57
5.0625
5560.834
167.78475
Egypt
2.31
69.88
5.3361
4882.655
161.41356
El Salvador
1.1
71.13
1.21
5059.192
78.2408
Equatorial Guinea
7.47
54.94
55.8009
3018.623
410.41674
Eritrea
0.12
60.70
0.0144
3683.883
7.2834
Estonia
14.22
73.78
202.2084
5443.193
1049.12316
Ethiopia
0.08
59.08
0.0064
3490.446
4.7264
Finland
12.51
79.54
156.5001
6326.771
995.05791
France
6.5
80.82
42.25
6531.226
525.304
Gabon
1.43
61.33
2.0449
3761.86
87.70762
Gambia
0.25
58.83
0.0625
3460.851
14.70725
Georgia
1.38
72.65
1.9044
5278.604
100.26252
Germany
10.22
79.73
104.4484
6357.351
814.87126
Ghana
0.43
60.02
0.1849
3602.881
25.81032
Greece
10.22
80.01
104.4484
6401.28
817.68176
Guatemala
0.97
70.50
0.9409
4970.814
68.38888
Guinea
0.14
55.45
0.0196
3075.035
7.76342
Guinea-Bissau
0.19
54.18
0.0361
2935.364
10.29401
Haiti
0.25
60.23
0.0625
3627.171
15.0565
Honduras
1.23
72.01
1.5129
5185.152
88.56984
Hungary
5.76
73.74
33.1776
5437.44
424.73664
Iceland
10.67
81.39
113.8489
6624.82
868.46331
India
1.38
65.57
1.9044
4298.9
90.48108
Indonesia
1.77
67.68
3.1329
4580.853
119.79714
Iran (Islamic Republic of)
6.85
72.73
46.9225
5289.071
498.1731
Iraq
3.4
68.01
11.56
4625.904
231.2476
Ireland
10.91
79.68
119.0281
6348.743
869.29789
Israel
9.63
80.94
92.7369
6550.798
779.42331
Italy
8.01
81.50
64.1601
6642.25
652.815
Jamaica
5.18
74.20
26.8324
5506.234
384.37672
Japan
10.23
82.65
104.6529
6831.518
845.54019
Jordan
3.61
73.00
13.0321
5329
263.53
Kazakhstan
14.76
66.08
217.8576
4365.906
975.267
Kenya
0.3
59.72
0.09
3566.956
17.9172
Kyrgyzstan
1.14
67.47
1.2996
4552.741
76.92036
Latvia
3.79
71.55
14.3641
5119.689
271.18208
Lebanon
3.21
77.74
10.3041
6043.819
249.55182
Liberia
0.19
58.11
0.0361
3377.237
11.04166
Libyan Arab Jamahiriya
9.29
71.79
86.3041
5154.235
666.95697
Lithuania
4.74
71.87
22.4676
5164.578
340.6401
Madagascar
0.12
62.23
0.0144
3872.324
7.46736
Malaysia
7.32
73.72
53.5824
5435.081
539.65236
Malta
6.71
79.40
45.0241
6304.519
532.78071
Mauritania
0.62
61.32
0.3844
3760.02
38.01778
Mauritius
3.06
72.76
9.3636
5294.163
222.64866
Mexico
4.39
75.72
19.2721
5733.821
332.41958
Morocco
1.49
72.89
2.2201
5312.369
108.60014
Mozambique
0.12
53.24
0.0144
2834.285
6.38856
Myanmar
0.27
64.26
0.0729
4129.476
17.35047
Namibia
1.45
54.98
2.1025
3022.251
79.71375
Nepal
0.12
66.79
0.0144
4460.904
8.0148
Netherlands
10.49
80.18
110.0401
6428.512
841.06722
New Zealand
8.4
80.32
70.56
6451.302
674.688
Nicaragua
0.82
72.83
0.6724
5304.5
59.72224
Nigeria
0.64
49.74
0.4096
2474.267
31.83488
Norway
9.53
80.60
90.8209
6496.682
768.13706
Oman
13.69
75.06
187.4161
5634.004
1027.5714
Pakistan
0.9
64.37
0.81
4143.497
57.933
Panama
2.17
76.36
4.7089
5831.002
165.70337
Papua New Guinea
0.52
64.18
0.2704
4118.559
33.37152
Paraguay
0.67
71.75
0.4489
5148.063
48.0725
Peru
1.51
73.15
2.2801
5350.923
110.4565
Philippines
0.8
68.05
0.64
4631.347
54.4432
Poland
8.61
75.56
74.1321
5709.162
650.56299
Portugal
5.9
79.28
34.81
6285.953
467.7756
Republic of Moldova
1.28
68.27
1.6384
4661.203
87.38944
Romania
5.17
73.08
26.7289
5340.102
377.80292
Russian Federation
11.13
67.14
123.8769
4508.048
747.29046
Rwanda
0.08
60.05
0.0064
3606.003
4.804
Sao Tome and Principe
0.81
65.47
0.6561
4286.19
53.02989
Saudi Arabia
16.31
73.22
266.0161
5361.608
1194.26713
Senegal
0.46
62.41
0.2116
3894.509
28.70676
Serbia and Montenegro
5.13
73.33
26.3169
5377.876
376.20342
Sierra Leone
0.24
45.88
0.0576
2105.341
11.01216
Singapore
12.08
81.21
145.9264
6594.902
981.00472
Slovakia
7.07
74.77
49.9849
5591.002
528.64511
Slovenia
8.45
78.55
71.4025
6170.731
663.7813
South Africa
8.82
53.07
77.7924
2816.213
468.05976
Spain
8.32
81.21
69.2224
6595.714
675.70048
Sri Lanka
0.62
74.07
0.3844
5486.365
45.9234
Sudan
0.28
61.50
0.0784
3782.496
17.22056
Sweden
5.64
81.06
31.8096
6571.372
457.20096
Switzerland
5.81
81.78
33.7561
6688.132
475.14761
Syrian Arab Republic
3.41
74.45
11.6281
5543.1
253.88132
Tajikistan
1.07
68.71
1.1449
4720.789
73.51756
Thailand
4.14
74.17
17.1396
5501.041
307.05966
The Former Yugoslav Rep. of Macedonia
5.53
73.15
30.5809
5351.215
404.53056
Togo
0.21
55.80
0.0441
3113.305
11.71737
Tunisia
2.37
74.56
5.6169
5559.79
176.71668
Turkey
4.17
73.37
17.3889
5383.157
305.9529
Turkmenistan
9.2
65.87
84.64
4338.593
605.9856
Uganda
0.1
55.15
0.01
3041.523
5.515
Ukraine
7.35
67.89
54.0225
4608.509
498.9621
United Kingdom
8.97
79.69
80.4609
6350.815
714.83724
United Rep. of Tanzania
0.15
58.82
0.0225
3459.204
8.82225
United States
19.74
78.16
389.6676
6109.611
1542.95736
Uruguay
1.86
76.20
3.4596
5806.135
141.72828
Uzbekistan
4.32
69.10
18.6624
4775.086
298.52064
Venezuela (Bolivarian Republic of)
5.99
73.35
35.8801
5380.809
439.39046
Viet Nam
1.29
74.69
1.6641
5578.447
96.34881
Yemen
0.99
62.75
0.9801
3937.186
62.11953
Zambia
0.22
52.93
0.0484
2801.161
11.64372
Zimbabwe
0.77
48.35
0.5929
2337.916
37.23104
Total
635.97
9620.76
5993.1427
681713.032
47782.595
Average
4.184013158
69.7156521
Table c : Calculations and values showing how equation for linear regression was calculated.
GDC Calculations
