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The study of factors influencing life expectancy is important not only for scientist and researchers, but also for the general public in the bid to live long in this world. For instance, some scientist have tried to come up with demands that eating certain food stuffs either increases or decreases the life span of an individual. Moreover, other factors like the income of people, a country’s GDP and level of education have also been studied regarding the longevity of an individual (Lin, Chen, Chien, & Chan, 2012).. However, this study tries to determine if there is a correlation between the level of alcoholism and the life expectancy of individuals in the world. In this regard, if in deed the two variables to be investigated will have a relationship, then the nature and strength of the relationship will be discussed. It is worth noting, that there has been an increase in general trend of life expectancy from the year 1800 to date.
Data Collection
The data to be analyzed in this study was obtained from gapminder.org website. The 2008 raw data for the two variables was used in developing a sample of 31 countries in Europe (“Gapminder Tools,” 2018). Since the countries were selected from one region, we expect the variability to be minimal unlike when we would have considered taking a sample that encompasses countries in the whole world. The distribution of the data points between the levels of alcohol consumption and life expectancy will be shown using scatter plot diagram for visual representation. Also, regression analysis will used to determine the strength of the model and the correlation between the explanatory and the response variable (Seber & Lee, 2012). In this regard, the former represents the level of alcohol consumption and the latter the life expectancy.
Sampling Technique
Since the population of interest is known, the lottery also known as the simple random sampling approach was employed. The countries in Europe were designated specific numbers from 1 to 50 and mixed in a box, after which a random sample of 31 nations was selected. Apparently, the sample size represents 62% of the total population which implies that the results in this study will be significant in making inference to the entire population.
Analysis
Scatter Plot Diagram
Regression Line
The least square method is used to determine the line of best fit. This is because it is more accurate than the other methods. The linear model is represented by,
Where, is the y-intercept, and
is the slope.
S/No.
7.29
76.8
559.872
53.1441
5898.24
-5.0432
-0.8742
25.43387
4.40877
10.17
84.6
860.382
103.4289
7157.16
-2.1632
6.9258
4.67943
-14.98189
13.66
72.3
987.618
186.5956
5227.29
1.3268
-5.3742
1.76040
-7.13049
12.4
80.4
996.96
153.76
6464.16
0.0668
2.7258
0.00446
0.18208
10.41
79.6
828.636
108.3681
6336.16
-1.9232
1.9258
3.69870
-3.70370
9.6
77.5
744
92.16
6006.25
-2.7332
-0.1742
7.47038
0.47612
11.4
73.2
834.48
129.96
5358.24
-0.9332
-4.4742
0.87086
4.17532
15
76.2
1143
225
5806.44
2.6668
-1.4742
7.11182
-3.93140
8.84
80
707.2
78.1456
6400
-3.4932
2.3258
12.20245
-8.12448
12.02
78.9
948.378
144.4804
6225.21
-0.3132
1.2258
0.09809
-0.38392
13.1
79.6
1042.76
171.61
6336.16
0.7668
1.9258
0.58798
1.47670
12.48
81.1
1012.128
155.7504
6577.21
0.1468
3.4258
0.02155
0.50291
12.14
80
971.2
147.3796
6400
-0.1932
2.3258
0.03733
-0.44934
11.01
80.2
883.002
121.2201
6432.04
-1.3232
2.5258
1.75086
-3.34214
16.12
73.9
1191.268
259.8544
5461.21
3.7868
-3.7742
14.33985
-14.29214
7.38
82.4
608.112
54.4644
6789.76
-4.9532
4.7258
24.53419
-23.40783
9.72
81.5
792.18
94.4784
6642.25
-2.6132
3.8258
6.82881
-9.99758
16.3
72.1
1175.23
265.69
5198.41
3.9668
-5.5742
15.73550
-22.11174
8.94
74.5
666.03
79.9236
5550.25
-3.3932
-3.1742
11.51381
10.77070
23.01
70.4
1619.904
529.4601
4956.16
10.6768
-7.2742
113.99406
-77.66518
9.75
80.3
782.925
95.0625
6448.09
-2.5832
2.6258
6.67292
-6.78297
8.35
80.8
674.68
69.7225
6528.64
-3.9832
3.1258
15.86588
-12.45069
14.43
75.4
1088.022
208.2249
5685.16
2.0968
-2.2742
4.39657
-4.76854
13.89
79.4
1102.866
192.9321
6304.36
1.5568
1.7258
2.42363
2.68673
16.15
73.2
1182.18
260.8225
5358.24
3.8168
-4.4742
14.56796
-17.07713
12.21
74.3
907.203
149.0841
5520.49
-0.1232
-3.3742
0.01518
0.41570
14.94
78.7
1175.778
223.2036
6193.69
2.6068
1.0258
6.79541
2.67406
9.5
81.1
770.45
90.25
6577.21
-2.8332
3.4258
8.02702
-9.70598
11.41
82
935.62
130.1881
6724
-0.9232
4.3258
0.85230
-3.99358
17.47
67.8
1184.466
305.2009
4596.84
5.1368
-9.8742
26.38671
-50.72179
13.24
79.7
1055.228
175.2976
6352.09
0.9068
2.0258
0.82229
1.83700
Total
382.33
2407.9
29431.758
5054.8625
187511.41
339.50028
-265.41642
Mean
12.3332
77.67419
But we know that,
On the other hand, the y-intercept is obtained from the value of the slope as follows,
In this regard, he regression line is given by,
From the liner regression line, there exists a negative relationship between the variables in this stud from the negative nature of the slope. The y- intercept value tells us that mean period of people from the sample population can live for a period of 87.3156 years if and only if they are not taking alcohol. However, their life expectancy decreases as soon as one starts to engage in drinking alcohol.
Correlation Coefficient
The correlation coefficient will be used to predict the relationship between the explanatory and the response variables.
In statistical analysis, the correlation value is used to determine if the explanatory and response variables have a relationship. It should be noted that the relationship can either be positive or negative (Zou, Tuncali, & Silverman, 2003). In this case, the correlation has a negative value which implies than an increase in one variable will cause a significant decrease in the other variable under study. However, the value of 0.6577 means that the two variables, that is life expectancy moderately depends on the level of alcohol consumption. In the bid to determine if the regression model better accounts for the variability we proceed to compute the R squared value (Montgomery, Peck, & Vining, 2012).
Coefficient of Determination (R Squared)
From the R squared value, it is seen that the model only accounts for 43.26% of the variability in the variables. Also, the value reveals that the data points are not close to the best fi line.
Data Grouping For Alcohol Consumption Level
Row Labels
Alcohol Consumption Level
CF
Sec. Angle
Mid-Point
Upper Class Limit
7.29-9.29
5
5
16%
8.29
9.29
9.29-11.29
7
12
23%
10.29
11.29
11.29-13.29
9
21
29%
12.29
13.29
13.29-15.29
5
26
16%
14.29
15.29
15.29-17.29
3
29
10%
16.29
17.29
17.29-19.29
1
30
3%
18.29
19.29
21.29-23.29
1
31
3%
20.29
21.29
Grand Total
31
23.29
Table 1: Grouped Frequency for alcohol consumption level.
The data in Table 1 was used in the drawing of histograms, frequency polygons, ogive curves, and pie charts to aid in the visual interpretation of data. Indeed, this is fundamental particularly for decision makers who have little or no knowledge in statistical analysis.
Histogram
The data from the sample is not normal since the histogram is skewed to the right. This implies that most of the countries have a mean value less than the mean of the sample in the study.
Frequency Polygon and Ogive Curve
Pie Chart
From the pie chart, a high percentage of the countries had alcohol levels ranging from 11.29 to 13.29 with 23%, followed by 27% for those countries ranging from 9.29 to 11.29 mean alcohol level.
Class
f
CF
x
fx
(x-Mean)^2
Upper Class Limit
7.29-9.29
5
5
8.29
41.45
16.5202
9.29
9.29-11.29
7
12
10.29
72.03
4.2622
11.29
11.29-13.29
9
21
12.29
110.61
0.0042
13.29
13.29-15.29
5
26
14.29
71.45
3.7462
15.29
15.29-17.29
3
29
16.29
48.87
15.4882
17.29
17.29-19.29
1
30
18.29
18.29
35.2302
19.29
21.29-23.29
1
31
20.29
20.29
62.9722
23.29
Grand Total
31
382.99
138.2231
Mean
Mode
Median
Standard Deviation
Inter-Quartile Range
Therefore, the inter-quartile range is given by,
Data Grouping For Life Expectancy
Row Labels
Count of Life expectancy
Mid-point
C.F
Sector angles
Upper Class Limit
67.8-70.8
2
69.3
2
6%
70.8
70.8-73.8
4
72.3
6
13%
73.8
73.8-76.8
5
75.3
11
16%
76.8
76.8-79.8
8
78.3
19
26%
79.8
79.8-82.8
11
81.3
30
35%
82.8
82.8-85.8
1
84.3
31
3%
85.8
Grand Total
31
Table 2: Grouped data for life expectancy
Histogram
The histogram above is skewed to the left meaning that it has a long left tail as compared to the right tail. Besides, this also implies that most of the values in the sample are on the highest side, the life expectancy in most countries is high.
The frequency polygon depicts the same results as the histogram but it uses lines at the midpoints of the range. A significant number of countries had a life expectancy of 81.3 years in accordance with the sharp peak in the frequency polygon. From the data grouping table, very few countries had a life expectancy ranging from 82.8 to 85.8 as evident in the frequency polygon and histogram above.
It is evident that the mean life expectancy of the countries in Europe significantly increases from 70.85 to 82.85. However, there is a small change from 82.85 to 85.85 as per the small nature of the slope at that point.
It should be noted that pie charts give a good visual representation than frequency polygons and ogive curves because they give the percentage score of each grouped category in relation to the total percentage in the sample. From the pie of life expectancy, most countries in Europe have a mean life expectancy ranging from 79.8 to 82.8 with 36% followed by 76.8 to 79.8 with 26%. On the other hand, only 3% of the sample population had a mean longevity between 82.8 to 85.8 years.
Class
f
x
C.F
fx
(x-Mean)^2
Upper Class Limit
67.8-70.8
2
69.3
2
138.60
70.89
70.8
70.8-73.8
4
72.3
6
289.20
29.37
73.8
73.8-76.8
5
75.3
11
376.50
5.85
76.8
76.8-79.8
8
78.3
19
626.40
0.34
79.8
79.8-82.8
11
81.3
30
894.30
12.82
82.8
82.8-85.8
1
84.3
31
84.30
43.30
85.8
Grand Total
31
2409.30
162.57
Mean
Mode
Median
Standard Deviation
Inter-Quartile Range
Therefore, the inter-quartile range is given by,
Data Interpretation
The life expectancy of people in a particular country is affected by various factors apart from the level of alcohol drinking. The political factors cannot be ignored since if a country is politically unstable and experiences either international or domestic wars, people in that country are unlikely to live for long. Besides, the literacy level falls under the umbrella of political factors that influence life expectancy. It is worth noting that a life expectancy is high in a country with low illiteracy rate (Lin et al., 2012). This is because life expectancy index does not account for the types of deaths of people. Life expectancy is also impacted by socioeconomic inequality and access to better health care services. This is because improved health care in a country is directly proportional to increase in life expectancy (Chan & Kamala, 2015).
Conclusion
The analysis above shows that indeed the alcohol consumption level in a country affects the longevity of individuals in that country. This has been confirmed from the correlation analysis even though the two variables have a moderate relationship. The variables in this study do not have a causal relationship because the longevity of people in the world is not caused by the amount of alcohol intake. Moreover, people take different types of drinks which have different effects to the alcohol consumers. It should be noted that the data in this study can be significantly inferred o the total population in Europe since the sample population was composed of the countries from the same continent. In this regard, researchers should advice the relevant bodies in different nation to advice the citizens on the reduction of alcohol drinking in the bid to live long. Also, the governments in the individual nations can provide a rule that limits the alcohol drinking hours to increase the life expectancy of its citizens. To improve this study, a multiple regression analysis should be encouraged since it is composed of more than one explanatory variable. The multiple regression model can then be used to determine which variables have greater effect to the life expectancy.
References
Chan, M. F., & Kamala Devi, M. (2015). Factors Affecting Life Expectancy: Evidence From 1980-2009 Data in Singapore, Malaysia, and Thailand. Asia Pacific Journal of Public Health, 27(2), 136-146.
Gapminder Tools. (2018). Retrieved from https://www.gapminder.org/tools/#_state_time_value=2008;&marker_select@_geo=usa&trailStartTime=2005;;&axis/_x_which=alcohol/_consumption/_per/_adult/_15plus/_litres&domainMin:null&domainMax:null&zoomedMin:null&zoomedMax:null&scaleType=linear;;;&data_/_lastModified:1523640284417&lastModified:1523640284417;&chart-type=bubbles
Lin, R. T., Chen, Y. M., Chien, L. C., & Chan, C. C. (2012). Political and social determinants of life expectancy in less developed countries: a longitudinal study. BMC Public Health, 12(1), 85.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (Vol. 821). John Wiley & Sons.
Seber, G. A., & Lee, A. J. (2012). Linear regression analysis (Vol. 329). John Wiley & Sons.
Zou, K. H., Tuncali, K., & Silverman, S. G. (2003). Correlation and simple linear regression. Radiology, 227(3), 617-628.
Appendix
Country List
S/NO.
Country
Alcohol Consumption Level (2008)
Life expectancy (2008)
Albania
7.29
76.8
Andorra
10.17
84.6
Armenia
13.66
72.3
Austria
12.4
80.4
Belgium
10.41
79.6
Bosnia and Herzegovina
9.6
77.5
Bulgaria
11.4
73.2
Croatia
15
76.2
Cyprus
8.84
80
Denmark
12.02
78.9
Finland
13.1
79.6
France
12.48
81.1
Germany
12.14
80
Greece
11.01
80.2
Hungary
16.12
73.9
Iceland
7.38
82.4
Italy
9.72
81.5
Lithuania
16.3
72.1
Macedonia [FYROM]
8.94
74.5
Moldova
23.01
70.4
Netherlands
9.75
80.3
Norway
8.35
80.8
Poland
14.43
75.4
Portugal
13.89
79.4
Romania
16.15
73.2
Serbia
12.21
74.3
Slovenia
14.94
78.7
Sweden
9.5
81.1
Switzerland
11.41
82
Ukraine
17.47
67.8
United Kingdom
13.24
79.7
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