Analysis of Social Progress Index data for Sustainable Development Goals

This report analyzes Social Progress Index data to inform on the Sustainable Development Goals set by the UN. Findings show differences in adult literacy rates, political rights, and premature deaths from non-communicable diseases between continents. Regression models are presented for predicting premature deaths and political rights.

Business Modelling
Name
Institution
Instructor
24th September 2017
Student Name: 1

Executive summary
The main aim of this report was to analyze Social Progress Index data and come up with findings
that can inform on the Sustainable Development Goals set by the UN. We analyzed data from 82
countries and results showed that when compared with European countries, African countries are
still lagging behind in terms of adult literacy rates. In terms of human rights (political rights),
countries in the Asian continent are still much behind countries in American continent. Analysis
of premature deaths from non-communicable diseases between American and European
countries showed that the two continents have no significant differences.
Lastly, we found out that Household air pollution attributable deaths significantly estimate
premature deaths from non-communicable diseases.
Student Name: 2

Introduction
The purpose of this report is to present analysis of Social Progress Index data. The report will
address the following questions;
 How does Basic Knowledge compare between European countries and African countries?
 Is there difference in terms of Personal Rights between Asian and American countries?
 Is there any difference in terms of Health and Wellness between European and American
countries?
Last, the report will present two regression models
 For predicting the premature deaths from non-communicable diseases using Household
air pollution attributable deaths.
 For predicting the political rights using Adult literacy rate.
The analysis utilizes the Social Progress Index data which comprises of 182 observations
(countries). Out this, a sample of 100 was selected where random numbers were generated in
excel and then sorted in ascending order. The first 100 samples was then selected. However, out
of the 100 selected samples there were missing values which resulted to the whole row being
deleted and thus we ended up with a sample of 82 observations (countries). The data is presented
in the appendix named A1.
Student Name: 3

Analysis
DESCRIPTIVE STATISTICS
A descriptive analysis of the data was done. Table 1 below gives the percent of continent
representation based on the number of countries.
Table 1: Frequency table for the continents representation
Continent Percent
Oceania 4.9%
Europe 14.6%
America 18.3%
Africa 29.3%
Asia 32.9%
Majority of the countries chosen were from the Asian continent (32.9%, n = 27) this was closely
followed by African countries (29.3%, n = 24). Countries from America and European continents
were represented by 18.3% (n = 15) and 14.6% (n = 12) respectively while Oceania was the least
represented (4.9%, n = 4).
Figure 1: Bar chart of continents representation
Student Name: 4

Table 2 below presents the summary statistics where it can be observed that the average child
mortality in the 82 countries chosen is 38.55 with minimum and maximum mortality rates being
3.40 and 138.70 respectively
Table 2: Descriptive statistics (Quantitative data)
Statistic Child
mortalit
y rate
Household air
pollution
attributable deaths
Adult
literacy
rate
Premature deaths
from non-
communicable
diseases
Political
rights
Discrimination
and violence
against
minorities
Minimum 3.40 0.00 19.10 183.10 -2.00 3.10
Maximum 138.70 296.50 99.89 1442.50 39.00 9.90
Range 135.30 296.50 80.79 1259.40 41.00 6.80
Median 21.05 30.70 92.80 440.05 21.00 6.70
Mean 38.55 70.18 81.39 469.56 19.91 6.56
CONFIDENCE INTERVAL
We computed the 95% confidence interval of the average child mortality rate as well as the
average rate of discrimination and violence against minorities. From the results, we are 95%
confident that the mean child mortality rate is between 28.00 and 42.80. Similarly, we are 95%
confident that the mean rate of discrimination and violence against minorities is between 5.89
and 6.71.
HYPOTHESIS TESTING
Three hypothesis were tested. The first hypothesis test was performed on the sample data to test
whether the adult literacy rate is higher among European countries than African countries. An
independent samples t-test was done to compare the mean adult literacy rate of the countries in
Europe and those in Africa. Results showed that the African countries (M = 60.53, SD = 21.47,
N = 24) had significant difference in terms of the adult literacy rates when compared to the
Student Name: 5

European countries (M = 98.51, SD = 1.61, N = 12), t (34) = -6.076, p < .05, one-tailed. The
difference of 37.98 showed a significant difference. Essentially results showed that European
countries had higher adult literacy rates when compared to the African countries.
The second hypothesis test was conducted on the sample data to test whether there is any
difference in terms of political rights between Asian and American countries. An independent
samples t-test was performed to compare the mean rate in political rights of the Asian countries
and countries in America. Results showed that the American countries (M = 27.00, SD = 10.34,
N = 15) had significant difference in terms of the political rights rates when compared to the
Asian countries (M = 14.15, SD = 9.54, N = 27), t (40) = -4.062, p < .05, two-tailed. The
difference of 12.85 showed a significant difference. Specifically, results showed that American
countries have higher political rights rates when compared to the Asian countries.
The third hypothesis test was performed on the sample data to test whether there is any
difference in terms of premature deaths from non-communicable diseases between European and
American countries than African countries. An independent samples t-test was conducted to
compare the mean premature deaths from non-communicable diseases of the European countries
and countries in America. Results showed that the American countries (M = 338.42, SD =
128.66, N = 15) had no significant difference in terms of the premature deaths from non-
communicable diseases when compared to the European countries (M = 434.98, SD = 139.81, N
= 12), t (25) = -1.865, p > .05, two-tailed. The difference of 96.56 showed an insignificant
difference. Essentially results showed that American countries and European countries have no
significant differences terms of premature deaths from non-communicable diseases.
Student Name: 6

CORRELATION and REGRESSION
Two regression models were estimated (Armstrong, 2012). The first regression model sought to
estimate premature deaths from non-communicable diseases (dependent variable) using
Household air pollution attributable deaths (independent variable). The correlation coefficient
was found to be 0.6423; this shows that a moderately strong positive linear relationship exists
between the two variables.
The value of R-Squared was found to be 0.4052; this implies that 40.52% of the variation in the
dependent variable (premature deaths from non-communicable diseases) is explained by the
independent variable (Household air pollution attributable deaths) in the model. The estimated
regression model is gives as follows;
y=344.19+ 1.7866 x
Where,
y= premature deaths¿ non−communicable diseases
x=Household air pollution attributable deaths
Student Name: 7

The p-value of the independent variable was found to be 0.000 (a value less than α = 0.05), this
leads to conclusion that the variable significantly estimates the dependent variable.
The coefficient of the independent variable is 1.7866; this suggests that a unit increase in the
independent variable results to an increase in the dependent variable by 1.7866. Similarly, a unit
decrease in the independent variable would result to a decrease in the dependent variable by
1.7866
The intercept coefficient is 344.19; this suggests that holding the independent variable constant
(zero value for the independent variable), we would expect the dependent variable to be 344.19.
The second regression model sought to estimate political rights (dependent variable) using Adult
literacy rate (independent variable). The correlation coefficient was found to be 0.1722; this
shows that a weak positive linear relationship exists between the two variables.
The value of R-Squared was found to be 0.0296; this implies that only 2.96% of the variation in
the dependent variable (political rights) is explained by the independent variable (Adult literacy
rate) in the model. The estimated regression model is gives as follows;
y=12.243+0.043 x
Student Name: 8

Where,
y=Political rights
x= Adult literacy rate
The p-value of the independent variable was found to be 0.1220 (a value greater than α = 0.05),
this leads to conclusion that the variable does not significantly estimate the dependent variable.
The coefficient of the independent variable is 0.043; this suggests that a unit increase in the
independent variable results to an increase in the dependent variable by 0.043. Similarly, a unit
decrease in the independent variable would result to a decrease in the dependent variable by
0.043.
The intercept coefficient is 12.243; this suggests that holding the independent variable constant
(zero value for the independent variable), we would expect the dependent variable to be 12.243.
Conclusion and limitations
This report sought to analyze the Social Progress Index data with an aim of informing on the
steps needed to achieve the Sustainable Development Goals (Andersson, et al., 2016). A total of
82 observations (countries) were analyzed. Results revealed that European countries are still far
ahead of African countries in terms of adult literacy rates. The average adult literacy rates in
African countries stands at 60.53 while in European countries it is at 98.51. In terms of human
rights (political rights), countries in the Asian continent are still much behind countries in
American continent. Analysis of premature deaths from non-communicable diseases between
American and European countries showed that the two continents have no significant
differences.
Student Name: 9

Lastly, we found out that Household air pollution attributable deaths significantly estimate
premature deaths from non-communicable diseases.
Some of the limitations of this study is the fact that the data used was merely a cross-sectional
data that does not give us a picture of how the trends have been. It would have been important to
look at the trends from the previous years to make a conclusive summary on the trends and
recommend on the potential actions to be undertaken.
Student Name: 10

References
Andersson, K., Dickin, S. & Rosemarin, A., 2016. Towards "Sustainable" Sanitation: Challenges
and Opportunities in Urban Areas. Sustainability, 8(12), p. 1289.
Armstrong, J. S., 2012. Illusions in Regression Analysis. International Journal of Forecasting ,
28(3), p. 689.
Student Name: 11

Appendix
A1: Data
Countries Names Continent
Child mortality rate
Household air pollution
attributable deaths
Adult literacy rate
Premature deaths from
non-communicable
diseases
Political rights
Discrimination and
violence against
minorities
Panama America 17.00 9.41 95.04 227.00 35.00 5.30
Chile America 8.10 1.91 96.63 242.90 37.00 3.80
Mauritania Africa 84.70 94.31 52.12 356.00 9.00 7.20
Jamaica America 15.70 10.68 88.50 398.00 34.00 3.60
Spain Europe 4.10 0.00 98.11 206.70 38.00 5.90
Malawi Africa 64.00 135.54 65.96 446.70 26.00 5.10
Serbia Europe 6.70 36.33 98.00 435.80 29.00 7.80
Costa Rica America 9.70 2.60 97.65 200.50 38.00 4.40
Tanzania Africa 48.70 141.08 80.36 451.20 25.00 5.40
Cameroon Africa 87.90 154.55 74.99 543.40 9.00 8.50
Turkey Asia 13.50 2.48 95.69 275.80 18.00 9.50
Hungary Europe 5.90 9.37 99.38 454.80 29.00 5.00
Mozambique Africa 78.50 150.77 58.84 595.90 20.00 5.60
Latvia Europe 7.90 8.07 99.89 474.10 36.00 7.70
Libya Africa 13.40 2.10 91.39 382.90 3.00 8.30
Georgia Asia 11.90 43.24 99.76 568.70 27.00 8.10
Cambodia Asia 28.70 109.19 78.35 532.00 11.00 7.10
Zambia Africa 64.00 187.19 85.12 725.10 23.00 5.40
Albania Europe 14.00 21.76 97.55 340.60 28.00 4.70
Egypt Asia 24.00 0.04 75.84 527.00 9.00 9.00
Tunisia Africa 14.00 0.41 81.05 305.20 36.00 7.90
Oman Asia 11.60 1.59 93.97 319.70 8.00 3.10
Romania Europe 11.10 21.73 98.76 462.50 35.00 7.00
Laos Asia 66.70 122.82 79.87 565.40 1.00 6.60
Philippines Asia 28.00 79.38 96.62 527.10 27.00 8.10
South Sudan Africa 92.60 183.17 31.98 596.40 -2.00 9.90
Korea, Republic of Asia 3.40 0.00 97.97 211.80 32.00 3.10
Congo, Republic of Africa 45.00 169.56 79.31 676.60 5.00 6.90
Cuba America 5.50 4.12 99.71 338.50 1.00 4.20
Student Name: 12

Liberia Africa 69.90 159.06 47.60 430.70 28.00 6.00
Mexico America 13.20 10.13 94.55 284.40 28.00 6.70
Nicaragua America 22.10 31.87 82.47 240.90 14.00 6.70
Kuwait Asia 8.60 0.09 96.12 237.90 13.00 5.20
Equatorial Guinea Oceania 94.10 158.20 95.20 576.90 1.00 6.30
Belarus Europe 4.60 1.79 99.72 684.30 5.00 7.10
Lesotho Africa 90.20 195.53 79.36 1037.70 27.00 4.10
Malta Europe 6.40 0.00 94.07 212.70 39.00 3.90
Bolivia America 38.40 29.40 95.14 319.40 29.00 6.20
Mauritius Africa 13.50 3.18 90.62 446.80 37.00 3.80
Armenia Asia 14.10 4.50 99.77 481.90 16.00 5.60
Kazakhstan Asia 14.10 22.76 99.79 657.80 5.00 7.30
Argentina America 12.50 1.62 98.09 365.90 33.00 4.70
Peru America 16.90 29.53 94.37 183.10 31.00 7.60
Thailand Asia 12.30 23.39 93.98 369.20 7.00 9.00
Poland Europe 5.20 9.02 99.79 393.70 37.00 4.90
Mongolia Asia 22.40 84.93 98.37 820.60 36.00 4.00
Gambia, The Africa 68.90 149.21 55.57 433.00 8.00 3.40
Central African Republic Africa 130.10 296.50 36.75 966.50 4.00 9.30
Niger Africa 95.50 174.50 19.10 414.60 21.00 7.70
Qatar Asia 8.00 0.01 97.76 229.20 9.00 4.60
Vietnam Asia 21.70 37.41 94.51 375.50 3.00 6.20
Azerbaijan Asia 31.70 17.06 99.81 503.20 4.00 6.70
Timor-Leste Oceania 52.60 86.98 64.07 365.20 29.00 6.70
Mali Africa 114.70 131.54 33.07 484.50 17.00 7.90
Chad Africa 138.70 197.90 40.02 517.60 4.00 8.50
Dominican Republic America 30.90 10.22 92.47 326.40 27.00 6.00
Turkmenistan Asia 51.40 1.02 99.69 597.60 0.00 6.80
United Arab Emirates Asia 6.80 0.02 92.99 444.30 7.00 3.60
Trinidad and Tobago America 20.40 0.86 98.97 512.90 33.00 4.40
Pakistan Asia 81.10 120.40 56.44 690.20 21.00 9.70
Benin Africa 99.50 188.36 38.45 563.20 33.00 3.60
Malaysia Asia 7.00 1.08 94.64 380.30 18.00 6.70
Honduras America 20.40 56.12 88.42 388.30 19.00 6.10
Papua New Guinea Oceania 57.30 191.82 63.43 1151.80 26.00 6.50
Guinea Oceania 93.70 210.63 30.47 564.90 17.00 8.80
Sri Lanka Asia 9.80 45.13 92.61 290.60 24.00 9.20
Montenegro Europe 4.70 41.10 98.72 423.60 26.00 7.60
Afghanistan Asia 91.10 251.92 38.17 1442.50 10.00 8.60
Iran Asia 15.50 0.94 87.17 415.40 7.00 8.80
Russia Europe 9.60 5.91 99.72 609.50 5.00 9.00
Senegal Africa 47.20 140.87 55.62 515.00 32.00 6.60
Maldives Asia 8.60 5.74 99.32 184.20 17.00 4.80
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Burkina Faso Africa 88.60 158.67 37.75 484.20 23.00 4.70
Lebanon Asia 8.30 0.01 94.05 320.40 14.00 8.70
Haiti America 69.00 144.91 60.69 678.90 15.00 6.70
Rwanda Africa 41.70 144.97 71.24 435.50 8.00 9.10
Nepal Asia 35.80 113.27 64.66 410.70 24.00 9.50
Brazil America 16.40 10.36 92.59 369.20 31.00 5.70
Bulgaria Europe 10.40 10.47 98.39 521.40 33.00 5.30
Nigeria Africa 108.80 109.17 59.57 284.90 25.00 9.40
Zimbabwe Africa 70.70 138.88 86.87 493.00 12.00 7.50
Myanmar Asia 50.00 91.98 93.09 583.60 14.00 9.90
A2: Confidence Interval
One-Sample T: Child mortality rate
Test of mu = 35.4 vs not = 35.4
Variable N Mean StDev SE Mean 95% CI
Child mortality 95 35.3979 36.3357 3.7280 (27.9959, 42.7999)
One-Sample T: Discrimination and violence aga
Test of mu = 6.301 vs not = 6.301
Variable N Mean StDev SE Mean 95% CI
Discrimination a 95 6.30105 2.01471 0.20670 (5.89064, 6.71147)
A3: Hypothesis Tests
A3.1: Hypothesis 1
t-Test: Two-Sample Assuming Equal Variances
African Europe
Mean
60.5296
9583
98.5088
3917
Variance
460.859
7462
2.58412
8207
Observations 24 12
Pooled Variance
312.594
1051
Hypothesized Mean Difference 0
df 34
t Stat -
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6.07574
819
P(T<=t) one-tail
3.43446
E-07
t Critical one-tail
1.69092
4255
P(T<=t) two-tail
6.86893
E-07
t Critical two-tail
2.03224
4509
A3.2: Hypothesis 2
t-Test: Two-Sample Assuming Equal
Variances
Asia
Ameri
ca
Mean
14.14
815 27
Variance
90.97
721
106.8
571
Observations 27 15
Pooled Variance
96.53
519
Hypothesized Mean
Difference 0
df 40
t Stat
-
4.061
87
P(T<=t) one-tail
0.000
11
t Critical one-tail
1.683
851
P(T<=t) two-tail
0.000
221
t Critical two-tail
2.021
075
A3.3 Hypothesis 3
t-Test: Two-Sample Assuming Equal
Variances
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Ameri
ca-
NCDs
Europ
e-
NCDs
Mean
338.4
2
434.9
75
Variance
16552
.11
19548
.06
Observations 15 12
Pooled Variance
17870
.33
Hypothesized Mean
Difference 0
df 25
t Stat
-
1.864
93
P(T<=t) one-tail
0.036
986
t Critical one-tail
1.708
141
P(T<=t) two-tail
0.073
972
t Critical two-tail
2.059
539
A4: Regression
A4.1: Regression model 1
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.6423
27
R Square
0.4125
84
Adjusted R
Square
0.4052
41
Standard Error
163.87
01
Observations 82
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ANOVA
df SS MS F
Significan
ce F
Regressi
on 1
15088
84
150888
4
56.189
67 7.8E-11
Residual 80
21482
73
26853.
41
Total 81
36571
58
Coefficients
Standar
d Error t Stat P-value
Lower
95%
Upper
95%
Intercept 344.1855 24.6420 13.967 3.68E-23 295.146 393.225
Household air pollution
attributable deaths 1.786605 0.23834 7.496 7.8E-11 1.3123 2.2609
A4.2: Regression model 2
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.1721
56
R Square
0.0296
38
Adjusted R
Square
0.0175
08
Standard Error
11.827
2
Observations 82
ANOVA
df SS MS F
Significan
ce F
Regressi
on 1
341.79
37
341.79
37
2.4434
33 0.121965
Residual 80
11190.
61
139.88
26
Total 81 11532.
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4
Coefficien
ts
Standa
rd Error t Stat P-value
Lower
95%
Upper
95%
Interce
pt 12.2427
5.0788
18
2.4105
4
0.0182
23
2.1355
26
22.349
87
Adult
literacy
rate 0.094257 0.0603
1.5631
48
0.1219
65
-
0.0257
4
0.2142
58
Student Name: 18