Analysis of Social Progress Index Data Set from 100 Countries

Report on the analysis of data from 100 countries across the world using different statistical techniques. Results showed significant differences in Women's average years in school among American and African countries. No significant differences in terms of traffic deaths between Asian and American countries. Access to water and sanitation facilities negatively correlated with greenhouse gas emissions.

Business Modelling and Analysis (FIN60003)
Name:
Institution:
26th May 2018
1

Table of Contents
Executive summary.........................................................................................................................3
Introduction......................................................................................................................................3
Analysis...........................................................................................................................................4
Descriptive Statistics....................................................................................................................4
Summary Statistics.......................................................................................................................4
Confidence intervals....................................................................................................................5
Water and Sanitation................................................................................................................6
Access to advanced education..................................................................................................6
Hypothesis Testing.......................................................................................................................7
Correlation and Regression........................................................................................................10
Scatter plot 1...........................................................................................................................10
Regression model...................................................................................................................11
Scatter plot 1...........................................................................................................................13
Regression model...................................................................................................................14
Conclusion and limitations............................................................................................................15
References......................................................................................................................................17
Appendix........................................................................................................................................18
2

Executive summary
The aim of this paper is to report on the analysis of data from 100 countries across the world.
Different statistical techniques were utilized to make concreate analysis of the presented data.
Results showed that the Women's average years in school is significantly higher among
American countries than African countries. Results also showed that no significant differences in
terms of traffic deaths between Asian and American countries.
Introduction
Margaret the CEO of a Human Capital Management company in Melbourne recently attended a
TED Talk in which Michael Green discussed the importance of the 2030 United Nation
Sustainable Development Goals. Michael presented challenges that countries around the world
would face to meet the UN Sustainable Development Goals and elaborated on the importance of
the Social Progress Index in that process. Some of the questions tackled include; what are the
UN Sustainable Development Goals? And how we can make the world a better place by 2030?
After the seminar, Margaret decided to further study the importance of the Social Progress Index
and compare countries based on their performance at each sub category of this index. This study
therefore sought to answer the CEO’s concerns in relation to the Social Progress Index.
Data for this study refers to the 2017 Social Progress Index data set and it contains about 182
countries. The sections of the report include statistical analysis (descriptive statistics, confidence
intervals, correlation and regression) conclusion and limitations.
3

Analysis
Descriptive Statistics
Most of the countries included in the study were countries in Africa (52%, n = 46). American
continent was represented by 26% (n = 23) while Asia had a 22% (n = 19) representation.
Summary Statistics
The average percent of people with access to improved sanitation facilities was found to be
58.73% with some countries having a 100% access to improved sanitation facilities and the
country with the lowest rate recording a mere 10.88% access to improved sanitation facilities. On
average, 21.99% of traffic deaths was recorded in the selected countries with some countries
recording as high as 73.4% while others recording as low as 3.60%. In terms of press freedom
index, an average index of 36.38% was recorded with the country with highest index score
scoring 83.92% and the lowest score being 11.10%.
4

The selected countries emitted an average of 725.93 greenhouse emissions with some countries
emitting as high as 11031.69 while others emitting as low as 168.22.
Average corruption index stood at 36.45% with the highest index being 82% and the lowest
index being 14%. Women’s average years in school for the selected sample was found to be
8.12, however, some countries had an average of 15.68 while others had an average of 0.98.
Access to
improved
sanitation
facilities
Traffic
deaths
Press
Freedom
Index
Greenhous
e gas
emissions
Corrupti
on
Women's
average
years in
school
Mean 58.73 21.99 36.38 725.93 36.45 8.12
Standard Error 3.23 0.99 1.60 124.78 1.56 0.41
Median 61.24 23.50 31.99 523.15 34.00 8.65
Mode 100.00 24.10 57.89 #N/A 26.00 #N/A
Standard
Deviation
30.32 9.26 14.98 1170.52 14.65 3.88
Sample
Variance
919.57 85.73 224.26 1370115.07 214.69 15.05
Kurtosis -1.49 9.70 1.20 70.98 0.78 -0.93
Skewness -0.13 1.69 1.07 8.06 0.99 0.07
Range 89.12 69.80 72.82 10863.47 68.00 14.70
Minimum 10.88 3.60 11.10 168.22 14.00 0.98
Maximum 100.00 73.40 83.92 11031.69 82.00 15.68
Sum 5168.09 1935.00 3201.35 63881.82 3208.00 714.94
Count 88 88 88 88 88 88
Confidence intervals
In this section, we estimate the 95% confidence interval for the two variables (one under water
and sanitation while the other under Access to advanced education).
5

Water and Sanitation
Under this we considered access to improved sanitation facilities. The 95% confidence
estimation is given as follows;
CI: x ± zα / 2 SE
x=58.73, zα / 2=1.96, SE=3.23
CI: x ± zα/ 2 SE →58.73 ± 1.96∗3.23
CI: 58.73 ±6.3308
Lower bound: 58.73−6.3308=52.3992
Lower bound: 58.73+6.3308=65.0608
From the above calculations, we are 95% confident that the true mean of access to improved
sanitation facilities is between 52.3992 and 65.0608.
Access to advanced education
Under this we considered Women's average years in school. The 95% confidence estimation is
given as follows;
CI: x ± zα / 2 SE
x=8.12, zα/ 2=1.96, SE=0.41
CI: x ± zα / 2 SE → 8.12± 1.96∗0.41
CI: 8.12 ± 0.8036
Lower bound: 8.12−0.8036=7.3164
6

Lower bound: 8.12+0.8036=8.9236
From the above calculations, we are 95% confident that the true mean of Women's average years
in school is between 7.3164 and 8.9236.
Hypothesis Testing
This section sought to carry out different hypothesis tests.
The first hypothesis that we tested was on whether there is evidence of significant differences in
the Women's average years in school. The following hypothesis was tested at 5% level of
significance.
H0: The women’s average years in school is not different for the American countries and African
countries.
H1: The women’s average years in school is not different for the American countries and African
countries.
This was tested at 5% level of significance and the results are presented below;
t-Test: Two-Sample Assuming Equal Variances
Africa
Americ
a
Mean 5.78413
11.2343
5
Variance
7.37124
3
4.34628
9
Observations 46 23
Pooled Variance
6.37797
4
Hypothesized Mean 0
7

Difference
df 67
t Stat
-
8.45066
P(T<=t) one-tail
1.85E-
12
t Critical one-tail
1.66791
6
P(T<=t) two-tail
3.71E-
12
t Critical two-tail
1.99600
8
An independent samples t-test was performed to compare the mean women’s number of years in
school for the African and American countries. Results showed that the African countries (M =
5.78, SD = 2.72, N = 46) had significant difference in terms of the mean women’s number of
years in school when compared to the American countries (M = 11.23, SD = 2.08, N = 23), t (67)
= -8.45, p < .05, two-tailed. The difference of 5.45 showed a significant difference. Essentially
results showed that the level of Access to Advanced Education is higher among American
countries than African countries.
The second hypothesis that we tested was on whether there is evidence of significant differences
in terms of Personal Safety between Asian and American countries. The following hypothesis
was tested at 5% level of significance.
H0: The average traffic deaths is not different for the Asian countries and American countries.
H1: The average traffic deaths is significantly different for the Asian countries and American
countries.
This was tested at 5% level of significance and the results are presented below;
t-Test: Two-Sample Assuming Equal Variances
8

America
n Asian
Mean
16.1521
7
15.5947
4
Variance
30.3798
8
49.7305
3
Observations 23 19
Pooled Variance
39.0876
7
Hypothesized Mean
Difference 0
df 40
t Stat
0.28760
2
P(T<=t) one-tail
0.38756
8
t Critical one-tail
1.68385
1
P(T<=t) two-tail
0.77513
6
t Critical two-tail
2.02107
5
An independent samples t-test was performed to compare the mean traffic deaths for the
American and Asian countries. Results showed that the American countries (M = 16.15, SD =
5.51, N = 23) had no significant difference in terms of the mean traffic deaths when compared to
the Asian countries (M = 15.59, SD = 7.05, N = 19), t (40) = 0.29, p > .05, two-tailed. The
difference of 0.56 showed no significant difference. Essentially results showed that there is no
significant difference in terms of Personal Safety between Asian and American countries.
The third hypothesis that we tested was on whether there is evidence of significant differences in
terms of Environmental Quality between African and American countries. The following
hypothesis was tested at 5% level of significance.
9

H0: The average Greenhouse gas emissions is not different for the African countries and
American countries.
H1: The average Greenhouse gas emissions is significantly different for the African countries and
American countries.
This was tested at 5% level of significance and the results are presented below;
t-Test: Two-Sample Assuming Equal Variances
Africa
America
n
Mean
975.116
8
434.213
4
Variance
248130
8
49631.4
9
Observations 46 23
Pooled Variance
168284
7
Hypothesized Mean
Difference 0
df 67
t Stat
1.63273
5
P(T<=t) one-tail
0.05360
8
t Critical one-tail
1.66791
6
P(T<=t) two-tail
0.10721
6
t Critical two-tail
1.99600
8
An independent samples t-test was performed to compare the mean Greenhouse gas emissions
for the African and American countries (Rice, 2006). Results showed that the African countries
(M = 975.12, SD = 1575.22, N = 46) had no significant difference in terms of the mean
Greenhouse gas emissions when compared to the American countries (M = 434.21, SD = 222.78,
10

N = 23), t (67) = 1.63, p > .05, two-tailed. The difference of 540.91 showed an insignificant
difference. Essentially results showed that the Greenhouse gas emissions is not significantly
different for the American and African countries.
Correlation and Regression
Scatter plot 1
The first scatter plot of Greenhouse gas emissions and access to improved sanitation facilities is
given below;
As can be seen, the plot shows that a negative relationship exists between access to water and
sanitation facilities and the greenhouse emissions.
Regression model
This is a continuation of the correlation between access to water and sanitation and greenhouse
gas emissions. The dependent variable is greenhouse gas emissions while the independent
variable is the access to water and sanitation services (Tofallis, 2009). The results of the
regression equation model are given below;
11

The value of the R-Squared is 0.0586; this means that only 5.86% of the variation in the
dependent variable is explained by the independent variable in the model. The overall model was
found to be significant and fit to estimate the dependent variable (p-value < 0.05).
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.242026
R Square 0.058576
Adjusted R Square 0.04763
Standard Error 1142.303
Observations 88
The correlation coefficient is -0.242; this means that a negative relationship exists between
access to water and sanitation facilities and greenhouse gas emissions (YangJing, 2009).
ANOVA
df SS MS F
Significan
ce F
Regressio
n 1
698230
2
698230
2
5.35100
9 0.023097
Residual 86
1.12E+0
8
130485
7
Total 87
1.19E+0
8
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Intercept 1274.579 266.6122 4.78065 7.15E-06 744.5719 1804.587
Access to improved -9.34216 4.038588 -2.31322 0.023097 -17.3706 -1.31371
12

sanitation facilities
The estimated regression model is;
y=1274.56−9.3422 x
Where;
y=greenhouse gas emissions
x=access ¿ water ∧sanitation facilities
The value of the coefficient (slope) for the access to water and sanitation facilities is -9.3422;
this means that an increase in the access to water and sanitation facilities would result to a
decrease in greenhouse gas emissions.
The p-value for the coefficient of access to improved sanitation facilities is 0.023 (a value less
than 5% level of significance). This means that the variable is significant in the model and that a
linear relationship exists between access to water and sanitation facilities and greenhouse gas
emissions.
Scatter plot 1
The second scatter plot of corruption and Press Freedom Index is given below;
13

As can be seen, the plot shows that a negative relationship exists between corruption and the
Press freedom Index. This means that an increase in the Press Freedom Index would result to a
reduction in corruption index.
Regression model
This is a continuation of the correlation between corruption and press freedom index. The
dependent variable is corruption while the independent variable is the press freedom index. The
results of the regression equation model are given below;
The value of the R-Squared is 0.1858; this means that only 18.58% of the variation in the
dependent variable is explained by the independent variable in the model (Good & Hardin,
2009). The overall model was found to be significant and fit to estimate the dependent variable
(p-value < 0.05).
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.431102
R Square 0.185849
14

Adjusted R
Square 0.176382
Standard Error 13.29739
Observations 88
The correlation coefficient is -0.4311; this means that a negative relationship exists between
corruption and press freedom index.
ANOVA
df SS MS F
Significanc
e F
Regressio
n 1 3471.248 3471.248 19.63147 2.75E-05
Residual 86 15206.57 176.8206
Total 87 18677.82
Coefficien
ts
Standar
d Error t Stat
P-
value
Lower
95%
Upper
95%
Intercept 51.79927
3.74210
6
13.8422
8
1.34E-
23
44.3602
1
59.2383
3
Press Freedom
Index -0.4218
0.09519
9
-
4.43074
2.75E-
05
-
0.61105
-
0.23255
The estimated regression model is;
y=51.8−0.4218 x
Where;
y=Corruption
x=Press Freedom Index
The value of the coefficient (slope) for the access to water and sanitation facilities is -0.4218;
this means that an increase in the press freedom index would result to a decrease in corruption
cases (Armstrong, 2012).
15

The p-value for the coefficient of press freedom index is 0.000 (a value less than 5% level of
significance). This means that the variable is significant in the model and that a linear
relationship exists between press freedom index and corruption cases.
Conclusion and limitations
This study sought to investigate and understand the social progress index data for 2017. The
study considered 6 different categories for analysis purposes. The categories are;
 Category 2: Water and Sanitation
 Category 4: Personal Safety
 Category 6: Access to Information and Communications
 Category 8: Environmental Quality
 Category 10: Personal Freedom and Choice
 Category 12: Access to Advanced Education
Three different hypothesis were tested. The first hypothesis tested whether there is evidence of
significant differences in the Women's average years in school. Results showed that the Women's
average years in school is higher among American countries than African countries. The second
hypothesis tested whether there is evidence of significant differences in terms of traffic deaths
between Asian and American countries. Results showed that there is no significant difference in
the traffic deaths between Asian and American countries. The last hypothesis tested whether
there is evidence of significant differences in terms of greenhouse gas emissions between
African and American countries. Results showed that the Greenhouse gas emissions is not
significantly different for the American and African countries.
16

In terms of limitations, this study only utilized the 2017 dataset. This makes it hard to gauge
whether the influence obtained is as a result of a particular change within the year. Next study
should attempt to use a panel data where different years are taken into consideration so as to
ascertain that the change is not sudden.
References
Armstrong, J. S., 2012. Illusions in Regression Analysis. International Journal of Forecasting
(forthcoming), 28(3), p. 689.
Good, P. I. & Hardin, J. W., 2009. Common Errors in Statistics.
Rice, J. A., 2006. Mathematical Statistics and Data Analysis.
Tofallis, C., 2009. Least Squares Percentage Regression. Journal of Modern Applied Statistical
Methods, 7(6), p. 526–534.
YangJing, L., 2009. Human age estimation by metric learning for regression problems.
International Conference on Computer Analysis of Images and Patterns, p. 74–82.
17

Appendix
Data
Countries Names Contin
ent
Access
to
improve
d
sanitatio
n
facilities
Traffic
deaths
Press
Freedom
Index
Greenho
use gas
emission
s
Corrupti
on
Women's
average
years in
random
Algeria AFRICA 87.60 23.80 41.69 380.96 34.00 7.74
0.8753
13
Angola AFRICA 51.59 26.90 39.89 995.89 18.00 5.31
0.2470
53
Benin AFRICA 19.72 27.70 28.97 650.83 36.00 2.73
0.8897
63
Botswana AFRICA 63.43 23.60 22.91 456.98 60.00 8.71
0.1597
28
Burkina Faso AFRICA 19.73 30.00 22.66 814.89 42.00 1.86
0.3769
8
Burundi AFRICA 48.01 21.30 54.10 404.14 20.00 4.23
0.1396
83
Cabo Verde AFRICA 72.22 26.10 19.82 204.55 59.00 6.14
0.5137
31
Cameroon AFRICA 45.80 27.60 40.53
1404.6
6 26.00 7.17
0.7669
8
Central African
Republic AFRICA 21.79 32.40 33.60
11031.
69 20.00 4.27
0.6877
28
Chad AFRICA 12.08 24.10 40.59
1122.7
1 20.00 1.63
0.0293
84
Comoros AFRICA 35.82 21.80 24.33 397.93 24.00 6.03
0.1353
07
Congo, Democratic
Republic of AFRICA 28.65 33.20 50.97 744.92 21.00 6.26
0.7938
39
Congo, Republic of AFRICA 15.01 26.40 35.84 314.38 20.00 8.85
0.5773
01
Côte d'Ivoire AFRICA 22.49 24.20 30.17 519.87 34.00 3.87
0.6678
83
Djibouti AFRICA 47.43 24.70 70.90 1067.2 30.00 4.05 0.7856
18

3 73
Eritrea AFRICA 15.73 24.10 83.92 895.58 18.00 3.95
0.1250
62
Ethiopia AFRICA 28.02 25.30 45.13
1037.2
9 34.00 3.75
0.8404
84
Gabon AFRICA 41.86 22.90 32.20 221.16 35.00 10.16
0.6882
07
Gambia, The AFRICA 58.88 29.40 46.53
2548.5
4 26.00 3.48
0.1192
27
Ghana AFRICA 14.87 26.20 17.95 293.80 43.00 7.12
0.7567
62
Guinea-Bissau AFRICA 20.85 27.50 29.03 964.53 16.00 2.36
0.9706
14
Kenya AFRICA 30.11 29.10 31.16 524.20 26.00 9.14
0.9491
4
Lesotho AFRICA 30.27 28.20 28.78 423.07 39.00 10.24
0.9004
5
Liberia AFRICA 16.89 33.70 30.71 523.13 37.00 4.37
0.0899
5
Libya AFRICA 96.56 73.40 57.89 921.35 14.00 6.27
0.3615
97
Madagascar AFRICA 12.00 28.40 27.04 884.35 26.00 6.46
0.6641
66
Malawi AFRICA 41.00 35.00 28.12 880.18 31.00 4.98
0.9175
57
Mali AFRICA 24.67 25.60 39.83
1179.8
4 32.00 1.56
0.3715
23
Mauritania AFRICA 40.01 24.50 24.03 990.97 27.00 4.44
0.2161
41
Mauritius AFRICA 93.15 12.20 27.69 264.02 54.00 10.36
0.4460
98
Morocco AFRICA 76.71 20.80 42.64 318.76 37.00 4.84
0.6188
12
Mozambique AFRICA 20.50 31.60 30.25 967.73 27.00 2.99
0.4778
77
Namibia AFRICA 34.39 23.90 15.15 708.78 52.00 9.79
0.2275
56
Niger AFRICA 10.88 26.40 24.62
1660.5
2 35.00 1.41
0.3756
33
Nigeria AFRICA 28.95 20.50 35.90 332.12 28.00 6.77
0.8029
83
Rwanda AFRICA 61.64 32.10 54.61 404.49 54.00 5.48
0.8532
26
Senegal AFRICA 47.59 27.20 27.99 726.07 45.00 3.11
0.4103
28
Sierra Leone AFRICA 13.26 27.30 29.94 753.01 30.00 2.58
0.4324
39
South Africa AFRICA 66.39 25.10 21.92 714.22 45.00 11.48
0.0667
54
Sudan AFRICA 23.64 24.30 72.53
1127.1
1 14.00 5.67
0.3364
72
Tanzania AFRICA 15.55 32.90 28.65 665.78 32.00 7.06
0.1733
82
Togo AFRICA 11.62 31.10 30.31 668.99 32.00 4.19
0.4448
23
Tunisia AFRICA 91.59 24.40 31.60 275.80 41.00 9.10
0.3975
74
Uganda AFRICA 19.08 27.40 32.58 523.17 25.00 6.16
0.7540
54
Zambia AFRICA 43.87 24.70 35.08 895.08 38.00 7.75
0.1490
82
Zimbabwe AFRICA 36.83 28.20 40.41
1050.1
2 22.00 10.20
0.9681
83
Bolivia
AMERIC
A 50.33 23.20 31.78 776.16 33.00 9.86
0.6198
5
Brazil
AMERIC
A 82.78 23.40 32.62 355.86 40.00 9.60
0.5139
86
Canada
AMERIC
A 99.82 6.00 15.26 494.63 82.00 15.65
0.9107
07
19

Chile
AMERIC
A 99.05 12.40 19.23 274.00 66.00 13.29
0.8846
34
Colombia
AMERIC
A 81.10 16.80 44.11 277.54 37.00 10.81
0.2672
93
Costa Rica
AMERIC
A 94.52 13.90 11.10 198.92 58.00 10.92
0.3554
6
Cuba
AMERIC
A 93.16 7.50 70.23 214.66 47.00 13.19
0.9761
12
Dominican Republic
AMERIC
A 83.99 29.30 27.90 265.76 31.00 11.12
0.7500
71
Ecuador
AMERIC
A 84.69 20.10 33.21 346.76 31.00 11.24
0.5915
1
El Salvador
AMERIC
A 74.99 21.10 27.20 249.88 36.00 9.21
0.2964
5
Guatemala
AMERIC
A 63.85 19.00 38.03 220.41 28.00 6.65
0.0449
53
Guyana
AMERIC
A 83.65 17.30 27.07 747.01 34.00 10.85
0.1882
88
Honduras
AMERIC
A 82.65 17.40 44.62 564.07 30.00 8.58
0.1719
11
Jamaica
AMERIC
A 81.78 11.50 12.45 382.78 39.00 12.81
0.5679
8
Mexico
AMERIC
A 85.16 12.30 49.33 367.10 30.00 10.77
0.1436
85
Nicaragua
AMERIC
A 67.90 15.30 28.82 530.89 26.00 9.04
0.9052
02
Panama
AMERIC
A 74.99 10.00 30.59 238.52 38.00 12.46
0.3945
27
Paraguay
AMERIC
A 88.60 20.70 33.63 790.19 30.00 10.21
0.4893
06
Peru
AMERIC
A 76.19 13.90 29.99 269.64 35.00 11.57
0.9726
66
Suriname
AMERIC
A 79.22 19.10 16.70 440.90 45.00 10.14
0.8449
74
Trinidad and Tobago
AMERIC
A 91.52 14.10 23.29
1030.2
1 35.00 13.28
0.3073
57
United States
AMERIC
A 99.99 10.60 22.49 392.70 74.00 15.06
0.6722
61
Uruguay
AMERIC
A 96.44 16.60 15.88 558.30 71.00 12.08
0.5169
48
Afghanistan ASIA 31.85 15.50 37.75 544.79 15.00 0.98
0.3134
21
Armenia ASIA 89.50 18.30 28.79 393.71 33.00 13.10
0.4779
44
Azerbaijan ASIA 89.35 10.00 57.89 469.46 30.00 13.16
0.8182
9
Bahrain ASIA 99.20 8.00 54.86 619.51 43.00 10.53
0.6406
83
Bangladesh ASIA 60.56 13.60 45.94 376.07 26.00 5.09
0.5443
18
Bhutan ASIA 50.40 15.10 30.73 168.22 65.00 3.23
0.4733
32
Cambodia ASIA 42.43 17.40 40.70 623.15 21.00 5.69
0.4301
96
China ASIA 76.47 18.80 80.96 755.44 40.00 9.82
0.4306
24
Egypt ASIA 94.72 12.80 54.45 334.09 34.00 9.06
0.8037
63
Georgia ASIA 86.26 11.80 27.96 472.99 57.00 14.57
0.0143
65
India ASIA 39.63 16.60 43.17 490.65 40.00 5.79
0.5660
86
Indonesia ASIA 60.83 15.30 41.72 348.04 37.00 9.56
0.1083
52
Iran ASIA 90.00 32.10 66.52 576.28 29.00 9.89
0.1939
99
Iraq ASIA 85.61 20.20 54.35 554.02 17.00 7.88
0.5026
7
Israel ASIA 100.00 3.60 32.58 388.92 64.00 15.68 0.4466
20

5
Japan ASIA 100.00 4.70 28.67 301.27 72.00 15.51
0.0153
88
Jordan ASIA 98.63 26.30 44.49 382.75 48.00 12.39
0.2162
03
Kazakhstan ASIA 97.54 24.20 54.55 805.55 29.00 13.93
0.0328
33
Korea, Republic of ASIA 100.00 12.00 28.58 434.63 53.00 14.62
0.9822
83
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.24202
6
R Square
0.05857
6
Adjusted R
Square 0.04763
Standard Error
1142.30
3
Observations 88
ANOVA
df SS MS F
Significa
nce F
Regression 1
69823
02
69823
02
5.3510
09 0.023097
Residual 86
1.12E+
08
13048
57
Total 87
1.19E+
08
Coefficie
nts
Standa
rd
Error t Stat
P-
value
Lower
95%
Upper
95%
Intercept
1274.57
9
266.61
22
4.7806
5
7.15E-
06 744.5719
1804.5
87
Access to
improved
sanitation
facilities -9.34216
4.0385
88
-
2.3132
2
0.0230
97 -17.3706
-
1.3137
1
21

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.43110
2
R Square
0.18584
9
Adjusted R
Square
0.17638
2
Standard Error
13.2973
9
Observations 88
ANOVA
df SS MS F
Significa
nce F
Regression 1
3471.2
48
3471.2
48
19.631
47 2.75E-05
Residual 86
15206.
57
176.82
06
Total 87
18677.
82
Coefficie
nts
Standa
rd
Error t Stat
P-
value
Lower
95%
Upper
95%
Intercept
51.7992
7
3.7421
06
13.842
28
1.34E-
23 44.36021
59.238
33
Press Freedom
Index -0.4218
0.0951
99
-
4.4307
4
2.75E-
05 -0.61105
-
0.2325
5
Hypothesis Tests;
t-Test: Two-Sample Assuming Equal
Variances
Africa
Ameri
ca
Mean
975.1
168
434.2
134
22

Variance
24813
08
49631
.49
Observations 46 23
Pooled Variance
16828
47
Hypothesized Mean
Difference 0
df 67
t Stat
1.632
735
P(T<=t) one-tail
0.053
608
t Critical one-tail
1.667
916
P(T<=t) two-tail
0.107
216
t Critical two-tail
1.996
008
t-Test: Two-Sample Assuming Equal
Variances
Ameri
ca Asia
Mean
16.15
217
15.59
474
Variance
30.37
988
49.73
053
Observations 23 19
Pooled Variance
39.08
767
Hypothesized Mean
Difference 0
df 40
t Stat
0.287
602
P(T<=t) one-tail
0.387
568
t Critical one-tail
1.683
851
P(T<=t) two-tail
0.775
136
t Critical two-tail
2.021
075
23

t-Test: Two-Sample Assuming Equal
Variances
Africa
Ameri
ca
Mean
975.1
168
434.2
134
Variance
24813
08
49631
.49
Observations 46 23
Pooled Variance
16828
47
Hypothesized Mean
Difference 0
df 67
t Stat
1.632
735
P(T<=t) one-tail
0.053
608
t Critical one-tail
1.667
916
P(T<=t) two-tail
0.107
216
t Critical two-tail
1.996
008
24