RMIT international university vietnam assignment cover page (individual) mortality rate neonatal (per 1,000 live births) and GNI per capita (current US) of the world in 2017

RMIT International University Vietnam
Assignment Cover Page (Individual)
Subject Code: ECON1193
Subject Name: Business Statistic 1
Location & Campus: RMIT SGS
Title of Assignment: A2 – Individual Case Study (40%)
Student Name: Nguyen Thi Thanh Van
Student Code: S3741087
Teachers Name: Tuan Chu Thanh
Class Group: 14
Assignment due date: August 22, 2021
Number of pages including this one: 13
Word Count 2818
(Excluding Cover Page and References):

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I.

Background Information:

According to UNICEF, the neonatal mortality rate means the proportion of child death within the
first 28 days of birth which is the most vulnerable period for a child’s survival. There are a lot of
reasons that can lead to the neonatal mortality as socio-economic or environmental factors.
Furthermore, the WHO Health Observatory Data Repository pointed out that one of the leading
triggers for deaths in newborns comes from congenital diseases or other infectious conditions.
Since 2015, the United Nations have established the Sustainable Development Goals (SDGs),
which include 17 different goals targeted to sustainably develop the balance and prosperity of
society, economy, and environment (UNDP, 2019). With SDG 3 about Good Health and Wellbeing at all ages, it aims to decrease the child morality to under 12 deaths per 1000 live births in
2030. Declining the rate of human infant deaths is one of the most essential parts to improve the

overall physical health of a community, which impacts public health and social policy. It also
demonstrates the rights of children who need to protect their healthy lives and increase the wellbeing of developing (UN). Globally, the percentage of neonatal deaths reduced significantly from
36,6 deaths per 1000 live births to 18 deaths per 1000 live births, about 51% between 1990 and
2017 (WHO, 2020). On the other hand, the proportion of infants’ deaths experienced
approximately 5.3 million children who died due to preventable reasons in 2018 (WHO, 2018).
Additionally, geographically, countries in sub-Saharan Africa and Southern Asia witnessed a
higher percentage of child mortality at about 24-27 deaths per 1,000 live births (more likely ten
times to die) than high-income countries (WHO, 2020). For this reason, it can be seen that the
child mortality rate and GNI have a sustainable connection. Most high-income countries often
keep a lower percentage of infant deaths than in lower or lower-middle countries because, in
wealthier countries, people tend to enhance the health care system to achieve progress in health

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indicators. To support for above points, Figure 1 shows that when the GNI per capita rose
gradually from $16,000 to $80,000, the child mortality rate fell marginally under 5 deaths per
1,000 live births. It can be concluded that the rate of infant deaths along with the growth of GNI
per capita.

Mortality Rate Neonatal vs. The Gross Nation Income (GNI) in 2017
40.00

90,000

35.00

80,000

30.00

70,000
60,000

25.00

50,000

20.00

40,000

15.00

30,000

10.00

20,000

5.00

10,000

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Mortality rate, neonatal (per 1,000 live births)

-

GNI per capita (current US$)

Figure 1. Mortality Rate Neonatal (Per 1,000 live births) and GNI per capita (current US$)
of the world in 2017
II.

Descriptive Statistic and Probability:
a. Probability

Low-income

Middle-income

High-income

countries (LI)

countries (MI)

countries (HI)

TOTAL

3


High mortality rate

5

5

0

10

neonatal (A)
Low mortality rate

0

16

13

28

neonatal (A’)
TOTAL
5
21
13
38
Table 1. Contingency Table of Mortality rate neonatal on three country categories
(Per 1,0000 live births)
Continuously, this part will compare two different probabilities to determine the rate of infant
deaths and income which are statistically independent or not. The marginal probabilities are

countries having a high mortality rate neonatal P (A), and the conditional probabilities are three
country categories Low-income P(LI), Middle-income P(MI), and High-income (HI). Checking
the independence of events:
P (A) = P (High mortality rate neonatal) = 10/38 = 0.263
P (A | LI) = P (High mortality rate neonatal WITH Low-income) = (5/38) / (5/38) = 1
P (A | MI) = P (High mortality rate neonatal WITH Middle-income) = (5/38) / (21/38) = 0.238
P (A | HI) = P (High mortality rate neonatal WITH High-income) = (0/38) / (13/38) = 0
Based on the observation from the data, the result presents that the conditional probabilities,
which are P(A|LI), P(A|MI), and P(A|HI), are all different from the marginal probability P(A).
Due to this reason, the rate of child mortality and income is not statistically independent. It
means the proportion of newborn death has a relationship with the countries’ GNI per capita.
Conclusion: According to the contingency table (Table 1), it witnesses those Low-income
countries (LI) tend to be more likely to have high popularity of mortality neonatal, at 100%,
being the highest percentage in three categories. Following by, Middle-income countries
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experience 23.8%; and Low-income countries illustrate with 0% dying of the children in the
neonatal period.
b. Descriptive Statistics
Mean
LI countries

Median
23.3

Mode
21.7

N/A

MI countries
12.6
10.75
5.3
HI countries
3.96
3
N/A
Table 2. Measures of central tendency for Mortality rate neonatal on three country
categories (Per 1,000 live births)
Comparing and Analysis: The second table illustrates measures of central tendency for infant
deaths of each country category. First of all, the average value of LI countries, at 23.3, is nearly
two times higher than MI countries with 12.6 and more than five times the rate of child deaths in
HI countries, at 3.96. Because of this, the figure points out that LI countries tend to have a higher
mean of child deaths than MI and HI countries. Furthermore, this table shows that the median
value of LI countries is highest with 21.7, come after is MI countries with 10.75 and HI countries
with 3 respectively. There are no many differences between the mean and median values of each
category, meaning third of country categories try to keep a low inflation rate with mortality rate
neonatal in 2017. In addition, the mode value shows in the dataset of MI countries, which is 5.3.
Besides that, LI and HI countries do not show the mode value in the dataset; and it means that LI
and HI countries do not have any value that occurs most often. By applying a calculation to find
outliers, both LI and HI nations appear outliers, while there are no outliers for MI countries.
-

LI countries: MIN > Q1 – 1,5*IQR = 13.05 or Q3 + 1.5*IQR = 32.65 < MAX

-

MI countries: MIN > Q1 – 1,5*IQR = -8.14 or Q3 + 1.5*IQR = 30.76 > MAX

-

HI countries: MIN > Q1 – 1,5*IQR = -1.65 or Q3 + 1.5*IQR = 7.55 < MAX

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To conclude, while MI countries do not appear outliers, LI and HI countries shows outliers in the
dataset. For this reason, the most appreciated measure of central tendency is the median due to
having outliers in the data as well as this method is not be impacted by extreme values.
Range
IQR
Variance
SD
CV (%)
18.3
4.9
46.38
6.81
29.23
LI countries
MI countries
25.6
9.73
59.14
7.69
61.06
HI countries
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2.3
10.13
3.18
80.33
Table 3. Measures of variation for Mortality rate neonatal on three country categories
(Per 1,000 live births)
Comparing and Analysis: Moving to the next part of the descriptive statistic, Table 3 presents
the measures of variation for infants’ deaths of each country category. Firstly, the range of MI
countries, at 25.6, is more slightly considerable than LI and HI countries, followed by 18.3 and
11. Besides that, HI countries recorded the lowest value of interquartile range with 2.3, while LI
countries, at 4.9, are approximately two times lower than MI countries with 9.73. It is obvious to
understand that the distance between the first and third quartile in HI countries data is more
closed strictly than remains. Regarding the variance, HI countries witness the most minimal
value with 10.13, which is about four to five times smaller than LI and MI countries, at 46.38
and 59.14. The standard deviation between LI and MI countries is not too different, at 6.81 and
7.69. However, the value of HI countries experienced a substantial decline to only 3.18.
According to the above table, the coefficient of variation in HI countries illustrates 80.33%,
which triples the percentage of LI countries by 29.23% and becomes higher than MI countries, at
61.06%. In this way, the outcome has shown that the rate of mortality neonatal in HI countries
prefers to stay unchanged. However, with the outliers in data, the coefficient of variation (%)
would be the most effective measure to identify the data dispersion precisely. Furthermore, the

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coefficient of variation is not affected by appearing of outliers as same as the attributions of
median.
III.

Confidence Intervals:

a. Calculating Confidence Intervals for The World Average of Mortality Rate Neonatal
Sample Mean

¯X

11.05
38

Sample Size (n)

8.83

Sample Standard Deviation (S)
Level of Significance

5% = 0.05

Confidence Level

95% = 0.95
37

Degree of Freedom (d.f)

±2,0262

T-value (t37 )
As the lack of the population standard deviation

σ , we have to replace Z-table with T-table,

being used for determining the confidence intervals.
-

1 - α = 0.95 -> the Level of Significance

-

Degree of Freedom d.f = n -1 = 38 -1 = 37

-

According to T-table: t37 = ±2,0262

α

= 0.05 -> σ /2

= 0.025

Confidence Intervals Formula:
μ= ¯X ± t .

s
√n

= 11.05 ± 2.0262.

8.83

√ 38

=>

8.14 ≤ μ≤ 13.95

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In conclusion, we are 95% ensure that the world average of mortality rate neonatal is between
8.14 deaths and 13.95 deaths (per 1,000 live births) in 2017.
b. Assumption
It is not necessary to have assumptions to calculate the variable’s confidence intervals above.
Even though the world’s standard population deviation of child mortality rate is unknown, the
sample size of the dataset is 38, more sustainable than 30, being large enough to apply for the
central limit theorem (CLT). That is why CLT is applicable, so that the distribution of the sample
mean become normally distributed, without regard to the shape of the population.
c. Supposing The World Standard Deviation of Each Mortality Rate Neonatal
In the other case, when the world’s population standard deviation of child mortality rate is
known, the confidence interval will experience a reduction. Because the sample standard
deviation arranges from sample to sample, it easily causes some confusion which can impact the
accuracy of final results. Instead of this, the population standard deviation may enhance a more
correct and precise outcome. In addition, if the sample size improves, the width of the confidence
interval will be narrower. It means the larger sample size will show the more accurate outcome.
IV.

Hypothesis Testing:

a. Testing the Hypothesis
Following to a report published by the World Health Organization (WHO), the world average

mortality rate neonatal is 18.6 deaths (per 1,0000 live births) in 2016. Besides that, in Part 3a, the
confidence interval with 95% confidence levels is calculated that varied from 8.14 deaths to
13.95 deaths (per 1,000 live births) in 2017, with the total average of 11.05 deaths in 2017.
Indeed, it is not sure to completely hold the stable of world average of mortality rate neonatal
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that can change or remain unchanged in upcoming years. Due to changing the sample mean
between 2016 and 2017, the mean value decrease by 7.55 deaths, therefore the global rate of
infant death is predicted to reduce in the future.
Sample Mean X¯

11.05
38

Sample Size (n)

8.83

Sample Standard Deviation (S)
Population Standard Deviation σ

Unknown

Population Mean μ

18.6

Confidence level (1- σ )*100%

95% = 0.95

Significance level α

5% = 0.05

Step 1: Check for CLT
Due to the sample size = 38 > 30, CLT is applicable. As well as, the sample size grows, hence the
sampling distribution of mean becomes normally distributed.
Step 2: State the null hypothesis, H0 and the alternative hypothesis H1
H 0: μ

≥ 18.6

H 1: μ

¿ 18.6

Step 3: Choose the level of significance α

= 0.05 and the sample size n = 38

It is a lower-tailed test
Step 4: Determine which table to use

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The population standard deviation is unknown and the sampling distribution of mean becomes
normally distributed, so that the T-table is applied.

Step 5: Determine the critical values
The significance level

α

= 0.05

Degree of freedom d.f = n – 1 = 38 = 37
 It is a lower-tailed test, hence CV = -t0.5,37 = -1.687
Step 6: Compute test statistic
t=

¯ −μ 11.05 −18.6
X
=−5.270
=
8.83
S
√n
√ 38

Step 7: Make the statical decision
After calculating, the ttest < tcv (-5.270 < -1,687)
 The test statistic does not belong to the rejection range; hence the null hypothesis is
acceptable.
Step 8: Make a managerial conclusion in the context of the real-world problem
As H0 is accepted and H1 is rejected. Therefore, we are 95% confidence to conclude that the rate
of newborn deaths has a tendency to decrease in the future.
Step 9: Determine the type of error
As the null hypothesis is not rejected, it means that we have a 5% probability to make a type-II

error. It is concluded that the mortality rate neonatal will not grow in the future, but actually the
rate of child deaths in neonatal time might have 5% opportunities to increase.
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b. Half The Number of Countries in The Dataset
If the number of countries is supposed to become half in the dataset, meaning also the sample
size is half, the statistical decision of accepting the null hypothesis might change. Since when the
sample size becomes smaller, the chance of making a Type-II error might be increase. Indeed, the
lower sample size with the same level of significance can make the sampling distribution become
smaller and expands the arrangement of the normal distribution. For this reason, the critical value
slightly outspread to the mean, also the test statistic cannot fall into the rejection range. The
lower sample size may reduce the correction of hypo testing outcomes because the standard
deviation of the sample distribution rises. This will not guarantee a more precise observation of
the mortality rate neonatal (per 1,000 live births).
V.

Overall Conclusion:

Overall, in 2015, the Agenda for Sustainable Development is conducted by the cooperation
between the United Nations and many nations, targeting to provide peace and prosperity for all
people around the world, of all ages. This program is planned in 15-year period with the 17
Sustainable Development Goals (SDGs) to deal with global issues such as ending poverty,
reducing environmental pollution, developing the quality of education, or improving health
(UN). The main findings, which are derived from the calculation and analysis in the dataset, help
me to gain better knowledge about the state, and it also reflects the stable connection between the
mortality rate neonatal and the Gross National Income (GNI).
To begin with, the correlation of the mean of child deaths rate in neonatal times and the GNI of
three country category shows the relationship between the rate of newborn deaths and the
economic development. Based on the mean value, it witnesses those High-income countries

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(GNI greater than $12,500 per capita) recorded the lowest rate with 3.96 deaths (per 1,000 live
births), while Low-income (GNI less than $1,000 per capita) is approximately five times higher,
at 23.3 deaths (per 1,000 live births). It illustrates that Low-income may have a poor health care
system and discourage human development. Besides that, the low mortality rate of neonatal in
High-income countries point out that decreasing the infants’ death can develop the public health
as same as improving the quality of living.
Next, we have 95% confidence that the world average mortality rate neonatal arranges from 8.14
deaths and 13.95 deaths (per 1,000 live births) in 2017. Furthermore, the global rate of child
deaths in 2016 is 18.6 deaths and this number plunge to 11.05 deaths in 2017, which prefer to
gradually fall in the future. However, by testing the hypothesis, it still has a fluctuation to climb
the mortality rate neonatal. In addition, a reduction in the sample size by half can make a change
to the statical decision of the mortality rate neonatal in the world.
To conclude all previous finding, I have some recommendations that both nations and
intergovernmental organizations should build more effective solutions achieve a high rate of
child survival by supporting to prevent the impact on children or developing the healthcare
systems to reach every child. Besides that, the reduction of mortality rate neonatal not only the
responsibility of global organizations or governments but also for individuals who is parents or
families.
References:
UNICEF Data 2020, Neonatal mortality – Child Survival, UNICEF, viewed 21 August, 2021,
< />
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The World Bank 2021, Mortality rate, infant (per 1,000 live births), World Bank, viewed 21
August, 2021, < />UNICEF Data 2020, Goal 3: Good Health and Well-Being, UNICEF, viewed 21 August, 2021,

< />World Health Organization 2020, Newborns: Improving survival and well-being, World Health
Organization, viewed 21 August, 2021, < />UN Chronicle 2019, Reducing Child Mortality – The challenges in Africa, United Nations,
viewed 21 August, 2021, < />UNDP, The Sustainable Development Goals in Actions, United Nations Development
Programme, viewed 21 August, 2021, < />
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