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 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): 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 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 indicators To support for above points, Figure shows that when the GNI per capita rose gradually from $16,000 to $80,000, the child mortality rate fell marginally under 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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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) = 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) = 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 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 N/A Table 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 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 not show the mode value in the dataset; and it means that LI and HI countries 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 To conclude, while MI countries 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 11 2.3 10.13 3.18 80.33 Table 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 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 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 - - 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 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 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 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 – = 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 10 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 11 (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, 12 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, 13 ... 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