The data, which was collected from World Bank data, is included in the Excel File

TABLE OF Team CONTENTS Report I/ Market ● Structure Firm’s behaviors Course Code: II/ ECON1193 Subject Name: Business Statistics Lecturer: Nguyen Thi Tuong Chau Market Words Count:III/ 3166 words Failure Group members: Nguyen Vu Hai Binh – S3878194 Tran Minh Phuong – S3877542 Ha Thien Loc – S3878211 Dao Thuy Minh Anh – S3878425 Nguyen Ngoc Phuong Mai – S3868202 IV/ Guest Speaker’s Reflection V/ References & Appendix First Name Student ID Parts Contributed Contribution % Hai Binh S3878194 Part 3,4,7 100% Minh Phuong S3877542 Part 5,6,7 100% Thien Loc S3878211 Part 3,4 100% Phuong Mai S3868202 Part 1,2 Minh Anh S3878425 Signature 100% TABLE OF CONTRIBUTIONS Part 5,6 100% TABLE OF CONTENTS I/ Data Collection II/ Descriptive Statistics III/ Regression IV/ Regression Conclusion V/ Time Series 10 VI/ Times Series Conclusion VII/ Overall Conclusion VIII/ References 17 19 21 IX/ Appendix I/ DATA COLLECTION The data, which was collected from World Bank data, is included in the Excel File In the collecting process, our team has successfully collected numerous data in the majority of countries in two regions including Asia and the America region, specifically in 2014 The variables to be included in the data set should be: Because the population and the number of countries in Asia are outnumbered in the America region; therefore, the collecting-data process is affected More countries in Asia are collected, with 40 countries, compared to 29 countries in the America region Since each region in a continent has a division in income category, it is advisable to continue taking the appropriate countries for each area, not evenly (Appendix1.1) For instance, most countries in Asia are in the middle-income segment, hence we choose more samples in this sector and the same with The America's samples Some countries' data is unavailable throughout the data-cleaning process, which is usually related to the recognition of sovereignty Data-cleaning methods are used to locate duplicates within a file or across sets of files to remove missing and unstructured data in these countries (Winkler 2002) II/ DESCRIPTIVE STATISTICS According to the table above, there is no detected Mode in both regions, hence it cannot be used to examine the GDP-GR Furthermore, as there are outliers existing in the dataset, the Mean measurement is not applicable Since Median is not affected by the outliers’ values in its calculation, it is considered the best descriptive measurement to analyse the central tendency As shown in table 2.1, the median number of GDP-GR in region A is 3.222%, nearly three times higher than that in region B with only 1.783%, which means in 2014, GDP-GR in Asia region is higher The America region on average The IQR is the reasonable measure for comparing variability in two regions’ datasets to avoid the effects of outliers on the results Moreover, all the other measurements are affected by outliers, except for CV, yet it is just suitable when there is a huge difference between mean value and sample sizes As shown in the table above, the IQR value of Asia, 3.141%, is 1.126 times higher than that of The America, which is 2.789% This indicates that in 2014, GDPGR varied amongst countries more in Asia There is no doubt that the box and whiskers plot is an excellent choice as the central tendency measurements' values and quartile values are clearly shown on the chart As a result, the comparison between the two regions will be more accurate Inferred from Figure1.1, two plots are left-skewed Because both Min and Max of Asia are higher than those of The America, GDP-GR of region A ranges higher and wider than B in 2014 Also, the Q1 of Asia approximately equals to the Q2 of the America, which means that just 25% of countries in Asia have a low GDP-GR ( |-0.0001|) In terms of The America, because there is only one significant independent variable which is GDP per capita, its influence level on GDP-GR is incomparable with any other variable Between two regions, the coefficient value of GDP per capita in Asia is higher than that of The America (|10 graphs simultaneously calculating the datasets, it can be inferred that countries in Asia not follow the same trend model as the America While the rate in Asia witnesses an upward Y =2.743+0.129(T ) ), the America observes a quadratic trend with a concave linear trend ( ^ T ¿ curve ( ) Hence, in the upcoming years, Asia’s GDP-GR is ¿ ^ Y =−2.101+0.591(T )−0.020 ¿ predicted to increase, and the America’s GDP-GR is forecasted to experience a decrease, as it has surpassed its vertex From table 6.1, the trend models of two regions have the same MAD values, meanwhile, for the SSE value, region B is lower than region A Moreover, the dataset of region B contains an extreme value (Table 6.2), hence, SSE is not a preferred measurement since it is sensitive to outliers In this case, MAD is used to determine the suitable trend model as it is not affected by outliers Besides, from the regression output of two representative countries, r-square and significance F can be considered to choose which region’s trend model is appropriate It is readily apparent that compared to region B, region A has a higher r-squared value and smaller significance F (Table 6.1) The higher the r-squared value and the smaller the significance F, the less error occurs Accordingly, the linear trend model of Asia is more appropriate as well as its equation is more reliable to predict the whole world GDP-GR Formula of the world’s linear trend model in GDP-GR: VII/ OVERALL TEAM CONCLUSION: Main factors that impact GDP-GR As analysed above, Asia’s GDP-GR is higher and more variable than that of The America resulted from some significant factors Specifically, GDP per capita had a negative relationship with the GDP-GR in both regions with a low level of significance, whereas the higher portion of population ages 15-64 will positively increase the GDP-GR in Asia Asia's working ages is important to the GDP-GR because of the extremely high level of the labour force in this area with around 1,876 billion people, which is nearly five times higher than that of The America with 388 million people (TheWorldBank 2014) 19 Besides significant variables detected throughout the analysis, there are other factors that can have an exclusive impact on GDP-GR Firstly, a research conducted in developing countries such as Banglades revealing that education is a crucial factor that have a close relationship with GDP (Islam 2007) It can equip residents with knowledge, empowering them to catch up with foreign technological innovations, thus can enhance economic growth (Berthelemy1996) Furthermore, inflation rate is also a significant factor affecting the GDPGR in many areas across the world (Sarel 1996) If an area has to face an extremely high inflation rate, a rapid increase in capita income will occur resulting in a downslope in economic growth and an escalating unemployment rate in the long-term (Sidrauski 1967) Prediction the world’s GDP-GR 2030 According to part recommendation, using Time-series forecasting (forecast future values based on previously observed values), GDP-GR of the world might follow a linear trend model basing on India’s (a LI-Asian country) data during 1990-2015, which has formula: ,as it seems to give us the most accurate prediction, compared to the other countries that we have analysed The formula suggests that with each year increase, the world's GDP-GR will increase by 0.129% Therefore, in 2030 (T=41), applying the formula, GDP-GR in the world is predicted to be about 8.032% Recommendation However, to predict the world’s GDP-GR more precisely, we recommend tha t the scope of analysis should be broader, also including countries from Europe, Africa and Australia, since larger samples brings us more precise estimation (Asiamah 2017) The economic growth varies widely among countries and regions; hence, by doing so, we can have a panoramic view from all regions, unlikely the narrow scope given- Asia and The America, especially the four chosen countries and India-the representative, cannot accurately reflect the whole world’s GDP-GR, which could cause over/underestimation Moreover, because the datasets collected in the period 1990-2015, considered fairly outdated, which might affect the prediction accuracy (Deloitte 2017), an updated GDP-GR version is recommended to analyse, especially taking the effects of the recent epidemic into account Particularly, the COVID-19 pandemic has exerted severe negative impacts on the global economy (Assessment 2020), which tends to deflect the increasing trend prediction above The pandemic causing fragmentation of supply chains, higher inflation (PHASE 2020), a depreciation in human capital due to unemployment or lost schooling (whereas education and inflation rate are two important factors mentioned above), plunges most countries into the deepest recession in decades Many economists predicted that this recession would leave lasting scars on the global economy growth (TheWorldBank 2020) Therefore, to make the growth rate recover from that recession, countries should strengthen public health systems, keep people access to education and their work, maybe through online-platform by improving technology if the country's conditions are capable, since 20 population (workforce) and education status contributes significantly to the economic growth as indicated previously 21 REFERENCE 2020, 'The Global Economic Outlook During the COVID-19 Pandemic: A Changed World', The World Bank, June, viewed 28 May 2021, Asiamah, N, Mensah, HK & Oteng-Abayie, EF 2017, 'Do larger samples really lead to more precise estimates? A simulation study', American Journal of Educational Research, vol 5, no 1, pp 9-17 Assessment, OIE 2020, 'Coronavirus: The world economy at risk', Organisation for Economic Cooperation and Development, France, vol Bartmann, R 2017, Causes and effects of 2008 financial crisis, Hochschule Furwange Deloitte 2017, 'Predictably inaccurate: The prevalence and perils of bad big data', 31 July, viewed 27 May 2021, Islam, TS, Wadud, MA & Islam, QBT 2007, 'Relationship between education and GDP growth: A multivariate causality analysis for Bangladesh', Economics Bulletin, vol 3, no 35, pp 1-7 Kihwan, K 2006, The 1997-98 Korean Financial Crisis: Causes, Policy Response, and Lessons , The International Monetary Fund and The Government of Singapore, Singapore, viewed 27 May 2021, PHASE, IL 2020, 'The Impact of COVID-19 on Inflation: Potential Drivers and Dynamics', vol Sarel, M 1996, 'Nonlinear effects of inflation on economic growth', Staff Papers, vol 43, no 1, pp 199-215 Sidrauski, M 1967, 'Inflation and economic growth', Journal of political economy, vol 75, no 6, pp 796-810 Upreti, P 2015, 'Factors affecting economic growth in developing countries', Major Themes in Economics, vol 17, no 1, pp 37-54 Winker, W 2002, Data Cleaning Methods U.S Bureau of the Census Statistical Research, p.1, viewed 20 May 2021, 22 APPENDICES FOR PART 1: DATA COLLECTION Appendix 1.1: Filtering and Collect appropriate number of countries according to Income Category APPENDICES FOR PART 3: BACKWARD ELIMINATION Dependent Variable ● GDP per capita growth (annual %) Independent Variables ● ● ● GDP per capita (current US$) Life expectancy at birth, total (years) GNI per capita, Atlas method (current US$) ● Foreign direct investment, net inflows (% of GDP) ● Exports of goods and services (% of GDP) ● Imports of goods and services (% of GDP) ● ● Trade (% of GDP) Population ages 15-64 (% of total population) The backward elimination method is used in hypothesis testing to find the significant the variables 23 H0: βi = (there is no relationship between the GDP per capita growth rate and the independent variables) H1: βi ≠ (there is a relationship between the GDP per capita growth rate and the independent variables) Level of significant α = 05 will be used for the p-value approach, given that: • p-value < α Reject H0 Significant variable • p-value ≥ α Do not reject H0 Not Significant variable In this case, there were connection between the exports of goods and services (% of GDP), imports of goods and services (% of GDP) To create a correct hypothesis test, there will be two data set for each region where each will both either have the exports of goods and services (% of GDP) or the imports of goods and services (% of GDP) I REGION A - Asia The first regression output of Asia Appendix 3.1 & 3.2: The first regression output of import and export for Asia In both of the first regression output, there are only one significant variable which is population ages 15-64 (% of total population), as their p-values are smaller than the level of significant (0.008 < 0.05) There are variables that are insignificant in both models to estimate the GDP per capita growth (annual %) (p-value >0.05) such as Life expectancy at birth, total (years), GNI per capita, Atlas method (current US$), Foreign direct investment, net inflows (% of GDP), Exports of goods and services (% of GDP), Imports of goods and services (% of GDP), Trade (% of GDP), thus, we not reject H0 Applying backward elimination, the predictor GNI per capita, Atlas method (current US$) in both models has the largest p-value (p-value = 0.979) Accordingly, in the second regression output this variable will be dropped from two models The second regression output of Asia Appendix 3.3 & 3.4: The second regression output of import and export for Asia In both of the second regression outputs, all the variables (except Population ages 15-64 (% of total population)) continued to have p-value exceeding the level of significance which means that we not reject H0 We continue to eliminate highest p-value which is Foreign direct investment, net inflows (% of GDP) The third regression output of Asia Appendix 3.5 & 3.6: The third regression output of import and export for Asia In the import third regression, the highest insignificance would be Trade (% of GDP) In the export third regression, the highest insignificance would be Exports of goods and services (% of GDP) Both are higher than the level of significance which means that theses variables will be eliminated from two models In both regression outputs, Population ages 15-64 (% of total population) are still the only one with variable that are significant The fourth regression output of Asia Appendix 3.7 & 3.8 The fourth regression output of import and export for Asia In the import fourth regression, the highest p-value would be Imports of goods and services (% of GDP) In the export fourth regression, the highest p-value would be Trade (% of GDP) Both are higher than the level of significance which means that they would be dropped from two models In both regression outputs, Population ages 15-64 (% of total population) are still the only one with variable that are significant 25 The fifth regression output of Asia Appendix 3.9 & 3.10: The fifth regression output of import and export for Asia In both of the fifth regression output, the life expectancy at birth, total (years) have the higher p-value as compared to the other independent variables and higher than 0.05 which means that it would be removed from the data set While both GDP per capita (current US$) and Population ages 15-64 (% of total population) are significant variables (p-value < 0.05) The final regression output of Asia Appendix 3.11 & 3.12: The final regression output of import and export for Asia After applying backward elimination to both regression output, it is noticeable that both GDP per capita (current US$) and Population ages 15-64 (% of total population) are significant (with export (0.00002