(TIỂU LUẬN) RMIT international university vietnam ASSINGMENT 3 PART a DATA COLLECTION the data for the total number of deaths due to COVID 19 between april 01

RMIT International University Vietnam ASSINGMENT PART A Subject code Subject name ECON1193B Business Statistic Location and campus Title of assignment RMIT Vietnam – South Saigon Team assignment report Lecture Greeni Maheshwari Assignment due date 18th September, 2020 Team Team 06 Number of page 12 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 TABLE OF CONTENTS PART 1: DATA COLLECTION PART 2: DESCRIPTIVE STATISTICS PART 3: MULTIPLE REGRESSION PART 4: TEAM REGRESSION CONCLUSION PART 5: TIME SERIES PART 6: TIME SERIES CONCLUSION 11 PART 7: OVERALL TEAM CONCLUSION REFERENCES 12 15 APPENDIX 16 CONTRIBUTION First name Student ID Parts contributed Contribution Khanh S3811511 1,3,5,7 100% Kha S3826384 1,5,6,7 100% Thinh S3818172 2,4,6 100% Lan S3836374 2,3,7 100% Hoang S3826384 1,3,7 100% Signature PART 1: DATA COLLECTION The data for the total number of deaths due to COVID 19 between April 01 to July 31, 2020, and five other variables including average temperature (in Celsius) and average rainfall (in mm) based on available data from 1991 to 2016, medical doctors ( per 10,000 people, latest available), hospital beds (per 10,000, latest available) and population of the country (in SGS RMIT – Business Statistic – ECON1193B – TEAM 06 millions, latest available) for 50 countries in Region A: Asia and 23 countries in Region B: North America were collected After the cleaning process, there are 46 countries remaining in Region A: Asia and 21 countries remaining in Region B: North America The datasets are presented in the attached Excel file PART 2: DESCRIPTIVE STATISTICS  Central Tendency Measurements Central Tendency Asia North America Mean 36.756 91.747 Median 10.031 27.09 Mode 0 Figure Measures of Central Tendency of total number of deaths due to COVID-19 between April 01 to July 31, 2020, in Asia and North America In comparing the total death in Asia and North America by using the Central Tendency measurements, there is nothing worth notice in the mode figure, which will not be considered Moreover, the mean will not be used to interpret since there is the existence of outliers, based on the calculation in appendix 1.1 and appendix 1.2 Consequently, the Median will be the most suitable measurement for the comparison which illustrates that 50 percent of the values are greater than the median and the remaining 50 percent are lower than the median.At first glance, it can be clearly defined that there is a significant difference between Asia and North America middle number of total deaths relating to the COVID-19 In addition, North America with the figure of 27.09, which is roughly three times higher than Asia with the median of 10.031 Therefore, it can be concluded that North American countries have more deaths relating to the Cocid-19 than the Asian countries  Box and whisker plot Figure Box-and-whisker plots of total number of deaths due to COVID 19 in Asia and EU countries SGS RMIT – Business Statistic – ECON1193B – TEAM 06 As can be seen from the box and whisker plot we drew above, the data distribution of Asia and North America region are both right-skewed Moreover, the right whiskers of Asia and North America are both longer than the left whiskers shows the presence of outliers in the datasets The box and whisker plots show that 75% of countries in North America have more than 27 deaths per million population while 75% of countries in Asia have only more than 10 deaths In addition, 25% of the number of deaths in Asia is around to 10 deaths and to 27 deaths in North America From which demonstrates that North American countries have a higher death rate than Asian countries  Measurements of variation Variation Measurements Asia North America Range 248.04 447.099 IQR 50.501 99.125 Variance 3170.34 17621.706 Standard Deviation 56.306 132.747 Coefficient of Variation 86.253 192.068 Figure Measures of Variation of total deaths in Asia and EU (Unit: number of deaths except for the Coefficient of Variation) In this scenario, the best measure of variation is the Interquartile Range (IQR) due to the existence of outliers In addition, standard deviation is not suitable to measure because it can be heavily influenced by the outliers, the coefficient of variation is also not a good choice as we can notice that the distribution of the datasets above is highly right-skewed The Interquartile Range of Asia region (50.501) is smaller than the Interquartile Range of North America (99.125), indicating that the dispersion of data of Asia region around the median is smaller In other words, the total number of deaths by Covid-19 in Asia are more consistent than in North America, or the Covid-19 pandemic has less impact on the Asia region than on North America PART 3: MULTIPLE REGRESSION Region A: Asian countries (FINAL) After applying backward elimination, we find that one variable which is the average rainfall is significant at a 5% level of significance The FINAL regression model for Asian countries is given below a Regression output SGS RMIT – Business Statistic – ECON1193B – TEAM 06 Figure 4: FINAL regression model of Region A: Asia b Regression Equation : = b0 + b1 * = 61.01 - 0.286* c Regression coefficient of the significant independent variable The slope b1= - 0.286 indicates that the total number of deaths due to COVID 19 between April 01 to July 31, 2020, decreased by 0.286 deaths with every mm increase in the amount of rainfall In this case, for no rainfall, b0 = 61.01, which makes sense as it is possible to have deaths regardless there is rain or not Also, the intercept indicates that over the sample size selected, the portion of the total number of deaths due to COVID 19 between April 01 and July 31, 2020, is not explained by the average rainfall (in mm) of a country is 61.01 deaths Therefore, the total number of deaths is 61.01 when there is no rainfall d The coefficient of determination The coefficient of determination (R square = 16.3%) shows that 16.3% of the total variation in the total number of deaths due to COVID 19 from April 01 to July 31, 2020, can be explained by the variation in the amount of rainfall, while 83,7% of the total variation in the total number of deaths due to COVID 19 between April 01 and July 31, 2020, is due to non included factors in the observation Region B: North American Countries (FINAL) After applying backward elimination, we find that only one variable named Population (in millions) is significant at a 5% level of significance The Final regression for North American countries is given below SGS RMIT – Business Statistic – ECON1193B – TEAM 06 a Regression Output Figure 5: FINAL regression model of Region B: North America b Regression Equation: = b0 + b1* = 52.98 +1.399* c The regression coefficient of the significant independent variables The slope b1= 1.399 indicates that the total number of deaths due to COVID 19 between April 01 to July 31, 2020, increased by 1.399 deaths with every million people increasing in the population of the country In this case, for no population, b0= 52.98, which makes no sense However, the intercept simply indicates that over the sample size selected, the portion of the total number of deaths due to COVID 19 between April 01 and July 31, 2020, not explained by the number of the population of the country is 52.98 deaths Also, when X1 = 0, that means it is impossible to have deaths when there is no population d The coefficient of determination The coefficient of determination (R Square = 61.1 %) shows that 61.1 % of the total variation in the total number of deaths due to COVID 19 from April to July 31, 2020, can be explained by the variation in the population of the country, while 38.9% of the total variation in the total number of deaths due to COVID 19 between April and July 31, 2020, is due to non included factors in this observation PART 4: TEAM REGRESSION CONCLUSION According to the study in Part 3, the final claim is that the two regions have the same amount of significant independent variables but in different types including average rainfall (in mm), hospital beds (per 10,000 population), medical doctors (per 10,000 population), average temperature (in Celsius) and population (in millions) In the Asia final regression model, the significant independent variable is the average rainfall (in mm) In the North America data set, the significant independent variable in the final regression model is Population (in millions) among the five listed above variables In comparison, the North America region has remarkably more total deaths according to the findings in part 2, which means the region has SGS RMIT – Business Statistic – ECON1193B – TEAM 06 been impacted more than the Asia Region due to the pandemic Moreover, from the study in part 3, 61.1% of the total variation in the total total deaths in North America due to COVID 19 can be explained by the population of the country (in millions) which illustrates that the variation of population contributes a major impact to the variation of the total number of deaths in the NA region Meanwhile, in Asia, only 16.3% of the variations in the total number of deaths can be explained by the variation of the average rainfall (in mm), which means that the average rainfall influence on the total deaths is not too great and a large amount of other considerable factors that are not included in the study leading to a lower reliable result compared to that of the North America region To conclude, by building the regression models and comparing the descriptive statistics of two regions, this study indicates that the average rainfall can be used to forecast the total number of deaths due to COVID 19 in Asia while in North America, the population of the country is the independent variable that can be utilized to predict the total number of deaths Also, the North American countries have suffered a higher impact due to the greater number of deaths due to the pandemic in comparison to Asian countries PART TIME SERIES In part 5, our group collected data for the total number of deaths per day in two regions Asia and North America from April 01 to July 31, 2020 In the collected datasets, if there are no deaths on a particular day and hence to build the exponential trend model, we will take 0.00005 instead of to build the exponential trend model as log(0) cannot be calculated The datasets are presented in the attached Excel file Build Linear, Quadratic and Exponential trend models 1.1 Region A: Asia After testing the Hypothesis for trend models in the Asia region (appendix 3.1), the findings indicate that linear, quadratic and exponential trend models are significant for this region  a Linear Trend Model Regression output Figure Time Series outputs for Region A: Asia linear trend b Formula: = 88.199 + 10.366* SGS RMIT – Business Statistic – ECON1193B – TEAM 06    a The slope b1= 10.366 indicates that the total number of deaths due to COVID 19 between April 01 to July 31, 2020, increased by 10.366 deaths every day b0 = 88.199 when T = 0, which illustrates that there were 88.199 deaths on 31 March, 2020 Quadratic Trend Model Regression output Figure Time Series outputs for Region A: Asia quadratic trend b Formula: =338.607–1.75*+ 0.0985*  The slope b2= 0.0985 indicates that the total number of deaths due to COVID 19 between April 01 to July 31, 2020, increased by 0.0985 deaths every  b0 = 338.607 when T = 0, which illustrates that there were 338.607 deaths on 31 March, 2020  Exponential Trend Model a Regression output Figure Time Series outputs for Region A: Asia exponential trend b Formula: in linear format: log() = 2.383 + 0.00653* In non-linear format: = 241.546 * SGS RMIT – Business Statistic – ECON1193B – TEAM 06 Interpretation: ( b1 - 1) * 100% = 1.5% is the estimated daily compound growth rate in percentage for the total number of deaths due to COVID 19 from April 01 to July 31, 2020 in Asia 1.2 Region B: North America After testing the Hypothesis for trend models in the North America region (appendix 3.2), the findings indicate that linear and exponential trend models are significant Linear Trend Model a Regression output  Figure Time Series outputs for Region B: North America linear trend b Formula: = 2056.42 - 5.37*  The slope b1= - 5.37 indicates that the total number of deaths due to COVID 19 between April 01 to July 31, 2020, decreased by 5.37 deaths every day  b0 = 2056.42 when T = 0, which illustrates that there were 2056.42 deaths on 31 March, 2020  Exponential Trend Model a Regression output Figure 10 Time Series outputs for Region A: Asia exponential trend SGS RMIT – Business Statistic – ECON1193B – TEAM 06 b Formula: In linear format: log() = 3.2840 - 0.00127* In non-linear format: = 1923.43 * Interpretation: ( b1 - 1) x 100% = 0.3% is the estimated daily compound decrease rate in percentage for the total number of deaths due to COVID 19 from April 01 to July 31, 2020 in North America Recommended Trend Models The Coefficient of Determination (R Square) will be used to determine the most suitable trend model for the regression outputs Higher the coefficient of determination, the more of the total variation in the number of deaths can be explained, which is better for the estimating the number of deaths due to COVID 19 a Region A: Asia R Square Linear Quadratic Exponential 67.62% 73.68% 80.60% Figure 11 Coefficient of determination of linear , quadratic and exponential trend models of NA (%) For region A, it can be seen in the figure that the exponential trend had the highest coefficient of determination, which means the exponential trend model will be the most suitable in region A's situation to predict the total number of deaths due to Covid-19 as it will produce fewer errors b Region B: North America R Square Linear Exponential 7.90% 7.26% Figure 12 Coefficient of determination of linear and exponential trend models of NA (%) For region B, with a slightly higher coefficient of determination; hence, the linear trend model will be the most suitable in region B's situation to predict the total number of deaths due to Covid-19 as it will produce fewer errors compared to the exponential trend model Predict the number of deaths on September 28, September 29, and September 30 10 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 a Region A: Asia As the above conclusion, the exponential trend is the best model for predicting the number of deaths due to COVID 19 in Asia, with the formula: = 241.546 * Date (T) Forecasted number of deaths September 28 (181) 3575.64 September 29 (182) 3629.27 September 30 (183) 3683.71 Figure 13 Forecasted number of deaths on September 28,29,30 in Asia b Region B: North America As the above conclusion, the linear trend is the best model to predict the number of deaths due to COVID 19 in North America, with the formula: = 2056.42 - 5.37* Date (T) Forecasted number of deaths September 28 (181) 1084.45 September 29 (182) 1079.08 September 30 (183) 1073.71 Figure 14 Forecasted number of deaths on September 28, 29, 30 in North America PART 6: TIME SERIES CONCLUSION a Line chart 11 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 Figure 15 Line graph of Daily total number of deaths due to COVID 19 in Asia and North America from April 01 to July 31,2020 b Explanation The line graph above presents the daily total number of Deaths in Asia and North America due to Covid 19 from April 01 to July 31, 2020 It can be concioused that the number of Deaths in Asia is more stable and significantly less (in number of deaths) compared to North America, although this is the region where the pandemic was spread There is an existence of irregular components in periods, once occurred in 15-April and once in 17-June, and started to increase steadily from 24-June to 29-June On the other hand, in North America was a chaos of fluctuation, the number of deaths reached the peak in 15-April, then started to move downward with the cyclical component of a days period until the end of the observation Also, the region has the irregular component of 24-June, which the number of deaths got higher than any other nearby period Relating to Part 5.3, Asia and North America not follow the same trend model in order to predict the numbers of death due to the Covid-19, which is the exponential trend model in Asia and the linear trend model in North America To come up with the conclusion, our team has compared the Coefficient of Determination (R Square), because the higher the Coefficient of Determination, the lesser error, the more total variation in the number of deaths can be explained The R Square of exponential trend mode of Asia is the highest (80.6%), similarity, the linear trend model of North America is higher than the other (7.9%) In conclusion, we want to use exponential trend model to predict the total number of death in the world since its R square is larger than the Linear trend model in North America (80.6% > 7.9%), presenting that 80.6% of the independent variable (number of deaths by the Covid 19) can be explained by exponential trend model PART : OVERALL TEAM CONCLUSION 7.1 Main factors impacting the total number of deaths Based on part 3, Multiple Regression analysis of Asia region, it indicates that there is only one significant independent variable that may affect the total number of deaths due to COVID 19 which is the average rainfall (in mm) at 95% level of confidence Based on the regression 12 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 equation in part (Total number of deaths= 61.01 - 0.286 *average rainfall), we can easily see that the coefficient of rainfall is negative, hence, the amount of rainfall has an inverse relationship with the total number of deaths due to COVID 19 which means that with every mm increasing in rainfall, the total number of deaths will decrease by 0.286 deaths However, the findings in Part show that the coefficient of determination of the average rainfall is only 16.3%, which could be inferred that the influence of rainfall on COVID 19 pandemic is not too great but still be considered Similarly with the Multiple Regression analysis of the North America region, the significant variable that may affect the total number of deaths due to COVID 19 is population (in millions) at 95% level of confidence By observing the regression equation for North America in Part ( Total number of deaths = 52.98 +1.399 *Population), we can conclude that population (in millions) has a direct positive relationship with the total number of deaths due to COVID 19 To be more specific, with every one million people increase in the population of a country, the total number of deaths due to COVID 19 will increase by 1.399 deaths which should be weighed more in the research and prevention process for the pandemic Each region has only one significant variable that affects the total number of deaths by COVID 19 However, as this study only analyses two specific regions (Asia and North America) and five variables, it can be obviously to define that there are more than five variables that may affect the total number of deaths due to COVID 19 which are not included in the observed study 7.2 Predicted number of deaths due to COVID-19 in the world on October 31 As mentioned in part 6, our team preferred to use the best trend model to predict the number of deaths due to COVID-19, which would be the exponential trend model of Asia region Moreover, to predict the number of Covid-19 deaths in the world on October 31, we would use the formula of the exponential trend model of Asia region with T equal to 214 to predict it Formula: = 241.546 * = 241.546 * = 5844.305 The calculation illustrates the numbers of deaths because of Covid-19 with a positive value on October 31 which means the disease will become more serious in the future To compare in the real world this can happen because nowadays Covid-19 is very complicated, which has more infections day by day 7.3 The number of deaths by COVID 19 by the end of year 2020 Based on the number of death evaluations of Asia and North America in part 5, it shows a gradual rise in the number of deaths due to COVID 19 Besides, the prediction of the total number of deaths by COVID 19 in the world on October 31 also illustrates a positive number (5844.305) indicating that the pandemic will continue to happen According to WHO, widespread vaccinations not expect to happen until the middle of next year, which means there are still risks that the number of cases and deaths may increase (CNBC 2020) Social distancing is one of the most efficient measures to help prevent COVID-19 ( Robert Preidt 2020) Unfortunately, Asia countries are having less restrictions with their earlier stringent measures as they move towards reopening their economies and they are facing an increase in the daily number of cases and deaths as stated by WHO (Salma Khalik 2020) The United States also begins to reopen the economy with relaxing social distancing restrictions and some states witness a rise in new cases ( USA Today 2020) To conclude, from our team’s perspective, by the end of 2020, the number of deaths by COVID-19 will increase 13 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 7.4 Two other variables impacting the total number of deaths To explain more about the factors impact the total number of deaths due to COVID 19 aside from the five variables in the first part Our team would recommend the two variables, the first one would be ages, which is a numerical variable and the second one is wearing masks behaviour, which is a categorical variable According to research by Clara et al (2020), ages have a positive relationship with the increase in the mortality rate of COVID 19 To be more specific, mortality was less than 1% in patients aged below 50 years, it increased significantly after that age and the highest rate was observed in patients aged above 80 years, at 29.6% In the report of the Centers for Disease Control and Prevention (CDC) (2020), the death rate ratio of ages after 29-year-old is higher in every 10 years Patients from 30 to 39-year-old and patients from 40 to 49-year-old will have times and 10 times higher death rate respectively compared to a group of patients from 18 to 29-year-old The higher the age, the more chances the patients get comorbidities, such as diabetes, hypertension, and cardiovascular disease, which have been associated with worse outcomes in COVID-19 and can positively impact the total number of deaths in this pandemic (Clara et al 2020) The wearing mask behaviour variable also impacts on the total number of deaths due to COVID-19 According to the University of Cambridge (2020), wearing masks is a cheap and effective way to reduce the transmission of the COVID-19 virus and keeps the coronavirus ‘reproduction number’ under 1.0 Hence, wearing crude homemade masks can reduce disease spread by catching the wearer’s virus particles, breathed directly into the fabric, whereas inhaled air is often sucked in around the exposed sides of the mask (University of Cambridge 2020) A study by Christopher Leffler from Virginia Commonwealth University indicates that wearing masks can help to lower the COVID-19 death rate not just by a few percent, but up to a hundred times lower mortality (Kate Marino 2020) Some countries that recommended mask-wearing within 15 days and 30 days, the death rate was far lower than the countries that waited longer or no policy recommended wearing masks (Kate Marino 2020) Several countries in Asia began using masks very early and still have mortality close to in million or less, while in the U.S the mortality is in 2,500 people in the population (Kate Marino 2020) In a forecast of the Institute for Health Metrics and Evaluation (IHME), if 95% of the people in the US wearing masks in public, the total number of deaths would decrease from 295,011 by December to 228,271, a 49% drop, which means more than 66,000 lives would be saved (IHME 2020) To conclude, wearing a mask is a cheap and effective solution to reduce the total number of deaths due to COVID-19 The more people wearing masks in public, the less total cases and total deaths in this pandemic In conclusion, the study in this report illustrates the comparison between Asia and North America in the total number of deaths due to COVID 19 from 01 April to 31 July Also, the findings from analyzing the regression and time series can be used to estimate the significant factors that affect the number of deaths as well as forecast the death cases in two aforementioned regions and even the world in the upcoming period In detail, countries in North America have suffered more than Asia’s countries with significant number of deaths due to the pandemic in the observed period; however, by the end of September, the number of deaths per day in North America is just about 1074 deaths compared to that of Asia is predicted to be times higher with 3684 deaths on 30 September Moreover, the world is also forecasted to be at the level of approximately 5844 deaths on 31 October Therefore, as the second wave of the pandemic is started and spreading widely again, our team would recommend everyone to strictly follow the preventive measures and practices such as continuing social distancing restrictions and patiently waiting for the completion of COVID 19 vaccine 14 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 References: CDC 2020, COVID-19 Hospitalization and Death by Age, cdc , viewed 10 September 2020, Clara Bonanad et al 2020, The Effect of Age on Mortality in Patients With COVID-19: A Meta-Analysis With 611,583 Subjects, sciencedirect, viewed 10 September 2020, CNBC 2020, WHO says widespread coronavirus vaccinations are not expected until mid2021, CNBC, viewed 10 September 2020 Coronavirus (COVID-19) deaths, 2020, Total number of deaths daily due to COVID-19 from 01 April to 31 July, 2020, data file, Our World in Data, viewed September 2020, Hospital beds (per 10,000 population) 2020, Hospital beds (per 10,000 population), data file, World Health Organization, viewed 28 August 2020, IHME 2020, New IHME COVID-19 Forecasts See Nearly 300,000 Deaths by December 1, However, Consistent Mask-Wearing Could Save about 70,000 Lives, viewed 10 September 2020, Kate Marino 2020, Early face mask policies curbed COVID-19’s spread, according to 198country analysis, viewed 10 September 2020, Medical doctors (per 10,000 population) 2020, Medical doctors (per 10,000 population), data file, World Health Organization, viewed 28 August 2020, Population, total 2019, Population, data file, The World Bank, viewed 28 August, Rainfall n.d., Average Rainfall (in mm) from 1991 to 2016, data file, Climate Change Knowledge Portal, viewed 28 August 2020, 15 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 Robert Preidt 2020, Benefits of Social Distancing Outweigh Economic Toll: Study,usnews, viewed 10 September 2020, Salma Khalik 2020, Pandemic has entered new phase in Asia-Pacific:WHO, Straitstimes, viewed 10 September 2020,< https://www.straitstimes.com/asia/pandemic-has-entered-newphase-in-asia-pacific-who> Temperature n.d., Average Temperature (in Celsius) from 1991 to 2016, data file, Climate Change Knowledge Portal, viewed 28 August 2020, Total confirmed COVID-19 deaths, 2020, Total number of deaths due to COVID-19 from 01 April to 31 July, 2020, data file, Our World in Data, viewed 28 August 2020, University of Cambridge 2020, Widespread facemask use could shrink the 'R' number and prevent a second COVID-19 wave, Eurek Alert, viewed 10 September 2020, USA today 2020, Coronavirus reopening, usatoday.com, viewed 10 September 2020, < https://www.usatoday.com/storytelling/coronavirus-reopening-america-map/#caseload > Appendix 1: Outlier calculation Outlier calculation Total deaths in Asia (per million population) Q1 - 1.5 x IQR Q3+ 1.5 x IQR 13.635 72.555 Min 29.08 Max 50.49 OUTLIER Appendix 1.1: Asia’s Outlier calculation Outlier calculation Total deaths in North America (Per million population) Q1 - 1.5 x IQR -143.6575 Q3+ 1.5 x IQR Min 252.8425 Max 248.04 OUTLIER Appendix 1.2: North America’s Outlier calculation Appendix 2: Backward Elimination procedures at 5% level of significance 16 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 In this part, the independent variables will be tested whether they are significant or insignificant at 5% level of significance by comparing p-value and the significant level Region A: Asia Step 1: Stating the null and alternative hypotheses H0; βj = (No variables have relationship with the total number of deaths due to COVID 19) H1; βj ≠ (At least one variable has a relationship with the total number of deaths due to COVID 19) j = 1, 2, 3, 4, Step 2: Full model with five variables Variable P-value Comparison Decision β Average rainfall 0.05 α Do not reject H0 β Medical doctors 0.34 >α Do not reject H0 β Average temperature 0.54 >α Do not reject H0 β Population 0.53 >α Do not reject H0 Because the p-value of average temperature variable output is the largest with 0.54 and greater than α, we eliminate this variable and continue the test Step 3: Four variables Variable P-value Comparison Decision β Average rainfall 0.02 α Do not reject H0 β Medical doctors 0.27 >α Do not reject H0 β Population 0.58 >α Do not reject H0 Because the p-value of population variable output is the largest with 0.58 and greater than α, we eliminate this variable and continue the test Step 4: Three variables Variable P-value Comparison 17 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 Decision β Average rainfall 0.02 α Do not reject H0 β Medical doctors 0.23 >α Do not reject H0 Because the p-value of hospital beds variable output is the largest with 0.45 and greater than α, we eliminate this variable and continue the test Step 5: Two variables Variable P-value Comparison Decision β Average rainfall 0.01 α Do not reject H0 Because the p-value of medical doctors variable output is the largest with 0.32 and greater than α, we eliminate this variable and continue the test Step 6: One variable Variable β1 Average rainfall P-value Comparison α Decision β1 18 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 Do not reject H0 β2 Hospital beds 0.536 >α Do not reject H0 Medical doctors 0.925 >α Do not reject H0 Average temperature 0.191 >α Do not reject H0 Population 0.002 α Do not reject H0 Hospital beds 0.403 >α Do not reject H0 Average temperature 0.177 >α Do not reject H0 Population 0.002 α Do not reject H0 Average temperature 0.128 >α Do not reject H0 Population 0.001 α Do not reject H0 Population 0.0009 >α Do not reject H0 β4 β5 Because the p-value of average temperature variable output is the largest with 0.119 and greater than α, we eliminate this variable and continue the test Step 6: One variable Variable P-value Comparison Population 0.0000284 Reject H0 Step 3: As H0 is rejected, it can be concluded that there is a linear trend at 5% level of significance b Quadratic Step 1: H0; β = (There is no quadratic trend) H1; β ≠ (There is quadratic trend) Step 2: p-value = 0.000 < α (0.05) => Reject H0 Step 3: As H0 is rejected, it can be concluded that there is a quadratic trend at 5% level of significance c Exponential Step 1: H0; β = (There is no exponential trend) H1; β ≠ (There is exponential trend) Step 2: p-value = 0.000 < α (0.05) => Reject H0 Step 3: As H0 is rejected, it can be concluded that there is an exponential trend at 5% level of significance Region 2: North America a Linear Step 1: H0; β = (There is no linear trend) H1; β ≠ (There is linear trend) Step 2: p-value = 0.0017 < α (0.05) => Reject H0 Step 3: As H0 is rejected, it can be concluded that there is a linear trend at 5% level of significance b Quadratic Step 1: H0; β = (There is no quadratic trend) H1; β ≠ (There is quadratic trend) Step 2: p-value = 0.2817 > α (0.05) => Do not reject H0 Step 3: As H0 is rejected, it can be concluded that there is not a quadratic trend at 5% level of significance c Exponential Step 1: H0; β = (There is no exponential trend) H1; β ≠ (There is exponential trend) Step 2: p-value = 0.00269 < α (0.05) => Reject H0 Step 3: As H0 is rejected, it can be concluded that there is an exponential trend at 5% level of significance 21 SGS RMIT – Business Statistic – ECON1193B – TEAM 06 ... Lan S3 836 374 2 ,3, 7 100% Hoang S382 638 4 1 ,3, 7 100% Signature PART 1: DATA COLLECTION The data for the total number of deaths due to COVID 19 between April 01 to July 31 , 2020, and five other variables... April 01 to July 31 , 2020, can be explained by the variation in the amount of rainfall, while 83, 7% of the total variation in the total number of deaths due to COVID 19 between April 01 and July 31 ,... of total number of deaths due to COVID- 19 between April 01 to July 31 , 2020, in Asia and North America In comparing the total death in Asia and North America by using the Central Tendency measurements,