Thông tin tài liệu
FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS GROUP ASSIGNMENT – ECONOMETRICS REPORT ON FACTORS ASSOCIATED WITH BODY MASS INDEX (BMI) IN VIETNAMESE ADOLESCENCE Class: KTEE218.1 Group Lecturer: Ms Nguyen Thuy Quynh Members: Phạm Nguyễn Xuân Lộc - 1816450047 Hoàng Ngọc Anh - 1814450010 Hoàng Hương Giang - 1814450026 Trần Thị Linh Chi - 1814450018 Nguyễn Thị Hạnh Hà - 1814450030 Hanoi 09/2019 I ABSTRACT The BMI is a convenient rule of thumb used to broadly categorize a person as underweight, normal weight, overweight, or obese based on tissue mass muscle, fat, bone and height The BMI is generally used as a means of correlation between groups related by general mass and can serve as a vague means of estimating adiposity BMI is easy to use as a general calculation On the whole, the index is suitable for recognizing trends within sedentary or overweight individuals because there is a small margin of error The BMI has been used by the WHO as the standard for recording obesity statistics since the early 1980s In this report we examine the Factors associated with Body Mass Index (BMI) in Vietnamese Adolescents The research was conducted on 152 people whose ages were ranging from 18 to 25 The data were collected using a questionnaire form that consisted of questions concerning general characteristics of individuals: Height, weight, average time spend on excercises… The average BMI of the individuals differs according to each person’s routine and how they consume calories The purpose of this report is to apply econometrics to examine the effect of different factors on the BMI and thus finding ways to have a healthier life, prevent overweight and underweight II CONTENTS I.ABSTRACT II CONTENTS III LIST OF ABBREVATIONS IV LIST OF TABLES V LIST OF FIGURES VI INTRODUCTION VII CHAPTER I: RATIONALE OF THE STUDY 7.1 Basis for variables and model choosing 7.2 Variables: 7.2.1 Dependent variable: BMI 7.2.2 Independent variables 7.2.3 Model 7.3 Assess about BMI metric VIII CHAPTER II: EMPIRICAL RESEARCH 8.1 Literature review 8.2 Objective 8.3 Quantitative analysis 8.4 Quanlitative analysis 8.4.1 Empirical model Multiple regression model 8.4.2 Methodology 10 8.4.3 Data sources 11 8.4.4 Expectations 11 8.4.5 Estimation results 12 IX CHAPTER III: HYPOTHESIS TESTING 17 9.1 P-value testing 17 9.2 Heteroscedasticity testing: 17 9.3 Correlation between variables testing 18 X RECOMMENDATIONS, DIFFICULTY AND LIMITATION OF THE STUDY: 19 10.1 Recommendation 19 10.2 20 Difficulty 10.3 Limitation 20 XI CONCLUSION 22 XII REFERENCES 23 XIII APPENDIX 24 III LIST OF ABBREVATIONS -OLS: Ordinary Least Square regression -BMI: Body Mass Index IV LIST OF TABLES Table 1: The BMI evaluation Table 2: Variables Table 3: Explanation variables Table 4: Table collected Table 5: Summary of simple statistics for variables Table 6: Tabulation of sex Table 7: Regression of bmi ( dependent) and sleep meal income exercise sex ( independent) Table 8: Vif Table 9: Des sleep, bmi, meal, income, exercise, sex Table 10: Imtest, white 24 24 28 28 28 29 29 29 V LIST OF FIGURES VI INTRODUCTION BMI (Body Mass Index) is a simple, inexpensive, and noninvasive surrogate measure of body fat In contrast to other methods, BMI relies solely on height and weight and with access to the proper equipment, individuals can have their BMI routinely measured and calculated with reasonable accuracy Furthermore, studies have shown that BMI levels correlate with body fat and with future health risks High BMI predicts future morbidity and death Therefore, BMI is an appropriate measure for screening for obesity and its health risks Lastly, the widespread and longstanding application of BMI contributes to its utility at the population level Its use has resulted in an increased availability of published population data that allows public health professionals to make comparisons across time, regions, and population subgroups Obesity is a medical condition that occurs when a person carries excess weight or body fat that might affect their health If a person does have obesity and excess weight, this can increase their risk of developing a number of health conditions, including metabolic syndrome, arthritis, and some types of cancer Causes of Obesity varies from consuming too many calories to leading a sedentary lifestyle Recent hypotheses in the scientific community suggest the current obesity epidemic is being driven largely by environmental factors (e.g., high energy/high fat foods, fast food consumption, television watching, "super-sized" portions, etc) Vietnamese people are bombarded with images and offers of high fat, high calorie, highly palatable, convenient, and inexpensive foods These foods are packaged in portion sizes that far exceed federal recommendations Furthermore, the physical demands of our society have changed resulting in an imbalance in energy intake and expenditure Today's stressful lifestyles compound the effects of environmental factors by impairing weight loss efforts and by promoting fat storage, increasing urbanization and changing modes of transportation, it is no wonder that obesity has rapidly increased in the last few decades, around the world To help ease this “epidemic” we conduct this report on Factors affecting BMI There are lots of factors that have impact to the BMI such as: age, sex, physical activities, individual’s income, number of calories consume per day, etc However, how these factors affect BMI and the extend of affection are still very ambiguous to most people So to clear the mist, our group has done a survey on this issue Nevertheless, due to our unexperience, we just focus on a a specific group of people Our topic is: “Factors associated with body mass index (BMI) in Vietnamese Adolescent” We give our everything into this report, but surely, making mistakes is inevitable We hope that after reading our report, you can give us some feedback on how we can improve the quality Thank you! VII CHAPTER I: RATIONALE OF THE STUDY 7.1 Basis for variables and model choosing ● Based on the characteristics of BMI, any factor alone can not show whether a person's weight is sensible or not, but using it in combination with other indicators can provide a more complete picture Therefore, we decided to choose variables that are both oriented and indefinitely affecting BMI for Vietnamese adolescence in general as: Height Weight Gender Personal income Numbers of minutes spending on exercising Number of sleeping hours per day Packets of milk drinks per day Total number of meals per day ● Multiple Regression Model is the model that we will use mainly in this report In this case, we want to examine the factors that affect to BMI of a Vietnamese so that we have both dependent and independent variables Based on the results of the independent variables, we can predict the dependent variables (BMI) 7.2 Variables: 7.2.1 Dependent variable: BMI BMI (Body Mass Index) is the body index used by doctors and health professionals to determine whether a person's body is obese, overweight or too thin Usually, people use to calculate the level of obesity The only downside of the BMI is that it cannot calculate the amount of fat in the body - the potential risk factor for future health Your BMI is calculated as follows: BMI = (body weight) / (height x height) - body weight: in kg; - height x height: in m; The BMI evaluation board follows World Health Organization (WHO) standards and is specifically for Asians (IDI & WPRO) You can assess your own BMI through the statistics table below: 1Wikipedia Table 1: The BMI evaluation BMI Below 18.5 18.5 – 24.9 25.0 – 29.9 30.0 and above Weight status Underweight Normal Overweight Obese 7.2.2 Independent variables ● Height: the distance from the top to the bottom of something, or the quality of being tall ● Weight: the amount that something or someone weighs ● Gender: the physical and/or social condition of being male or female ● Personal income: money earned by a person over a particular period of time ● Numbers of minutes spending on exercising: is any bodily activity that enhances or maintains physical fitness and overall health and wellness ● Number of sleeping hours per day: is a naturally recurring state of mind and body, characterized by altered consciousness, relatively inhibited sensory activity, inhibition of nearly all voluntary muscles, and reduced interactions with surroundings ● Packets of milk drunk per day: a packet of milk is 180ml ● Total number of meals per day: when food is eaten 7.2.3 Model Multiple regression model: Multiple regression model is a statistical method used to predict the value a dependent variable based on the values of two or more independent variables.The variable called the dependent variable sometimes can be the outcome, target or criterion variable The variables we are using to predict the value of the dependent variable, called the independent variables sometimes can be the predictor, explanatory or regressor variables 7.3 Assess about BMI metric ● BMI is a quick and simple way to get an overall view of your health: It is easy to use formula and makes it useful for measuring across populations With its simplistic design, it can easily be applied to research that compares data on obesity rates between different age ranges in geographical locations The simplicity of calculating a 2statistics.laerd.com BMI also makes it easy for anyone to quickly assess basic information about their physical health at home without having to go to a medical professional or buy expensive equipment ● BMI works extremely well when used for what it’s designed for — to calculate in measure obesity and weight across large populations Because weight is not a direct correlation to fat, and amount of fat on one’s body is not always directly correlated to health issues, BMI measurements are more accurate when used to study the rates of obesity and malnutrition among populations When used in this way, BMI can lead to productive conversations about health while still encouraging body positivity and self-love ● BMI is a widely used metric: Many people, including physicians, use BMI as a measure of health and fitness According to the National Heart, Lung, and Blood Institute, it is a measure of body fat based on weight that applies to both men and women VIII CHAPTER II: EMPIRICAL RESEARCH 8.1 Literature review In 1972, Keys et al severely criticized the validity of Metropolitan Life Insurance published data Instead, Keys et al, using better documented weight for height data, popularized the Quetelet Index in population-based studies They referred to it as the body mass index (BMI) The distribution of BMIs in adult American men and women was determined in 1923 in 1026 individuals The median BMI was 24, but the mean BMI was 25 The distribution curve clearly indicated a skewing toward an increase in BMI, and this trend has continued The reason for choosing this topic: Our topic focuses on people in Vietnam with some new variables: “average hours of exercise per day”, “income per day”, “meals per day” Similarities are we both using quantitative analysis methods and about factors affecting BMI 8.2 Objective Examine the effect of different sociodemographic factors on the BMI 8.3 Quantitative analysis Obesity is a widespreading disease and It is compared to be dangerous as an epidemic Obesity is when someone is so overweight that it is a threat to their health People can become obese in simples way like eating junk food or not excercise In our modern world with increasingly cheap, high calorie food, prepared foods that can be found anywhere having high percentage of sugar combined with our increasingly automatically lifestyles, increasing urbanization and the development of automobiles, it is no wonder that obesity has rapidly increased in the last few decades, around the world A person who is overweight is at risk He has to face health problems such as heart disease, diabetes, and cancer In England in 2016, 34% of men and 46% of women had a very high waist circumference These proportions rose from 20% and 26% respectively in 1993 to 31% and 38% in 2001 As with obesity, there were 617,000 admissions to NHS hospitals in 2016/17 where obesity was recorded as either a primary or secondary diagnosis, an increase of 18 per cent on 2015/16 (525,000) Around two thirds of the admissions where obesity was recorded as either a primary or secondary diagnosis in 2016/17 were for women (66 per cent) Websites or mobile phone apps were used by 8% and activity trackers or fitness monitors by 6% Overall 47% of adults said they were trying to lose weight 66 per cent of men and 58 percent of women aged 19 and over met the government's aerobic guidelines in 2016 21 percent of men and 25 percent of women were classed as inactive in 2016 24 per cent of men and 28 per cent of women consumed the recommended five portions of fruit and vegetables a day in 2016 Half of the people who reported they were trying to lose weight were not using any of the aids or support asked about 3 Health Survey for England, 2016: Summary of key findings 8.4 Quanlitative analysis 8.4.1 Empirical model Multiple regression model According to the basis of the BMI was devised by Adolphe Quetelet, a Belgian astronomer, mathematician, statistician and sociologist, from 1830 to 1850 during which time he developed what he called "social physics", The modern term "Body Mass Index" (BMI) for the ratio of human body weight to squared height was coined in a paper published in the July 1972 edition of the Journal of Chronic Diseases by Ancel Keys and others We classified the independent variables in two categories: (1) individual factors: sex, height, weight, the number of meals per day, physical activities, sleeping hours (2) family and social factors: income The dependent variable is BMI (Body Mass Index) We entered all of the predictors (individual, family) into one model using a multiple linear regression model However, the BMI depends on some determinants: average hours of exercise per day, income per day As a result, we will set up to represent those disturbances The model is: Y= X0 + X11+ X22+ X33+ X44+ X55 + ui Table 2: Variables NAME TYPE X Independent variable (Quantitative variable) Sleep hours sleep Time (hours) X Independent variable (Quantitative variable) Meals per day meal Number of meals X Independent variable (Quantitative variable) Income per month income VND X Independent variable Average minutes for doing exercise per day excercise Time (minutes) EXPLANATION SIGH UNIT https://files.digital.nhs.uk/pdf/s/q/hse2016-summary.pdf IX CHAPTER III: HYPOTHESIS TESTING 9.1 P-value testing Back to the stata table ( = 0.05): -The coefficient sleeping hours has p-value = 0.000 < 0.05, so rejecting Ho, accepting H1 Thus the coefficient sleeping hours is statistically significant at the 5% significance level -The coefficient, meals per day has p-value = 0.178 chi < , then reject H If Prob > chi > , then fail to reject H 2 The result from the table above shows that: P = Prob > chi = 0.03022, and α = 0.05 F → P < α, so that we reject H and accept H F We can conclude that this is a model has unrestricted heteroskedasticity 9.3 Correlation between variables testing We applied “vif ” command in STATA in order to test the correlationship among variables, we obtained the result table as follow: vif Variable | VIF 1/VIF -+ -meal | exercise | 1.04 0.965463 1.03 0.969234 18 sleep | income | 1.01 0.992842 1.00 0.997146 -+ Mean VIF | 1.02 As the result of table, the VIF value of all variables is smaller than 10, so that there would be no collinearity between variables X RECOMMENDATIONS, DIFFICULTY AND LIMITATION OF THE STUDY: 10.1 Recommendation By constructing the regression model, we concluded that the variables included in the index were statistically significant for the BMI, including: sleeping hours, income per month, meals per day, sex and average minutes for doing exercise per day In addition, there are omitted variables, which means, in addition to the variables presented there are some independent variables that impact on the BMI that were not yet included in the model After analysing this data, based on the results obtained, the research team made the following recommendations and measures to increase the BMI: We recommend recording your food or calorie intake for a few days to understand what you eating habits are truly like It may be the reality check you need to change your habits Use whatever method you feel most comfortable with, whether that’s writing it in a journal or using an app on your smartphone As with monitoring your food intake, you’ve got to know what your physical activity level is like Not just plan exercises, we are going to exercising immediately and more Commit to walking for 20 minutes three times this week, and plan the days you’re going to it and what time — for instance after work on Mondays, Wednesdays, and Fridays And if something comes up, know that you can shorten it to or 10 minutes — everything counts Even if the weight doesn’t seem like it’s coming off fast enough, stay the course It’s only with consistent efforts to eat well, move more, and maintain other healthy 19 habits that affect weight (like getting enough sleep) that the pounds come off permanently, research suggests 10.2 Difficulty Difficulty in choosing the topic: We had so many topics such as the factors affect economic indicators, the factors impacting on the choice of adolescents in services, and so on But, we care more about health problems which is more and more complicated So the final topic was chosen based on its meaningful for young people, in order that they can know the way to have a more confident body and a good health and reduce risky by taking care of their health and body Difficulty in choosing the model: Our team had discussed and had run a trial model to choose which model is the most suitable for this topic The final answer is to choose the linear regression model due to its simplicity and accuracy Difficulty in choosing the variables: choosing the variables which are suitable for the topic is also very difficult Taking the variables is not a hard problem but deciding if when run model, the variables has capacity to impact on each other and the model to survey is controversial Because social characteristics of those variables, It is also quite hard to decide if it is quantitative variable or qualitative variable Difficulty in surveying and conducting: Because of the limitation in condition the survey was not clearly The sample is quite small and limited in Ha Noi So in some aspect, the sample is not yet comprehensive Difficulty in running the model: We met some technique problems We were wrong when run vif and because of our inexperience as well 10.3 Limitation Choosing variables: Some chosen variables are not very meaningful in real life When establishing the model, we have to eliminate some variables not necessary and also might lack some important variables which have strong impact on this model Data source: The sample size is not big enough and the level of accuracy is still limited These may affect the inclusiveness of model We collect data of Body Mass Index (BMI)- an indicator of body fatness and not all of us knew what is it so it is our big limitation 20 21 XI CONCLUSION The analysis in this report has revealed that there is an inverse relationship between sleeping hours, exercising hours, income and the BMI This once again proves the significant impact of the variables mentioned above on BMI Obviously, if we not allocate the hours spent on sleeping and exercising, the money earned and money spent on eating properly, the BMI will not in its usual number As a consequence, our body will be unhealthy and not in a good shape, we might be too skinny, or we might be fat Sleeping hours and exercising hours affect the BMI, the more hours spent on sleeping and less numbers of meal we eat, the greater BMI will be This might be abnormal because in the reality, if we ear more, we might gain weight and get higher, therefore, the BMI must increase But this analysis show the opposite It might be explained as follow: -The less meal we eat, but if each meal has a large proportion can cause the metabolic system in the body to not working properly So in the end, the weight rise and BMI increase -The sample size is not big enough to have a good result -Finally, income and expenditure on eating reflect the capacity of a person to pay for eating On average, the more money earned and the more money spent on eating, the stronger impact on BMI Taking all of these into consideration, a person can adjust the BMI through a number of things such as sleeping hours, exercising hours, income, and expenditure on eating However, the conclusion above are also subject to a number of limitations First, it is unclear to what extent the results can be generalized to other countries apart from the Viet Nam Each countries has its own weather, eating habits and people’s shape form which affect strongly on BMI Second, there may be other variables that affect the BMI such as genetic, working conditions, family relationship and so on Including these in the regression would increase the precision of estimates as well as eliminate the potential of omitting the bias variable This investigation, however, is left for future research Therefore, BMI is an appropriate measure for screening for obesity and its health risks Lastly, the widespread and longstanding application of BMI contributes to its utility at the population level Its use has resulted in an increased availability of published population data that allows public health professionals to make comparisons across time, regions, and population subgroups 22 XII REFERENCES Further detail about trends in obesity can be found in the Adult Health Trends report: https://digital.nhs.uk/pubs/hse2016 Health Survey for England, 2016: Summary of key findings: https://files.digital.nhs.uk/pdf/s/q/hse2016-summary.pdf BMI Wiki: https://en.wikipediah.org/wiki/Body_mass_index Obesity Wiki: https://en.wikipedia.org/wiki/Obesity Limitations of BMI: https://www.medicalnewstoday.com/articles/323543.php Metric of BMI: https://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmi-m.htm Factors affecting BMI: http://applications.emro.who.int/imemrf/Professional_Med_J_Q/Professional_Med_J _Q_2013_20_6_956_964.pdf BMI on Child & Teen: https://www.cdc.gov/healthyweight/assessing/bmi/childrens_bmi/about_childrens_bm i.html 23 XIII APPENDIX Part A: Data Table 3: Explanation variables NAME TYPE X Independent variable Sleep hours sleep Time (hours) X Independent variable Meals per day meal Number of meals X Independent variable Income per month income VND X Independent variable Average minutes for doing exercise per day exercise Time (minutes) X Independent variable Sex sex Y Dependent variable BMI bmi Table 4: Table sex SIGH UNIT Males: 1; females: collected bmi 1 1 0 1 1 EXPLANATION 20.51509 28.68514 21.20311 28.53746 20.8307 20.60378 21.05171 39.00119 27.5802 27.51338 18.50777 29.21841 22.76147 24.31412 height 1.78 1.67 1.71 1.45 1.78 1.83 1.51 1.45 1.66 1.64 1.66 1.48 1.61 1.66 weight 65 80 62 60 66 69 48 82 76 74 51 64 59 67 exercise income meal 0.75 0.25 2 0.75 0.25 2.25 0.25 0.5 0.25 500000 4500000 5000000 3000000 4500000 5000000 500000 2500000 2000000 5000000 3000000 1000000 4000000 sleep 1 3 4 1 5.5 5 5.5 10 7 4.5 7.5 5.5 24 1 1 0 1 1 0 0 0 1 0 0 0 1 1 0 0 16.97959 19.95728 25.84777 21.91358 19.84066 16.52893 14.53296 23.23346 24.7768 21.49029 24.03441 24.25684 22.4323 27.6601 13.7253 20.28651 31.64429 28.25097 19.59646 30.11938 27.88762 22.20633 30.86301 19.13265 30.49353 18.42404 24.80159 23.01118 24.6755 14.00511 21.08281 19.04432 29.93759 12.98152 23.1405 22.22222 16.61327 31.46837 31.59626 20.56933 21.67211 25.59221 23.82813 14.70538 1.75 1.69 1.61 1.8 1.81 1.65 1.74 1.58 1.53 1.51 1.54 1.45 1.58 1.69 1.77 1.79 1.57 1.54 1.66 1.65 1.55 1.63 1.61 1.68 1.46 1.68 1.68 1.56 1.72 1.69 1.54 1.52 1.54 1.82 1.65 1.8 1.59 1.47 1.52 1.65 1.81 1.79 1.6 1.71 52 57 67 71 65 45 44 58 58 49 57 51 56 79 43 65 78 67 54 82 67 59 80 54 65 52 70 56 73 40 50 44 71 43 63 72 42 68 73 56 71 82 61 43 1.75 1.5 2.5 0.5 2.5 0.25 0.5 2.5 1.25 0.25 2.5 2.25 0.25 0.75 2.25 0.5 0.75 1.25 1.25 1.75 1.25 0.75 2.25 2.5 1.75 1.5 0.25 1.25 1.5 0.25 2.5 1.5 2.5 2.25 1.5 2.25 2500000 1500000 1500000 3500000 4500000 4000000 4500000 3000000 3500000 3000000 1000000 500000 4500000 2000000 5000000 3000000 2000000 2500000 1000000 1500000 1500000 2500000 1500000 5000000 1000000 2000000 500000 3500000 500000 3000000 3500000 3500000 3500000 1500000 1500000 4000000 3000000 4500000 5000000 4500000 2 4 3 2 4 4 4 2 2 1 4 4 4 6.5 5.5 3.5 6 5.5 6 5.5 3.5 7.5 5.5 7.5 7.5 4.5 6 3.5 5.5 7.5 5.5 8 5.5 6.5 3.5 25 1 1 1 1 0 0 0 1 0 0 0 1 0 0 1 0 0 14.53287 30.07813 13.29159 27.73438 20.2848 21.71807 29.04866 36 16.97531 21.22789 25.82645 28.13366 22.35434 23.78121 30.58802 31.86683 33.31945 24.44444 31.98054 32.43543 26.63496 13.21178 29.77778 18.14487 34.24032 20.82466 17.23905 32.88241 23.1405 31.22945 22.06035 19.22769 28.617 31.5011 27.6601 22.58271 30.47722 14.36735 28.69898 24.39482 28.19692 15.73134 25.56495 23.24459 1.7 1.6 1.84 1.6 1.57 1.73 1.53 1.5 1.8 1.55 1.76 1.52 1.45 1.74 1.48 1.45 1.47 1.5 1.49 1.59 1.45 1.74 1.5 1.66 1.48 1.47 1.72 1.55 1.65 1.56 1.55 1.58 1.46 1.48 1.69 1.63 1.61 1.75 1.68 1.46 1.62 1.71 1.78 1.51 42 77 45 71 50 65 68 81 55 51 80 65 47 72 67 67 72 55 71 82 56 40 67 50 75 45 51 79 63 76 53 48 61 69 79 60 79 44 81 52 74 46 81 53 1.25 0.25 1.75 1.75 0.75 0.5 1.75 2.25 0.75 2.25 0.25 2.5 2.5 0.75 0.5 1.25 1.75 2.5 2.5 1.75 1.75 2.5 1.5 0.5 0.25 1 1.25 1.25 0.75 1.25 2.5 0.25 1.75 2 1.5 2500000 1500000 4000000 1500000 1500000 4000000 4500000 3500000 5000000 4500000 2500000 2000000 4500000 5000000 2500000 3500000 4000000 1500000 3500000 5000000 3500000 2500000 500000 3500000 5000000 3500000 1500000 500000 3500000 3500000 500000 3000000 1500000 500000 2000000 1000000 4500000 4500000 2500000 4500000 4 3 4 2 4 4 4 1 2 2 1 1 3.5 7.5 3.5 5.5 7.5 5.5 6.5 5.5 7.5 8.5 8 6.5 3.5 7.5 4.5 8.5 4.5 8 5.5 5.5 7.5 3.5 7 6.5 26 0 0 1 1 0 1 1 1 0 1 1 0 1 1 1 0 29.13632 31.86683 13.73584 27.51338 33.76039 24.38653 36.14744 20.83082 12.76772 15.82609 17.14678 21.20845 23.50356 29.66655 29.7442 20.0796 38.52556 30.70041 17.0433 25.45236 21.10727 25.24934 23.12467 14.34257 21.21832 28.53746 22.12974 17.71542 20.44914 23.09541 14.31702 17.42675 24.12175 22.21297 32.89329 18.93878 17.09928 17.95918 18.49112 33.26707 29.6875 15.24158 16.07012 12.54143 1.55 1.45 1.81 1.64 1.52 1.62 1.45 1.82 1.77 1.83 1.62 1.52 1.81 1.59 1.64 1.67 1.45 1.51 1.78 1.47 1.7 1.78 1.57 1.67 1.61 1.45 1.74 1.68 1.64 1.79 1.85 1.84 1.51 1.47 1.51 1.75 1.71 1.75 1.56 1.57 1.6 1.62 1.85 1.83 70 67 45 74 78 64 76 69 40 53 45 49 77 75 80 56 81 70 54 55 61 80 57 40 55 60 67 50 55 74 49 59 55 48 75 58 50 55 45 82 76 40 55 42 0.25 1.25 2.5 0.75 0.25 1.5 1.75 2.5 0.25 1.25 1.5 0.5 2.5 0.25 0.25 0.25 2.5 1.5 1.25 2.25 0.75 1.75 0.25 1.5 2.5 1.25 0.5 2.25 1.25 2.25 1.25 0.5 1.75 1.25 1.5 0 3500000 4500000 1500000 3500000 3000000 4500000 1500000 4500000 1000000 2000000 3000000 2000000 5000000 1000000 2000000 4000000 4500000 2000000 3000000 2500000 1000000 2500000 2500000 1500000 1000000 2500000 500000 3000000 1000000 2000000 2500000 4000000 2000000 500000 4000000 1000000 1000000 5000000 1000000 0 1000000 2500000 4500000 3 2 4 2 1 4 2 2 3 3 4 3 7.5 3.5 8.5 4.5 5.5 7.5 7.5 9.5 7.5 4.5 6.5 5.5 6.5 3.5 5.5 5.5 4.5 3.5 4.5 5.5 4.5 4.5 4.5 4.5 8.5 7.5 4 27 0 0 0 21.87755 25.1559 22.74338 30.84442 32.41417 25.9909 1.75 1.68 1.84 1.58 1.48 1.71 67 71 77 77 71 76 1.5 1.25 2.5 2.25 1500000 1000000 500000 2000000 1500000 3 5.5 6.5 5.5 7.5 6.5 Part B: Stata output of simple statistics for variables Table 5: Summary Variable | Obs Mean Std Dev Min Max -+ - sex | height | weight | bmi | exercise | 152 152 152 152 152 4671053 5005661 1.630526 118354 1.45 1.85 62.14474 12.39601 40 82 23.8024 6.03558 12.54143 39.00119 1.253289 8253972 2.5 -+ - income | 152 2503289 1553139 meal | 152 2.480263 1.139132 sleep | 152 5.957237 1.519132 Table 6: Tabulation of sex tabulate sex sex | Freq Percent 5000000 Cum + 0| 81 53.29 53.29 1| 71 46.71 100.00 + Total | 152 100.00 Table 7: Regression of bmi ( dependent) and sleep meal income exercise sex ( independent) regress bmi sleep meal income exercise sex Source | SS df MSNumber of obs = 152 -+ -F(5, 146) = 3264.04 Model | 5451.89011 1090.37802 Prob > F = 0.0000 Residual | 48.7724273 146 334057721 R-squared = 0.9911 -+ -Adj R-squared = 0.9908 Total | 5500.66254 151 36.4282287 Root MSE = 57798 -28 bmi | - Coef Std Err t P>|t| [95% Conf Interval] + sleep | 3.960645 0313438 126.36 0.000 3.898699 4.022591 meal | -.0575852 0425167 -1.35 0.178 -.1416128 0264425 income | 2.17e-08 3.05e-08 0.71 0.478 -3.86e-08 8.20e-08 exercise | 0173715 0581383 0.30 0.766 -.0975299 1322728 sex | 0965581 0965238 1.00 0.319 -.0942064 2873225 _cons | 2295313 2371914 0.97 0.335 -.2392409 6983034 -Table 8: Vif Variable | VIF 1/VIF - + meal | 1.04 0.965463 exercise | 1.03 0.969234 sleep | 1.01 0.992842 income | 1.00 0.997146 - + Mean VIF | 1.02 Table 9: Des sleep, bmi, meal, income, exercise, sex des sleep bmi meal income exercise sex storage display value variable name type format labelvariable label -sleep float %8.0g bmi float %8.0g meal byte %8.0g income long %8.0g exercise float %8.0g sex byte %8.0g Table 10: Imtest, white imtest, white White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(14) = Prob > chi2 = 16.19 0.3022 Cameron & Trivedi's decomposition of IM-test Source | chi2 df p 29 -+ Heteroskedasticity | 16.19 14 0.3022 Skewness | 8.59 0.0721 Kurtosis | 76.23 0.0000 -+ Total | 101.01 19 0.0000 - 30 PART C: MEMBERS INTRODUCTION Member’s Member’s name ID Pham Nguyen Xuan Loc 1816450047 Tasks ● ● ● ● ● ● Nguyen Thi Hanh Ha 1814450030 ● Member Appendix Data treatment and Data sources P-value testing Difficulty and limitation Conclusion ● ● Member ● Carry out STATA ● Data treatment and Data sources ● F - test, Analysis of variance, correlation testing ● ● ● ● ● ● ● ● ● ● ● ● Tran Thi Linh Chi Hoang Huong Giang 1814450018 1814450026 Responsibly ● Submit works on time ● Enthusiasm ● ● 1814450010 ● Member Rationale of the study Definition of dependent/ independent variables Assess the metric and regression model References Finish the final report ● Hoang Ngoc Anh Leader Introduction Methodology Expectations Confident interval Estimation results, interpreting Working attitude Member ● Literature review, objective ● Quantitative analysis Responsibly ● Submit works on time ● Enthusiasm Responsibly ● Submit works on time ● Enthusiasm Responsibly ● Submit works on time ● Enthusiasm Negligent 31 ... early 1980s In this report we examine the Factors associated with Body Mass Index (BMI) in Vietnamese Adolescents The research was conducted on 152 people whose ages were ranging from 18 to 25... the Quetelet Index in population-based studies They referred to it as the body mass index (BMI) The distribution of BMIs in adult American men and women was determined in 1923 in 1026 individuals... FIGURES VI INTRODUCTION BMI (Body Mass Index) is a simple, inexpensive, and noninvasive surrogate measure of body fat In contrast to other methods, BMI relies solely on height and weight and with access
Ngày đăng: 22/06/2020, 21:35
Xem thêm:
