FOREIGN TRADE UNIVERSITY
FACULTY OF INTERNATIONAL ECONOMICS
----------

ECONOMETRICS REPORT
Class: KTEE218.1
GROUP 11

Name
Đàm Thanh Bình
Thái Mỹ Hạnh
Phạm Khắc Dương
Nguyễn Thị Hằng
Đoàn Thanh Tùng

Students’ ID
1814450016
1814450038
1814450025
1814450106
1814450072

Lecturer: MSc. Quynh Thuy Nguyen
Hanoi, 26th September 2019


FOREIGN TRADE UNIVERSITY
FACULTY OF INTERNATIONAL ECONOMICS
----------

ECONOMETRICS REPORT

Class: KTEE218.1
GROUP 11

Name
Đàm Thanh Bình
Thái Mỹ Hạnh
Phạm Khắc Dương
Nguyễn Thị Hằng
Đoàn Thanh Tùng

Students’ ID
1814450016
1814450038
1814450025
1814450106
1814450072

Lecturer: MSc. Quynh Thuy Nguyen
Hanoi, 26th September 2019


TABLE OF CONTENTS
ABSTRACT

5

INTRODUCTION 6

1.


Research objective.....................................................................6

2.

Rationale of study......................................................................6

3.

Object and scope of study.........................................................7

4.

Structure of scope......................................................................7
SECTION I: OVERVIEW OF THE TOPIC 8

1.

General definitions and economic theories................................8
1.1 General definitions.......................................................................................8
1.2 Economic theories related to the research....................................................9

2.

Literature view...........................................................................12
2.1 Related published researches.....................................................................12
2.2 Research hypotheses..................................................................................12
2.3 BMR...........................................................................................................13
2.4 Calorie Expenditure of Exercise................................................................13
2.5 Calories expenditure of Common Foods...................................................14
SECTION II: MODEL SPECIFICATION


16

1. Methodology.................................................................................16
1.1. Method you use to derive the model.........................................................16

2. Theoretical model specification....................................................17
2.1. Specification of the model........................................................................17

2.3 Describe the data..........................................................................21
SECTION III: ESTIMATED MODEL and statistical inferences.24

1. Estimated model:...........................................................................24
1.1Estimation result:........................................................................................24


1.3 Explain the meanings of estimated coefficients.........................................25
1.4 The coefficient of determination................................................................26

2. Hypothesis Testing.........................................................................26
2.1. Testing the significance of an individual regression coefficient ..............26
2.2 The confidence interval approach..............................................................27
2.3. The T-distribution approach......................................................................27

3. The P-value approach....................................................................28
4. Testing the overall significance.....................................................28
4.1. The F-test of significance approach...........................................28
4.2. The P-value approach................................................................................29
Conclusion: 30



ABSTRACT
In the last a few decades, BMI index has been becoming one of the growing
concerns all over the world, especially to the health conscious as well as health
public researchers. BMI is a measurement of a person's leanness or corpulence
based on their height and weight, and is intended to quantify tissue mass. It is
widely used as a general indicator of whether a person has a healthy body weight
for their height. Specifically, the value obtained from the calculation of BMI is used
to categorize whether a person is underweight, normal weight, overweight, or obese
depending on what range the value falls between. This is the World Health
Organization's (WHO) recommended body weight based on BMI values for adults.
It is used for both men and women, age 18 or older.
This provides the answer to our question why BMI is one of the most reliable
index reflecting the recent health situation. BMI plays a crucial role in analyzing
the status of well-being, measuring the risk of hazardous diseases resulting from
overweight like obesity, cardiovascular diseases, high blood pressure or from
underweight like malnutrition, vitamin deficiencies, etc. However, in reality, to
achieve an ideal BMI index, we have to pay more attention to a number of factors
directly affecting it, namely nutritious intake in each portion, daily eating habit,
average workout hours, sex, age, etc. So in this study we will gain deeper insight
into the primary factors affecting BMI index and shed light on the optimal
approaches to reach standardized BMI, improve the general public well-being.
We would like to give our appreciation to your useful lectures of this course
and instruction to fulfill this report. Throughout process of making this report, our
team did try our best to gather data, conduct researches and utilize available
materials to analyze the result. However, mistakes are inevitable. Therefore, please
let us know if there are some ones that must be fixed to achieve more accurate
result.



INTRODUCTION
1.

Research objective

The main purposes of this study is to provide thorough understanding of
BMI index, and analyze fundamental elements contributing to fluctuation of BMI
ranging from nutritious regime, eating habit to workout routine. To be more
specific, it also supplies data about every single substance in daily diets, frequency
and the typical amount of calorie burned in exercising, the number and rational
distribution of daily meals. This study also enables people to foresee the positive or
negative trend of BMI in the future, gives recommendations for them to propose
long-term plan for the sustainable development of public health.

2.

Rationale of study

There are a few motivations prompting us to do this research:
- The lifestyle in the modern societies pose various threats to our health,
particularly for youngster. The consumption of junk food has been considerably
increasing year by year. Besides, the rate of children as well as adults becoming
obese is so alarming, which has been linked to a lack of physical exercises. On the
contrary, in the most remote areas, many families have to struggle to feed
themselves so malnutrition is a very common tendency. The underweight and
overweight is associated with a plenty of dangerous diseases for health:
- Being overweight increases the risk of a number of serious diseases and
health conditions. Below is a list of said risks, according to the Centers for Disease
Control and Prevention (CDC):
 High blood pressure

 Higher levels of LDL cholesterol, which is widely considered "bad
cholesterol," lower levels of HDL cholesterol, considered to be good
cholesterol in moderation, and high levels of triglycerides
 Type II diabetes
 Coronary heart disease
 Stroke
 Gallbladder disease
 Osteoarthritis, a type of joint disease caused by breakdown of joint cartilage
 Sleep apnea and breathing problems
 Certain cancers (endometrial, breast, colon, kidney, gallbladder, liver)
 Low quality of life
 Mental illnesses such as clinical depression, anxiety, and others
 Body pains and difficulty with certain physical functions
 Generally, an increased risk of mortality compared to those with a healthy
BMI
- Being underweight has its own associated risks, listed below:


Malnutrition, vitamin deficiencies, anemia (lowered ability to carry blood
vessels)
 Osteoporosis, a disease that causes bone weakness, increasing the risk of
breaking a bone
 A decrease in immune function
 Growth and development issues, particularly in children and teenagers
 Possible reproductive issues for women due to hormonal imbalances that can
disrupt the menstrual cycle. Underweight women also have a higher chance
of miscarriage in the first trimester
 Potential complications as a result of surgery
 Generally, an increased risk of mortality compared to those with a healthy
BMI

Therefore, more attention should be placed on and more investigation should be
conducted on a regular basis to get deep understanding of primary elements having
influences on BMI index. From those data and results of this paper, a specific plan
can be set up to regulate the amount of nutrition intake, time for exercise and
outdoors activities, which can help control weight, height and lead a healthier
lifestyle.


3.

Object and scope of study

In this study, we will concentrate on young people at ages ranging from 18 to
22 both female and male. These are the most adequate and ideal ages to get
variables directly affecting BMI as well as the most accurate consequences. A
range of research methodologies was used to investigate current practice and to
capture data about the scope and fundamental contributors in BMI index. Using a
software Stata used for statistical analysis; descriptive statistics and summary
methods to analyze the information from the survey.

4.

Structure of scope

The report has been structured to reflect the different research goals for the
project. This report is organized as follows: Sections I will overview definitions.
Section II will explore methodology of study: Factors that affect BMI index and
analyze dependent and independent variables in the OLS model. Section III will
explain result we get from the Stata and test initial hypotheses. Finally, give some
recommendations and effective way to positively alter daily diets and workout

routine, improving the state of well-being and reaching an ideal BMI.


SECTION I: OVERVIEW OF THE TOPIC
1. General definitions and economic theories
1.1 General definitions
1.1.1. Definition and formula of BMI
a. Definition of BMI
- The BMI formula uses your weight (in kg or pounds) and your height (in
meters or inches) to form a simple calculation that provides a measure of
your body fat. The formula for BMI was devised in the 1830s by Belgian
mathematician Adolphe Quetelet and is universally expressed in kg/m2.
- Body mass index is a measure of body fat and is commonly used within the
health industry to determine whether your weight is healthy. BMI applies to
both adult men and women and is the calculation of body weight in relation
to height. This article delves into the BMI formula and demonstrates how
you can use it to calculate your own BMI.
b. Formula of BMI
The first formula we've listed is the metric BMI formula, using kilograms
and meters. The second one is the imperial BMI formula, which uses units of
pounds and inches.




Metric BMI Formula:

BMI =

Imperial BMI Formula:


BMI = 703

1.1.2. BMI categorization
The BMI statistical categories below are based on BMI scores and apply to
adults of age 20 years and upwards. The World Health Organisation (WHO)
regards a healthy adult BMI to be between 18.5 and 25.


BMI

BMI Category

Less than 15

Very severely underweight

Between 15 and 16

Severely underweight

Between 16 and 18.5

Underweight

Between 18.5 and 25

Normal (healthy weight)

Between 25 and 30


Overweight

Between 30 and 35

Moderately obese

Between 35 and 40

Severely obese

Over 40

Very severely obese
Table 1

1.1.3. Current problems around BMI
It is a common argument that the results the BMI formula provides are too
general and do not consider the gender, build, age or ethnicity of a person. For
example, professional athletes are often considered overweight or obese when
using BMI measurements due to their muscle content, which weighs more than fat.
Similarly, as people age their bone density decreases. So, although they may
seem to have a weight within the normal BMI range, their measurement actually
needs to be scaled-down to reflect this. In a study published in the Journal of
Economics in 2008, John Cawley, professor at Cornell University, was able to
demonstrate that, relative to percent body fat, BMI appears to misclassify
substantial fractions of individuals as obese or non-obese.

1.2 Economic theories related to the research
a. The three-variable model




Population regression function:

E (YX2i, X3i) = β1 + β2X2i + β3X3i




Stochastic form

Yi = β1 + β2X2i + β3X3i + ui
Where:

Y: dependent variable
X2, X3: independent variables

β 1: intercept term
β 2, β 3: partial regression coefficients
ui: disturbance



The meaning of partial regression coefficients
- β 2 measures the change in the conditional mean value of Y, E(YX2i, X3i),
per unit change in X2, holding the value of X3 constant.

β2=
- If we increase X2 by one unit and keep other variables constant, the

expected value of Y increase by β2 units.
- Similarly, β3 measures the change in the mean value of Y, E(Y), per unit
change in X3, holding the value of X2 constant.

β3=
b. Coefficient of determination R2 and the adjusted R2



The multiple coefficient of determination R2
The extent to which all the independent variables jointly (i.e., the model)
explain the variation in the dependent variable.

R2 = = 1 - = 1 

Problems with R2


R2 is a non-decreasing function of the number of independent variables in
the model.
- Model with more terms may appear to have a better fit.
- If there are too many predictors, it will result in over-fitting the model:
misleading high R2 and a lessened ability to make predictions.



The adjusted R2

=
- k is the number of parameters in the model

- “Adjusted”: adjusted for the degree of freedom associated with the sums
of squares.
- R2 and are related:

=1–



Properties of the adjusted R2
- If k > 1 then < R2 ≤ 1
penalizes models with a larger number of parameters to be estimated.
-

can be less than zero

- is used for the following purposes:
+ is used to compare the fitness between models with different number of
explanatory variables.
The sample size n and the dependent variable must be the same.
+ is used to consider adding one more variable into the model. The
variable will be added if:
 Increases
 The coefficient associated with the added variable is statistically
different from 0.
c. K-variable regression model
 Population regression model:


E (YX2,…Xk) = β1 + β2X2i +…+ βkXki
Yi = β1 + β2X2i +…+ βkXki + ui

 Sample regression model:

Yi = + X2i +…+ Xki
Yi = + X2i +…+ Xki + ui
2. Literature view
2.1 Related published researches
Obesity is a world-wide health problem across the lifespan that also affects
the elderly in developed and emerging countries. In these countries, their
populations have proportionally greater numbers of older adults living to older
ages, and the prevalence of obesity is increasing rapidly even at these oldest ages.
In the United States, the prevalence of obesity in the elderly ranges from 42.5% in
women aged 60–69 years to 19.5% in those aged 80 years or older. The prevalence
of obesity is 38.1% in men aged 60–69 years and 9.6% for those men aged 80 years
or older. In Europe, the prevalence is slightly lower but it is still a significant health
issue. In the United Kingdom for example, 22% of women and 12% of men aged
75 years or older are obese. These statistics bode ill as the proportion of world’s
elderly population continues to increase.
Obesity in adulthood is associated with increased mortality, and data from
the Framingham Heart Study report that obese adults (BMI ≥ 30) at age 40 years
lived 6–7 years less than did their normal-weight counterparts. Another study based
on several U.S. data sets (US Life Tables (1999), the third National Health and
Nutrition Examination Survey (NHANES III), NHANES I and II, and the
NHANES II Mortality Study) also reported that obesity reduces life expectancy,
particularly so in younger adults. For example, in obese (BMI ≥ 45) white men and
women aged 20–30 years, the minimum years of life lost was 13 and 8 years,
respectively.
The aim of this systematic literature review is to collate, review and critically
assess current scientific and clinical information on the impact of obesity on



mortality in the elderly so as to help clarify and improve our understanding of the
complex relationship between the increasing health problem of obesity in elderly
adults and the risk of mortality. This knowledge can help to reduce the cost of
health care and improve the quality of life in this segment of the world’s
population.

2.2 Research hypotheses
H1: Are the factors such as Age, Gender, Eating habit, Average calories
burned per day and Rate of absorbed calories over necessary calories influenced
more or less on the BMI of 18-22 years old people? And how does it affect?
2.3 BMR
Calculate your Basal Metabolic Rate (BMR)
Our bodies are like engines that are constantly running. They're always
burning fuel or calories (even during sleep). BMR is the number of calories you
burn each day simply by being alive.
Your Basal Metabolic Rate (BMR) can vary based on your age, sex, size, and
genetics. To get an accurate picture of the amount of calories you burn per day, start
by calculating a value for your BMR.
Use the following equations to find your BMR by hand:
 Men: (0.1 × weight) + (6.25 × height*100) - (5 × age) +5
 Women: (0.1 × weight) + (6.25 × height*100) - (5 × age) -161
 Doing exercises is an important factor which accelerates the calorieburning process.
 No exercise = BMR× 1.2
 1-3 days/week = BMR × 1.375
 3-5 days/week = BMR × 1.55
 Most days = BMR × 1.725
 Everyday = BMR × 1.9
2.4 Calorie Expenditure of Exercise
Exercises
Golf


Calories
113

Exercises
Gate-ball

Calories
122


Walking

128

Yoga

128

Badminton

145

Table Tennis

145

Tennis

192


Bicycling

192

Boxing

192

Basketball

192

Mountain Climbing

209

Jumping Rope

224

Aerobics

224

Jogging

224

Soccer


224

Swimming

224

Japanese Fencing

320

Racquetball

320

Squash

320

Taekwondo

320

Table 2

 Based on your current weight
 Based on 30 mins duration
2.5 Calories expenditure of Common Foods
Food


Serving Size

Calories

kJ

Fruit
Apple

1 (4 oz.)

59

247

Banana

1 (6 oz.)

151

632

Grapes

1 cup

100

419


Orange

1 (4 oz.)

53

222

Pear

1 (5 oz.)

82

343


Peach

1 (6 oz.)

67

281

Pineapple

1 cup


82

343

Strawberry

1 cup

53

222

Watermelon

1 cup

50

209

Vegetables
Asparagus

1 cup

27

113

Broccoli


1 cup

45

188

Carrots

1 cup

50

209

Cucumber

4 oz.

17

71

Eggplant

1 cup

35

147


Lettuce

1 cup

5

21

Tomato

1 cup

22

92

Proteins
Beef, regular, cooked

2 oz.

142

595

Chicken, cooked

2 oz.


136

569

Tofu

4 oz.

86

360

Egg

1 large

78

327

Fish, Catfish, cooked

2 oz.

136

569

Pork, cooked


2 oz.

137

574

Shrimp, cooked

2 oz.

56

234

Table 3


SECTION II: MODEL SPECIFICATION
1. Methodology
1.1. Method you use to derive the model
The process using in this research is called Multiple Linear Regression.
This is a linear approach to modeling the statistical relationship of a dependent
variable on one or more explanatory variables. Specifically, in our research, it is the
statistically dependent relationship between eating habit, exercising routine and
BMI index
Methods used to collect and analyze the data
- Collect: At first we do many types of research: do survey by passing out
answer sheets, gather a number of related researches on World Bank and many
preference books.
- Therefore, we decide to find have another methods to collect our data,

which is mail interview. It took quite a lot of time to draw up an attractive mail
form to send to our subjects of research but one of the most benefit of this method
is that we can send to many people in a very polite way. Nevertheless, the response
rate is often low, it takes a lot of time to wait for outgoing messages and replies,
uncontrolled respondents can reply to the wrong target …
- We also collect the data by telephone interview. During the process of
collecting information by telephone interviewing we were able to better understand
their personal opinions and the response rate was also very high when call people
directly. Best of all, we can improve the questionnaire during the interview process
(improve the questionnaire, or change the order of the questions) .However, the
interview time is limited because the respondent is often not willing to talk for a
long time on the phone, sometimes people need to ask to refuse to answer or not at
home.
- Especially, we get a lot of data from Worldbank, which is a very reliable
open data site. The searching is very fast because we only need to sort out the data
and search directly on the Worldbank website. This is really convenient because we
do not have to spending a lot of time and giving us a high level of confidence in the
accuracy of the numbers.


- Analyze: With the assistance of Stata 14 software and the following step to
analyze data
+ Step 1: Data Validation
The purpose of data validation is to find out, as far as possible, whether the
data collection was done as per the pre-set standards and without any bias. It is a
four-step process, which includes…
 Fraud, to infer whether each respondent was actually interviewed or
not.
 Screening, to make sure that respondents were chosen as per the
research criteria.

 Procedure, to check whether the data collection procedure was duly
followed.
 Completeness, to ensure that the interviewer asked the respondent all
the questions, rather than just a few required ones.
 To do this, we would need to pick a random sample of completed
surveys and validate the collected data. For example, we sort out
people into many ranges of age, many kinds of activities to specify to
suitable burned calories. After that we can reach out to them through
email or phone and check their responses to a certain set of questions.
+ Step 2: Data Editing
Typically, large data sets include errors. To make sure that there are no such
errors, we conduct basic data checks, check for outliers, and edit the raw research
data to identify and clear out any data points that may hamper the accuracy of the
results.
+ Step 3: Data Coding
This is one of the most important steps in data preparation. It refers to
grouping and assigning values to responses from the survey.For example, some of
the acronyms we use to assign values such as: Height-hght, Weight- wght,...

2. Theoretical model specification
2.1. Specification of the model
According to previous published researches, our group has established a


function to analyze the relationships between related variables and the
BMI index as well as the effects of those variables toward the dependent
variable:
BMI = f(Ag, Gdr, Eht, Avgcalo, Rarcnca)
Where:
BMI: Body Mass Index

Ag: Age
Gdr: Gender
Eht: Eating habit
Avgcalo: Average calories burned per day
Rarcnca: Rate of absorbed calories over necessary calories
 Thus, according to the economic theories, in order to analyze the factors
influencing the BMI index, our group has discussed and decided to choose the
regression analysis models.
2.1.1 POPULATION REGRESSION MODEL (PRM)

Where:
β0: the intercept term of the model
β1: the regression coefficient of “age”
β2: the regression coefficient of “gender”
β3: the regression coefficient of “eating habit”
β4: the regression coefficient of “average calories burned per day”
: the regression coefficient of “rate of absorbed calories over necessary
calories”


: the disturbance term of the model, represents other factors that affect UEM
but not mentioned in the model
+ Explain the variables, proxies to measure and their units
Name
Y

Acronym Method

Body Mass BMI
Index


Unit

weight (kg) / height2 (m)

kg/m
2

X1 Age

Ag

Do survey

Year

X2 Gender

Gdr

Do survey. With Female - 0 and Male - free
1

X3 Eating habit

Eht

Do survey and summary base on the free
amount of water per day, level of
healthy eating time, numerous of means

per day. Interval of Eht is [1,4], the
higher Eht is, the healthier eating habit
they have.

X4 Average
Avgcalo
calories burned
per day

Do survey about kind of activities,
amount of time for it and frequency per
day .Then use Table 2 to calculate.

Calo

X5 Rate
of Rarcnca
absorbed
calories over
necessary
calories

absorbed calories/ necessary calories

Calo

Height

hght


Do survey

m

Weight

wgth

Do survey

kg


absorbed
calories

arc

Basal BMR
Metabolic Rate

Do survey and calculate by Table 3

calo

Use the following equations to find calo
your BMR by hand.
Men: (0.1 × weight) + (6.25 ×
height*100) - (5 × age) +5
Women: (0.1 × weight) + (6.25 ×

height*100) - (5 × age) -161

Necessary Nca
calories

An average woman needs to eat about calo
2000 calories per day to maintain, and
1500 calories to lose one pound of
weight per week. An average man needs
2500 calories to maintain, and 2000 to
lose one pound of weight per week.
Table 4

 Dependent variables :
Y = BMI
 Independent variables :
X1 = Ag

which stands for: Age

X2 = Gdr

which stands for: Gender

X3 = Eht

which stands for: Eating habit

X4 = Avgcalo which stands for: Average calories burned per day
X5 = Rarcnca

calories

which stands for: Rate of absorbed calories over necessary

According to our aforementioned research, many factors can affect your BMI
and lead to overweight or obesity. Some of these factors may make it hard for you
to lose weight or avoid regaining weight that you’ve lost.


- Age: Many people gain weight as they age. Adults who have a normal BMI
often start to gain weight in young adulthood and continue to gain weight until they
are ages 60 to 65. In addition, children who have obesity are more likely to have
obesity as adults.
- Gender: A person’s gender may also affect where the body stores fat. Women
tend to build up fat in their hips and buttocks. Men usually build up fat in their
abdomen or belly. Extra fat, particularly if it is around the abdomen, may put
people at risk of health problems even if they have a normal weight.
- Eating and physical activity habits: Your eating and physical activity habits
may raise your chances of becoming overweight and having obesity if you
· eat and drink a lot of foods and beverages that are high in calories, sugar, and
fat
· drink a lot of beverages that are high in added sugars
· spend a lot of time sitting or lying down and have limited physical activity
2.1.2 Sample Regression Model (SRM)
Sample Regression Function:

Where:
: the estimator of β1
: the estimator of β2
: the estimator of β3

: the estimator of β4

: the estimator of β5
: the estimator of − the residuals term

2.3 Describe the data
+ Specify the sources of data
+ Descriptive statistics and interpretation for each variable


Using STATA 14 for model description with des command, we have collected this
result:

We continue use “sum” command for data description. “sum” has shown us
number of observation (Obs), mean, standard deviation (std. dev.), and also
maximum value (Max), minimum value (Min) of variables.

+ Correlation matrix between variables
Using “corr” command to test the correlation of variables


Explaining variables relationship:
- Correlative coefficient between BMI and Ag is 0.1827 => positive relationship
- Correlative coefficient between BMI and Gdr is 0.4213 => positive relationship
- Correlative coefficient between BMI and Eht is 0.2142 => positive relationship
- Correlative coefficient between BMI and Avgcalo is 0.3499 => positive
relationship
- Correlative coefficient between BMI and Rarcnca is -0.0972 => negative
relationship
- According to the figures from the table, there are no coefficient greater than 0.8.

=> Therefore, the multicollinearity didn’t occur in our model


SECTION III: ESTIMATED MODEL AND STATISTICAL
INFERENCES.
1. Estimated model:
1.1Estimation result:
- Run regression model diagnosis: Using “reg” command to run regression
model in STATA, we have:
Y = BMI
X1 = Ag, X2 = Gdr, X3 = Eht, X4 = Avgcalo, X5 = Rarcnca

+
1.2 Sample regression model (SRM)
SAMPLE REGRESSION FUNCTION (SRF)

The model studies about dependence between level of BMI and Age (Ag),
Height (Hght), Gender (Gdr), Eating habit (Eht), Average calories burned per day
(Avgcalo), rate of absorbed calories over necessary calories (Rarcnca).
 According to this result, = 0.3488639,
0.0033525, = 7.416015, = -3.089573
 Therefore, we have SRM is:

= 2.442524, = 0.7689835,

=


1.3 Explain the meanings of estimated coefficients
Regression

Coefficient

Value

Meanings

0.3488639

> Ceteris paribus, when Age increase by 1
0 unit, BMI increase by 0.3488639 unit.
The increase in BMI goes hand in hand
with the increase of age. That means
normally the older you are, the higher
BMI is.

2.442524

> Ceteris paribus, when Gdr increase by 1
0 unit, BMI increase by 2.442524 unit. The
BMI varies from sex to sex. In general,
BMI of men is higher than that of women.
In fact, as the result of the survey we
conducted, this element has minor effect
on our dependent variable.

0.7689835

> Ceteris paribus, when Eht increase by 1
0 unit, BMI increase by 0.7689835 unit.The
habit of eating clean exerts strong

influences on BMI. Should you digest
more food rich in high-quality protein,
vital nutrition and mineral and distribute
daily meals appropriately, your BMI can
definitely reach the standardized level.

0.0033525

> Ceteris paribus, when avegcalo increase
0 by 1 unit, BMI increase by 0.0033525
unit. The amount of calorie burned every
single day via exercising, fitness regime
determines largely the variation of BMI
Although β4 is small, it’s interval is about