Tiểu luận the impact of lover on study results of foreign trade university students

Foreign Trade University
Faculty of Finance and Banking
*********

ECONOMETRICS ASSIGNMENT
Topic: “The impact of lover on study results of Foreign
Trade University students”


Contents
I. Introduction................................................................................3
II. Data description.........................................................................3
1.Scope.........................................................................................................3
2.Sources of data..........................................................................................3
3.Investigated factors and expectations.......................................................3
III. Empirical Results.......................................................................5
1.Building Regression Model.........................................................................5
a.Model 1...............................................................................................5
b.Model 2...............................................................................................6
2.Assumption Tests.......................................................................................8
a.Multicollinearity..................................................................................8
b.Heteroskedasticity..............................................................................9
c.Autocorrelation.................................................................................10
d.Normality..........................................................................................11
IV. Conclusion...............................................................................13
1.Interpretion..............................................................................................13
2.Suggestions.............................................................................................14
3.Limitations...............................................................................................14
4.Final words...............................................................................................15

2

Part I: Introduction
Love is inherently a part of human life, particularly with the young. Entering college
marks the stage of adult. From then on, you have the right to have a boy/girl friend
and you also have more opportunities to expand your relationship than high school
period. Percentage of college students who are in love is great. Besides issues such
as part time jobs, school work, or social activities, we cannot deny that love is an
important part of university student life. However, the last question is whether
university students should love or not. Love has positive or negative impacts on
academic performance. Indeed, it depends on the way you love and people you
choose. Therefore, our group decided to choose the topic “The impact of lover on
study results of Foreign Trade University students”. We hope we can bring a more
fully comprehensive view of students’ love, which suggests a reasonable and helpful
advice for you to balance between love and learning.

Part II: Data description
1. Scope:
Data collected from students in Foreign Trade University who already have a lover
by November 2012.
2. Sources of data:
We have conducted survey on totally 150 students, through both online-form and
offline-form, to run the model.
Of all the 150 answer sheets, we have had 111 acceptable results. The rest cannot
be used because the respondents omitted some questions, or had some unrealistic
answers
3. Investigated factors and expectations:
Dependent variable
Variable

Description

GPA

The GPA at the nearest semester of a student
3


Independent variable – Quantitative variable
Expec
Variable

Description

-

Note

tation
The gap between the
Age

Finance

The “Age” variable can have

age of the respondent

+/-

positive or negative impact

and her/his lover.

on the study results.

The finance condition of

The “Finance” variable can

the respondent’s

+/-

boy/girl friend

have positive or negative
impact on the study results.
The “Time” variable can
have negative impact on the

The average number of
Time

study results. If students

hours per week the

-

respondent spends with

spend more time on love,
they have to cut back the

his/her lover

time spending on studying

Qualitative variable
Variable

Description

Denote
0

Expec1

tation

Note
The “Gender”
variable can be

Gender

Gender of the
respondent

Femal

Male

e

+/-

positive/ or
negative impact on
the study results.

Appearance

The

Averag

above

appearance of

e

Above

+/-

The “Appearance”
variable can be

the

positive/ or

respondent’

negative impact on

boy/girl friend

the study results.

is above

4


average level
The

The “Appearance”

appearance of
Appearance
below

the
respondent’s
boy/girl friend

variable can be
Averag

Below

e

+/-

positive/ or
negative impact on
the study results.

is below
average level
The capacity

The higher

of study of the
Capacity

respondent’s

Averag

above

boy/girl friend

e

capacity of boy/girl
Above

+

level of motivation

is above the

for respondent

average level
The capacity

The lower capacity

of study of the
Capacity

respondent’s

Averag

below

boy/girl friend

e

of boy/girl friend,
Below

-

the lower level of
motivation for

is below the

respondent

average level

Distance

friend, the higher

The geography

The “Distance”

distance

variable can have

between the
respondent’s

Close

Far

+/-

positive or
negative impact on
the study results

place and
his/her lover’s

Extra activities

High
concentrate

Extra activities

Taking part in extra

implies part

activities may have

time jobs or

no

yes

+/-

positive or

other social

negative impact on

activities

the study result
The higher

Respondent
concentrate

no

yes

well on study.

+

concentration on
study, the higher
the study result

5


The worse

Respondents

Low
concentrate

concentrate

no

yes

-

concentration on

badly on study.

study, the lower
the study result

Part III: Empirical Results
1. Building regression model:
a. Model 1:
Y =  0 +  1*Gender +  2*Age +  3*Appearance_above +

 4*Appearance_below +  5*Capacity_above +  6*Capacity_below +
 7*Distance +  8*Finance +  9*Dedicated_time +
 10*Low_concentrate +  11*High_concentrate +  12*Extra_activities
+ ui

By using Gretl following OLS method, we have the result below:

6




R-squared = 0,511498, which means all the independent variables explain



about 51,15% of the real outcome.
There are only 4 variables which have statistical significance (p-values ≤ 0.1).
These

are:

Age,

Dedicated_time,

Low_concentrate,

High_concentrate,

Extra_Activities. The signs of the coefficients of these variable are followed
our expectation. Other variables do not have statistical significance, so we
will omit them from the model and run another regression model.
b. Model 2:
After omitting insignificant variables, we run the model with the other 5 variables:
Y =  0 +  1*Age +  2*Dedicated_time +  3*Low_concentrate +
 4*High_concentrate +  5*Extra_activities + ui

7


The R-squared now is 0,475943, which is smaller than the old R-squared. Thus, we
run the Ramsey RESET test to see if there is mispecification in our model or not.

As we can see, all the p-values are larger than
specification is adequate.

8

α

= 0,05. So we conclude that


The signs of the variables follow our expectations: The variables of age, dedicated
time and low concentration have negative signs, means that they have negative
relationship with the increase of GPA. On the other hand, the variables of high
concentration and extra activities have positive signs, indicating that they have
positive relationship with the increase of GPA.
Our SRF now is:

Y = 7,76941 -0,0326185*Age – 0,0129144*Dedicated_time –
0,556837*Low_concentrate + 0,655956*High_concentrate +
0,3368*Extra_activities + ui

2. Assumption Tests
a. Multicollinearity:
At first, we use a correlation matrix to detect the presence of multicollinearity.
By using gretl, we have the following correlation matrix:

We can see from the matrix that all the correlation coefficients between the
variables have small absolute value. Thus, we can not conclude that variables are
9


strongly associated with each other. But we also can not conclude that
multicollinearity does not appear. That is why we have to run another test to see if
there is multicollinearity in the model.

According to this test, all the variables have Variance Inflation Factors (VIF) smaller
than 10. Therefore, we can conclude that the model does not have the problem of
multicollinearity.

b. Heteroskedasticity:
We try to see if there are any signs of heteroskedasticity by creating a scatter plot
of the model:

10


There is no sign of heteroskedasticity, so we move on to run the White Test. And

here is the result from gretl:

The p-value here is 0,505189 >

α = 0,05; so we can come to the conclusion that

there is no heteroskedasticity in this model.

11


c. Autocorrelation:
Because we use a cross-sectional data, we cannot just run the autocorrelation test.
In this case, we change our data to time-series and have the result of the BG test as
below:

In this case, all the p-values are larger than

α

= 0,05; so we can come to the

conclusion that the model does not face autocorrelation.

d. Normality
This part is to find out whether the error term ui in the model has normal
distribution. After running the Test statistic for Normality, here is our result:

12

The p-value in this case is 0,0153 <

α = 0,05; so we conclude that the error term is

not normally distributed.
To fix this, we add the variable of l_age to the current model:

13


We run the test again to see if the problem has been cured:

14


The p-value is 0,6013 >

α

= 0,05; so we can conclude that the error term is now

normally distributed.

Part III: Conclusion
1. Interpretation:
After running regression and testing all the assumptions for multiple regressions, we
have the final regression function with R-squared = 41, 3070%. This is not a high
value, but still can be accepted. It implies that our model could explain about 41,3%
of the outcome.

Our final regression model is:

15


Y = 7,85175 – 0,0154247*Dedicated_time – 0,481006*Low_concentrate +
0,517323*High_concentrate

+

0,367198*Extra_activities

–

0,162235*l_age + ui
From our final regression model, we can conclude that among 5 variables:
Dedicated–time, low–concentrate, high-concentrate, extra-activities and l_age, there
are 3 variables having negative impacts on GPA and 2 variables having positive
ones. Their influence mostly follow our first expectations.


 1 = -0, 0154247 < 0: means that one hour increased in time for love leads
to 0,0154247 unit less in Y if other factors remain unchanged.



 2 = -0, 481006 < 0: means that low concentration leads to 0,481006 unit
decreased in Y if other factors remain unchanged.



 3= 0,517323 >0: means that high concentration leads to 0,517323 unit
more in Y if other factors remain unchanged.



β4 = 0,367198 > 0: means that taking part in extra-activities leads to
0,367198 unit increase in GPA.



β5 = –0,162235 < 0: means that a year increased in age gap leads to
-0,162235 unit decrease in GPA.

2. Suggestions:
Below are some suggestions for Foreign Trade University’s students that we
conclude from our analysis result:
 Gender, the capacity/appearance/finance condition of the lover and distance
do not have affect on study result. Thus, we are free to love who we want
redardless of these factors.
 As students, we should spend less time for love as studying is the most
important thing at this time. It seems like an opportunity cost if we dedicate
too much to dating. Love should be the motivation to archive higher marks, not
a reason for going backward.

16


 Practicing

self-concentration

is

the

most

important

factor

on

study

performance. We should try to manage time effectively as well as identify our
goals clearly; by doing this, not only studying but other issues will get better.
 Extra-activities are very essential, especially with students, it not only help us
develop social skills but also have positive effects on studying result. However,
we should balance between time for studying and time for these activities.

3. Limitations:
From our regression model, we can conclude that among 13 variables, there are
some variables that follow our expectation but some do not. This indicates the gap
between theory and reality, which can be unpredictable and impossible to fulfill
without the help of subjects like Econometrics.
Besides, during the process of preparing this report, we have to face some
problems. The most challenged problem arising from the subject econometric itself.
Econometrics is a difficult subject which requires good nationality, diligence and

time for research as well as analysis. However, because we had to complete
assignments

of

different

subjects

simultaneously

and

we

received

the

announcement in hurry, we had to worked in a rush and did not have enough time
to proofread this paper. Furthermore, our knowledge of the subject still has
limitations, which led us to choosing unsuitable variables. In other words, the model
which was run by us still had unavoidable mistakes. Besides, our topic is about
“Impacts of lovers on study results of FTU's students” – a sensitive one, so very few
people can provide exact data leading to limited number of observations: we
received only 150 surveys, and luckily 110 ones are accepted. Moreover, many
different groups are conducting surveys at this time, which makes students get
bored of filling surveys. Last but not least, it is also not easy to draw the right and
meaningful conclusion from the result of the research, which is an inevitably
important step in the process of research. However during the time of doing this

exercise we had chance to practicing team-working and understanding more about
the econometrics and its application in life.

4. Final words

17


Our group, thanks to the instructions of Dr Tu Thuy Anh and lecturer Thai Long,
has made great effort in collecting data and implementing the model. Though the
result did not turn out to be as well as we had expected, we have gained a lot
experiences in building the regression model.

18