# Chapter by year and try to find out the

Chapter 3

METHODOLOGY

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3.1
Introduction

This chapter
consists with seven parts such as Research Framework or Model of the Analysis,
Hypotheses Development,
Research Design,
Operational Definitions,
Measurement of Variables,
Data Collection & Sampling,
Data Collection Procedures,
and Techniques of Data Analyses.

3.2 Research Framework:

Y= GDP per capita

X2=G.
N. Savings

X1=
Inflation

X4=
Remittance

X3=
Export

3.3 Model:

Y = ? + ?1X1 +
?2X2  +  ?3X3  +
?4X4  + € ,

Where,X1 shows the inflation rate , X2 shows the Gross
national savings, X3 shows the Export and X4 Shows Remittance of Bangladesh . All
data of this model are collected annual basis.

3.4
Hypotheses Development

Y
> 0; self-dependent GDP per capita
when inflation, Gross national savings,
Export and remittance are zero though it is not very
important.

?1>0If
inflation increases, then
the GDP per capita will be increased.

?2>0If
savings increase, then
the GDP per capita will be increased.

?3>0
If
export increases,
then the GDP per capita will be increased.

?4>0
If
remittance
increases, then the GDP per capita will be increased.

3.5
Measurement of Variables

In order to examine the impact of
inflation, Gross national savings, Export and remittance on GDP per capita, we
have specified following econometric model. The independent variables are inflation,
Gross national savings, export and remittance, while the dependent variable is
economic GDP per capita. The model is stated as follows :

Y = ? + ?1X1 +
?2X2  +  ?3X3  +
?4X4  + € ,

GDP per capita income= f(Inf.r,
Sav.r, Exp.r, Rem.r,)

In linear form,
equations can be written as:

GDP per capita income
=f(?1 Inf.r+ ?2 Sav.r+ ?3Exp.r + ?4Rem.r)

3.6
Operational Definitions

In this paper I
am collecting annual based time series data, from various source and tabulate data
in ms excel year by year and try to find out the relationship among the various
annual based data. Then set econometric model , among this data we set GDP per
capita income as a dependent variable(which is shown by US\$), and all others
are independent variable which are influence the GDP per capita income of
Bangladesh .after getting result we see all variable are very important for GDP
per capita income .

3.7
Data Collection, Procedures and Sampling

Data were
collected from the secondary sources and taken the period of 1976-2016, this
means that we have 41 years data (n=41). And this data collected from based on
web-side of WB, BB, IMF and from
various economic review yearly book. For robustness of analysis, variables under
consideration are economic GDP per capita income (proxied by gross domestic
product),  inflation rate, gross national
savings rate, Export and remittance of Bangladesh.

3.8 Techniques of Data Analyses

All data have
been tabulated by sequence number for final use. MS Excel 2007 and SPSS
software version 12, use also stata 10.0are used to reach the destination in
this project.  To make a proper regression we need to cheek the Descriptive
statistics, correlation analysis ,ANOVA, model summery, Regression Analysis for  getting proper result.

Chapter 4

FINDINGS
OF THE STUDY

4.1 Introduction

This chapter is very
important of my study paper. In this chapter first explain descriptive summery
, then find-out the correlation among the variables , 5 percent significant
level among the variables and overall model discussion.

Table 4.1.1 :Descriptive Statistics

N

Minimum

Maximum

Mean

Std. Deviation

Year

41

1976.00

2016.00

1.9960E3

11.97915

Y

41

128.80

1358.70

4.4597E2

297.36092

X1

41

-17.60

25.60

6.8195

6.50601

X2

41

-2.90

24.90

13.8585

6.94863

X3

41

.50

36.80

8.7356

10.49943

X4

41

.02

15.20

3.9512

4.96175

Valid N
(listwise)

41

In the above table, there are five group
of data set( GDP per capita US\$ , Inflation rate , Gross national savings as
percent of total GDP of year , export and remittance are  collected by billions of dollars). The mean
value of GDP per capita, inflation rate, national savings , export earnings and
remittance is 445.69, 6.81, 13.85, 8.73 and 3.95 respectively .

Table 4.1.
2:Correlations

GDP(US\$)

Inflation.R

G.savings

Export

Remittance

GDP(US\$)

1

41

Inflation.R

-.024

1

41

G.savings

.765**

-.110

1

41

Export

.988**

-.042

.733**

1

41

Remittance

.957**

-.028

.714**

.981**

1

41

Note: **Significant at .01
level

In the above table shows the correlation among five
variables. (GDP per capita, inflation rate , gross national savings , export
and remittance). The total number of observations  are 41, the correlation between GDP per
capita and inflation rate is -0.0240 correlation coefficient , the correlation
between GDP per capita and gross savings is
0.7654 , and 0.9880 and 0.9567
correlation between GDP per capita and export, and correlation between
GDP per capita and remittance respectively .

Table: 4.1.3: ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

3483854.888

4

870963.722

590.641

.000a

Residual

53085.881

36

1474.608

Total

3536940.769

40

a.
Predictors: (Constant), X4, X1, X2, X3

b.
Dependent Variable: Y

The one-way ANOVA compares the means between the groups you are
interested in and determines whether any of those means are statistically
significantly different from each other. Specifically, it tests the null
hypothesis:

H0:µ1 = µ2 = µ3= …= µK

where µ = group mean and k =
number of groups. If, however, the one-way ANOVA returns a statistically
significant result, we accept the alternative hypothesis (HA), which
is that there are at least two group means that are statistically significantly
different from each other.

The SPSS output for the ANOVA is shown in the above
table, indicating whether we have a statistically significant difference
between our four group means. We can see that the significance level is 0.00 (p =
.00), which is below 0.05. and, therefore, there is a statistically significant
difference in the mean productivity between the four  different groups of the independent
variable, (inflation rate ,
Gross National savings, Export , Remittance).

Table 4.1.4 : Model Summary

Model

R

R Square

Std. Error of the Estimate

1

.992a

.985

.983

38.40062

a.
Predictors: (Constant), X4, X1, X2, X3

From t-statistics and probability value we
can test (inflation rate
, Gross National savings, Export,
Remittance). Significantly affection  GDP per capita (US\$) or not.

Now, we can set null and alternate hypothesis
as follows-

H0: ? 1=0 : ? 1 ?0
and
H0: ? 2=0 : ? 2?0

Here,
we see that the probability value of coefficient of (inflation rate , Gross National
savings, Export , Remittance)are
.168,  0.005,0.00 and .002 respectively,
which without inflation rate all are less than 0.05 where the level of significance
is 5%. We can reject null. Only inflation rate is more than .05% so only for X1
we can accept null hypothesis.

In our study we consider the following model
because of economic significance. This study proceeds with the OLS method.

Y = ? + ?1X1
+ ?2X2  +  ?3X3  +
?4X4  + €

Estimated results with Ordinary Least Square
method has been reported in

Table : 4.1.5 : Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

152.612

16.727

9.124

.000

X1

1.325

.942

.029

1.406

.168

X2

3.895

1.293

.091

3.012

.005

X3

35.826

3.103

1.265

11.545

.000

X4

-20.910

6.391

-.349

-3.272

.002

a.
Dependent Variable: Y (GDP US\$)

In
the above table hare we calculate the statistically significance about
Inflation rate, gross national savings , remittance and export on GDP per
capita income (US\$) of Bangladesh .As the p-value is much more
than 0.05, we accept the null hypothesis that ? = 0. In the above model there are four variables. First one
is X1 ( inflation rate ) which is statistically insignificant because the
result is more than 0.05 percent .here shows the value is .689, so the
relationship between inflation rate and GDP in this model is not
significant.

Second variable is X2(Gross national savings) which is
statistically significant because the result is .005 percent because the result
is less than .05. So the relationship between Gross national savings and GDP in
this model is significant.  Third
variable is X3(Export) which is also statistically significant because the
result is .00 percent which is fully significant, so the relationship between export
rate and GDP in this model is positive and also significant.  Second variable is X4(remittance) which is
statistically highly significant because the result is .002percent. So the
relationship between remittance and GDP in this model is positive and also significant.  We can see
that the significance level is 0.015 (p = .015), which is above
0.05. and, therefore, there is a statistically insignificant difference in the
mean productivity between the four  different groups of the independent variable.
In this model we can see.

4.2R2 vs.