This chapter focuses on the determination and information analysis. All the informations collected in this survey were processed utilizing the SPSS plan. SPSS plan was used to analyse the information from the correlativity and arrested development analysis. The method was used to analyse the information was Multiple Regression Correlation Analysis. A multiple arrested development analysis involves more than one independent variable.

The procedure of evaluating is the same with simple arrested development, but in order to deduce the estimated arrested development, a computing machine is employed due to the complex nature of informations and clip required. The presentation of findings is made to analyze the relationship among independent variables ( rising prices rate, gross domestic merchandise of Malaysia ( GDP ) , involvement rate or base loaning rate ( BLR ) and exchange rate ) and dependent variable ( public presentation of ASB ) . Besides that, this research besides wants to analyze the relationship between profitableness ( independent variable ) and dividend of ASB ( dependent variable ) .

This survey used Multiple Regression Method Analysis which is the reading of Regression Analysis which includes Coefficient of Correlation ( R ) , Coefficient of finding ( R-Square ) , F-Statistic and T-Statistic.

## 4.1 REGRESSION EQUATION

General Function:

P = a ( INF, GDP, BLR, EXC )

Multiple Regression Equation:

P = – 1.738 – 11703.166 INF -3.912 GDP + 3.428 BLR – 4.813 EXC + vitamin E

Where,

P = Profitability

a = invariable

b1, b2, b3, b4 = coefficient

INF = Inflation rate

GDP = Gross Domestic Product ( GDP )

BLR = Interest rate ( BLR )

EXC = Exchange rate

vitamin E = Error term

## 4.2 REGRESSION COEFFICIENT ANALYSIS

## Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

T

Bacillus

Std. Mistake

Beta

( Constant )

-1.738E7

3.505E7

-.496

GDP

-11703.166

4655.202

-1.421

-2.514

BLR

-3.912E6

1.764E6

-.324

-2.217

INF

3.428E6

1.121E6

2.076

3.059

EXC

-4.813E6

7.839E6

-.116

-.614

Dependent Variable: Net income

Table 4.1 ( The Regression Result )

4.2.1 RELATIONSHIP BETWEEN DEPENDENT VARIABLE AND INDEPENDENT

Variables

The above tabular array shows the consequence of coefficient that will be explicating the relationship between dependant variable and independent variables. The positive mark will be explained when the independent variables increasing, dependant variable besides will be increasing but for the negative mark will be meant for the opposite relationship, when independent variables increasing, the dependant variable will be diminishing.

## 4.3 REGRESSION COEFFICIENT INTERPRETATION

4.3.1 CONSTANT

The invariable is equal to 1.738 represents the per centum of public presentation of ASB when the all independent variables which are rising prices, gross domestic merchandise ( GDP ) , involvement rate ( BLR ) , and exchange rate equal to zero.

4.3.2 GROSS DOMESTIC PRODUCT ( GDP )

Variable

REGRESSION COEEFICIENT

GROSS DOMESTIC PRODUCT ( GDP )

-11703.166

Table 4.2.1 Table Regression Coefficient between GDP and Performance of ASB

An estimated coefficient of gross domestic merchandise ( GDP ) is equal to -11703.166. It means, when the gross domestic merchandise addition by 1 % , the public presentation of ASB will be diminishing by 11703.166 % . It shows that GDP has negative relationship with the public presentation of ASB.

4.3.3 Interest Rate ( BASE LENDING Rate )

Variable

REGRESSION COEEFICIENT

Interest Rate ( BASE LENDING Rate )

-3.912

Table 4.2.2 Table Regression Coefficient between BLR and Performance of ASB

An estimated coefficient of involvement rate ( BLR ) is equal to -3.912. It means, when the involvement rate ( BLR ) addition by 1 % , the public presentation of ASB will be diminishing by 3.912 % . It shows that involvement rate has negative relationship with the public presentation of ASB.

4.3.4 INFLATION Rate

Variable

REGRESSION COEEFICIENT

Inflation Rate

3.428

Table 4.2.3 Table Regression Coefficient between Inflation Rate and Performance of ASB

An estimated coefficient of rising prices rate is equal to 3.428. It means, when the rising prices rate addition by 1 % , the public presentation of ASB will be increasing by 3.428 % . It shows that rising prices rate has positive relationship with the public presentation of ASB.

4.3.5 Exchange Rate

Variable

REGRESSION COEEFICIENT

Exchange Rate

-4.813

Table 4.2.4 Table Regression Coefficient between Exchange Rate and Performance of ASB

An estimated coefficient of exchange rate is equal to -4.813. It means, when the exchange rate addition by 1 % , the public presentation of ASB will be diminishing by 4.813 % . It shows that exchange rate has negative relationship with the public presentation of ASB.

## 4.4 T-STATISTIC ( T-TEST )

T-Test is used to find if there is a important relationship between the dependant variable and each of the independent variables.

Variables

COMPUTED

T-VALUE

Sign

CRITICAL

T-VALUE

Consequence

HYPHOTESIS

Net income

-.496

## –

## –

## –

## –

GROSS DOMESTIC PRODUCT ( GDP )

-2.514

## & A ; gt ;

2.0

SIGNIFICANT

REJECT HO

Interest Rate ( BLR )

-2.217

## & A ; gt ;

2.0

SIGNIFICANT

REJECT HO

Inflation Rate

3.059

## & A ; gt ;

2.0

SIGNIFICANT

REJECT HO

Exchange Rate

-.614

## & A ; lt ;

2.0

INSIGNIFICANT

ACCEPT HO

Table 4.3 Table of T-Statistic Consequence

Rule of Thumbs T-Statistic = 2

Interpretation

4.4.1 GROSS DOMESTIC PRODUCT ( GDP )

Since Rule of Thumb is 2, hence at 95 % assurance degree, the deliberate T value is more than critical T value from the distribution tabular array ( 2.514 & A ; gt ; 2.000 ) . Therefore there is important relationship between the profitableness and gross domestic merchandise. So, from the hypothesis, H0 will be rejecting and H1 will be accepted. This determination is supported by Robert E. Hall and Marc Lieberman ( 2001 ) , when the economic system is spread outing, existent GDP is lifting, houses is general tend to gain high net incomes, and these net incomes are less hazardous. This is in add-on to the normal rise in existent GDP that would be happening anyhow, as income growing. In the typical enlargement, net incomes will lift along with GDP. Higher net incomes are adequate to do stocks look more attractive.

4.4.2 Interest Rate ( BLR )

Since Rule of Thumb is 2, hence at 95 % assurance degree, the deliberate T value is more than critical T value from the distribution tabular array ( 2.217 & A ; gt ; 2.000 ) . Therefore there is important relationship between the profitableness and involvement rate. So, from the hypothesis, H0 will be rejecting and H1 will be accepted. This determination is supported by Lawrence J. Gitman Jeff Madura ( 2001 ) , the alterations in involvement rate affect consumer ‘s purchases with borrowed financess and the house ‘s cost of funding. Most of the houses are unfavourably affected by upward motions in involvement rates and are favourably affected by downward motions in involvement rates.According to Gonzales et Al. ( 2000 ) , the involvement rate are a placeholder for the stance of pecuniary

policy and this is why it is sensible to believe that they could foretell the stock returns. The spread between long and short term involvement rates is besides the stance for the pecuniary policy. Long term outputs contain a hazard premium above the norm of expected future short term outputs. A mark of pecuniary easiness is when long term outputs are high than short term outputs.

4.4.3 INFLATION Rate

Since Rule of Thumb is 2, hence at 95 % assurance degree, the deliberate T value is more than critical T value from the distribution tabular array ( 3.059 & A ; gt ; 2.000 ) . Therefore there is important relationship between the profitableness and rising prices rate. So, from the hypothesis, H0 will be rejecting and H1 will be accepted. This determination is non supported by Chen et Al. ( 1986 ) . They used monthly informations for the period 1958 to 1984 to prove the impact of the rising prices rate on stock monetary values. In fact, they defined three variable related to the rising prices rate: expected rising prices ; the alteration in expected rising prices ; and unforeseen rising prices, and found a significantly negative relationship between the rising prices and stock monetary values. Besides that, Geske and Roll ( 1983 ) and Chen, Roll and Ross ( 1986 ) in their research show a negative relationship between rising prices and equity returns. This is same with ulterior consequence from Murkherjee and Naka ( 1995 ) that show a negative relationship between Tokyo Stock Exchange and rising prices.

4.4.4 Exchange Rate

Since Rule of Thumb is 2, hence at 95 % assurance degree, the deliberate T value is less than critical T value from the distribution tabular array ( .614 & A ; lt ; 2.000 ) . Therefore there is undistinguished relationship between the profitableness and exchange rate. So, from the hypothesis, H1 will be rejecting and Ho will be accepted. Harmonizing to Aggarwal ( 1981 ) stated that the relationship between exchange rates and stock monetary values utilizing monthly informations from 1974 to 1978 by utilizing correlativity arrested development analyses. The survey found that the trade-weighted exchange rate and the stock market indices were positively correlated during this research period. Movement of exchange rate could straight impact the stock monetary values of transnational houses by act uponing the value of its abroad operations, and indirectly consequence domestic houses through act uponing the monetary values of its exports or imported inputs. Furthermore, harmonizing to Ma and Kao ( 1990 ) , they stated that relationship between exchange rates and the stock monetary values in six industrialised economic systems, the U.K, Canada, France, West Germany, Italy and Japan utilizing monthly informations from January 1973 to December 1983. They tested the grade of stock monetary value reaction to interchange rate alterations and their findings were consistent with the exchange rate motion caused the stock monetary value motion therefore will impact the unit trust as whole.

## 4.5 F-STATISTIC ( F-TEST )

An F-test is a statistical trial which most used when comparing statistical theoretical accounts that have been fit to a information set, in order to place the theoretical account that best fits the population from which the informations were sampled, Exact F-Test ever arise when the theoretical accounts have been fit to the informations utilizing least squares.

## Computed F-Stat

## Critical F-Value

## Interpretation

23.383

4 ( RULE OF THUMBS )

Significant

Table 4.4 Table of F-Statistic Consequence

F-Statistic = 4 ( RULE OF THUMBS )

Last, by looking at the F -Stats, the value is equal to 23.383. This shows that the deliberate F value is higher than the value of F in tabular array ( 23.383 & A ; gt ; 4 ) . It means that the information taken is dependable for the overall theoretical account. This information besides provide strong grounds to measure the important of each constituent of the full theoretical account. So, from hypothesis, H1 will be accepted and H0 will be rejected.

## 4.6 COEFFICIENT OF DETERMINATION ( R2 )

R2 is the coefficient of finding where it is used to prove the goodness of tantrum. It measure how many % of a alteration / fluctuation in the dependant variable and be measured or explained by in the independent variables. The coefficient of finding, R2, is utile because it gives the proportion of the discrepancy ( fluctuation ) of one variable that is predictable from the other variable. It allows us to find how certain one can be in doing anticipations from a certain model/graph.

## Model Summary

Model

Roentgen

R Square

Adjusted R Square

1

.928a

.862

.825

Forecasters: ( Constant ) , Exc, Interest, Gdp, Inflation

Table 4.5 Table of R-Squared

From the above consequence, it shows that R-squared ( R? ) is 0.862. It can be considered as giving a really high explanatory power for the estimated equation. In other words, it means 86.2 % of the alteration in the variables can be explained by independent variables but merely 13.8 % can be explained by others factor. Adjusted R-squared is a alteration of R? that adjusts for the figure of explanatory footings in a theoretical account.

## 4.7 LINEAR REGRESSION EQUATION

( PROFITABILITY AND DIVIDEND OF ASB )

DIV = -10.00 – 2.79 P + vitamin E

Where,

P = Profitability

a =constant

b1 =coefficient

DIV = Dividend of ASB

vitamin E = mistake term

## 4.8 REGRESSION COEFFICIENT ANALYSIS

( PROFITABILITY AND DIVIDEND OF ASB )

## Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

T

Bacillus

Std. Mistake

Beta

1

( Constant )

10.001

.671

14.894

Net income

-2.794E-7

.000

-.556

-2.838

a. Dependent Variable: Dividend

Table 4.6 Table of Regression Result

Relationship between dependant variable and independent variables.

The above tabular array shows the consequence of coefficient that will be explicating the relationship between dependant variable and independent variables. The positive mark will be explained when the independent variables increasing, dependant variable besides will be increasing but for the negative

mark will be meant for the opposite relationship, when independent variables increasing, the dependant variable will be lessening

## 4.9 REGRESSION COEFFICIENT INTERPRETATION

4.9.1 CONSTANT

The invariable is equal to 10.001 represents the per centum of dividend of ASB when the independent variables which are profitableness equal to zero.

4.9.2 Profitableness

Variable

REGRESSION COEEFICIENT

Profitableness

-2.794

Table 4.7 Table Regression Coefficient between Profitability and Dividend of ASB

An estimated coefficient of profitableness is equal to -2.794. It means, when the profitableness addition by 1 % , the dividend of ASB will be diminishing by -2.794 % . It shows that profitableness has negative relationship with the dividend of ASB.

## 5.0 T-STATISTIC ( T-TEST )

T-Test is used to find if there is a important relationship between the dependant variable and each of the independent variables.

Variables

COMPUTED

T-VALUE

Sign

CRITICAL

T-VALUE

Consequence

HYPHOTESIS

DIVIDEND OF ASB

14.894

## –

## –

## –

## –

Profitableness

-2.838

## & A ; gt ;

2.0

SIGNIFICANT

REJECT HO

Table 4.8 Table of T-Statistic Consequence

RULE OF THUMBS T-STATISTIC = 2

Interpretation

4.9.1 Profitableness

Since Rule of Thumb is 2, hence at 95 % assurance degree, the deliberate T value is more than critical T value from the distribution tabular array ( 2.838 & A ; gt ; 2.000 ) . Therefore there is important relationship between the dividend of ASB and profitableness. So, from the hypothesis, H0 will be rejecting and H1 will be accepted.

## 5.1 F-STATISTIC ( F-TEST )

An F-test is a statistical trial which most used when comparing statistical theoretical accounts that have been fit to a information set, in order to place the theoretical account that best fits the population from which the informations were sampled, Exact F-Test ever arise when the theoretical accounts have been fit to the informations utilizing least squares.

## Computed F-Stat

## Critical F-Value

## Interpretation

8.054

4 ( Rule Of Thumbs )

Significant

4.9 Table of F-Statistic Consequence

F-Statistic = 4 ( Rule of Thumbs )

Last, by looking at the F -Stats, the value is equal to 8.054. This shows that the deliberate F value is higher than the value of F in tabular array ( 8.054 & A ; gt ; 4 ) . Therefore, there is important relationship between the independent variables and dependent variable. It means that the information taken is dependable for the overall theoretical account. This information besides provide strong grounds to measure the important of each constituent of the full theoretical account. So, from hypothesis, H1 will be accepted and H0 will be rejected.

## 5.2 COEFFICIENT OF DETERMINATION ( R2 )

R2 is the coefficient of finding where it is used to prove the goodness of tantrum. It measures how much per centum of a alteration / fluctuation in the dependant variable and be measured or explained by in the independent variables. The coefficient of finding, R2, is utile because it gives the proportion of the discrepancy ( fluctuation ) of one variable that is predictable from the other variable. It allows us to find how certain one can be in doing anticipations from a certain model/graph.

## Model Summary

Model

Roentgen

R Square

Adjusted R Square

1

.556a

.309

.271

a. Forecasters: ( Constant ) , Net income

5.0 Table of R-Squared

From the above consequence, it shows that R-squared ( R? ) is.309. It is really low and can be considered as non really high explanatory power for the estimated equation. In other words, it means 30.9 % of the alteration in the variables can be explained by independent variables and 69.1 % can be explained by others factor. Adjusted R-squared is a alteration of R? that adjusts for the figure of explanatory footings in a theoretical account.