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This research employs multiple-linear regression as it has four independent variables and one dependent variable. The model is explained in mathematical
equation as follows:
Y = α + β
1
PROD + β
2
PRDT + β
3
DIST + β
4
PROM + ℮ 3.6
Where,
Y = Stock Price
α = Intercept
β = Regression coefficient
PROD = Product strategy
PRDT = Production strategy
DIST = Distribution strategy
PROM = Promotion strategy
a. T test
T statistics test basically indicates how strong influence one independent variable partially in explaining variation of dependent variable. The null hypothesis
H
O
to be tested is if a parameter bi equals to zero or H
O
: bi = 0 It implies if an independent variable is not significant explainer towards
dependent variable. The alternative hypothesis H
A
would be parameter of a variable does not equal to zero or H
A
: bi ≠ 0
Ghozali 2006: 89 further elaborates, if the value of t test is more than the value of t table in positive region and if the value of t test is more than the value of t
70
table in negative region, therefore H
O
should be rejected and H
A
should be accepted, means that independent variables partially as influence significantly towards
dependent variable. On the contrary, when t test t table therefore H
O
accepted and H
A
rejected, means that independent variable partially has no significant influence towards dependent variable. Level of significant used in this test is 5 or
α 0.05.
b. Determination Coefficient
Determination coefficient R
2
aims to measure how good model in explaining variation of dependent variable Ghozali, 2006: 87. Determination coefficient score
is between zero and one. Small R
2
score indicates ability of independent variables in explaining variation of dependent variable is constrained. On the other hand, score
that approaches one indicates independent variables provide almost all information needed to predict variation of dependent variable. The closer adjusted R
2
score to 1, the better independent variables explaining dependent variable.
R
2
often formulated in mathematical formula as follows:
= = 1
− 3.7
R
2
, thus defined, ranging from 0 to 1. The closer it is to 1, the better is the fit. However, there are problems with R
2
. “First, it measures in-sample goodness of fit in the sense of how close an estimated Y value is to its actual value in the given sample.
Second, in comparing two or more R
2
’s, the dependent variable, or regress and, must be the same. Third, an R
2
cannot fall when more variables are added to the model” Agustina, 2011.
