r
xy
: coefficient of correlation between variables X and Y N
: number of respondents ∑XY
: multiplication of the number of variables X and Y ∑X
: the amount of the value of the variable X ∑Y
: the amount of the value of the variable Y ∑X
2
: the sum of the square of the value of the variable X ∑Y
2
: the number of the square of the value of the variable Y Suharsimi, 2010: 213
If the coefficient of correlation between the free variables is smaller or equal to 0.600, then do not occur Multicolinearity between
free variables, regression test can be continued Danang Sunyoto, 2007: 89. Whether or not, to know the existence of Multi co-linearity
might be used other ways, namely by values of tolerance ɑ and the value of the variance inflation factor VIF. Free variables are
experiencing Multicolinearity if ɑ
count
ɑ and VIF
count
VIF. Instead, free variables are not subjected to Multicolinearity if ɑ ɑ
count
and VIF
count
VIF.
3. Hypothesis Test
a. Simple Regression Analysis
This data analysis technique used to know if there is an effect between independent variable toward the Students Perception on
Teacher Performance toward Accounting Learning Outcomes hypothesis 1 and influence between Learning Motivation toward
Accounting Learning Outcomes hypothesis 2. The next steps in the simple regression analysis is:
1 Finding the coefficient of correlation between X
1
and X
2
with Y. Formula:
∑ √ ∑
∑ Description:
r
xy
: coefficient of correlation between X
1
and X
2
with Y X
: Student Perceptions on Teacher Performance Learning Motivation
Y : Accounting Learning Result
∑
xy
: total between X and Y ∑
x 2
: quadratic sum score X ∑
y 2
: the sum of quadratic score Y Sutrisno Hadi, 2004: 4
Has a positive correlation Direction if the results of the calculation of the correlation of at least the plus +. If the sign
minus -, then the direction of the negative correlation Suharsimi, 2010: 213.
Find the coefficient of determination r
2
The coefficient of determination is the level of influence of the free variable X
1
or X
2
against variables bound Y. Formula:
Description: r
2
: determination of coefficient
r : correlation of coefficient
If the influence of free variables X
1
or X
2
against variables bound Y of the square of the correlation coefficient.
Furthermore the results of the determination of the coefficient multiplied 100 to find out the influence level of both free
variable against bound variable in the form of a percentage Darwyan Syah, 2009: 94.
a Testing the significance of the correlation coefficients with t
Test t
Test was used to test the significance between the variables. Formula:
√ √
Description: t : t calculate
r : correlation coefficient n : number of respondents
r
2
: quadratic correlation coefficient Sugiyono, 2010: 230
Taking the conclusion is to compare t
count
with t
table
. If t
count
is greater than or equal to t
table
with 5 significance level then the influential variables significantly against variables
bound. Otherwise, if t
count
is less than t
table
then the influence of these variables was not significant.
b Create a simple linear regression line Formula:
Y = aX + K Description:
Y : Accounting Learning Outcomes a : numbers in a coefficient
X :Student Perceptions on Teacher Performance Learning Motivation
K : constant Sutrisno Hadi, 2004: 1
b. Multiple Analysis Regression
