GROUP 7 - Multiple Correlation - STATISTICS - Prof Baso Jabu - Semester I 2021.pdf

Statistics multiple correlation

1. MULTIPLE CORRELATION
STATISTICS

2. GROUP 7
1.
2.
3.
4.
5.
6.
Muh Khalid Bilal B (210025301044)
Muliyana(210025301047)
Nurul Ashiqin Binti Larawi (210025301052)
Syahrullah (210025301065)
Muihaturrahma (0210025301042)
JusmawatI (0210025301038)

3. Get to know STATISTICS of Multiple
Correlation and examples of questions
•Correlation
Is a statistical technique used to test whether or not there is a relationship
and the direction of
the relationship of two or more variables

• Multiple Correlation
Correlation is used to test the relationship of two or more independent
variables with one
dependent variable simultaneously. So by using this correlation, we can
nd out whether the
variables are interconnected or not.

4. MULTIPLE CORRELATION
Meanwhile, according to Ridwan (2012:238) multiple correlation is a value
that gives a strong inuence or relationship between two or more
variables together with a variable 

Multiple correlation is a correlation consisting of two independent
variables (X1, X2) and one dependent variable (Y). If the formulation of
the problem consists of three problems, then the relationship between
each variable is carried out by means of calculation a simple correlation.
Therefore, the following will only present a calculation method double
between X1 and X2 with Y.

5. •
•
•
•
Make a problem formulation 
Make a research hypothesis 
Make a formal hypothesis
Enter statistical numbers the correlation
formula:

Steps for Solving
Multiple Correlations

6. Steps for Solving Multiple Correlation 
Where:


•
•
•
•
•
•
Rx/y = is the Product moment correlation between X, with Y X = is the
sum of the
variables X 
∑Y = is the amount of the variable Y 
N = is the number of samples
Σ y² = is the square of the total amount of variable Y 
Σxy = is the multiplication results of the Total number of variables X and
Y variables 
Xj      = is the variable X to so many ... (example: Xl, X2, X3, etc....) 

7. Steps for Solving Multiple Correlation
Next the results of the correlation then calculate the multiple
correlation (R) with the formula:
•
•
•
•
R
YX1X2
= correlation between variables X
1
with X
2
simultaneously with the
variable Y 
r
YX1
= Product moment correlation between X
1
and Y 
r
YX2
= Product moment correlation between X
2
and Y 
r
X1X2
= Product moment correlation between X
l
and X
2

8. Steps for Solving Multiple Correlation 




5. Test the signicance with the F arithmetic formula: 
Where: 
• R =value of multiple correlation coecient 
• k = number of independent variables
(independent) 
• n = number of samples 
• F = F count which will then be compared with
F table

9. Steps -Steps for Solving Multiple Correlation 

From the above calculation, draw conclusions, using the rules
of signicance: 

• If F count > F table then it is signicant 
• If F count < F table then it is not signicant 
• Looking for F table with table F with using formula : 
   Level of Signicance is a = 0.01 or a = 0.05 
   F table = F (1-a)(db= k)(db = n-k-1)

10. EXAMPLE OF MULTIPLE CORRELATION
PROBLEMS
•In the following table is a study of the
relationship between self-condence
and learning motivation on statistical
learning presentations for students of
the Faculty of Economics, INAIS. For
this purpose, data was collected on 10
respondents who were taken randomly
with a = 0.05. Based on the 10
respondents, the data obtained from
the level of condence (X l), Motivation
(X2), and learning presentations (Y), as
follows:

11. SOLUTION

•
•
•
•
•
It is known: 
XI = self-condence 
X2 = motivation 
Y = learning presentation 
N = 10 
 A = 0.05
•
Formulation of the problem 
Is there a signicant relationship between
the level of self-condence and learning
motivation on the statistical learning
presentation of the students of the Faculty
of Economics, INAIS 
•
Research hypothesis: 
There is a signicant relationship between
the level of self-condence and learning
motivation on the statistical learning
presentation of the students of the Faculty
of Economics, INAIS ?


•
•
Formally determine the hypothesis: 
Ho: R = 0
Hi: R 0

12. SOLUTION
Correlation X1 with Y

13. SOLUTION
Correlation X1

with Y  Correlation X2 With Y

14. Solution
Correlation X1 with X2
Multiple Correlation Formula (R)

15. SOLUTION
From the results of these calculations, it can be said that there is a correlation between
self-condence (x1) motivation (x2) and learning presentation (y) in INAIS Economics
students. The correlation between self-condence (x1) and motivation (x2) on learning
presentation (y) in INAIS Economics students is quite strong because the results of the
above calculation, the value of R is 0.67 ≈ 1. Then, to state the size of the contribution of
variables x1 and x2 with variables y or determinant coecient uses formula R
2
x 100% or
(0.67² x 100% = 45%). Fuhermore, to determine the signicance of the multiple
correlation (R), the following F test is calculated: 

16. SETTLEMENT
•
•
•
•
•
•
Test the signicance with the F arithmetic formula:
 Signicance test rules: 

If F count > F table then it is signicant 
if F count < F table then it is not signicant 
with a = 0.05 for 2-sided test 
F table = F (1 -a) ((db=k)(db= nk- 1)) 
= F(1-a)((db=2)(db=10-2-1))
= F(1-0.05)(2.7)
= F (0.95) (2.7) 
How to nd F table: 2 as the numerator; 7 as the
denominator. 
F table = 4.74 

17. RESULT AND CONCLUSION
•Having calculated turns F count < F table or 2,88 < 4,74. So Hi is
rejected and H
0
is accepted. It can be concluded that "there is
no signicant relationship between self-condence (X
l
) and
motivation (X
2
) on learning presentation (Y) for students of the
Faculty of Economics, INAIS". 

18. THANK YOU