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38
Display
1.
2.
3.
4.
6.
(Note:
To input multiple
identical
samples,
pro-
ceed as indicated.)
79
so
87
96
73
73
y
Student No.
Mark
in Math.
ll
x
82
2
53
3
61
4
74
5
51
6
51
Key
in:
82
@
79
@ill]
53
~
50 ~
61
@
87
@ill]
74
~
96
@ill]
51
~
73
00
2
@mi
Example
1:
If we know a student's mark
in
mathematics,
can we predict the mark
in
English?
The
exam marks for five
students
chosen
at
random are given
in
the following
table:
Mark
in English
S
=
Lx2
-
(Lx)2
xx
n
(I;y)2
Syy
=
LY2
----
n
.l:x· 1:y
Sxy
=
LXy-~---
n
a:
a=
ji
-
bi
l
Coefficient
of linear
b:
b=
Sxy
regression
equation
Sxx
y=
a+ bx
r:
Correlation
coefficient
Sxy
r
= --;:::=====-
.../
Sxx.
Syy
Negative
Correlation
Positive
Correlation
Using
as
a Calculat or
Value
of
r
Call
it
+0.80 to
+
1.00
Extra
High
+0.60 to +0.80
High
+0.40
to +0.60
Moderate
+0.20
to +0.40
Low
-0.20
to
+0.20
Nil
-0.20
to -0.40
Low
-0.40
to -0.60
Moderate
-0.60
to
-0.80
High
-0.BO
to
-1.00
Extra High
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