Geoscience Reference
In-Depth Information
7.17. 3 . 2 I n t e r a c t i o n s
Suppose that there is a simple linear relationship between Y and X 1 . If the slope ( b ) of this relation-
ship varies, depending on the level of some other independent variable ( X 2 ), then X 1 and X 2 are said
to interact. Such interactions can sometimes be handled by introducing interaction variables. To
illustrate, suppose that we know that there is a linear relationship between Y and X 1 :
Y = a + bX 1
Suppose further that we know or suspect that the slope ( b ) varies linearly with Z :
b = a ′ + b Z
This implies the relationship
Y = a + ( a ′ + b Z ) X 1
or
Y = a + a X 1 + b X 1 Z
where X 2 = X 1 Z , an interaction variable.
If the Y -intercept is also a linear function of Z , then
a = a ′′ + b ′′ Z
and the form of relationship is
Y = a ′′ + b ′′ Z + a X 1 + b X 1 Z
7.17.4 g roup r egressions
Linear regressions of Y on X were fitted for each of two groups:
Group A
Sum
Mean
Y
3
7
9
6
8
13
10
12
14
82
9.111
X
1
4
7
7
2
9
10
6
12
58
6.444
where
2
2
2
n
=
9
,
Y
=
848
,
XY
=
609
,
X
=
480
,
y
=
100 8889
.
ˆ
2
xy
=
80 5556
.
,
x
=
106 2222
.
,
Y
=
4 224
.
+
0 7584
.
X
Residual
SS = 39 7980
.
,
with7degrees of freedom
Group B
Sum
Mean
Y
4
6
12
2
8
7
0
5
9
2
11
3
10
79
6.077
X
4
9
14
6
9
12
2
7
5
5
11
2
13
99
7.616
where
2
2
2
n
=
13
,
Y
=
653
,
XY
=
753
,
X
=
951
,
y
=
172 9231
.
ˆ
2
xy
=
151 3846
.
,
x
=
197 0796
.
,
Y
=
0 228
.
+
0 7681
.
X
Residual
SS = 56 6370
.
,
with11degrees of free
dom
 
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