 
Single
Regressor or Simple Regression Model:
Single Regressor or variable (x) that has a
relationship with response (y). Creates a straight line.
What to look for, example from our book:
Bruce L. Bowerman, and Richard T. O'Connell, From Miami
University, Business Statistics in Practice, 3rd, McGrawHill Irwin, Page 547,
Example 11:1 and Figure 11.1.
Regression
Lines
by
Venita
Twitty,
(UoPhx
2008)
The
regression
lines
are used
for a
visual
method
of
relating
the
relationship
between
(x) and
(y) in a
graph.
The (x)
is
independent
variable
and (y)
is a
dependent
variable.
Independent
refers
to the
process
of one
event
has no
impact
on
another
event.
Dependent
is if
the
process
has an
impact
on an
event.
The
equation
for the
line
used to
estimate
Y based
on X
referrers
to as
the
regression
equation
(Lind,
Marchal
& Wathen,
2005, p.
440).
The
Regression
Equation
is a
line in
a
twodimensional
or
twovariable
space is
defined
by the
equation
Y=a+b*X;
in full
text:
the Y
variable
can be
expressed
in terms
of a
constant
(a) and
a slope
(b)
times
the X
variable.
The
constant
is also
referred
to as
the
intercept,
and the
slope as
the
regression
coefficient
or B
coefficient
(Linear
Regression
in
Excel,
2004).
An
example
of
solving
and
defining
regression
is
outlined
in the
following
example.
The
example
is found
at North
Carolina
University
Labwrite
website.
A method
for
making
predictions
given 2
related
variables
(related
means
variables
which
are
correlated).
The form
of the
prediction
is
usually:
what is
the
expected
y value
given
some x
value.
The form
of the
regression
line is:
Predicted
y =
(slope)
multiplied
by (X
value) +
intercept.
Steps to
compute
regression
line:

Find
slope
(correlation
multiplied
by
the
standard
deviation
of
y)
*divided
by*
(standard
deviation
of
x)

Find
intercept
(mean
of y
minus*
(slope
multiplied
by
mean
of
x))

Plug
in x
and
solve
for
y
Note,
predictions
are only
good
when:
References
Lind,
Marchal
& Wathen.
(2005).
Statistical
Techniques
in
Business
&
Economics,
12 ed.
[University
of
Phoenix
Custom
Edition
etext].
New
York:
The
McGrawHill
Companies.
Retrieved
July 10,
2008,
from
University
of
Phoenix,
rEsource,
MBA/510
Statistical
Techniques
in
Business
&
Economics
Web
site.
North
Carolina
University. (2004).
Linear
Regression
in
Excel.
Retrieved
August
18,
2008
from: http://www.ncsu.edu/labwrite/res/gt/gtreghome.html

Multiple
Regressors or Multiple Regression Model:
More than one Regressor or variable (Xi) that has
a relationship with response (y). Creates a straight plane.
What to look for, example from our book:
Bruce L. Bowerman, and Richard T. O'Connell, From Miami
University, Business Statistics in Practice, 3rd, McGrawHill Irwin, Page 526
and 527, Example 12:1, Table 12.1 and Figure 12.3.
Curvilinear
or Polynomial Regression Model or Quadratic Regression Model:
Curve or variable (X squared) that has a
relationship with response (y). Creates a curved line.
What to look for, example from our book:
Bruce L. Bowerman, and Richard T. O'Connell, From Miami
University, Business Statistics in Practice, 3rd, McGrawHill Irwin, Page 552
and 553, Example 12:11, Table 12.8 and Figure 12.14.
 
