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HP 95LX - Page 69

HP 95LX
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6.
Analyze
the
data:
Press
(Regression).
You
will
see:
r
~
C22:
[W151]
FE
HL
1
-Data
0
Z
tercept
413.49488215
=
d
Err
of
¥Y
Est
843.99857721
i
Squared
}
8.9731872975
=
.
of
Observations
11
=
ope
8.8040123457
Ee]
d
Err
of Coef.
0.4688880934
x
Predicted
Value
413.48488215
alue
\_
_/
The
macro
has
calculated
a
two-variable
linear
regression:
Intercept
is
the
y-axis
intercept
for
the
regression
line.
Std Err
of
Y
Est
isthe
standard
error
of
the
estimated
y
values.
R
squaredis
the
reliability
of
the
regression
(between
0
and
1).
Slope
is
the
slope
for
the
independent
variable.
Std Err
of
Coef
isthe
standard
error
of
the
x
coefficient
(the
slope).
Y¥
Predicted
Value
is
the
predicted
value
for
the
y-variable
for
the
given value
of
the
x-variable.
Since
no
x-value
is
given
yet,
x
is
assumed
to
be
equal
to zero.
#
Yalue
is
a
value
for
the
independent
variable
(i.e.
floor
space)
that
you
enter
in
order
to
predict
a
corresponding
y-value
(i.e.
rental
income),
based
on
the
current
regression.
7.
Enter
a
different
x-value.
How
much
income
can
you
expect
from
a
building
with
2985
square
feet
of
floor
space?
Press
(2]9)]8]5)[ENTER).
The
predicted
y-
value
is
26693
.381
734
or
about
$26,
690
of
rental
income.
8.
Plot
the
regression
line.
Press
(ALT)}{P).
The
plot
is
good
enough
to
give
you
a
visual
estimate
for
how
good
the
fit
is.
Of
course,
the
r-squared
value
(see
above)
gives
you
a
calculated
estimate
of
the
same
thing.
9.
Ifyou
want
to
save
the
data,
press
(MENU),
(Flile,
(Slave,
type
in
a
new
name,
and
press
(ENTER).
Statistics
and
Databases
69

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