TM 5-6675-317-14
Keystrokes
Display
n L.R.
-118,290.6295
The y-intercept of the line.
H
61.1612
Slope of the line.
(6) Linear estimation.
With data accumulated in registers R.O through R.5 a
predicted value for y (denoted y) can be calculated by keying in a new value for x
and pressing
q
y .
A predicted value for x (denoted x) can be calculated by
keying in a new value for y and pressing
q
~ .
Example:
With data intact from previous example in registers R.O through R.5
to predict demand for motor fuel for the years 1980 and 2000, key in new x values
and press
q
~ .
To determine the year that the demand for motor fuel is expected
to pass 3,500 million barrels,
key in 3,500 (new value for y) and press
q
E*
Keystroke
Display
1980
q
$
2,808.6264
Predicted demand in millions
of barrels for the year 1980.
2000
q
j
4,031.8512
Predicted demand in millions
of barrels for the year 2000.
35
q
ill
1,991.3041
The demand is expected to pass
3,500 million barrels during
1992.
(7) Correlation coefficient.
Both linear regression and linear estimation
presume that the relationship between x and y data values can be approximated, to
some degree, by a linear function (a straight line).
q
(correlation coefficient)
can be used to determine how closely the data “fits” a straight line.
The correla-
tion coefficient can range from r = + 1 to r = - 1. At r = + 1, data falls exactly
onto a straight line with positive slope.
While at r = -1, data falls exactly onto
a straight line with negative slope. At r = O, data cannot be approximated by a
straight line.
Example:
To calculate the correlation coefficient for previous example press:
Keystrokes
Display
E?IH
0.9931
The data very closely
approximates a straight line.
6-7. OPERATION UNDER UNUSUAL CONDITIONS.
This equipment is designed for
operation only in a controlled environment.
6-34
