TM 5-6675-324-14
Keystrokes
Display
The y-intercept of the line.
-118,290.6295
L.R.
Slope of the line.
61.1612
mation. With data accumulated in registers R.0 through R.5 a
(6) Linear esti
(denoted y) can be calculated by keying in a new value for x
predicted value for y
A predicted value for x (denoted x) can be calculated by
and pressing
.
for y and pressing
keying in a new value
Example: With data intact from previous example in registers R.0 through R.5
to predict demand for motor fuel for the years 1980 and 2000, key in new x values
. To determine the year that the demand for motor fuel is expected
and press
to pass 3,500 million barrels, key in 3,500 (new value for y) and press
.
Keystroke
Display
2,808.6264
Predicted demand in millions
of barrels for the year 1980.
4,031.8512
Predicted demand in millions
of barrels for the year 2000.
The demand is expected to pass
1,991.3041
35
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).
(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.
To calculate the correlation coefficient for previous example press:
Example:
Display
Keystrokes
0.9931
The data very closely
approximates a straight line.
only in a controlled environment.
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