TM 5-6675-313-14
Keystrokes
Display
E 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.0 through R.5 a
predicted value for y (denoted y) can be calculated by keying in a new value for x
and pressing
q
~ .
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.0 through R.5
to predict demand for motor fuel for the years 1980 and 2000, key in new x values
and-press
q
to pass 3,500
Keystroke
9 “
To determine the year that the demand for-motor fuel is expected
million barrels, key in 3,500 (new value for y) and press
q
;.
u
Display
1980
q
j
2000 El ;
35
Em
2,808.6264
4,031.8512
1,991.3041
Predicted demand in millions
of barrels for the year 1980.
Predicted demand in millions
of barrels for the year 2000.
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
B
q
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
