TM 5-6675-322-14
Keystrokes
D i s p l ay
L . R .
-118,290.6295
T h e y - i n t e r c e p t o f t h e l i n e.
61.1612
Slope of the line.
( 6 ) L i n e a r e s t i m a t i o n.
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
a n d p r e s s i ng
. A predicted value for x (denoted x) can be calculated by
keying in a new value for y and pressing
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
. To determine the year that the demand for motor fuel is expected
t o p a s s 3 , 5 0 0 m i l l i o n b a r r e l s ,
key in 3,500 (new value for y) and press
Keystroke
Display
1980
2,808.6264
Predicted demand in millions
of barrels for the year 1980.
2
0
0
0
4,031.8512
Predicted demand in millions
of barrels for the year 2000.
35
1,991.3041
The demand is expected to pass
3 , 5 0 0 m i l l i o n b a r r e l s d u r i ng
1992.
( 7 ) C o r r e l a t i o n c o e f f i c i e n t .
Both linear regression and linear estimation
presume that the relationship between x and y data values can be approximated, to
some degree,
b y a l i n e a r f u n c t i o n ( a s t r a i g h t l i n e ) . ( c o r r e l a t i o n c o e f f i c i e n t )
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
o n t o a s t r a i g h t l i n e w i t h p o s i t i v e s l o p e .
While at r = -1, data falls exactly onto
a s t r a i g h t l i n e w i t h n e g a t i v e s l o p e .
A t r = 0, data cannot be approximated by a
s t r a i g h t l i n e .
Example:
To calculate the correlation coefficient for previous example press:
Keystrokes
D i s p l a y
0.9931
The data very closely
approximates a straight line.
3 - 7.
OPERATION UNDER UNUSUAL CONDITIONS. This equipment is designed for operation
only in a controlled environment.
3-34
