HP·38E/38C - Slide Rule Museum

74 Statistical Functions
An example of a negative trend is declining property values or declining
sales. If r = 0, the data values are spread out and do not come close to a
straight line. It would be useless to find linear estimates from unrelated
data.
In the example of lot frontage related to value, the correlation coefficient
is close to I, so we can feel comfortable using linear regression.
Suppose, though, that the correlation coefficient was not close to I but
instead was 0.5 or 0.6. This would indicate that a straight line is not a
very good fit to the data. Then you might try to fit a curve to the data.
Refer to the applications books for a description of three other types of
curves: exponential, logarithmic, and power. A correlation coefficient
can be calculated for each of these curves and should be interpreted
similarly: if r is close to ± I , the curve is a reasonable approximation of
the data. If not, try a different curve.
Linear Estimates X, y
Now that we know our data fits a line closely, we can trust results of our
linear estimates. With the data totaled in registers Rl through R 6 , a
predicted y (designated 9) can be calculated by keying in an x-value and
pressing [I!J . And a predicted x (designated x) can be calculated by
keying in a y-value and pressing I x,r I
Example 1: For the previous example, find projected values for 80-,
95- , and loo-foot frontages.
Keystrokes
80 1]] [I!J
1001]][I!J
Display
11,922.65
14,084.29
14,804.83
80-foot frontage
projected value.
95-foot frontage
projected value.
100-foot frontage
projected value .