STAR*NET V6 - Circe

Chapter 5 Preparing Input Data
Again, it must be emphasized that you should use realistic values. As explained in
Chapter 8, “Analysis of Adjustment Output,” the entire adjustment is checked with a
statistical test. Frequently, a cause for failing this test is the assignment of unrealistically
small standard errors to your input values. On the other hand, do not make your global
standard errors overly large, just to ensure that the adjustment passes the test. This will
have the effect of unnecessarily increasing the size of the error ellipses around the
computed points, and unfairly representing the confidence of your computed coordinates.
If you observe multiple readings for your observations and are entering the mean values
into STAR*NET, the question is sometimes asked as to whether you should use a
different standard error as computed from averaging each set, or use the same standard
error for all averaged sets based on the manufacturer’s “population” standard deviations.
The answer is that we recommend you base your standard errors on the manufacturer’s
stated specifications – or the results of a controlled field test. Standard errors computed
from such small observation samples (i.e. sets containing 2, 4, 10, 20, etc. repeats) are
not that statistically significant!
Calculating the proper standard errors to use based on a manufacturer’s stated standard
deviations is discussed in detail on page 22 in Chapter 4, “Options.”
The following table summarizes the relationship between weights, precision and the
influence on the adjustment.
“Strong”
Measurement
101
“Weak”
Measurement
Standard Error LOW HIGH
Weight HIGH LOW
Precision HIGH LOW
Influence HIGH LOW
Weights