The sample variance is the mean of the square of the deviations
of each measurement from the mean (called mean square deviation). Enter x_{i}
values: (hit **Autofill** and **Plot) t**o see the sample
variance s^{2} and sample standard deviation s. The
sum of deviations å(x_{i}
- )= 0 is expected, since
it follows that
åx_{i }= N,
i.e. . which
is the definition of . Most
points (approx 2/3) lie within ± s of the mean . N
must be at least 2, since we cannot estimate a spread in values from
a single measurement. s is an
estimate of s, the standard deviation of a single measurement. It is **not
**an estimate of s(),
the standard
deviation of the mean ... next. |