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Name
Remarks (see p.
(2) For a linear fit model, the line parameters are
and
a
1
L.R.
be calculated using
Generally, a regression parameter is significant if
side being the inverse of the t-distribution.
(1) For a linear fit model, the sample covariance is
s
XY
s
n
xy
(2) The sample variance is
s,
SERR
The sample standard deviation (SD) is
And the standard error (i.e. the SD of the mean
(1) The sample SD for weighted data (where the weight
entered via
s
,
W
SERR
And the respective standard error (the SD of the mean
W
s
Ew
x
(2) The arithmetic mean (or average) is calculated as
x
(2) The geometric mean is calculated as
g
x
(1) The arithmetic mean for weighted data (see s
w
(2) The scattering factor
WP 34S Owner's Manual
74
for general information)
s
s
xy
y
(see CORR above, s
r
2
s
s
x
x
s
s
(
a
)
E
1
s
x
y
x
y
n
i
i
i
i
2
s
x
s
) is
w
y
y
1
i
i
y
i
ε
for a sample of log-normally distributed data is calculated via:
x
and s below). Their standard errors can
XY
2
1
r
y
and
s
(
E
n
2
x
(
n
) 1
. Compare COV above.
2
2
n
x
x
i
i
n
(
n
) 1
2
s
s
x
x
x
) is
2
y
y
x
i
i
i
y
1
i
2
2
x
y
x
i
i
i
y
1
i
x
n
g
) is calculated as
W
ln(
)
x
Edition 3.1
2
x
y
i
a
0
n
n
1
a
)
s
(
a
)
0
E
1
n
a
1
i
t
. 0
995
s
a
n
2
v
i
2
2
x
n
x
i
.
n
1
.
s
s
x
.
Ex
n
y
of each data point
i
2
y
x
i
i
.
y
i
x
) is
w
.
1
x
x
.
i
n
1
ln
x
n
x
e
.
x
w
2
ln
x
2
n
ln
i
n
1
x
x
y
i
i
i
i
2
n
1
s
x
2
2
.
s
x
x
, with the right
x
was
i
x
y
i
i
.
y
i
x
g
. Compare s.
Page 205 of 211
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