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1-4 Distribution (DIST)
There is a variety of different types of distribution, but the most well-known is "normal
distribution," which is essential for performing statistical calculations. Normal distribution is a
symmetrical distribution centered on the greatest occurrences of mean data (highest
frequency), with the frequency decreasing as you move away from the center. Poisson
distribution, geometric distribution, and various other distribution shapes are also used,
depending on the data type.
Certain trends can be determined once the distribution shape is determined. You can
calculate the probability of data taken from a distribution being less than a specific value.
For example, distribution can be used to calculate the yield rate when manufacturing some
product. Once a value is established as the criteria, you can calculate normal probability
when estimating what percent of the products meet the criteria. Conversely, a success rate
target (80% for example) is set up as the hypothesis, and normal distribution is used to
estimate the proportion of the products will reach this value.
Normal probability density calculates the probability density of normal distribution from a
x
specified
value.
Normal distribution probability calculates the probability of normal distribution data falling
between two specific values.
Inverse cumulative normal distribution calculates a value that represents the location
within a normal distribution for a specific cumulative probability.
t
Student-
probability density calculates
t
Student-
distribution probability calculates
between two specific values.
t
Like
distribution, distribution probability can also be calculated for
and Geometric distributions.
On the initial STAT Mode screen, press 5 (DIST) to display the distribution menu, which
contains the following items.
• 5(DIST)b(Norm) ... Normal distribution (p. 1-4-3)
1-4-1
Distribution (DIST)
t
probability density from a specified x value.
c(T) ... Student-
t
distribution (p. 1-4-7)
d(
2
2
) ...
distribution (p. 1-4-9)
e(F) ...
F
distribution (p. 1-4-12)
f(Binmal) ... Binomial distribution (p. 1-4-16)
g(Poissn) ... Poisson distribution (p. 1-4-19)
h(Geo) ... Geometric distribution (p. 1-4-21)
20010101
the
t
probability of
distribution data falling
2
,
F
, Binomial, Poisson,
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