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Θ Generate the list of 200 number by using RDLIST(200) in ALG mode, or
200 ` @RDLIST@ in RPN mode.
Θ Use program LXC (see above) to convert the list thus generated into a
column vector.
Θ Store the column vector into
ΣDAT, by using function STOΣ.
Θ Obtain single-variable information using: ‚Ù @@@OK@@@. Use Sample for
the Type of data set, and select all options as results. The results for this
example were:
Mean: 51.0406, Std Dev: 29.5893…, Variance: 875.529…
Total: 10208.12, Maximum: 99.35, Minimum: 0.13
This information indicates that our data ranges from values close to zero to
values close to 100. Working with whole numbers, we can select the range of
variation of the data as (0,100). To produce a frequency distribution we will
use the interval (10,90) dividing it into 8 bins of width 10 each.
Θ Select the program
2. Frequencies.. by using ‚Ù˜ @@@OK@@@. The
data is already loaded in
ΣDAT, and the option Col should hold the value 1
since we have only one column in
ΣDAT.
Θ Change X-Min to 10, Bin Count to 8, and Bin Width to 10, then press
@@@OK@@@.
Using the RPN mode, the results are shown in the stack as a column vector in
stack level 2, and a row vector of two components in stack level 1. The vector
in stack level 1 is the number of outliers outside of the interval where the
frequency count was performed. For this case, I get the values [ 25. 22.]
indicating that there are, in my
ΣDAT vector, 25 values smaller than 10 and 22
larger than 90.
Θ Press ƒ to drop the vector of outliers from the stack. The remaining result
is the frequency count of data. This can be translated into a table as shown
below.
This table was prepared from the information we provided to generate the
frequency distribution, although the only column returned by the calculator is
the Frequency column (f
i
). The class numbers, and class boundaries are easy