HP 50g HP 50g_user's manual_English_HDPSG49AEM8.pdf - Page 158

Obtaining frequency distributions, Frequencies.., X-Min, Bin Count, Bin Width

Page 158 highlights

Obtaining frequency distributions The application 2. Frequencies.. in the STAT menu can be used to obtain frequency distributions for a set of data. The data must be present in the form of a column vector stored in variable ΣDAT. To get started, press ,Ù˜@@@OK@@@. The resulting input form contains the following fields: ΣDAT: Col: X-Min: Bin Count: Bin Width: the matrix containing the data of interest. the column of ΣDAT that is under scrutiny. the minimum class boundary to be used in the frequency distribution (default = -6.5). the number of classes used in the frequency distribution (default = 13). the uniform width of each class in the frequency distribution (default = 1). Given a set of n data values: {x1, x2, ..., xn} listed in no particular order, one can group the data into a number of classes, or bins by counting the frequency or number of values corresponding to each class. The application 2. Frequencies.. in the STAT menu will perform this frequency count, and will keep track of those values that may be below the minimum and above the maximum class boundaries (i.e., the outliers). As an example, generate a relatively large data set, say 200 points, by using the command RANM({200,1}), and storing the result into variable Page 16-3

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Page 16-3
Obtaining frequency distributions
The application
2. Frequencies..
in the STAT menu can be used to
obtain frequency distributions for a set of data.
The data must be present
in the form of a column vector stored in variable
Σ
DAT.
To get started,
press
‚Ù˜
@@@OK@@@
.
The resulting input form contains the following
fields:
Given a set of n data values: {x
1
, x
2
, …, x
n
} listed in no particular order,
one can group the data into a number of classes
, or bins
by counting the
frequency
or number of values corresponding to each class.
The
application
2. Frequencies..
in the STAT menu will perform this
frequency count, and will keep track of those values that may be below the
minimum and above the maximum class boundaries (i.e., the outliers
).
As an example, generate a relatively large data set, say 200 points, by
using the command RANM({200,1}), and storing the result into variable
Σ
DAT:
the matrix containing the data of interest.
Col:
the column of
Σ
DAT that is under scrutiny.
X-Min:
the minimum class boundary to be used in the frequency
distribution (default = -6.5).
Bin Count:
the number of classes used in the frequency distribution
(default = 13).
Bin Width:
the uniform width of each class in the frequency
distribution (default = 1).