Page 18-6
Suppose that the classes, or bins, will be selected by dividing the interval (x
bot
,
x
top
), into k = Bin Count classes by selecting a number of class boundaries,
i.e., {xB
1
, xB
2
, …, xB
k+1
}, so that class number 1 is limited by xB
1
-xB
2
, class
number 2 by xB
2
- xB
3
, and so on. The last class, class number k, will be
limited by xB
k
- xB
k +1
.
The value of x corresponding to the middle of each class is known as the class
mark, and is defined as xM
i
= (xB
i
+ xB
i+1
)/2, for i = 1, 2, …, k.
If the classes are chosen such that the class size is the same, then we can
define the class size as the value Bin Width =
∆
x = (x
max
- x
min
) / k,
and the class boundaries can be calculated as xB
i
= x
bot
+ (i - 1) *
∆
x.
Any data point, x
j
, j = 1, 2, …, n, belongs to the i-th class, if xB
i
≤
x
j
< xB
i+1
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).
Example 1 -- In order to better illustrate obtaining frequency distributions, we
want to generate a relatively large data set, say 200 points, by using the
following:
•
First, seed the random number generator using:
RDZ(25)
in ALG mode,
or
25
`
RDZ
in RPN mode (see Chapter 17).
•
Type in the following program in RPN mode:
«
n
«
1 n FOR j RAND 100 * 2 RND NEXT n
LIST
»
»
and save it under the name RDLIST (RanDom number LIST generator).
•
Generate the list of 200 number by using RDLIST(200) in ALG mode, or
200
`
@RDLIST@
in RPN mode.
•
Use program LXC (see above) to convert the list thus generated into a
column vector.
•
Store the column vector into
Σ
DAT, by using function STO
Σ
.
