HP 48gII hp 48gII_user's manual_English_E_HDPMSG48E67_V2.pdf - Page 577

Suppose that the classes, or bins, will be selected by dividing the interval (xbot, xtop), into k = Bin Count classes by selecting a number of class boundaries, i.e., {xB1, xB2, ..., xBk+1}, so that class number 1 is limited by xB1-xB2, class number 2 by xB2- xB3, and so on. The last class, class number k, will be limited by xBk - xB k +1. The value of x corresponding to the middle of each class is known as the class mark, and is defined as xMi = (xBi + 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 = (xmax - xmin) / k, and the class boundaries can be calculated as xBi = xbot + (i - 1) * ∆x. Any data point, xj, j = 1, 2, ..., n, belongs to the i-th class, if xBi ≤ xj < 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 ΣDAT, by using function STOΣ. Page 18-6