EDF - Empirical Distribution Function

Calculates the empirical distribution function, or empirical cdf, of the sample data.

Syntax

EDF(X, target, Ret_type)

X is the input data series (one/two dimensional array of cells (e.g. rows or columns)).

target is the target value to compute the underlying cdf for.

Ret_type is a switch to select the return output (1= CDF (default), 2= Inverse CDF).
Order Description
1 Cumulative EDF (default)
2 Inverse Cumulative EDF

Remarks

  1. For the inverse cumulative EDF calculation, the target value must be between 0 and 1 (exclusive).
  2. The input data series may include missing values (e.g. #N/A, #VALUE!, #NUM!, empty cell), but they will not be included in the calculations.

Examples

Example 1:

  A B
1 Date Data
2 1/1/2008 #N/A
3 1/2/2008 -1.28
4 1/3/2008 0.24
5 1/4/2008 1.28
6 1/5/2008 1.20
7 1/6/2008 1.73
8 1/7/2008 -2.18
9 1/8/2008 -0.23
10 1/9/2008 1.10
11 1/10/2008 -1.09
12 1/11/2008 -0.69
13 1/12/2008 -1.69
14 1/13/2008 -1.85
15 1/14/2008 -0.98
16 1/15/2008 -0.77
17 1/16/2008 -0.30
18 1/17/2008 -1.28
19 1/18/2008 0.24
20 1/19/2008 1.28
21 1/20/2008 1.20
22 1/21/2008 1.73
23 1/22/2008 -2.18
24 1/23/2008 -0.23
25 1/24/2008 1.10
26 1/25/2008 -1.09
27 1/26/2008 -0.69
28 1/27/2008 -1.69
29 1/28/2008 -1.85
30 1/29/2008 -0.98


  Formula Description (Result)
  =EDF(\$B\$2:\$B\$30,0.5) Density Probability (0.207)
  =EDF(\$B\$2:\$B\$29,0.24,2) Inverse Cumulative Probability (-1.28)

Files Examples


References

  • Balakrishnan, N., Exponential Distribution: Theory, Methods and Applications, CRC, P 18 1996.
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