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:

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

Comments

Article is closed for comments.

Was this article helpful?
0 out of 0 found this helpful