Beta
33557

DETERMINATION OF THE GOODNESS OF FIT OF A DISTRIBUTION TO A SET OF EXPERIMENTAL DATA

Article

Last updated: 24 Dec 2024

Subjects

-

Tags

-

Abstract

Abstract
Many systems of interest involve phenomena that exhibit unpredictable variation and
randomness. For example, communication systems must provide continuous and error free
communication over channels that are subject to random noise. Probability models are one of
the tools that enable the designer to successfully build systems that are efficient and reliable.
Processing of random signals postulates a probability model that is defined by the probability
density function of the random signal. In this paper, we propose a method to determine the
goodness of fit of a distribution to a set of experimental data. The proposed method depends
on the chi-square test. It is applied to different examples of different probability density
functions. The proposed method is proved to be efficient.

DOI

10.21608/iceeng.2006.33557

Keywords

Probability density function – Fit of a distribution to data – Probability models – Histogram of random variables

Authors

First Name

Ashraf

Last Name

Aziz

MiddleName

Mamdouh A.

Affiliation

Associate Professor, Electrical Eng. Dept., Military Technical College, Cairo, Egypt.

Email

-

City

-

Orcid

-

Volume

5

Article Issue

5th International Conference on Electrical Engineering ICEENG 2006

Related Issue

5615

Issue Date

2006-05-01

Receive Date

2019-05-28

Publish Date

2006-05-01

Page Start

1

Page End

15

Print ISSN

2636-4433

Online ISSN

2636-4441

Link

https://iceeng.journals.ekb.eg/article_33557.html

Detail API

https://iceeng.journals.ekb.eg/service?article_code=33557

Order

32

Type

Original Article

Type Code

833

Publication Type

Journal

Publication Title

The International Conference on Electrical Engineering

Publication Link

https://iceeng.journals.ekb.eg/

MainTitle

DETERMINATION OF THE GOODNESS OF FIT OF A DISTRIBUTION TO A SET OF EXPERIMENTAL DATA

Details

Type

Article

Created At

22 Jan 2023