Home > Humanities and the Arts > Education > Volume-3 > Issue-4 > Simulating Multivariate Random Normal Data using Statistical Computing Platform R

Simulating Multivariate Random Normal Data using Statistical Computing Platform R

Call for Papers

Volume-6 | Issue-5

Last date : 27-Aug-2022

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

Processing Charges : 700/- INR Only OR 25 USD (for foreign users)

First Update : Within 7 Days after submittion

Submit Paper Online

For Author

Research Area



Simulating Multivariate Random Normal Data using Statistical Computing Platform R


Mehmet Turegun

https://doi.org/10.31142/ijtsrd23987



Mehmet Turegun "Simulating Multivariate Random Normal Data using Statistical Computing Platform R" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4, June 2019, pp.1126-1132, URL: https://www.ijtsrd.com/papers/ijtsrd23987.pdf

Many faculty members, as well as students, in the area of educational research methodology, sometimes have a need for generating data to use for simulation and computation purposes, demonstration of multivariate analysis techniques, or construction of student projects or assignments. As a great teaching tool, using simulated data helps us understand the intricacies of statistical concepts and techniques. The process of generating multivariate normal data is a nontrivial process and practical guides without dense mathematics are limited in the literature (Nissen and Saft, 2014). Hence, the purpose of this paper is to offer researchers a practical guide for and a quick access to generating multivariate random data with a given mean and variance-covariance structure. A detailed outline of simulating multivariate normal data with a given mean and variance-covariance matrix using Eigen (or spectral) and Cholesky decompositions is presented and implemented in statistical computing platform R version 3.4.4 (R Core Team, 2018).

Cholesky decomposition, Eigen decomposition, simulation of multivariate random normal data, variance-covariance matrix, R


IJTSRD23987
Volume-3 | Issue-4, June 2019
1126-1132
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin