Effects of Missing Data on the Parameters of Multiple Regression Model (MRM)

Harrison Etaga, Ngonadi Lilian, Aforka Kenechukwu F and Etaga Njideka C
Additional contact information
Ngonadi Lilian: Department of Statistics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
Aforka Kenechukwu F: Department of Statistics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
Etaga Njideka C: Department of Statistics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria

International Journal of Research and Scientific Innovation, 2023, vol. 10, issue 2, 63-73

Abstract: Multiple Regression Models are used in prediction the nature of relationship between one dependent variable and more than more independent variables. There are so many assumptions the guide the estimation of the parameters of the model. The interpretations of parameters are always subjected to the nature of data involved. Missing values tends to limit the fullness of information in analysis. It is therefore necessary to check for the effect of missing data on the parameters of the Multiple Regression Model. Data were simulated using Binomial, Geometric, Normal and Exponential Distribution. The simulation was done at different sample sizes of 15, 25, 50 and 100. The level of missingness was moderated at 5%, 10%, 25% and 35%. Two methods of handling missing data were employed, listwise deletion and Mean imputation. Data were analysis using multiple regression and Analysis of Variance. The results shows that the least Mean Square Error (MSE) were obtained at different level of missingness depending of the distribution. There was a significant effect on the parameters of the multiple Regression base on sample sizes.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrsi/d ... 10-issue-2/63-73.pdf (application/pdf)
https://www.rsisinternational.org/virtual-library/ ... tm_campaign=Krishuo1 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjc:journl:v:10:y:2023:i:2:p:63-73

Access Statistics for this article

International Journal of Research and Scientific Innovation is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Scientific Innovation from International Journal of Research and Scientific Innovation (IJRSI)
Bibliographic data for series maintained by Dr. Renu Malsaria ().