IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF APPLIED SCIENCES AND MATHEMATICAL THEORY (IJASMT )

E- ISSN 2489-009X
P- ISSN 2695-1908
VOL. 8 NO. 4 2022
DOI: https://doi.org/10.56201/ijasmt.v8.no4.2022.pg26.77

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Multivariate Analysis of Some Economics Data and Crime Figures in Nigeria

EZE-Emmanuel, Peace

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Abstract

Multivariate Analysis of some Gross Domestic Product variables and crime figures in Nigeria was investigated to determine the important economic variables and crime figure variables that have positive effect on Nigerian society. This study examined the performance of these variables using yearly Crime data betwixt 2012 and 2020 and quarterly Nigerian Gross Domestic Product data from 1981Q1-2019Q1.The methods utilised are principal components analysis(PCA), factor analysis(FA) and cluster analysis(CA) multivariate techniques. Using R statistical software, the data were analysed. This research used three Rotation Methods of the Principal Components Factor Analysis to describe the variability in both data sets. Then, find the optimal number of clusters using four Clusters Identification approaches, and group variables into more homogenous groups. This research was able to identify significant differences between (None and Varimax rotation methods) and the Promax rotation methods of the Nigeria Economic variables data considered, while there are no significant differences among the three different rotation method results for crime data (i.e. large and small sample sizes). The communality and uniqueness of the factor analysis for the economic variables showed that principal components lies between 78.2% and 99.5% respectfully, of the total variability; the communality and uniqueness of the factor analysis for the large sample size (crime variables) principal components lies between 17.7% and 99.5% respectfully, of the total variability while the communality and uniqueness of the factor analysis for the small sample size (crime variables) principal components lies between 18.1% and 99.3% respectfully, of the total variability. This study was able to determine seven cluster groups for the economic variables, also seven cluster groups for the large sample size of crime rate variables and five cluster homogenous groups for the small sample size of crime

keywords:

Eigenvalues, Eigenvectors, Rotation, Cluster Analysis (CA), Factor Analysis (FA) And Principal Component Analysis (PCA)

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References:

Abdi, H. & Williams, L. J. (2010). Principal Components Analysis. Wiley Interdisciplinary
Journal of Reviews in Computational Statistics, 2(4), 433–459.
Abhishek, B. (2012). Impact of principal components analysis in the application of image
processing. International Journal of Advanced Research in Computer Science and
Software Engineering, 2(1).
Aebi, M.F., Aubusson de Cavarlay, B., Barclay, G., Gruszezynska, B., Harrendorf, B. &
Heiskanen, M. (2010). European source book of crime and criminal justice statistics (4th
edition). Den Haag: Boom Juridische Uitgevers. Available online at:
http://english.wode.nl/onderzocksdatabase/europeansourcebook-4c-editie.aspx?cp=
45&sc=6796.
Andresen, M. A. (2006). A spatial analysis of crime in Vancouver, British Columbia: A
synthesis of social disorganization and routine activity theory. Can. Geogr., 50(4), 487–
502.
Arash A., (2005). Color image processing using principal component analysis. Unpublished
Master’s Thesis, Mathematics Science Department, Sharif University of Technology,
Tehran, Iran.
Arash, A. & Shohreh, K. (2008). Colour PCA Eigen Images and their Application to
Compression and watermarking, 26(7), 0260-8858
Baumer, E.P. & Lauritsen, J.L. (2010). Reporting crime to the police: A multivariate analysis of
long-term trends in the national crime survey (NCS) and national crime victimization
survey (NCVS). Criminology, 48, 131-186.
Bocuvxa L., Vacek O. & Jehlicka J. (2005). Principal Component Analysis as a Tool to
Indicative the Origin of Potentially Toxic Elements in Soils. Geoderma, 128, 289-300.
Bosick, S., Rennison, C., Gover, A. & Dodge, M. (2012). Reporting violence to the police:
Predictos through the life course. Journal of Crime Justice, 441-451.
https://doi.org/10.1016/j.jcrimjus.2012.05.001.
Brook, J. S., Whiteman, M. & Nomura, C. (2008). Personality, family, and ecological influences
on adolescent drug use: A developmental analysis. Journal of Chemical Dependency
Treatment, 1, 123-161.
Carreira-Perpinan, M. A. (2001). Continuous latent variable models for dimensionality reduction
and sequential data reconstruction, Unpublished PhD Thesis.
Chang, Y., Cesarman, E., Pessin, M. S., Lee, F., Culpepper, J., Knowles, D. M. & Moore, P. S.
(2013). Identification of herpes virus-like DNA sequences in AIDS-associated Kaposi's
sarcoma. Science; 266, 1865–1869

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