MULTIVARIATE TECHNIQUES IN CRIME DATA ANALYSIS: AN ASSESSMENT OF UTILIZED AND ALTERNATIVE STATISTICAL METHODS

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dc.contributor.authorOsi, Abdulhameed A.
dc.date.accessioned2015-05-20T12:22:32Z
dc.date.available2015-05-20T12:22:32Z
dc.date.issued2015-05-20
dc.descriptionA THESIS SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTERS DEGREE IN STATISTICSen_US
dc.description.abstractThe scope of crime and the concern for its prevention/control has grown considerably in the last few years in Nigeria. Therefore, how to discover the variables which have salience in affecting crime rate has become crucial. In this thesis, some of Multivariate Statistical Techniques were utilized on the crime data of Nigeria. Specifically, Principal Component Analysis (PCA) is used to discover the distinct influential variables in the identification of State with high or low crime rate; evaluates and compares the performances of five different Classification Techniques (i.e., Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbour Analysis (KNN), Classification Trees (CT) and Logistic Discriminant Analysis (LgDA)) in the classification of States of Nigeria as high and low crime rate (unsafe and safe). Each method has unique assumptions about the data, so each may be appropriate for different situations. The results show that four Principal components have been retained using both scree plot and Kaiser’s criterion which accounted for 75.024% of the total variation. QDA had the best overall classification performance by classifying 100% of the States correctly, followed by LDA which had only 13.9% apparent error rate. LgDA is recommended to be used when QDA assumption failed while CT are the recommended alternative when LDA’s assumptions are not met. Though CT’s performance is likely lower than that of LDA, it offers many advantages that make it a useful method, such as its lack of data assumptions.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/6442
dc.language.isoenen_US
dc.subjectMULTIVARIATE,en_US
dc.subjectTECHNIQUES,en_US
dc.subjectCRIME,en_US
dc.subjectDATA ANALYSIS:,en_US
dc.subjectASSESSMENT,en_US
dc.subjectUTILIZED,en_US
dc.subjectALTERNATIVE,en_US
dc.subjectSTATISTICAL,en_US
dc.subjectMETHODS.en_US
dc.titleMULTIVARIATE TECHNIQUES IN CRIME DATA ANALYSIS: AN ASSESSMENT OF UTILIZED AND ALTERNATIVE STATISTICAL METHODSen_US
dc.typeThesisen_US
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