Professor, Statistics, College of Engineering and Computing
Building: Nguyen Engineering Building
Mail Stop: 4A7
Signature statistics sleuth. Linda J. Davis uses her skill and interest in categorical data to propel innovations in handwriting analysis for forensic science. Davis has co-authored many articles for publications including Forensic Science International, Journal of Forensic Sciences, Law, Probability, and Risk, and a special issue of Science and Justice for the 5th Triennial Conference of the European Academy of Forensic Science. Davis teaches both undergraduate and graduate courses in applied statistics. Her research interest is in categorical data analysis, and that research seeks to find better ways of grouping and sampling handwriting samples for evidence discovery. Davis challenges graduate-level students to explore advanced techniques in analyzing non-parametric statistics, interpretation and generation of SAS statistical graphics, and case studies in data analysis.
2010 - 2014 : Quantifying the Effects of Database Size and Sample Quality on Measures of Individualization Validity and Accuracy in Forensics. Funded by National Institute of Justice.
Categorical Data Analysis
- PhD, Statistics, Rutgers University