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Littlejohn, Ray Lynn Scholar in Residence and W. Edwards Deming Professor of Management

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Teaching

courses taught

  • EMEN 5005 - Introduction to Applied Statistical Methods
    Primary Instructor - Spring 2018 / Fall 2018 / Spring 2019 / Fall 2019 / Spring 2020 / Fall 2020 / Spring 2021
    Covers statistical reasoning and analysis in support of business and engineering decision making. Topics include: engineering and applied research, descriptive and inferential statistics to include estimation and hypothesis testing using both traditional parametric as well as nonparametric procedures for research situations involving one or two groups of treatment conditions. The R statistical analysis and programming system is used.
  • EMEN 5610 - Advanced Statistical Methods for Engineering Research
    Primary Instructor - Fall 2018 / Fall 2019 / Fall 2020
    Combines intermediate and advanced statistical methods (Two- and Three-Way ANOVA and post hoc analyses for a large variety of specific designs). Real data sets are employed permitting a focus on engineering research in support of business decision making through the integration of cost benefit analysis and process performance. Parametric as well as nonparametric methods of analysis are included.
  • EMEN 5620 - Data Mining and Screening Experiments for Engineering Research
    Primary Instructor - Spring 2018 / Spring 2019 / Spring 2020 / Spring 2021
    This advanced course focuses on: a) Regression Analyses (Simple and Multiple Linear Regression as well as Nonlinear Binomial and Multinomial Logistic Regression), b) Data Mining/Statistical Learning (Classification and Regression Decision trees) to include Neural Networks and c) Screening (aka Fractional Factorial) Experimental Designs. The R statistical analysis and programming language is used for all of the analyses in the course.
  • EMEN 5900 - Research Methods and Experimental Design
    Primary Instructor - Spring 2018 / Spring 2019 / Spring 2020 / Spring 2021
    The analytical tools used in this course involve both Parametric and Nonparametric methods for one experimental factor, with a specific intense focus on the Analysis of Variance and accompanying post hoc analysis methods. The R statistical analysis and programming language is used for all of the analyses in the course.

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