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Publications in VIVO
 

Zaharatos, Brian R Senior Instructor

Positions

Research Areas research areas

Research

research overview

  • My primary interests are in applied statistics and the philosophy of statistics. Most of my work has been in statistical methods for photovoltaic (solar cell) performance modeling. I have also worked on applications for residential building energy analysis and on a consulting team that provides statistical litigation support. In addition to math and statistics, I am also interested in a number of areas in philosophy, including ethics, philosophy of science, and phenomenology.

Publications

Teaching

courses taught

  • APPM 3310 - Matrix Methods and Applications
    Primary Instructor - Spring 2018
    Introduces linear algebra and matrices with an emphasis on applications, including methods to solve systems of linear algebraic and linear ordinary differential equations. Discusses vector space concepts, decomposition theorems, and eigenvalue problems. Degree credit not granted for this course and MATH 2130 and MATH 2135.
  • APPM 4570 - Statistical Methods
    Primary Instructor - Spring 2018
    Covers basic statistical concepts with accompanying introduction to the R programming language. Topics include discrete and continuous probability laws, random variables, expectation and variance, central limit theorem, testing hypothesis and confidence intervals, linear regression analysis, simulations for validation of statistical methods and applications of methods in R. Same as APPM 5570.
  • APPM 5570 - Statistical Methods
    Primary Instructor - Spring 2018
    Covers basic statistical concepts with accompanying introduction to the R programming language. Topics include discrete and continuous probability laws, random variables, expectation and variance, central limit theorem, testing hypothesis and confidence intervals, linear regression analysis, simulations for validation of statistical methods and applications of methods in R. Same as APPM 4570.
  • APPM 6940 - Master's Degree Candidate
    Primary Instructor - Fall 2020
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  • DTSA 5011 - Modern Regression Analysis in R
    Primary Instructor - Summer 2021 / Fall 2021
    Modern Regression Analysis in R provides foundational statistical modeling tools for data science. Introduction to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse.
  • DTSA 5012 - ANOVA and Experimental Design
    Primary Instructor - Summer 2021 / Fall 2021
    Introduction to the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, provide mathematical basis for designing experiments for data science applications. Emphasis placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Attention will also be given to ethical issues raised in experimentation.
  • DTSA 5013 - Generalized Linear Models and Nonparametric Regression
    Primary Instructor - Fall 2021
    Generalized Linear Models and Nonparametric Regression teaches generalized linear models (GLMs), which provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools. Attention will also be given to ethical issues raised by using complicated statistical models.
  • MATH 4520 - Introduction to Mathematical Statistics
    Primary Instructor - Fall 2018 / Fall 2019 / Fall 2020
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Same as MATH 5520 and STAT 4520 and STAT 5520.
  • MATH 5520 - Introduction to Mathematical Statistics
    Primary Instructor - Fall 2019
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Department enforced prerequisite: one semester calculus-based probability course, such as MATH 4510 or APPM 3570. Same as STAT 4520 and MATH 4520 and STAT 5520.
  • STAT 3400 - Applied Regression
    Primary Instructor - Spring 2019
    Introduces methods, theory, and applications of linear statistical models, covering topics such as estimation, residual diagnostics, goodness of fit, transformations, and various strategies for variable selection and model comparison. Examples will be demonstrated using statistical programming language R.
  • STAT 4010 - Statistical Methods and Applications II
    Primary Instructor - Spring 2019 / Spring 2020 / Spring 2021
    Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 5010.
  • STAT 4520 - Introduction to Mathematical Statistics
    Primary Instructor - Fall 2018 / Fall 2019 / Fall 2020
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Same as STAT 5520 and MATH 4520 and MATH 5520.
  • STAT 4700 - Philosophical and Ethical Issues in Statistics
    Primary Instructor - Fall 2019 / Fall 2021
    Introduces students to philosophical issues that arise in statistical theory and practice. Topics include interpretations of probability, philosophical paradigms in statistics, inductive inference, causality, reproducible, and ethical issues arising in statistics and data analysis. Same as STAT 5700.
  • STAT 5000 - Statistical Methods and Application I
    Primary Instructor - Fall 2021
    Introduces exploratory data analysis, probability theory, statistical inference, and data modeling. Topics include discrete and continuous probability distributions, expectation, laws of large numbers, central limit theorem, statistical parameter estimation, hypothesis testing, and regression analysis. Considerable emphasis on applications in the R programming language. Same as STAT 4000.
  • STAT 5010 - Statistical Methods and Applications II
    Primary Instructor - Spring 2019 / Spring 2020 / Spring 2021
    Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 4010.
  • STAT 5520 - Introduction to Mathematical Statistics
    Primary Instructor - Fall 2018 / Fall 2019 / Fall 2020
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Department enforced prerequisite: one semester calculus-based probability course, such as MATH 4510 or APPM 3570. Same as STAT 4520 and MATH 4520 and MATH 5520.
  • STAT 5700 - Philosophical and Ethical Issues in Statistics
    Primary Instructor - Fall 2019 / Fall 2021
    Introduces students to philosophical issues that arise in statistical theory and practice. Topics include interpretations of probability, philosophical paradigms in statistics, inductive inference, causality, reproducible, and ethical issues arising in statistics and data analysis. Same as STAT 4700.

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