Master's candidates present results of their thesis research to faculty and graduate students at a specially scheduled seminar.

Selected research problems not related to a candidate's Master's thesis.

Graduate level independent project or research, unrelated to students' thesis or dissertation.

With lab. The acquisition, translocation, distribution, and function of the essential inorganic elements in plants. Genetic control of plant nutrition and ecological adaptation to nutritional variables. Diagnosis of plant nutritional disorders. Prerequisites: STOCKSCH 105 and STOCKSCH 108, and either CHEM 110 or 111 or equivalent courses.

With lab. The acquisition, translocation, distribution, and function of the essential inorganic elements in plants. Genetic control of plant nutrition and ecological adaptation to nutritional variables. Diagnosis of plant nutritional disorders. Prerequisites: STOCKSCH 105 and STOCKSCH 108, and either CHEM 110 or 111 or equivalent courses.

An overview course designed to provide First-Year students with information, opportunities, and skills to ease their transition into college and build a successful foundation necessary to reach their educational goals.

Applications of Statistics for Data Analysis using R software. There will be a project report at the end of this 1-credit enrichment colloquium.

This course will cover several workhorse models for analysis of time series data. The course will begin with a thorough and careful review of linear and general linear regression models, with a focus on model selection and uncertainty quantification. Basic time series concepts will then be introduced. Having built a strong foundation to work from, we will delve into several foundational time series models: autoregressive and vector autoregressive models.

Distribution and inference for binomial and multinomial variables with contingency tables, generalized linear models, logistic regression for binary responses, logit models for multiple response categories, loglinear models, inference for matched-pairs and correlated clustered data. Prerequisites: Previous course work in probability and mathematical statistics including knowledge of distribution theory, estimation, confidence intervals, hypothesis testing and multiple linear regression; e.g. Stat 516 and Stat 525 (or equivalent). Prior programming experience.