Introduction to insect recognition, development, damage, and control, focusing on insect identification, classification, biology, and physiology. It covers the role of insects in ecosystems, emphasizing their direct benefits (e.g., honey, silk, wax) and their importance as pollinators, natural enemies of pests, scavengers, and food for other organisms. Additionally, it addresses the challenges of insects as crop pests and disease vectors.

Introduction to insect recognition, development, damage, and control, focusing on insect identification, classification, biology, and physiology. It covers the role of insects in ecosystems, emphasizing their direct benefits (e.g., honey, silk, wax) and their importance as pollinators, natural enemies of pests, scavengers, and food for other organisms. Additionally, it addresses the challenges of insects as crop pests and disease vectors.

Contact department for description.

Contact department for description.

Contact department for description.

The increasing production of descriptive data sets and corresponding software packages has created a need for data visualization methods for many application areas. Data visualization allows for informing results and presenting findings in a structured way. This course provides an introduction to graphical data analysis and data visualization. Topics covered include exploratory data analysis, data cleaning, examining features of data structures, detecting unusual data patterns, and determining trends.

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.

This course will introduce students to Bayesian data analysis, including modeling and computation. We will begin with a description of the components of a Bayesian model and analysis (including the likelihood, prior, posterior, conjugacy and credible intervals). We will then develop Bayesian approaches to models such as regression models, hierarchical models and ANOVA. Computing topics include Markov chain Monte Carlo methods. The course will have students carry out analyses using statistical programming languages and software packages.