BTRY 5020 - Statistics II

(crosslisted) STSCI 5201

Spring. 4 credits. Student option grading.

Forbidden Overlap: due to an overlap in content, students will receive credit for only one course in the following group: BTRY 3020, ILRST 2110, STSCI 2110, STSCI 3200.
Prerequisite: BTRY 3010 or equivalent. Basic knowledge of R programming. Co-meets with BTRY 3020/STSCI 3200.

J. Entner.

Applies linear statistical methods to quantitative problems addressed in biological and environmental research. Methods include linear regression, inference, model assumption evaluation, the likelihood approach, matrix formulation, generalized linear models, single-factor and multifactor analysis of variance (ANOVA), and a brief foray into nonlinear modeling. Carries out applied analysis in a statistical computing environment.

Outcome 1: Students will be able to design a statistical experiment using randomization techniques.

Outcome 2: Students will be able to analyze multivariate linear and nonlinear data that include quantitative and qualitative variables.

Outcome 3: Students will be able to apply generalized linear model, generalized additive models, and mixed effects models to appropriately collected data.

Outcome 4: Students will be able to formulate and evaluate parametric and nonparametric methods for determining model uncertainty.

Outcome 5: Students will be able to employ matrix methods to effectively design and implement linear models.

Outcome 6: Students will be able to assess the quality of a statistical analysis.