The page uses Browser Access Keys to help with keyboard navigation. Click to learn moreSkip to Navigation

Different browsers use different keystrokes to activate accesskey shortcuts. Please reference the following list to use access keys on your system.

Alt and the accesskey, for Internet Explorer on Windows
Shift and Alt and the accesskey, for Firefox on Windows
Shift and Esc and the accesskey, for Windows or Mac
Ctrl and the accesskey, for the following browsers on a Mac: Internet Explorer 5.2, Safari 1.2, Firefox, Mozilla, Netscape 6+.

We use the following access keys on our gateway

n Skip to Navigation
k Accesskeys description
h Help
Cornell University    
  Nov 24, 2017
Courses of Study 2017-2018
[Add to Favorites]

BTRY 3020 - Biological Statistics II

(crosslisted) STSCI 3200  
Spring. 4 credits. Student option grading.

Forbidden Overlap: due to an overlap in content, students may receive credit for only one course in the following group: BTRY 3020, STSCI 3200 , and ILRST 2110 .
Prerequisite: BTRY 3010  or equivalent.

C. Earls.

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.

[Add to Favorites]