BTRY 3010

BTRY 3010

Course information provided by the Courses of Study 2017-2018.

In this course, students develop statistical methods and apply them to problems encountered in the biological and environmental sciences. Methods include data visualization, population parameter estimation, sampling, bootstrap resampling, hypothesis testing, the Normal and other probability distributions, and an introduction to linear modeling. Applied analysis is carried out in the R statistical computing environment.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: one semester of calculus.
Forbidden Overlaps Forbidden Overlap: due to an overlap in content, students will receive credit for only one course in the following group: AEM 2100, BTRY 3010, BTRY 6010, ENGRD 2700, HADM 2010, ILRST 2100, ILRST 6100, MATH 1710, PAM 2100, PAM 2101, PSYCH 3500, SOC 3010, STSCI 2100, STSCI 2150, STSCI 2200.

Distribution Category (OPHLS-AG)

  • Students will be able to design an experiment using randomization techniques.
  • Students will be able to use R Markdown for reproducible research.
  • Students will be able to produce effective graphical summaries of collected data.
  • Students will learn how sampling distributions are determined and utilized for statistical analysis.
  • Students will understand why some estimators are more desirable than others.
  • Students will be able to perform a variety of basic statistical analyses including: t-tests, ANOVA, two-sample t-tests, tests for categorical data, linear regression and multiple linear regression.
  • Students will be able to assess the quality of a statistical analysis.

View Enrollment Information

  •   Regular Academic Session.  Choose one lecture and one laboratory. Combined with: STSCI 2200

  • 4 Credits Stdnt Opt

  •  4962 BTRY 3010   LEC 001

  • Prerequisites: one semester of calculus.

  •  4963 BTRY 3010   LAB 401

  •  4964 BTRY 3010   LAB 402

  •  4965 BTRY 3010   LAB 403

  •  4966 BTRY 3010   LAB 404

  •  4967 BTRY 3010   LAB 405

  •  4995 BTRY 3010   LAB 406