INTRODUCTORY APPLIED STATISTICS FOR THE LIFE SCIENCES

STAT 371

Course Description

Introduction to modern statistical practice in the life sciences, using the R programming language. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.

Prerequisties

(MATH 112 and placed out of MATH 113 ), (MATH 113 and placed out of MATH 112 ), (MATH 112 and MATH 113 ), MATH 114 , MATH 171 , MATH 211 , MATH 221 , or placement in MATH 221 . Not open to students with credit for STAT 302 or STAT 324

Satisfies

SOILSCI 585 , CNSRSCI 201 , PLPATH 602 , F&WECOL 458 , ISYE 315 , F&WECOL/LANDARC/ZOOLOGY 565 , SOC 674 , MATH/STAT 310 , PSYCH 602 , PUBAFFR 340 , ISYE 320 , SOC 351 , PLPATH 315 , ISYE/PSYCH 349 , COMPSCI 566 , ME/STAT 424 , STAT 333 , FOODSCI 412 , BOTANY/PLPATH 563 , ECON/FINANCE 300 , BMI/COMPSCI 567 , KINES 330 , PSYCH 225 , STAT 324 , STAT 303 , AAE/ECON 421 , STAT 301 , and C&ESOC/SOC 618

Credits

Offered

Not Applicable

Grade Point Average

2.8

-10.1% from Historical

Completion Rate

89.79%

-6.21% from Historical

A Rate

25.26%

-23.68% from Historical

Class Size

578

21.38% from Historical

2025 Fall Grade Distribution

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

DATA SCIENCE MODELING II

STAT 340

Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation. Features the analysis of real-world data sets and the communication of findings in a clear and reproducible manner within a project setting.

INTRODUCTION TO STATISTICS FOR SCIENCE AND ENGINEERING

STAT 324

Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Utilizes the R programming language.

R FOR STATISTICS I

STAT 303

An understanding of the commonly used statistical language R. Topics will include using R to manipulate data and perform exploratory data analysis.

DATA SCIENCE MODELING I

STAT 240

Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown with applications to a wide variety of data to address open-ended questions.

STATISTICAL METHODS FOR BIOSCIENCE I

F&WECOL/STAT 571

Descriptive statistics, distributions, one- and two-sample normal inference, power, one-way ANOVA, simple linear regression, categorical data, non-parametric methods; underlying assumptions and diagnostic work.