INTRODUCTORY APPLIED STATISTICS FOR THE LIFE SCIENCES

Name: INTRODUCTORY APPLIED STATISTICS FOR THE LIFE SCIENCES
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Rating: 3.9 (17104 reviews)

STAT 371

과목 설명

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.

선수과목

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

충족 요건

BOTANY/PLPATH 563 , SOILSCI 585 , SOC 351 , FOODSCI 412 , KINES 330 , ISYE/PSYCH 349 , ISYE 320 , STAT 324 , ISYE 315 , BMI/COMPSCI 567 , CNSRSCI 201 , STAT 303 , ECON/FINANCE 300 , F&WECOL 458 , STAT 333 , PLPATH 315 , STAT 301 , SOC 674 , AAE/ECON 421 , PSYCH 225 , PUBAFFR 340 , C&ESOC/SOC 618 , F&WECOL/LANDARC/ZOOLOGY 565 , COMPSCI 566 , ME/STAT 424 , MATH/STAT 310 , PLPATH 602 , and EDPSYCH 360

학점

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미보고

평점

2.89

-7.1% 과거 데이터 대비

수료율

92.68%

-3.11% 과거 데이터 대비

A 비율

28.83%

-12.59% 과거 데이터 대비

학급 규모

437

-8.02% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

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유사 과목

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.

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.

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.