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DESIGN AND ANALYSIS OF BIOLOGICAL STUDIES

ANSCI 865
과목 설명

Experimental design and proper data analysis are critical processes for scientific research. Planning and performing research studies have statistical implications that influence how results are interpreted. Learn the fundamentals of generalized linear models, experimental design, and data analysis using common examples from biological studies.

선수과목

Graduate/professional standing

충족 요건
학점

미보고

개설 시기

미보고

평점
3.32

0.18% 과거 데이터 대비

수료율
100%

과거 데이터 대비 변화 없음

A 비율
32%

4% 과거 데이터 대비

학급 규모
25

-3.85% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

EXPERIMENTAL DESIGN AND ANALYSIS

PLANTSCI 771

Review of methods for controlling error in research experiments; review and in-depth development of factorial treatment designs; theory, analysis, and examples of advanced experimental designs for plant and animal research.

STATISTICAL EXPERIMENTAL DESIGN

ME/STAT 424

Introduction to statistical design and analysis of experiments. Topics include: principles of randomization, blocking and replication, randomized blocking designs, Latin square designs, full factorial and fractional factorial designs and response surface methodology. Substantial focus will be devoted to engineering applications.

EXPERIMENTAL DESIGN AND STATISTICAL METHODOLOGY

NTP 701

Application of the scientific method and experimental design, with a focus on experimental neuroscience. Topics include best practices that underlie robust and unbiased experimental approaches, methods, analyses, data interpretation and transparent reporting of results.

STATISTICAL METHODS FOR BIOSCIENCE II

F&WECOL/STAT 572

Polynomial regression, multiple regression, two-way ANOVA with and without interaction, split-plot design, subsampling, analysis of covariance, elementary sampling, introduction to bioassay.

EXPERIMENTAL DESIGN AND ANALYSIS IN BIOTECHNOLOGY

ABT 720

Principles of descriptive and inferential statistics with applications in biotechnology including experimental design, quantitative data analysis, and bioinformatic evaluation of complex molecular and biological data sets.