APPLICATIONS IN ANOVA AND MIXED MODELS

Name: APPLICATIONS IN ANOVA AND MIXED MODELS
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PLANTSCI 772

과목 설명

Development of models, programs, inferences, and interpretations of analysis of variance in biological research; mixed models and their development; choosing the correct inference range; variance and covariance analyses; repeated measures; dealing with missing data.

선수과목

(F&WECOL/STAT 572 or concurrent enrollment or ANSCI 865 or concurrent enrollment) and (PLANTSCI 771 or concurrent enrollment)

충족 요건

This course does not satisfy any prerequisites.

학점

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개설 시기

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평점

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변화 계산 불가

수료율

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변화 계산 불가

A 비율

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변화 계산 불가

학급 규모

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과거 데이터 대비 변화 없음

데이터 없음

강사 (2026 Summr)

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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.

DESIGN AND ANALYSIS OF PSYCHOLOGICAL EXPERIMENTS II

PSYCH 710

Statistical power, paired-samples t-test, within-subjects ANOVA, mixed models, mediation in within-subjects designs, contrast analysis (= the analysis of categorical predictors with 3 or more levels), multilevel modeling, linear mixed-effects models, restricted maximum likelihood, signal detection theory, logistic regression, exploratory factor analysis, missing data.

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.

ENVIRONMENTAL BIOPHYSICS

ATMOCN/PLANTSCI 532

Plant-environment interactions with particular reference to energy exchanges and water relations. Models are used to provide a quantitative synthesis of information from plant physiology, soil physics, and micrometeorology with some consideration of plant-pest interactions.

DESIGN AND ANALYSIS OF BIOLOGICAL STUDIES

ANSCI 865

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.