Random Models, Nested and Split-plot Designs
- 4.6
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Course Summary
Learn how to design and analyze random models and nested split-plot designs in this advanced statistics course.Key Learning Points
- Understand the principles and assumptions of random models and nested split-plot designs.
- Learn how to analyze data using mixed-effects models and ANOVA.
- Explore real-world applications of random models and nested split-plot designs.
Related Topics for further study
Learning Outcomes
- Ability to design and analyze random models and nested split-plot designs
- Understanding of mixed-effects models and ANOVA
- Application of statistical models in real-world scenarios
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and probability
- Familiarity with R programming language
Course Difficulty Level
AdvancedCourse Format
- Online self-paced course
- Video lectures
- Quizzes and assignments
Similar Courses
- Design and Analysis of Experiments
- Regression Models
Related Education Paths
Notable People in This Field
- Andrew Gelman
- Bradley Efron
Related Books
Description
Many experiments involve factors whose levels are chosen at random. A well-know situation is the study of measurement systems to determine their capability. This course presents the design and analysis of these types of experiments, including modern methods for estimating the components of variability in these systems. The course also covers experiments with nested factors, and experiments with hard-to-change factors that require split-plot designs. We also provide an overview of designs for experiments with response distributions from nonnormal response distributions and experiments with covariates.
Knowledge
- Design and analyze experiments where some of the factors are random
- Design and analyze experiments where there are nested factors or hard-to-change factors
- Analyze experiments with covariates
- Design and analyze experiments with nonnormal response distributions
Outline
- Unit 1: Experiments with Random Factors
- Instructor Welcome
- Course Introduction
- Estimating the Variance Components
- Maximum Likelihood Approach
- The Two-Factor Mixed Model
- Measurement System Study Example
- Course Description
- Course Textbook and Resources
- Best Practices in Online Learning (or How to Succeed in This Class)
- Project Report
- Unit 1: Introduction
- Unit 1: Concept Questions
- Exercise 1
- Unit 2: Nested and Split-Plot Designs
- Two-Stage Nested Design
- Variations of the Nested Design
- The Split-Plot Design
- Unreplicated Designs and Fractional Factorial Designs in a split-plot framework
- Purity Example
- Tensile Strength of Paper Example
- Unit 2: Introduction
- Unit 2: Concept Questions
- Exercise 2
- Unit 3: Other Design and Analysis Topics
- The Generalized Linear Model
- The Grill Defects Experiment
- The Analysis of Covariance
- Design of Experiments with Known Covariates
- Motor Oil Additive Experiment
- Consumer Product Coupons Example
- Factorial with Covariate Example
- Unit 3: Introduction
- Unit 3: Concept Questions
- Exercise 3
Summary of User Reviews
Random Models and Nested Split Plot Designs is a highly rated course on Coursera that teaches learners about experimental design and data analysis. Many users appreciate the course's practical applications and clear explanations.Key Aspect Users Liked About This Course
The course is praised for its emphasis on real-world scenarios and hands-on exercises.Pros from User Reviews
- Clear explanations of complex topics
- Practical applications for experimental design and data analysis
- Engaging and interactive course materials
- Great instructor support and feedback
- Flexible scheduling and self-paced learning
Cons from User Reviews
- Some users found the course content to be too basic or simplistic
- Limited opportunities for in-depth discussion or peer collaboration
- Occasional technical issues with the online platform or course materials
- Not suitable for learners with little background in statistics or mathematics
- Some users felt that the course could benefit from more interactive elements or multimedia resources
Douglas C. Montgomery
4.6 Raiting
Comprehensive Linear Modeling with R
- 3.9
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Learn to model with R: ANOVA, regression, GLMs, survival analysis, GAMs, mixed-effects, split-plot and nested designs The course showcases the use of R Commander in performing these tasks....
Random Models, Nested and Split-plot Designs
- 4.6
-
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Many experiments involve factors whose levels are chosen at random. A well-know situation is the study of measurement systems to determine their capability....
