Random Models, Nested and Split-plot Designs (Coursera)

Random Models, Nested and Split-plot Designs (Coursera)

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

We also provide an overview of designs for experiments with response distributions from nonnormal response distributions and experiments with covariates.
What You Will Learn

  • 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

Course 4 of 4 in the Design of Experiments Specialization.

Syllabus

WEEK 1: Experiments with Random Factors
WEEK 2: Nested and Split-Plot Designs
WEEK 3: Other Design and Analysis Topics

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Aléatoire : une introduction aux probabilités - Partie 1 (Coursera) Coursera
École Polytechnique

Aléatoire : une introduction aux probabilités - Partie 1 (Coursera)

Ce cours d'introduction aux probabilités a la même contenu que le cours de tronc commun de première année de l'École polytechnique donné par Sylvie Méléard. Le cours introduit graduellement la notion de variable aléatoire et culmine avec la loi des grands nombres et le théorème de la limite centrale. Les notions mathématiques nécessaires sont introduites au fil du cours et de nombreux exercices corrigés sont proposés.

Aug 10th 2026
5-12 Weeks
Think Again III: How to Reason Inductively (Coursera) Coursera
Duke University

Think Again III: How to Reason Inductively (Coursera)

Want to solve a murder mystery? What caused your computer to fail? Who can you trust in your everyday life? In this course, you will learn how to analyze and assess five common forms of inductive arguments: generalizations from samples, applications of generalizations, inference to the best explanation, arguments from analogy, and causal reasoning. The course closes by showing how you can use probability to help make decisions of all sorts.

Aug 17th 2026
4 Weeks
A Crash Course in Causality: Inferring Causal Effects from Observational Data (Coursera) Coursera
University of Pennsylvania

A Crash Course in Causality: Inferring Causal Effects from Observational Data (Coursera)

We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment).

Aug 17th 2026
5-12 Weeks
Analysis of Algorithms (Coursera) Coursera
Princeton University

Analysis of Algorithms (Coursera)

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

Aug 3rd 2026
5-12 Weeks
Introduction to Probability and Data with R (Coursera) Coursera
Duke University

Introduction to Probability and Data with R (Coursera)

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.

Aug 3rd 2026
5-12 Weeks
Materials Data Sciences and Informatics (Coursera) Coursera
Georgia Institute of Technology

Materials Data Sciences and Informatics (Coursera)

This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts.

Aug 10th 2026
5-12 Weeks
Exploration et production de données pour les entreprises (Coursera) Coursera
University of Illinois at Urbana-Champaign

Exploration et production de données pour les entreprises (Coursera)

Ce cours fournit un cadre analytique afin de vous aider à évaluer les problèmes clés de manière structurée. Il vous procurera également des outils afin de mieux gérer les incertitudes qui envahissent et compliquent les processus des entreprises. Plus précisément, vous serez initié(e) aux statistiques et à la manière de résumer les données. Vous découvrirez les concepts de fréquence, de loi normale, d’études statistiques, de l’échantillonnage et des intervalles de confiance.

Aug 17th 2026
4 Weeks