We may earn an affiliate commission when you visit our partners.
Douglas C. Montgomery
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/o1b8su/random
Here's a deal for you
Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Get offer
What's inside
Syllabus
Unit 1: Experiments with Random Factors
Unit 2: Nested and Split-Plot Designs
Unit 3: Other Design and Analysis Topics
Read more
Syllabus
Unit 1: Experiments with Random Factors
Unit 2: Nested and Split-Plot Designs
Unit 3: Other Design and Analysis Topics
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides strong basis for experimenting with factors whose levels are chosen randomly
Examines components of variability of a measurement system
Explores designs for experiments with hard-to-change factors, such as split-plot designs
Taught by Douglas C. Montgomery, author of popular textbooks in experimental design
Covers both theoretical concepts and practical applications
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Advanced experimental design in statistics
According to the course description, this course provides an in-depth exploration of experimental designs involving random factors, focusing on their analysis and application. Learners can expect to cover modern methods for estimating variability components, essential for fields like measurement systems analysis. The curriculum also details nested and split-plot designs, which are crucial for complex experimental setups, along with an overview of nonnormal response distributions and covariates. This content is geared towards those needing to understand advanced statistical methodologies.
Overview of designs for nonnormal responses and covariates.
"I hope to get an overview of designs for experiments with nonnormal response distributions, a common challenge."
"It's valuable that covariates are also covered, broadening the applicability of design principles."
"The course provides a necessary overview of specialized design and analysis topics."
Teaches modern methods for estimating variability components.
"I need modern methods for estimating components of variability in complex systems and processes."
"This course directly addresses analysis of measurement system capability, which is highly practical."
"I expect to learn techniques for quantifying different sources of variation in my experiments accurately."
Covers complex experimental structures and their analysis.
"I anticipate gaining expertise in nested designs, vital for multi-level data structures."
"Understanding split-plot designs is essential for experiments with hard-to-change factors."
"The detailed coverage of these designs addresses critical gaps in complex research methods."
Core focus on experiments with random factors.
"I expect to learn how to design and analyze experiments where factors are chosen at random, vital for my work."
"The course's focus on measurement systems to determine their capability is exactly what I need."
"This is essential for mastering the analysis of random components and their variability."
Assumes solid foundation in advanced statistical methods.
"I expect this course requires a solid foundation in experimental design and statistical methods."
"This seems like a specialized, graduate-level course, not suitable for statistical beginners."
"I assume learners should be comfortable with statistical concepts and software before enrolling."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Random Models, Nested and Split-plot Designs with these
activities:
Review key concepts of probability and statistics
Show steps
Review the basic concepts of probability and statistics to enhance your foundational understanding necessary for this course.
Browse courses on
Probability
Show steps
-
Read textbooks or online resources on probability and statistics
-
Solve practice problems related to probability and statistics
-
Take a refresher course or participate in online discussions
Explore online tutorials on statistical software
Show steps
Gain proficiency in using statistical software to analyze and interpret data in the context of experimental design.
Browse courses on
Statistical Software
Show steps
-
Identify online tutorials or courses for statistical software
-
Follow the tutorials to learn the software's capabilities
-
Practice using the software to analyze sample datasets
Complete practice problems on experimental design
Show steps
Engage in practice drills to solidify your understanding of experimental design principles and techniques.
Browse courses on
Experimental Design
Show steps
-
Access online resources or textbooks for practice problems
-
Solve problems covering various aspects of experimental design
-
Analyze and interpret the results of your solutions
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in peer-led study groups
Show steps
Enhance your learning through collaborative discussions and knowledge exchange with peers.
Browse courses on
Experimental Design
Show steps
-
Join or create a study group with fellow students
-
Meet regularly to discuss course concepts and work on assignments
-
Share perspectives, ask questions, and provide feedback
Attend specialized workshops on experimental design
Show steps
Expand your knowledge and skills by attending workshops led by industry experts and researchers.
Browse courses on
Experimental Design
Show steps
-
Identify workshops related to experimental design
-
Register and attend the workshop
-
Actively participate in discussions and hands-on activities
Develop a presentation on a case study in experimental design
Show steps
Deepen your understanding of experimental design by analyzing and presenting a real-world case study.
Browse courses on
Experimental Design
Show steps
-
Identify a case study related to experimental design
-
Analyze the case study, focusing on the experimental design and its impact
-
Create a presentation to showcase your analysis and insights
Participate in data analysis competitions
Show steps
Test and refine your skills by applying experimental design and data analysis techniques in competitive settings.
Browse courses on
Data Analysis
Show steps
-
Identify data analysis competitions related to experimental design
-
Form a team or work independently to participate in the competition
-
Apply experimental design principles and data analysis techniques to solve the competition's challenges
Review key concepts of probability and statistics
Show steps
Review the basic concepts of probability and statistics to enhance your foundational understanding necessary for this course.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Read textbooks or online resources on probability and statistics
- Solve practice problems related to probability and statistics
- Take a refresher course or participate in online discussions
Explore online tutorials on statistical software
Show steps
Gain proficiency in using statistical software to analyze and interpret data in the context of experimental design.
Browse courses on
Statistical Software
Show steps
Show steps
Show steps
- Identify online tutorials or courses for statistical software
- Follow the tutorials to learn the software's capabilities
- Practice using the software to analyze sample datasets
Complete practice problems on experimental design
Show steps
Engage in practice drills to solidify your understanding of experimental design principles and techniques.
Browse courses on
Experimental Design
Show steps
Show steps
Show steps
- Access online resources or textbooks for practice problems
- Solve problems covering various aspects of experimental design
- Analyze and interpret the results of your solutions
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in peer-led study groups
Show steps
Enhance your learning through collaborative discussions and knowledge exchange with peers.
Browse courses on
Experimental Design
Show steps
Show steps
Show steps
- Join or create a study group with fellow students
- Meet regularly to discuss course concepts and work on assignments
- Share perspectives, ask questions, and provide feedback
Attend specialized workshops on experimental design
Show steps
Expand your knowledge and skills by attending workshops led by industry experts and researchers.
Browse courses on
Experimental Design
Show steps
Show steps
Show steps
- Identify workshops related to experimental design
- Register and attend the workshop
- Actively participate in discussions and hands-on activities
Develop a presentation on a case study in experimental design
Show steps
Deepen your understanding of experimental design by analyzing and presenting a real-world case study.
Browse courses on
Experimental Design
Show steps
Show steps
Show steps
- Identify a case study related to experimental design
- Analyze the case study, focusing on the experimental design and its impact
- Create a presentation to showcase your analysis and insights
Participate in data analysis competitions
Show steps
Test and refine your skills by applying experimental design and data analysis techniques in competitive settings.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify data analysis competitions related to experimental design
- Form a team or work independently to participate in the competition
- Apply experimental design principles and data analysis techniques to solve the competition's challenges
Career center
Learners who complete Random Models, Nested and Split-plot Designs will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians apply mathematical and statistical techniques to a variety of fields, including business, economics, and healthcare. They use their skills to analyze data, identify trends, and make predictions. The course **Random Models, Nested and Split-plot Designs** can be extremely helpful to aspiring statisticians as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all essential concepts for statisticians to understand.
Data Scientist
Data Scientist
Data scientists use their skills in programming, mathematics, and statistics to extract insights from data. They work in a variety of industries, including finance, healthcare, and retail. The course **Random Models, Nested and Split-plot Designs** can be helpful to aspiring data scientists as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of data scientists.
Market Researcher
Market Researcher
Market researchers use their skills in statistics and data analysis to understand consumer behavior. They work in a variety of industries, including marketing, advertising, and public relations. The course **Random Models, Nested and Split-plot Designs** could be helpful to aspiring market researchers as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of market researchers.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use their skills in mathematics, statistics, and computer science to solve problems in a variety of industries, including manufacturing, logistics, and healthcare. They use their skills to develop models and simulations to improve efficiency and productivity. The course **Random Models, Nested and Split-plot Designs** may be helpful to aspiring operations research analysts as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of operations research analysts.
Actuary
Actuary
Actuaries use their skills in mathematics, statistics, and computer science to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. The course **Random Models, Nested and Split-plot Designs** could be helpful to aspiring actuaries as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of actuaries.
Biostatistician
Biostatistician
Biostatisticians use their skills in statistics and data analysis to solve problems in the field of healthcare. They work in a variety of settings, including hospitals, universities, and government agencies. The course **Random Models, Nested and Split-plot Designs** may be helpful to aspiring biostatisticians as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of biostatisticians.
Financial Analyst
Financial Analyst
Financial analysts use their skills in mathematics, statistics, and finance to analyze and evaluate financial data. They work in a variety of industries, including investment banking, asset management, and corporate finance. The course **Random Models, Nested and Split-plot Designs** may be helpful to aspiring financial analysts as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of financial analysts.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use their skills in mathematics, statistics, and computer science to develop and implement models for financial trading. They work in a variety of industries, including investment banking, asset management, and hedge funds. The course **Random Models, Nested and Split-plot Designs** may be helpful to aspiring quantitative analysts as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of quantitative analysts.
Software Engineer
Software Engineer
Software engineers use their skills in computer science to design, develop, and maintain software applications. They work in a variety of industries, including technology, finance, and healthcare. The course **Random Models, Nested and Split-plot Designs** could be helpful to aspiring software engineers as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of software engineers.
Data Analyst
Data Analyst
Data analysts use their skills in statistics and data analysis to extract insights from data. They work in a variety of industries, including business, healthcare, and finance. The course **Random Models, Nested and Split-plot Designs** may be helpful to aspiring data analysts as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of data analysts.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers use their skills in computer science, mathematics, and statistics to develop and implement machine learning models. They work in a variety of industries, including technology, healthcare, and finance. The course **Random Models, Nested and Split-plot Designs** may be helpful to aspiring machine learning engineers as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of machine learning engineers.
Research Scientist
Research Scientist
Research scientists use their skills in science, engineering, and mathematics to conduct research and develop new technologies. They work in a variety of industries, including healthcare, technology, and manufacturing. The course **Random Models, Nested and Split-plot Designs** may be helpful to aspiring research scientists as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of research scientists.
Economist
Economist
Economists use their skills in mathematics, statistics, and economics to analyze economic data and develop economic models. They work in a variety of industries, including government, academia, and business. The course **Random Models, Nested and Split-plot Designs** could be helpful to aspiring economists as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of economists.
Operations Manager
Operations Manager
Operations managers use their skills in business, management, and operations to oversee the day-to-day operations of an organization. They work in a variety of industries, including manufacturing, retail, and healthcare. The course **Random Models, Nested and Split-plot Designs** could be helpful to aspiring operations managers as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of operations managers.
Project Manager
Project Manager
Project managers use their skills in project management to plan, execute, and close projects. They work in a variety of industries, including technology, construction, and healthcare. The course **Random Models, Nested and Split-plot Designs** could be helpful to aspiring project managers as it provides a foundation in experimental design and data analysis. The course covers topics such as random factors, nested designs, and split-plot designs, which are all relevant to the work of project managers.
For more career information including salaries, visit:
OpenCourser.com/course/o1b8su/random
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Random Models, Nested and Split-plot Designs.
Provides a more advanced treatment of experimental design and analysis, including coverage of nested and split-plot designs. It would be a valuable reference for students who want to learn more about these topics.
Provides a comprehensive treatment of measurement systems analysis, including coverage of experimental design and analysis. It would be a valuable reference for students who want to learn more about this topic.
Provides a comprehensive treatment of experiments with mixtures. It would be a valuable reference for students who want to learn more about this topic.
Provides a comprehensive treatment of response surface methodology. It would be a valuable reference for students who want to learn more about this topic.
Provides a comprehensive treatment of design of experiments. It would be a valuable reference for students who want to learn more about this topic.
Provides a comprehensive treatment of statistical methods for quality improvement. It would be a valuable reference for students who want to learn more about this topic.
Provides a comprehensive treatment of experiments with nonnormal distributions. It would be a valuable reference for students who want to learn more about this topic.
Provides a comprehensive treatment of statistical methods for the analysis of repeated measurements. It would be a valuable reference for students who want to learn more about this topic.
Provides a comprehensive treatment of statistical methods for longitudinal data analysis. It would be a valuable reference for students who want to learn more about this topic.
Provides a comprehensive treatment of analysis of variance for experimental designs. It would be a valuable reference for students who want to learn more about this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/o1b8su/random
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Effort
3 weeks of study, 4-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Arizona State University
Instructor
Douglas C. Montgomery
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
This course belongs to a
collection
called
Design of Experiments. It's comprised of four courses:
- Factorial and Fractional Factorial Designs
- Experimental Design Basics
- Random Models, Nested and Split-plot Designs (viewing)
- Response Surfaces, Mixtures, and Model Building
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser