Experimentation for Improvement

  • 4.8
Approx. 13 hours to complete

Course Summary

Learn the fundamentals of experimentation, including hypothesis testing, causal inference, and A/B testing. This course covers statistical concepts such as sample size, significance, and power, and teaches you how to design and analyze experiments that can help you make better decisions.

Key Learning Points

  • Understand the key concepts of experimentation
  • Learn how to design and analyze experiments
  • Gain practical skills in hypothesis testing and causal inference

Related Topics for further study


Learning Outcomes

  • Design and analyze experiments to make better decisions
  • Understand statistical concepts such as sample size, significance, and power
  • Gain practical skills in hypothesis testing and causal inference

Prerequisites or good to have knowledge before taking this course

  • Basic knowledge of statistics
  • Familiarity with spreadsheet software

Course Difficulty Level

Intermediate

Course Format

  • Self-paced
  • Video lectures
  • Quizzes

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Related Education Paths


Related Books

Description

We are always using experiments to improve our lives, our community, and our work. Are you doing it efficiently? Or are you (incorrectly) changing one thing at a time and hoping for the best?

Outline

  • Introduction
  • Promotional video for this course
  • 1A: Why experiments are so important
  • 1B: Some basic terminology
  • 1C: Analysis of your first experiment
  • 1D: How NOT to run an experiment
  • Materials for this section
  • Ungraded practice quiz 1
  • Module 1 quiz
  • Analysis of experiments by hand
  • 2A: Analysis of experiments in two factors by hand
  • 2B: Numeric predictions from two-factor experiments
  • 2C: Two-factor experiments with interactions
  • 2D: In-depth case study: analyzing a system with 3 factors by hand
  • Enrichment: Made for you by Madeleine: an interview with Joy
  • Materials for this section
  • Ungraded practice quiz 2
  • Module 2 quiz
  • Using computer software to analyze experiments
  • 3A: Setting up the least squares model for a 2 factor experiment
  • 3B: Solving the mathematical model for a 2 factor experiment using software
  • 3C: Using computer software for a 3 factor experiment
  • 3D: Case study: a 4-factor system using computer software
  • Enrichment: Dr. Soo Chan Carusone talks about experiments in a medical context
  • Materials for this section
  • Ungraded practice quiz 3
  • Module 3 quiz
  • Getting more information, with fewer experiments
  • 4A: The trade-offs when doing half-fraction factorials
  • 4B: The technical details behind half-fractions - math warning!
  • 4C: A case study with aliasing in a fractional factorial
  • 4D: All about disturbances, why we randomize, and what covariates are
  • 4E: All about blocking
  • 4F: Introducing aliasing notation
  • 4G: Using aliasing notation to plan experiments
  • 4H: An example of an analyzing an experiment with aliasing
  • Enrichment: My colleague, David, and his student Jeff, talk about water treatment experiments
  • Materials for this section
  • Ungraded practice quiz 4: [4A,B,C,D]
  • Ungraded practice quiz [4E, 4F, 4G, 4H]
  • Module 4 quiz [4A to 4H]
  • Response surface methods (RSM) to optimize any system
  • 5A: Response surface methods (RSM): an introduction
  • 5B: Response surface methods (RSM): one variable
  • 5C: Why changing one factor at a time (OFAT) will mislead you
  • 5D: The concept of contour plots and which objectives should we maximize
  • 5E: RSM in 2 factors: introducing the case study
  • 5F: RSM case study continues: constraints and mistakes
  • 5G: RSM case study continues: approaching the optimum
  • Enrichment: An interview with Dr. Joe Kim (McMaster University)
  • Materials for this section
  • Ungraded practice quiz [5A, 5B, 5C, 5D]
  • Module 5 quiz [5A, 5B, 5C, 5D]
  • Ungraded practice quiz [5E, 5F, 5G]
  • Module 5 quiz [5E, 5F, 5G]
  • Wrap-up and future directions
  • 6: The big picture (wrapping it up, and other topics)
  • Materials for this section
  • Final survey: your feedback and comments

Summary of User Reviews

Key Aspect Users Liked About This Course

Many users found the course material to be insightful and informative.

Pros from User Reviews

  • The course is well-structured and easy to follow.
  • The instructors are knowledgeable and engaging.
  • The assignments and quizzes help reinforce the concepts taught.

Cons from User Reviews

  • Some users found the course to be too basic and not challenging enough.
  • The course may not be suitable for those with advanced knowledge in statistics.
  • The discussion forums can be overwhelming and difficult to navigate.
English
Available now
Approx. 13 hours to complete
Kevin Dunn
McMaster University
Coursera

Instructor

Kevin Dunn

  • 4.8 Raiting
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