Design and Analysis of Experiments | DoE
What you'll learn
- Fundamentals of the Design of Experiments (DoE).
- Basic concepts of hypothesis testing, analysis of variance and mean comparison.
- Factorial designs, single-replicate designs, blocking and confounding, fractional designs.
- How to present the final results (bar charts, contour plots, tables) and how to interpret them.
Requirements
- No specific pre-requisite is needed.
Description
This course covers the fundamentals of the design and analysis of experiments (DoE).
Experimentation plays an important role in science, technology, product design and formulation, commercialization, and process improvement. A well-designed experiment is essential once the results and conclusions that can be drawn from the experiment depend on the way the data is collected.
This course is about planning and conducting experiments and about analysing the resulting data in a way that valid and objective conclusions are obtained.
The course begins with some basic statistics concepts to understand the fundamentals of hypothesis testing and analysis of variance. Then we introduce the idea of factorial designs, with the definition of effects and interactions between factors. The following sections will focus on the widely used 2-level factorial designs. We will cover full homogeneous designs, blocked designs, and fractional designs. The whole course is illustrated with practical examples to help with understanding.
The course focuses on the understanding of the principles used in the design of experiments and on the critical analysis and discussion of the results.
The analysis of the data will use MS Excel and R-Studio. Although this is not an R course, even students that are not familiar with R can enrol in it. The R codes used can be downloaded, the functions will be briefly explained, and the codes can be easily adapted to analyse the student’s own data.
Any person who performs experiments will benefit from this course.
By the end of this course, the student will be able to:
- Choose the most suitable experimental design;
- Analyse the experimental data with confidence;
- Present and discuss the results based on charts, contour plots, and tables.
Who this course is for:
- Master and Ph.D. students;
- Researchers;
- Engineers;
- Undergraduate students who need to run and analyze experimental data;
- Anyone who performs and analyze experiments.
Course content
- Preview06:35
- Preview03:53
- 01:21Basic Statistical Concepts
- 02:17Sampling and Sample Properties
- 04:28Hypothesis Testing: t-Test and F-Test
- Preview04:44
Instructor
![Rosane Rech](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.6 Instructor Rating
- 383 Reviews
- 2,003 Students
- 2 Courses
Hi there,
My name is Rosane Rech.
I am a Chemical Engineer with a Ph.D. in Molecular and Cellular Biology. During the last 25 years, I have been developing research on bioprocess, food and chemical engineering.
During this time, I became passionate about going over data, statistical analysis, and data visualisation. I have helped dozens of people in designing and analysing their experiments and interpreting experimental results.
I have 19 years of experience in post-secondary teaching, including eight years of teaching “Statistics and Experimental Design.”
In my classes, I always strive to uncomplicate complex subjects in easy logical steps. I truly believe that students must understand why, not only how.
I hope you enjoy my courses and get what you were expecting from them!