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Design of Experiments for Optimisation

DoE using R: Response Surface Methodology, Lack-of-Fit, Central Composite Designs, Box-Behnken Designs
5.0
5.0/5
(2 reviews)
44 students
Created by Rosane Rech

9.6

CourseMarks Score®

9.9

Freshness

9.3

Feedback

9.1

Content

Platform: Udemy
Price: $11.99
Video: 2h 38m
Language: English
Next start: On Demand

Top Design of Experiments (DOE) courses:

Detailed Analysis

CourseMarks Score®

9.6 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.9 / 10
This course was last updated on 3/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.3 / 10
We analyzed factors such as the rating (5.0/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.1 / 10
Video Score: 7.9 / 10
The course includes 2h 38m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 9.9 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
16 resources.
0 exercise.
0 test.

Table of contents

Description

Welcome to “Design of Experiments for Optimisation”!
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 will cover the basic concepts behind the Response Surface Methodology and Experimental Designs for maximising or minimising response variables.
This is not a beginner course, so to get the most of it, you need to be familiar with some basic concepts underlying the design of experiments, such as analysis of variance and factorial designs.
You can find it in my course “Design and Analysis of Experiments” or on several other courses and resources on the market.
The course starts with a basic introduction to linear regression models and how to build regression models to fit experimental data and check the model adequacy. The next section covers experimental designs for linear models and the use of central points to check the model’s linearity (lack-of-fit). By the end of the section, we will be using linear models to fit experiments with inaccurate levels in the design factors and missing observations.
By then, we will be ready for Response Surface Methodology. We will start with a factorial design to fit a linear model and find the path of the steepest ascent. And then, we are going to use a central composite design to fit a quadratic model and find the experimental conditions that maximise the response. Moreover, we will see how to analyse several responses simultaneously using two very illustrative and broad examples.
Finally, we will see how to use three-level designs: Box-Behnken and face-centred composite designs.
The whole learning process is illustrated with real examples from researchers in the industry and in the academy.
The analysis of the data will be performed using R-Studio. Although this is not an R course, even students that are not familiar with R can enrol in it. The R codes and the data files used in the course can be downloaded, the functions will be briefly explained, and the codes can be easily adapted to analyse the student’s own data.
However, if you are already familiar with using other DoE software, feel free to download the data and reproduce the analysis using the software of your choice. The results will be exactly the same.
Any person who performs experiments can benefit from this course, mainly researchers from the academy and the industry, Master and PhD students and engineers.

Requirements

• The student must be familiar with the basic concepts of the design of experiments such as:- Analysis of variance (ANOVA)- Factorial Designs
• Concepts as designs in blocks and fractional designs also help with the understanding.

You will learn

✓ – Basic concepts of regression models;
✓ – Factorial designs with central points;
✓ – Lack-of-fit test;
✓ – Inaccurate levels in the design factors and missing observations
✓ – Response surface methodology;
✓ – Path of the steepest ascent;
✓ – Central Composite Designs;
✓ – Face-Centred Composite Designs;
✓ – Box-Behnken Designs.
✓ – Analysing several responses simultaneously.

This course is for

• Researchers;
• Graduate students;
• Engineers;
• Anyone who performs and analyse experiments.

How much does the Design of Experiments for Optimisation course cost? Is it worth it?

The course costs $11.99. And currently there is a 87% discount on the original price of the course, which was $94.99. So you save $83 if you enroll the course now.

Does the Design of Experiments for Optimisation course have a money back guarantee or refund policy?

YES, Design of Experiments for Optimisation has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Design of Experiments for Optimisation course, but there is a $83 discount from the original price ($94.99). So the current price is just $11.99.

Who is the instructor? Is Rosane Rech a SCAM or a TRUSTED instructor?

Rosane Rech has created 2 courses that got 191 reviews which are generally positive. Rosane Rech has taught 1,217 students and received a 4.4 average review out of 191 reviews. Depending on the information available, Rosane Rech is a TRUSTED instructor.

More info about the instructor, Rosane Rech

Chemical Engineer, PhD
I am a Chemical Engineer with a Ph.D. in Molecular and Cellular Biology. I have 19 years of experience in post-secondary teaching, including eight years of teaching “Statistics and Experimental Design.”I am passionate about going over experimental data, and I have helped dozens of people in designing and analyzing their experiments and interpreting experimental results.I have been teaching my whole life. 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!

9.6

CourseMarks Score®

9.9

Freshness

9.3

Feedback

9.1

Content

Platform: Udemy
Price: $11.99
Video: 2h 38m
Language: English
Next start: On Demand

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