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Design of Experiments Training | DOE Training By TONEX for Non-Engineers

The design of experiments training for non-engineers course is designed to teach you how to analyze root causes of failures using applied statistics. DOE also will provide you the ability of problem solving, designing experiments, conducting them, and analyzing the results. Through this hands-on course, we will teach you how to make analyzing complex, multilevel systems simpler without loosing the accuracy of analysis.


Since statistics and mathematical analysis is not usually taught to non-engineering majors at school, professionals with no engineering background might find it hard to conduct such analysis at work. The design of experiments for non-engineers seminar is ideal for those non-engineer individuals who need to use root cause analysis, problem-solving, optimization, and/or designing products/processes in their job, while they don’t have a very strong math background.

Experiment refers to the orderly procedure conducted under controlled conditions with the purpose of discovering an unknown, examining an assumption, or demonstrating and proving an established effect. During evaluating a process, experiments are conducted to determine the effect of each input factor on a desired result. There are several ways to design an experiment, but this doesn’t mean they all are going to be efficient and accurate. The Design of Experiment (DOE), aka Designed Experiments or Experimental Design. DOE is often used to find the best design of experiment; the one that costs the least and takes the shortest time to generate results, in other word, the most efficient and effective design. DOE often offers a design that requires little or no late engineering adjustments and reduces the complication of material and workforce.

In its simplest application, an experiment intends to anticipate the results by changing the input, reflected in a predictor. Such change often changes a second variable, called the outcome variable. DOE provides you with the most suitable predictor and outcome variables.

To give you a brief description of Design of Experiments (DOE), it is a powerful tool that helps you evaluating the effects of multiple input factors on a target response variable. The advantage of using DOE over the conventional One-Factor-At-A-Time (OFAT) method of studying various factors’ impacts on an experiment is that you can change several factors at the same time and then observe and analyze their effect on the results simultaneously. With DOE, you can study a combination of full factorial or fractional (partial) factorials at the same time.

There are four main problem categories that DOE can be used in: Comparative, Screening/Characterizing, Modeling, and Optimizing. During this seminar, you will get introduced to all of these problems and learn how to deal with them in DOE. During this 2-day workshop, we will teach you how to design and experiment, how to manipulate the input factors to get the desired results, how to set up the process windows, and how to use robust designs.

Have you always been nervous around mathematical and engineering problems? Are you worried your lack of background in engineering and statistics would affect your job? This seminar is for you. Our instructors will teach you all you need to know in the simplest and least complicated way. The design of experiments for non-engineers course will cover both the theory of designing effective experiments and a lot of hands-on practices on simulated and live processes.

Who Should Attend?

The design of experiments for non-engineers training is a 2-day course designed for the individuals who need to apply statistics in their work, yet have no engineering background. The workshop will benefit quality managers and technicians, scientists, research and development personnel, marketing and business analysts, and testing and manufacturing professionals.

What Will You Learn?

Below are the major topics this seminar will cover (more details can be found under the full course description):

  • Introduction To Design of Experiments
  • Components of Experimental Design
  • Purpose of Experimentation
  • Requirements Prior To DOE Execution
  • Comparative Experiments
  • Single Factor Experiments, ANOVA, In A Completely Randomized Design (CRD)
  • Blocking
  • Basic Factorial Designs
  • The 2^k Factorial Design
  • Confounding And Blocking in 2^k Factorial Designs
  • The Two-level Fractional Factorial Designs
  • The Three-level And Mixed-Level Factorials and Fractional Factorials
  • Response Surface Designs
  • Robust Designs
  • Experiments with Random Factors
  • Nested and Split Plot

Design of Experiments Training | DOE Training for Non-Engineers

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