Design of Experiments Training Course

Module 1: Fundamentals of Experimentation and Key Terminology

• The strategy of experimentation: Comparing one-factor-at-a-time (OFAT) vs. Factorial designs
• Key principles: Randomization, Replication, and Blocking
• Defining independent (factors/treatments) and dependent (response) variables
• Types of experimental errors and how to minimize them
• Introducing the statistical model for an experiment
• Practical session: Identifying factors, responses, and potential noise variables for a process study

Module 2: Simple Comparative Experiments and Hypothesis Testing

• Review of statistical inference and the role of the t-test
• Comparing two treatment means: Independent and Paired samples t-tests
• One-Way Analysis of Variance (ANOVA) for more than two treatments
• Decomposing the total variability into treatment and error components
• Multiple comparison procedures (e.g., Tukey's HSD) for post-hoc analysis
• Practical session: Running a One-Way ANOVA and post-hoc tests to compare different formulation yields

Module 3: Introduction to Factorial Designs

• Advantages of Factorial Designs over single-factor experiments
• Main effects and interaction effects: Definition and interpretation
• General notation for Factorial Designs (a × b or k factors)
• Constructing the statistical model for a two-factor Factorial Design
• Using interaction plots to visually interpret two-way interactions
• Practical session: Analyzing a simulated two-factor experiment and calculating main and interaction effects

Module 4: The 2ᵏ Factorial Design: Screening for Effects

• Characteristics and structure of the 2ᵏ design (two levels for k factors)
• Calculating the estimated effects and sums of squares for 2ᵏ designs
• The Normal Probability Plot for identifying significant effects
• Setting up and running the experiment using a design matrix
• Using Yates' Algorithm for rapid hand calculation of effects
• Practical session: Designing a 2³ experiment and analyzing the effects using statistical software

Module 5: Blocking and Confounding in Factorial Designs

• The concept of blocking to handle nuisance variables (e.g., batches, operators)
• Randomized Block Designs (RBD) and their analysis
• Introduction to Incomplete Block Designs (IBD)
• Confounding effects in 2ᵏ designs and the need for partial replication
• Principles of designing experiments with nested factors
• Practical session: Analyzing an RBD experiment to remove the effect of a known nuisance factor

Module 6: Fractional Factorial Designs

• When and why to use Fractional Factorial Designs (2ᵏ⁻ᵖ designs)
• Defining the generating relation and the design resolution (III, IV, V)
• Understanding the concept of aliases and confounding patterns
• Selecting the appropriate fraction (1/2, 1/4) based on sparsity of effects principle
• Analysis and interpretation of highly fractionated designs
• Practical session: Setting up a 2⁵⁻² Fractional Factorial design and determining the alias structure

Module 7: Response Surface Methodology (RSM)

• Introduction to RSM for process optimization and finding optimal operating conditions
• Method of Steepest Ascent/Descent for moving toward the optimum
• Central Composite Designs (CCD) and Box-Behnken Designs
• Fitting the second-order model (quadratic terms) to the experimental data
• Using contour and surface plots to visualize the response surface
• Practical session: Designing a CCD, analyzing the second-order model, and visualizing the optimal operating region

Module 8: Analysis of Covariance (ANCOVA) in Experimental Design

• The concept of a covariate and when to use ANCOVA
• Statistical model for ANCOVA and its assumptions
• Adjusting treatment means for the effect of the continuous covariate
• Testing the homogeneity of regression slopes assumption
• Running ANCOVA and interpreting the adjusted treatment effects
• Practical session: Applying ANCOVA to a designed experiment to control for a factor that could not be blocked

Module 9: Robust Parameter Design and Taguchi Methods

• The philosophy of Robust Design: Minimizing quality variation
• Classification of factors: Control factors vs. Noise factors
• The Taguchi Loss Function and the concept of minimizing loss to society
• Using the Signal-to-Noise (S/N) Ratio as a performance measure
• Designing experiments with Inner and Outer Arrays (Product Array)
• Practical session: Analyzing a Taguchi Inner/Outer Array experiment to identify robust settings

Module 10: Case Studies and Industrial Applications of DOE

• Reviewing best practices for implementing DOE in industrial settings
• Design considerations for experiments in service and transactional processes
• Case studies in manufacturing process optimization and drug formulation
• Presenting and communicating DOE results to non-statisticians
• Introduction to modern computational approaches in experimental design
• Practical session: Reviewing a complete DOE case study (from problem definition to final recommendation) and proposing follow-up experiments

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588