## Design and Analysis of Experiments DoE

### What you’ll learn

Fundamentals of the Design of Experiments (DoE) using simple to understand examples.

Screening (Plackett-Burman), Modelling (Full, Fractional Factorial and Split Plot Designs) and Optimizing (Central Composite Design) Designs

A quick refresher on ANOVA and Regression Analysis is also included to help you understand the results of analysis clearly.

Clear understanding of Blocking, Analysis of Covariance, Replication, Confounding and Design Resolutions.

### Requirements

Basic understanding of statistics is preferred, however we will cover the concepts required to understand DoE and interpret the alaysis results.

### Description

The design of experiments is a systematic approach of studying the relationship between various inputs (factors) on the key output (response).This is the basics to the intermediate level course. In this course, we start with a basic understanding of the Design of Experiments (DoE) process by performing manual calculations on simpler processes.Because this course will be taken by students from various sectors, we have kept the case studies simpler by using examples such as coffee tasting and catapult. These simple examples will help student focus on the concepts rather than focusing on the specific case studies.This course assumes that you do not have any prior knowledge of the Design of Experiments, but you do have a basic understanding of statistics principles, such as ANOVA and Regression. However, we will review these two topics (ANOVA and Regression) to provide adequate knowledge to interpret the DoE results.The course consists of video lectures, readings, and quizzes that help build upon each other so that by the end of the course, you have gained a firm grasp of the topics covered.Topics Covered:Section 1. Basics of Design of Experiments: We will start this course by understanding the definitions of common terms used in DoE. You will clearly understand factorial and partial factorial designs, as these will be explained by using an example of coffee tasting. In addition, we will also use the catapult experiment to understand the variation in processes.Other concepts that are covered in this section include: Blocking, Analysis of Covariance, Replication, Confounding and Design Resolutions.This section will set a strong foundation for you to understand foundational concepts.Section 2. ANOVA and Regression: Even though it is expected that you have some basic understanding of these concepts, we will still cover these two topics to provide you with sufficient knowledge to interpret the results of an experiment.Section 3. Screening, Modelling and Optimizing: In this section, we will cover three main milestones in any designed experiment. For screening, we will use Plackett-Burman Design to reduce the number of factors to be studied. In modelling, we will use full factorial, fractional factorial and Split Plot Designs (for Hard to Change Factors). In the last, we will optimize the process, and for that, we will use Central Composite Design (CCD).

### Overview

Section 1: Foundation of Design of Experiments (Definitions and Basic Concepts)

Lecture 1 1a Introduction to Design of Experiments

Lecture 2 1b Dependent and Independent Variables

Lecture 3 1c Purpose of DoE

Lecture 4 1d Stages of DoE

Lecture 5 1e Factor, Level and Treatment

Lecture 6 1f Two Factors Two Levels Experiment

Lecture 7 1g Plots for Two Factors Two Levels Experiment

Lecture 8 1h Regression Equation for Two Factors Two Levels Experiment

Lecture 9 1i Minitab Demonstration: Two Factors Two Levels Experiment

Lecture 10 1j Two Factors Two Levels with Interaction

Lecture 11 1k Regression Equation for for 2×2 Experiment with Interaction

Lecture 12 1l Minitab Demo 2×2 Experiment with Interactions

Lecture 13 1m Catapult Experiment with 2 Factors

Lecture 14 1n Noise Factors Three Types

Lecture 15 1o Blocking

Lecture 16 1p Analysis of Covariance

Lecture 17 1q Replication and Repetition

Lecture 18 1r Catapult Experiment with 2 Replications

Lecture 19 1s Minitab Demo – Catapult Experiment with 2 Replications

Lecture 20 1t Adding the Third Factor

Lecture 21 1u Three Factors Regression Equation

Lecture 22 1v Results of Three Factors Experiment

Lecture 23 1w Minitab Demo – Three Factors Experiment

Lecture 24 1x Three Factors Experiment with Center Point

Lecture 25 1y Minitab Demo Experiment with Center Point

Lecture 26 1z Partial Factorial Design Introduction

Lecture 27 1z1 Confounding in Partial Factorial Design

Lecture 28 1z2 Design Resolution

Section 2: Understanding ANOVA and Regression (A Quick Overview)

Lecture 29 2a ANOVA

Lecture 30 2b Regression Getting Started

Lecture 31 2c Assumptions in Regression

Lecture 32 2d Comparing Models

Lecture 33 2e R Square Predicted

Lecture 34 2f Model Simplification

Section 3: Screening, Modelling and Optimizing Designs

Lecture 35 3a Screening Designs

Lecture 36 3b Plackett Burman Design Demonstration

Lecture 37 4a Types of Factorial Designs

Lecture 38 4b Split Plot Design

Lecture 39 4c Minitab Demo – Split Plot Design

Lecture 40 4d Catapult Full Factorial Design

Lecture 41 4e Minitab Demo – Catapult Full Factorial Design – Part 1

Lecture 42 4f Minitab Demo – Catapult Full Factorial Design – Part 2

Lecture 43 5a Response Surface Designs

Lecture 44 5b Central Composite Design

Lecture 45 5c Minitab Demo – Central Composite Design

Section 4: Resources

Lecture 46 Course Slides

Section 5: BONUS

Lecture 47 BONUS LECTURE

Quality Engineers,Quality Managers,All Engineers,Performance Improvement Professionals,Any one who wants to understand the behaviour of a complex process to achieve desired outcome

#### Course Information:

Udemy | English | 5h 4m | 2.02 GB

Created by: Sandeep Kumar, Quality Gurus Inc.

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