Design Of Experiments (Doe) With Minitab

你会学到什么

• 使用简单易懂的示例进行实验设计 (DoE) 的基础知识。
• 筛选（Plackett-Burman）、建模（完整、部分析因和裂区设计）和优化（中心复合设计）设计
• 还包括对方差分析和回归分析的快速复习，以帮助您清楚地了解分析结果。
• 清楚地了解分块、协方差分析、复制、混杂和设计解决方案。

要求

对统计学有基本的了解是首选，但是我们将涵盖理解 DoE 和解释分析结果所需的概念。

描述

实验设计是研究各种输入（因素）与关键输出（响应）之间关系的系统方法。这是中级课程的基础知识。在本课程中，我们从对实验设计 (DoE) 过程的基本理解开始，通过对更简单的过程进行手动计算。考虑到本课程将由来自各个部门的学生学习，我们通过以下方式简化了案例研究使用咖啡品尝和弹射器等示例。这些简单的示例将帮助学生专注于概念，而不是专注于具体的案例研究。本课程假定您没有任何关于实验设计的先验知识，但您对统计学原理有基本的了解，例如方差分析和回归。然而，我们将回顾这两个主题（方差分析和回归），以提供足够的知识来解释 DoE 结果。该课程包括视频讲座、阅读和测验，它们相互帮助，以便在课程结束时，您已经获得牢牢掌握所涵盖的主题。涵盖的主题：第 1 部分。实验设计基础：我们将通过理解 DoE 中常用术语的定义来开始本课程。您将清楚地理解析因设计和偏析因设计，因为这些设计将通过咖啡品尝示例进行解释。第 1 部分。实验设计基础：我们将通过理解 DoE 中常用术语的定义来开始本课程。您将清楚地理解析因设计和偏析因设计，因为这些设计将通过咖啡品尝示例进行解释。第 1 部分。实验设计基础：我们将通过理解 DoE 中常用术语的定义来开始本课程。您将清楚地理解析因设计和偏析因设计，因为这些设计将通过咖啡品尝示例进行解释。

此外，我们还将使用弹射器实验来理解过程中的变化。本节涵盖的其他概念包括：分块、协方差分析、复制、混杂和设计分辨率。本节将为您的理解打下坚实的基础基础概念。第 2 节方差分析和回归：尽管我们希望您对这些概念有一些基本的了解，但我们仍将涵盖这两个主题，以便为您提供足够的知识来解释实验结果。第 3 节筛选、建模和优化：在本节中，我们将介绍任何设计实验中的三个主要里程碑。对于筛选，我们将使用 Plackett-Burman 设计来减少要研究的因素数量。在建模中，我们将使用全阶乘，部分因子和裂区设计（对于难以改变的因素）。最后，我们将优化流程，为此，我们将使用中央复合设计 (CCD)。

概述

第 1 节：实验设计基础（定义和基本概念）

第 1 讲 1a 实验设计简介

第 2 讲 1b 因变量和自变量

第 3 课 1c DoE 的目的

第 4 课 DoE 的第 1d 阶段

第 5 讲 1e 因素、水平和治疗

第6讲1f二因素二水平实验

第 7 讲 双因素两水平实验的 1g 图

第八讲双因素两水平实验的1h回归方程

第 9 讲 1i Minitab 演示：双因素两水平实验

第10讲1j两因素两水平相互作用

第 11 课 2×2 交互实验的 1k 回归方程

第 12 课 1l Minitab 演示 2×2 交互实验

第 13 讲 1m 弹射器实验，有 2 个因素

第14讲1n噪声因子的三种类型

第 15 课 1o 阻塞

第16讲1p协方差分析

第 17 课 1q 复制和重复

第 18 讲 1r 弹射器实验，重复 2 次

第 19 课 1s Minitab 演示 – 具有 2 个重复的弹射器实验

第20讲 1t 添加第三个因素

第21讲1u三因子回归方程

第22讲三因素实验的1v结果

第 23 讲 1w Minitab 演示 – 三因素实验

第 24 讲 1x 三因素中心点试验

第 25 课 1 年 Minitab 中心点演示实验

第26讲1z偏析因设计介绍

第 27 讲 偏因子设计中的 1z1 混杂

第28讲1z2设计分辨率

第 2 部分：了解方差分析和回归（快速概述）

第29讲2a方差分析

第 30 课 2b 回归入门

第 31 课 回归中的 2c 假设

第 32 课 2d 比较模型

第 33 讲 2e R 平方预测

第34讲2f模型简化

第 3 部分：筛选、建模和优化设计

第 35 课 3a 筛选设计

第 36 课 3b Plackett Burman 设计演示

第 37 课 4a 因子设计的类型

第 38 课 4b 裂区设计

第 39 课 4c Minitab 演示 – 裂区设计

第 40 讲 4d 弹射器全因子设计

第 41 课 4e Minitab 演示 – Catapult 全因子设计 – 第 1 部分

第 42 课 4f Minitab 演示 – Catapult 全因子设计 – 第 2 部分

质量工程师、质量经理、所有工程师、绩效改进专业人员、任何想要了解复杂过程的行为以实现预期结果的人

DoE Using Minitab: From Basics to Plackett-Burman, Full/Fractional Factorial, Response Surface (CCD) Designs

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 the basic understanding of the Design of Experiments (DoE) process by performing manual calculations on simpler processes.Considering the fact that 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 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

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