Foundations In Statistical Decision Making

Hypothesis Testing, ANOVA, and Design and Analysis of Experiments (DOE) for the Manufacturing Professional

What you'll learn

How to conduct experiments and analyze the resulting data to help make better technical decisions about equipment, processes and measurement systems

An intermediate-level statistical tool kit aimed at the manufacturing professional

Practical examples and case studies from a manufacturing setting

Hypothesis Testing - What is it and How to apply it?

T tests, Z tests - With examples in Microsoft Excel

Design and Analysis of Experiments (DOE)

DOE terminology and techniques

ANOVA, One and Two Factor - Also with examples in Microsoft Excel

Full Factorial Experiments

Fractional Factorial Experiments

Taguchi Experimental Methods

Requirements

General understanding of manufacturing

General understanding of spreadsheets

Basic understanding of math and statistics

Desire to learn intermediate-level statistical tools

Description

Effective decision making is what separates successful manufacturing professionals from everyone else. And to make effective technical decision, you must correctly understand, analyze and interpret the data.More than hazarding a guess or using simple tools like averages and visualizations, this class will teach you a broad selection of intermediate-level statistical tools useful in solving your difficult quality, engineering and process improvement problems.Topics in Foundations in Statistical Decision Making include:The benefits and advantages of statistical experimentsHypothesis testing - where and why it's used.Error in hypothesis testingDesigning a statistical experimentT tests for meansZ tests for means and proportionsDesign and analysis of experiments (DOE)Practical tips for a successful DOEOne and two factor analysis of variance (ANOVA)Full factorial experimentsFractional factorial experimentsAn introduction to Taguchi MethodsA case study showing an L8 Taguchi experimentLots of real-life examples from manufacturingReferences for your further studyAnd MUCH moreUnlike some classes taught from a purely academic perspective with little connection to the real world, this class was designed and taught by manufacturing professionals for manufacturing professionals. By the time you are done with this course, you will have a clear understanding how to use statistical models in your work, and be prepared to continue your training onto to more advanced statistical tools.So if you're a manufacturing, quality, process or industrial engineer or manager looking to take the next step in your decision making skills, this is the class for you!!Sign up today!!

Overview

Section 1: Introduction

Lecture 1 Introduction to the Course

Lecture 2 Comments on Software

Lecture 3 Course Topics

Lecture 4 Overview of Course Topics

Lecture 5 Why Statistical Experiments

Lecture 6 Alternatives to DOE

Lecture 7 Why Hypothesis Testing

Lecture 8 The Statistical View of Data

Lecture 9 Sampling and the Hypothesis Test

Lecture 10 Errors in Hypothesis Testing

Lecture 11 Tools and Requirements of Statistical Design

Lecture 12 Tools and Requirements of Statistical Design

Lecture 13 T test Examples in Hypothesis Testing

Lecture 14 T tests in Excel

Lecture 15 Z tests in Hypothesis Testing, Pt 1

Lecture 16 Z tests in Hypothesis Testing, Pt 2

Lecture 17 More Z test Examples

Lecture 18 Z test in Excel

Lecture 19 Z tests of Proportions

Lecture 20 Conclusion to Hypothesis Testing

Lecture 21 Introduction to a DOE, Pt 1

Lecture 22 Introduction to a DOE, Pt 2

Lecture 23 DOE Terminology

Lecture 24 Tips for a Successful DOE

Lecture 25 Types of Experimental Designs

Lecture 26 Additional DOE concepts

Lecture 27 ANOVA and the F Distribution

Lecture 28 ANOVA in Excel

Lecture 29 Two-way ANOVA Overview

Lecture 30 Two-way ANOVA in Excel

Lecture 31 Full Factorial Experiments, Pt 1

Lecture 32 Full Factorial Experiments, Pt 2

Lecture 33 Fractional Factorial Designs and Taguchi Methods

Lecture 34 Taguchi Case Study, Pt 1

Lecture 35 Taguchi Case Study, Pt 2

Lecture 36 Taguchi Case Study, Pt 3

Lecture 37 Concluding Notes and References

Lecture 38 Conclusion to the Course

Lecture 39 Bonus Lecture

Industrial engineers, Manufacturing engineers,Quality engineers and quality technicians,Process engineers and process technicians,Manufacturing managers