Tutorials > Foundations In Statistical Decision Making

Foundations In Statistical Decision Making


Date: 24 January 2023 | Posted by: voska89
Foundations In Statistical Decision Making
Published 1/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.86 GB | Duration: 3h 14m
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


Homepage
https://www.udemy.com/course/foundations-in-statistical-decision-making/


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