Udemy - Advanced Lean Six Sigma

What you'll learn
Advanced Tools of Lean Six Sigma
Control Charts
Continuous Data Analysis
Descrete Data Analysis
Hypothesis testing
Linear Regression
Requirements
Six Sigma Fundamentals, General Understanding of statistics
Description
In this course we will:Review six sigma tools such as PDCA, DMAIC, and SIPOCReview statistics, sampling, and how to use excel to do statistical analysisWe will learn аbout:Measurement system analysis and attribute agreement analysisThe baseline of process performance for discrete data including Yield, DPU, DPMOStatistical process control and control charts, ImR, XbarR, and XbarS charts.Statistical process control charts analysis and how to identify if a process has an issue.The baseline for continuous data and continuous data analyses, sampling distribution, standard error and coefficient of variation, and Z-score, and T-scoresHypothesis testing, confidence interval and central limit theorem, one-sample z-test, t-test, and ANOVALinear regression, covariance, and correlation, use excel for linear regression analysisAt the end of this course, we will complete the Lean Six Sigma project togetherThis course requires you to be familiar with key concepts of quality and lean six sigma as well as some basic knowledge of statistics.This course includes multiple examples. Every time a new concept is introduced, I provide real-life examples and how to apply the new concept.I will walk you through difficult statistics concepts and will teach you how to use statistical tools and interpret the results of the analysis.We will use excel, do to all the heavy lifting for us, and our job will be to learn how to apply the tools and understand the results.I am including all templates and supporting materials and a lot of useful tables that are needed for the analysis, Templates, and tables will be very helpful for you to get started with your lean six sigma project.
Overview
Section 1: Introduction
Lecture 1 Course outline
Section 2: Introduction
Lecture 2 Sig Sigma tools review
Lecture 3 Intorduction to statistics
Lecture 4 2.3 Introduction to sampling
Lecture 5 2.4 Excel Statistics Introduction
Section 3: 3 Measurement System Analysis
Lecture 6 3.1 Measurement System Analysis Introduction
Lecture 7 3.2 Components of Measuring system
Lecture 8 3.3 MSA Example
Lecture 9 3.4 MSA Attribute agreement
Section 4: 4 Baseline and Process Performance
Lecture 10 4.1 Baseline
Lecture 11 4.2 Yield
Lecture 12 4.3 DPU AND DPMO
Section 5: 5 Statistical Process Control
Lecture 13 5.1 Statistical Process Control
Lecture 14 5.2 Individual and Moving Range Control Charts
Lecture 15 5.3 XbarR Charts
Lecture 16 5.4 XbarS Charts
Lecture 17 5.5 Chart Analysis
Section 6: 6 Baseline - Continuous Data
Lecture 18 6.1 Continuous data analysis
Lecture 19 6.2 Normal distribution and Z score
Lecture 20 6.3 Z Score
Section 7: 7 Hypothesis testing
Lecture 21 7.1 Confidence interval
Lecture 22 7.2 Central Limit Theorem
Lecture 23 7.3 Hypothesis Testing
Lecture 24 7.4 Hypothesis Testing in Excel
Lecture 25 7.5 Thought experiment
Lecture 26 7.6 ANOVA
Section 8: 8 Linear Regression
Lecture 27 8.1 Covariance & Correlation coefficient
Lecture 28 8.2 Regression
Lecture 29 8.3 Regression in Excel
Section 9: 9 Project
Lecture 30 9.1 Review of the key concepts
Lecture 31 9.2 Project review
Section 10: Final Word
Lecture 32 Final Word
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Homepage

https://www.udemy.com/course/advanced-lean-six-sigma/