Quality Engineering Statistics

The tools, theories, and case studies you need to understand the analytical methods of quality engineering

What you'll learn

Collecting and summarizing data including type of data, measurement scales, collection methods, visualization techniques, and descriptive statistics

Statistics and probability terminology and concepts

Statistical decision making including point estimates, confidence intervals, hypothesis testing, paired comparison tests, goodness of fit tests, ANOVA and more

Tools for examining the relationships between variables such as linear regression, correlation, and time series analysis.

Control charting: Objective and benefits, common and special causes, variable charts, attribute charts, interpreting the results, and short run SPC

Process capability analysis: Pp, Ppk, Cp, Cpk, control limits, specification limits, and interpreting actual histograms and capability indices

Design and analysis of experiments: Terminology, planning and organizing experiments, replication, balance, order and more; full and fractional factorial

All topics in the "Quantitative Methods and Tools" section of the ASQ Certified Quality Engineer Body of Knowledge

Requirements

Basic understanding of manufacturing

Basic math and spreadsheet skills

Description

This course, Quality Engineering Statistics, is the most comprehensive course of its kind on Udemy. Featuring over 100 videos, this course covers all the analytical methods you need to succeed as a quality engineer, technician or manager. Plus its analytical methods – many of which are detailed in Microsoft Excel – will also serve industrial, manufacturing and process engineers and managers very well.For those interested in preparing for the ASQ Certified Quality Engineer's exam, this course covers all topics in the "Quantitative Methods and Tools" section of their July 2022 Body of Knowledge.But for those not interested in taking a certification exam, this course covers a very wide range of topics that will certainly help advance your career as a quality professional.Topic covered include:A. Collecting and Summarizing Data 1. Types of data 2. Measurement scales 3. Data collection methods 4. Data accuracy and integrity 5. Data visualization techniques 6. Descriptive statistics 7. Graphical methods for depicting distributions B. Quantitative Concepts 1. Terminology 2. Drawing statistical conclusions 3. Probability terms and concepts C. Probability Distributions 1. Continuous distributions 2. Discrete distributions D. Statistical Decision-Making 1. Point estimates and confidence intervals 2. Hypothesis testing 3. Paired-comparison tests 4. Goodness-of-fit tests 5. Analysis of variance (ANOVA) 6. Contingency tables E. Relationships Between Variables 1. Linear regression 2. Simple linear correlation 3. Time-series analysis F. Statistical Process Control (SPC) 1. Objectives and benefits 2. Common and special causes 3. Selection of variable 4. Rational subgrouping 5. Control charts 6. Control chart analysis 7. Short-run SPC G. Process and Performance Capability 1. Process capability studies 2. Process performance vs. specifications 3. Process capability indices 4. Process performance indices H. Design and Analysis of Experiments 1. Terminology 2. Planning and organizing experiments 3. Design principles 4. Full-factorial experiments 5. Two-level fractional factorial experimentsFar more than a simple exam prep class, Quality Engineering Statistics is taught by two "hands on", senior manufacturing professionals that share DOZENS of real-life examples and case studies drawn from their decades of experience.If want to advance your analytical skill set and prepare yourself to solve increasingly complex problems in the workplace, then this is the class for you! Quality Engineering Statistics will give you teach you the skills you need to tackle the toughest problems in industry, and as a result, advance your career as a manufacturing quality professional. SIGN UP TODAY!!

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction to the Course

Lecture 2 Course Contents

Lecture 3 Comments about the Use of Software

Section 2: Section A: Collecting and Summarizing Data

Lecture 4 Data Types

Lecture 5 Data Scales

Lecture 6 Data Coding

Lecture 7 Data Integrity

Lecture 8 Introduction to Data Visualizations

Lecture 9 Stem and Leaf Diagram

Lecture 10 The Histogram in Excel

Lecture 11 Dashboards

Lecture 12 Introduction to Descriptive Statistics

Lecture 13 Graphical Depiction of Standard Deviation

Lecture 14 Measures of Dispersion in Excel

Lecture 15 The Shape of Data, Pt 1

Lecture 16 The Shape of Data, Pt 2

Lecture 17 Median, Quartiles, and IQR in Excel

Lecture 18 The Box Plot in Excel

Section 3: Section B: Quantitative Concepts

Lecture 19 Statistics and Probability Concepts, Pt 1

Lecture 20 NORM.DIST in Excel

Lecture 21 Statistics and Probability Concepts, Pt 2

Section 4: Section C: Probability Distributions

Lecture 22 Overview of Probability Distributions, Pt 1

Lecture 23 Overview of Probability Distributions, Pt 2

Lecture 24 Overview of Probability Distributions, Pt 3

Lecture 25 Poisson in Excel

Lecture 26 Using Test Statistics

Section 5: Section D: Statistical Decision Making

Lecture 27 Point Estimates

Lecture 28 Point Estimates in Excel

Lecture 29 Confidence Intervals, Pt 1

Lecture 30 Confidence Intervals, Pt 2

Lecture 31 Confidence Intervals in Excel

Lecture 32 Standard Error of the Mean

Lecture 33 Z Score and Z Table

Lecture 34 Confidence Intervals for Variance

Lecture 35 Chi Squared Coordinates

Lecture 36 Tolerance Intervals

Lecture 37 Why Hypothesis Testing

Lecture 38 The Statistical View of Data

Lecture 39 Sampling and the Hypothesis Test

Lecture 40 Errors in Hypothesis Testing

Lecture 41 Statistical Power

Lecture 42 Statistical vs Practical Significance

Lecture 43 Tools and Requirements of Statistical Design, Pt 1

Lecture 44 Tools and Requirements of Statistical Design, Pt 2

Lecture 45 T-test Examples in Hypothesis Testing

Lecture 46 T Tests in Excel

Lecture 47 Z-tests in Hypothesis Testing, Pt 1

Lecture 48 Z-tests in Hypothesis Testing, Pt 2

Lecture 49 More Z-Test Examples

Lecture 50 Z Test in Excel

Lecture 51 Z Tests of Proportions

Lecture 52 Goodness of Fit

Lecture 53 Goodness of Fit in Excel

Lecture 54 The Normal Probability Plot in Excel

Lecture 55 ANOVA and the F Distribution

Lecture 56 ANOVA in Excel

Lecture 57 Two-way ANOVA Overview

Lecture 58 Two-way ANOVA in Excel, Rev 1

Lecture 59 Contingency Tables

Section 6: Section E: Relationships Between Variables

Lecture 60 Understanding Linear Regression, Pt 1

Lecture 61 Understanding Linear Regression, Pt 2

Lecture 62 Correlation and the Coefficient of Determination

Lecture 63 Linear Regression in Excel

Lecture 64 Overview of Time Series Analysis

Section 7: Section F Statistical Process Control (SPC)

Lecture 65 Introduction to SPC Section

Lecture 66 Overview of SPC

Lecture 67 Control Chart Terminology

Lecture 68 What to Chart

Lecture 69 Variation and Subgrouping

Lecture 70 X-bar and R Chart, Pt 1

Lecture 71 X-bar and R Chart, Pt 2

Lecture 72 X-bar and R chart, Pt 3

Lecture 73 X-bar and s Chart, Pt 1

Lecture 74 X-bar and s Chart, Pt 2

Lecture 75 IXMR Chart, Pt 1

Lecture 76 IXMR Chart, Pt 2

Lecture 77 p Chart, Part 1

Lecture 78 p Chart, Part 2

Lecture 79 np Chart, Pt 1

Lecture 80 np Chart, Pt 2

Lecture 81 c Chart, Pt 1

Lecture 82 c Chart, Pt 2

Lecture 83 u Chart, Pt 1

Lecture 84 u Chart, Pt 2

Lecture 85 Interpreting Control Charts, Pt 1

Lecture 86 Interpreting Control Charts, Pt 2

Lecture 87 Interpreting Control Charts, Pt 3

Lecture 88 Control Chart Selection Process

Lecture 89 Short Run SPC

Section 8: Section G: Process and Performance Capability

Lecture 90 Introduction to Process Capability Analysis

Lecture 91 Specification Limits

Lecture 92 Sampling Frequency

Lecture 93 Understanding Capability Indices

Lecture 94 Sample Size and the Normal Distribution

Lecture 95 Arithmetic Mean

Lecture 96 The Histogram

Lecture 97 Excel's Data Analysis Add In

Lecture 98 Overview of the Normal Distribution

Lecture 99 Standard Deviation

Lecture 100 Properties of the Normal Distribution Curve

Lecture 101 Plotting the Normal Curve

Lecture 102 Pp and Ppk, Pt 1

Lecture 103 Pp and Ppk, Pt 2

Lecture 104 Pp and Ppk, Pt 3

Lecture 105 Pp and Ppk of a Sample

Lecture 106 Cp and Cpk, Pt 1

Lecture 107 Cp and Cpk, Pt 2

Lecture 108 Cp and Cpk, Pt 3

Lecture 109 Interpreting the Capability Indices

Lecture 110 The Difference Between Cpk and Ppk

Lecture 111 Summary of Process Capability

Section 9: Section H: Design and Analysis of Experiments

Lecture 112 Introduction to a DOE, Pt 1

Lecture 113 Introduction to a DOE, Pt 2

Lecture 114 DOE Terminology

Lecture 115 Tips for a Successful DOE

Lecture 116 Types pf Experimental Designs

Lecture 117 Additional DOE concepts

Lecture 118 Full Factorial Experiments, Pt 1

Lecture 119 Full Factorial Experiments, Pt 2

Lecture 120 Fractional Factorial Designs and Taguchi Methods

Section 10: Conclusion to the Course

Lecture 121 Conclusion to the Course

Lecture 122 Bonus Lecture

Quality engineers, quality technicians, quality managers,Industrial engineers, manufacturing engineers,Manufacturing managers, operations managers,Students studying for the ASQ Certified Quality Engineer exam