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Foundations of Statistical Decision Making
Rating: 4.4 out of 5(24 ratings)
268 students

Foundations of Statistical Decision Making

Hypothesis Testing, ANOVA, and Design and Analysis of Experiments (DOE) for the Manufacturing Professional
Last updated 5/2026
English

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

Course content

1 section44 lectures3h 31m total length
  • Introduction to the Course6:33
  • Comments on Software6:18
  • Introduction to Statistical Decision Making1:56
  • Slide Deck1:15

    Download the free pdf slide deck included with this course, featuring all slides discussed, designed with white space for notes to boost learning retention and course value.

  • Decoding Statistical Decisions1:49
  • What is Hypothesis Testing1:39
  • Sampling and the Normal Distribution3:27
  • Type I and II Errors6:48
  • Tests for Means4:28

    Compare means using t tests, z tests, and analysis of variance, and explore the F distribution, full factorials, and Taguchi fractional factorial designs.

  • Z Testing a Hypothesis10:09
  • t Testing a Hypothesis7:37
  • Commercial Software2:46

    learn how hypothesis testing and experimental design inform statistical decisions, and use Excel, Minitab, and key macros to perform t-tests and z-tests with the Analysis Toolpak.

  • t-Test in Excel6:43
  • Paired t Test in Excel, Example #15:31
  • Paired t Test in Excel, Example #24:06
  • t Tests, Additional Examples8:24
  • t Test Homework2:17
  • Z Testing a Hypothesis, Revisited5:01

    Use a z test with known sigma to test mu=15; sample mean 14.8 from 50 lines at 1% alpha yields z = -2.83, leading to rejecting the null.

  • Z Testing Example7:11

    Perform a two-tailed z-test comparing the new nylon coating tooling to the 0.0064 mm standard, using 25 samples; since z = -1.875 is within ±1.96, we accept the null.

  • Z Testing in Excel7:34
  • Another Z Test Example2:29
  • Z Tests, Additional Examples7:07
  • Z Test Homework0:51
  • Hypothesis Testing for Proportions5:25
  • Testing for Proportion Example2:00
  • Summarizing Hypothesis Testing1:00
  • Glossary of Terms2:16

    Access a downloadable glossary of terminology that consolidates key terms from the course. Responding to student feedback, it adds terms like logistic regression and Poisson distribution, with a reference pdf.

  • Understanding Statistical Experiments6:50
  • Key Terms and Their Definitions6:04
  • ANOVA and the F Distribution8:39
  • One Way ANOVA Example3:56

    The one-way ANOVA compares four groups—education, business, behavioral science, and Fine Arts—with 32 students’ percent scores on a US history test, and finds no significant difference at alpha 0.05.

  • Additional Examples of One Way ANOVA10:00
  • One Way ANOVA Homework0:49
  • Additional Examples of Two Way ANOVA8:28
  • Two Way ANOVA Homework0:34
  • Full Factorial Experimental Methods3:41
  • Taguchi Fractional Factorial Methods4:30
  • Taguchi Example12:18
  • Taguchi L4 Example in QI Macros3:54

    Taguchi L4 example shows drill speed as the significant factor in drilling quality. Drill type and lubrication do not matter, so use the cheapest drill and save lubricant.

  • Taguchi L8 Example5:41
  • Final L8 Example from Product Design5:36

    Assess a Taguchi designed, fractional factorial study of a laptop fan to reduce noise by evaluating five factors, including fan blade surface and air gap, at two levels.

  • Closing Comments0:49

    Apply t-tests, z-tests, and tests for proportions to improve decision making, and use ANOVA with full and fractional factorials to evolve processes.

  • Conclusion to the Course2:55

    Conclude the foundations of statistical decision making course by applying intermediate tools to break down data and make accurate manufacturing decisions, with lifetime access and ideas you can apply.

  • Bonus Lecture3:47

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 of Statistical Decision Making include:

  • The benefits and advantages of statistical experiments

  • Hypothesis testing - where and why it's used.

  • Error in hypothesis testing

  • Designing a statistical experiment

  • T tests for means

  • Z tests for means and proportions

  • Design and analysis of experiments (DOE)

  • Practical tips for a successful DOE

  • One and two factor analysis of variance (ANOVA)

  • Full factorial experiments

  • Fractional factorial experiments

  • An introduction to Taguchi Methods

  • A case study showing an L8 Taguchi experiment

  • Lots of real-life examples from manufacturing

  • References for your further study

  • And MUCH more

Unlike 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.

Listen to what other students have said about Foundations of Statistical Decision Making:

  • "A great introduction on how to perform design of experiments and analysis of variance." - Don M.

  • "Both speakers presented very interesting topics on ... statistical decision making for process improvement" - Jacky F.

  • "It's excellent ... exceeded my expectations" - Molas S.

When you enroll in Foundations of Statistical Decision Making, you get:

  • 3+ Hours of high quality lecture video

  • LOTS of real life examples of statistical experiments

  • 15 Excel templates detailing the statistical design techniques taught in this class.

  • The COMPLETE SET OF COURSE SLIDES

  • LIFETIME ACCESS to all course materials AND all other materials we may add later.

  • A Certificate of Completion with your name, the course's name, and the time duration of the course (useful for fulfilling the need of some CEU requirements)

  • Q&A access through the Udemy platform to a 40+ year manufacturing, quality, engineering, and business professionals.

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!!

Who this course is for:

  • Manufacturing Engineers, Quality Engineers, Process Engineers, Industrial Engineers, Product Development Engineers
  • Reliability Engineers, Test Engineers, R&D Engineers, Design Engineers, Materials Engineers, Production Supervisors
  • Operations Managers, Quality Managers, Manufacturing Managers, Engineering Managers, Continuous Improvement Managers
  • Lean Six Sigma Black Belts, Lean Six Sigma Green Belts, Technical Project Managers, Six Sigma Practitioners
  • Statistical Analysts, Validation Specialists, Regulatory Affairs Professionals, Metrologists, Calibration Specialists
  • Process Validation Engineers, Junior Engineers, Technicians