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QUALITY TRAINER – Part of the Minitab Education Hub

Blended Learning Paths Outline

A comprehensive statistics course brought to you by experts in quality improvement.

Quality Trainer was developed by seasoned statisticians with more than 150 years of industry experience. The Learning Paths guide you through over 35 exercises using Minitab Statistical Software to solve real-world quality improvement challenges.

Learning Path 1: Foundations of Data Analysis

Descriptive Statistics and Graphical Analysis

  • Types of Data
  • Using Graphs to Analyze Data
  • Using Statistics to Analyze Data

Analysis of Variance (ANOVA)

  • Fundamentals of ANOVA
  • One-Way ANOVA
  • Two-Way ANOVA

Statistical Inference

  • Fundamentals of Statistical Inference
  • Sampling Distributions
  • Normal Distribution

Correlation and Regression

  • Relationship Between Two Quantitative Variables
  • Simple Regression
  • Trend Analysis in Time Series Primer

Hypothesis Tests and Confidence Intervals

  • Confidence Intervals for Population Parameters Primer
  • Tests and Confidence Intervals
  • 1-Sample t-Test
  • 2 Variances Test
  • 2-Sample t-Test
  • Paired t-Test
  • 1 Proportion Test
  • 2 Proportions Test
  • Chi-Square Test

Learning Path 2: Statistical Quality Analysis

Control Charts

  • Phase 1 and 2 Control Charts Primer
  • Statistical Process Control
  • Control Charts for Variables Data in Subgroups
  • Control Charts for Individual Observations
  • Control Charts for Attributes Data

Measurement Systems Analysis

  • Fundamentals of Measurement Systems Analysis
  • Repeatability and Reproducibility
  • Graphical Analysis of a Gage R&R Study
  • Variation
  • ANOVA with a Gage R&R Study
  • Gage Linearity and Bias Study
  • Attribute Agreement Analysis

Process Capability

  • Process Capability for Normal Data
  • Capability Indices
  • Process Capability for Nonnormal Data

Learning Path 3: Design of Experiments

Analysis of Variance (ANOVA)

  • Fundamentals of ANOVA
  • One-Way ANOVA
  • Two-Way ANOVA

Design of Experiments

  • T Tests for Effects in DOE Primer
  • Factorial Designs
  • Blocking and Incorporating Center Points
  • Fractional Factorial Designs
  • Response Optimization Using Desirability Primer
  • Response Optimization

Learning Path 4: Predictive Analytics

Correlation and Regression

  • Relationship Between Two Quantitative Variables
  • Simple Regression
  • Trend Analysis in Time Series Primer

Predictive Analytics

  • Predictive Analytics
  • Model Validation
  • Tree Based Methods
  • CART Classification Splitting Primer
  • CART Classification Trees
  • CART Regression Splitting Primer
  • CART Regression Trees
  • Random Forests Classification Primer
  • Random Forests Classification
  • TreeNet Regression Primer
  • TreeNet Regression

Multiple Regression

  • Relationships Between Multiple Quantitative Variables
  • Multiple Regression
  • Polynomial and Interacting Terms
  • Model Selection
  • Binary Logistic Regression

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