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