• Quality Trainer

    Course 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 course covers everything from Basic Statistics to DOE, and guides you through 31 exercises using Minitab Statistical Software to solve real-world quality improvement challenges.

    Chapter One

    Descriptive Statistics and Graphical Analysis

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

    Chapter Two

    Statistical Inference

    • Fundamentals of Statistical Inference
    • Sampling Distributions
    • Normal Distribution

    Chapter Three

    Hypothesis Tests and Confidence Intervals

    • 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

    Chapter Four

    Control Charts

    • Statistical Process Control
    • Control Charts for Variables Data in Subgroups
    • Control Charts for Individual Observations
    • Control Charts for Attributes Data

    Chapter Five

    Process Capability

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

    Chapter Six

    Analysis of Variance (ANOVA)

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

    Chapter Seven

    Correlation and Regression

    • Relationship Between Two Quantitative Variables
    • Simple Regression

    Chapter Eight

    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

    Chapter Nine

    Design of Experiments

    • Factorial Designs
    • Blocking and Incorporating Center Points
    • Fractional Factorial Designs
    • Response Optimization

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