Statistical Methods for Quality Improvement

Process Overview

The Process Overview tool in the SPC app offers reports with charts and capability statistics. It helps you to identify problem areas and gain insights into your data:

• Xbar / Individuals charts
• Probability plot
• Capability histogram
• Capability plot
• Capability statistics

Capability Analysis

Capability analysis is used to detect whether a process meets specifications. The SPC app provides 5 types of capability analysis tools for different data types:

• Normal
• Between/Within
• Non-normal
• Binomial
• Poisson

Data Transformation

The SPC app provides 3 transformation functions to make data approximately normal:

• Box-Cox Transform
• Johnson Transform
• Yeo-Johnson Transform

The SPC App provides 14 distributions and 3 transformation functions to help to identify your data distribution:

• Normal
• Logistic
• Lognormal / 3-Parameter-Lognormal
• Log-logistic
• Weibull / 3-Parameter Weibull
• Gamma
• Rayleigh
• Smallest Extreme Value
• Largest Extreme Value
• Exponential / 2-Parameter Exponential
• Gaussian Mixture
• Folded Normal
• Box-Cox Transformation
• Johnson Transformation
• Yeo-Johnson Transformation

Gage R&R Study

The Gage Study - Continuous Data App supports the following features:

• Type 1 Gage Study
• Gage Linear Bias Analysis
• Crossed Gage R&R Study
• Nested Gage R&R Study
• Expanded Gage R&R Study
• Create Gage Worksheet

Attribute Agreement Analysis

The Attribute Agreement Analysis App is designed to assess the degree of agreement between appraisers' ratings and known standards. This App supports the following:

• Kappa Statistics
• Kendall's Coefficient of Concordance
• Within Appraisers Analysis
• All Appraisers vs. Standard Analysis
• Agreement Graphs
• Confidence Intervals
• Misclassification Rates

Origin offers Tolerance intervals app, which is important tool in quality improvements. It can help to understanding and managing variability in production processes. The tolerance intervals app in Origin provides following features

• Support multiple distributions
  • Normal
  • Lognormal
  • Gamma
  • Exponential
  • Smallest Extreme Value
  • Weibull
  • Largest Extreme Value
  • Logistic
  • Loglogistic
  • Nonparametric
• Parametric and non-parametric tolerance intervals
• Graphical report including interval plot, histogram and probability plots

Power and sample size

• Determine the minimum sample size required to achieve a certain level of power for detecting a specified effect size
• Find the right balance that ensures robust and valid experimental outcomes

Design

• Factorial designs, including 2-level factorial, Plackett-Burman and general full factorial
• Response surface design, including central composite and Box-Behnken

Analysis

• Design evaluation tools including coefficients, standard error and p-value.
• Build models with linear+interaction+square, or customize terms in regression model.
• Statistics table to examine the goodness-of-fit, and analysis summary with ANOVA
• Fits and residual diagnostics
• Find Y from X to extract fitted data.

Graphics

• Fitted Plot
• Residual Plot
• Effects Plot
• Main Effects Plot
• 2-way Interaction Plots
• Contour Plot
• Surface Plot

Optimize

• Search for optimal responses based on the goal:minimize, maximize and target.
• Constraint factors to a region or hold at a fixed value.
• Display the optimization plot, in which you can tune factor settings to improve the solution.