## Frequency Plot

Updated:It is generally easier to understand what your data is telling you if you can visualize it rather than view it in rows and columns of numbers. A frequency plot is a great tool for doing that. A frequency plot is a graphical representation of the distribution of continuous data based on how often a […]

Read more## Hidden Factory, The

Updated:You claim your process yield is 98%. Are you sure? Have you considered your hidden factory? Let’s find out why you should.

Read more## The Role of DOE in Quality Control

Updated:DOE (Design of Experiments) can be defined as a set of statistical tools that deal with the planning, executing, analyzing, and interpretation of controlled tests to determine if there is a casual relationship between your process factors or variables and your process output. Overview: What is DOE? Two common approaches to DOE are a full […]

Read more## General Linear Model (GLM)

Published:If you understand multiple linear regression, you already know a lot about the General Linear Model (GLM). Let’s learn some more about how and when to use the GLM.

Read more## Genchi Gembutsu

Published:How much can you learn about your process sitting in your office? According to Genchi Gembutsu, you can’t learn much. In Japanese, genchi means “actual place,” and gembutsu means “actual thing.” In the Toyota Production System (TPS), this concept is interpreted as “to go and see.” Let’s explore this a little further.

Read more## Degrees of Freedom and Sample Size: How to Determine the Right Amount of Data for Your Analysis

Updated:Degrees of Freedom (DF) can be thought of as the amount of information you have to compute certain statistics. The more you want to compute, the more data and information you need. Let’s learn more about how to compute and use DF. You can think of DF as statistical money that you can spend to […]

Read more## Design of Experiments (DOE)

Published:A design of experiments (DOE) is a set of powerful designed experimentation statistical tools for understanding the impact that varying your process inputs will have on the outputs of your process.

Read more## Understanding the basics of ANOVA and Dunnett’s 1-way ANOVA

Updated:If you are curious as to whether the means of greater than 2 groups are statistically different or not you could use a 1-way ANOVA. But, if you have multiple treatment groups and want to compare the means to a control group, then you can use Dunnett’s 1-way ANOVA. ANOVA stands for Analysis of Variance. […]

Read more## How the Deming Cycle (PDCA) Can Help Improve Organizational Efficiency

Updated:Never-ending improvement is the heart of any continuous improvement effort. The Deming Cycle, or PDCA, is one of the first formalized approaches to utilize an iterative approach to improving processes, and it still serves as a fundamental tool today for continuous improvement. This article will describe the stages of the Deming Cycle, the benefits of […]

Read more## Discover the Power of DMEDI in Process Improvement

Updated:DMEDI stands for: Define, Measure, Explore, Develop, and Implement. This differs from the basic Six Sigma methodology of DMAIC which stands for: Define, Measure, Analyze, Improve, and Control. Both methodologies are designed to improve your processes and products. Whereas, DMAIC is designed for incremental improvements of existing processes, DMEDI focuses on more robust improvements for […]

Read more## The Importance of Understanding and Implementing Directives

Updated:Directives are an important tool for organizations that seek to align employees with their goals, increase efficiency, and promote a culture of continuous improvement. Understanding Directives: Benefits, Best Practices, and Common Questions Directives are a critical tool for organizations seeking to improve their performance and achieve their goals. By providing clear guidance and expectations, directives […]

Read more## The Importance of Selecting the Proper Type of Control Chart

Updated:Since there is variation in all processes, how can you tell whether the variation is due to random expected variation, or something unexpected? The control chart is a tool for making that distinction. Let’s learn how that is done.

Read more## Define, Measure, Analyze, Design, Verify (DMADV)

Published:The DMAIC methodology of Six Sigma is a well-known tool for improving existing products and processes. But, what if our existing process or product can’t be improved enough to meet expectations? What if we are developing a process that hasn’t existed before?

Read more## Understanding Statistical Distributions: A Comprehensive Guide

Updated:If you plot your data on a histogram, the resulting graphic will illustrate how your data is distributed. This distribution can provide a lot of information about your data and the process from which it came from. Let’s learn some more about distributions. Once you have collected your desired data, you can display the data […]

Read more## Defects Per Million Opportunities (DPMO)

Published:Lean Six Sigma professionals, much like every other professional, love to talk in acronyms. The acronym of the day is DPMO. Let’s check out what it means.

Read more## What you need to Know to Calculate Cp and Interpret Its Value

Updated:Cp is one of the metrics calculated for determining whether your process is capable of meeting customer specifications or requirements. It is useful in comparing different functions and their ability to meet their unique specifications.

Read more## What is a Defective? A Complete Guide

Updated:Unfortunately, sometimes your process produces unacceptable output. These are usually referred to as defects or defectives. Let’s examine what a defective item is and contrast it with a defect. The Merriam-Webster dictionary defines a defective as: having a defect or flaw or imperfect in form, structure, or function. Simply put, a defect is a flaw […]

Read more## Confidence Interval

Published:Would you expect the average of a sample to be the same as the average of the population from which it came? No. Why not? Because of randomness and sampling error. How should we handle that? Let’s find out.

Read more## Consumers Risk

Published:When doing hypothesis testing, there is a risk of making two types of errors. One of them is the consumer’s risk, which is when the null hypothesis is rejected when it shouldn’t have been.

Read more## The Power of Data: Understanding What It is and Why It Matters

Updated:The famed statistician, Dr. W. Edwards Deming, frequently talked about data and said, “Without data, you’re just another person with an opinion.” Let’s learn some more about data. Data refers to any information, facts, or statistics that can be collected, stored, and analyzed. It can be in the form of numbers, text, images, audio, or […]

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