Category: Sampling/Data

Random sampling is a statistical method of selecting a sample of data from a larger set in such a way that each data point has an equal chance of being selected so that the sample represents the population from which it was drawn. What Is Random Sampling? Random sampling is a method of […]

When performing statistical data analyses, quality professionals are always challenged to maintain data integrity. When should you round up the answer; when should you round down? How many significant figures are appropriate for the data set that has been taken? Below is a set of simple rules that should help you traverse the […]

Do you know how to account for the margin of error in your customer surveys? A survey is a valuable assessment tool in which a sample and information relate to a larger population. Surveying is like tasting a soup, a few spoonfuls tell what the whole pot tastes like. An Example of Margin […]

In the Analyze phase of a DMAIC (Define, Measure, Analyze, Improve, Control) Six Sigma project, potential root causes of variations and defects are identified and validated. Various data analysis tools are used for exploratory and confirmatory studies. Descriptive and graphical techniques help with understanding the nature of data and visualizing potential relationships. Statistical analysis techniques, […]

This paper looks at some of the methods of estimating standard deviation (which I will usually refer to as ‘sigma’). Additionally, I propose a new formula for estimating sigma for small sample sizes and also present a means to mathematically evaluate these competing estimates of sigma. The question was posed to me: “I have five […]

It is often not possible or practical to collect all the data from your process. It can be too costly or take too much time. It may not be possible to access all the data. If this situation exists in your process, then you will need to sample your data. Overview: What is sampling?  There […]

What do Goldilocks and statistical analysis sample size have in common? A razor-sharp focus on “just right.” A sample size that is too big or too small leads to inaccurate data and wasted resources (although hopefully not three bears).

When doing statistical analysis, it is impractical and often impossible to gather all the available data. That is when you would seek to sample the data to analyze it and make decisions about your process. In statistics, a sample refers to a group of individuals, objects, or events that are selected from a larger population […]

Censored data is commonly used in reliability studies to determine the mean time to failure in order to establish warranty and maintenance periods for products. A large number of samples are subjected to either normal-use or accelerated-use conditions. Failure modes and occurrences are logged. Plotting the distribution of the sample failures over time allows the […]

TaaG (trends at a glance) analysis is a fast way to compare trends of subsets of data across large data sets. It is an ideal tool to use in the Measure and Control phases of DMAIC (Define, Measure, Analyze, Improve, Control) projects. The value of TaaG analysis is best understood by way of example. Suppose […]

When the Ritz-Carlton Hotel Company won the Malcolm Baldrige National Quality Award for the second time in 1999, companies across many industries began trying to achieve the same level of outstanding customer satisfaction. This was a good thing, of course, as CEOs and executives began incorporating customer satisfaction into their company goals while also communicating […]

Collecting data – be it voice of the customer or otherwise – requires a plan. Details of the plan should include what data to collect, how to get the information, where the information will come from and so on. Before any of these details are defined, however, the first step is to identify what a […]

Reducing wait times is a perennial challenge for the service industry, particularly if you haven’t rooted out all the causes for the delays. The mind mapping visualization tool can help you make sense of large volumes of interconnected data and fast-track implementation of Lean Six Sigma.

Manufacturing facilities can be faced with major challenges when it comes to process improvement, largely because practitioners don’t always know enough about the underlying process factors (x’s) are that drive the improvement metric (Y). Practitioners might have a brainstorming session to tap into the collective experience of experts involved in the process, and design experiments […]

Most manufacturing processes are controlled by sampling a product at some regular interval. Often, when a process is running normally, this interval is once every shift. It is not too surprising that in today’s economic climate, where cutting cost is of paramount importance, reducing sampling to save money is inviting, especially at large manufacturing facilities, […]

By definition, a sample of size n is random if the probability of selecting the sample is the same as the probability of selecting every other sample of size n. If the sample is not random, a bias in introduced which causes a statistical sampling or testing error by systematically favoring some outcomes over others. […]

We have heard about GE being one of the biggest proponents of Six Sigma, both for their own processes and for their customers. We’ve also heard how much GE has saved by implementing Six Sigma. This article is not a regurgitation of the existing rhetoric. Instead, I’d like to focus on an aspect of how […]

Engineers, Six Sigma practitioners and other researchers often work with “hard” data – discrete data that can be counted and legitimately expressed as ratios. But what of “soft” data, things like opinions, attitudes and satisfaction? Can statistical process controls (SPC) be applied here? Can process variation in customer satisfaction, for example, be measured and then reported to […]

A repeated series of actions and variables is a process. A collection of processes is a system. Virtually perfect Six Sigma quality results from an optimal interaction of all the variables in a given system. Process and system questions we all face at work include: Which variables are the most important to the customer? Am […]

Based on experience with property and casualty insurers (P&C), one of the biggest profitability drivers is the expense incurred staffing and settling claims. Many P&C insurers struggle with timely and efficient claims processing. In working with P&C companies consulting groups found three common challenges that project teams must address to improve the overall claims settlement […]

Black Belts and Six Sigma practitioners who are leading DMAIC (Define, Measure, Analyze, Improve, Control) projects should develop a sound data collection plan in order to gather data in the measurement phase. There are several crucial steps that need to be addressed to ensure that the data collection process and measurement systems are stable and […]

The beauty of Six Sigma, over other decision-making strategies is that it is, by nature, data driven – it involves making decisions backed by evidence. In the absence of data, then, what good is Six Sigma? A Black Belt without data is like a navigator without a compass; finding north becomes complicated, but not impossible. […]

One of the pleasures of teaching Green Belts is helping to eliminate the fear of statistical analysis. One technique is to place an emphasis on not only when and why a tool or methodology is used but also what the data says in “plain English.” Memorizing complex formulas may be the goal of many Master […]

In order to prove that a process has been improved, you must measure the process capability before and after improvements are implemented. This allows you to quantify the process improvement (e.g., defect reduction or productivity increase) and translate the effects into an estimated financial result – something business leaders can understand and appreciate. If data […]