The cumulative sum (or Cusum) technique is occasionally offered as an alternative to process behavior charts, even though they have completely different objectives. Process behavior charts characterize whether a process has been operated predictably. Cusums assume that the process is already being operated predictably and look for deviations from the target value. Thus, by replacing process characterization with parameter estimation, Cusums beg the very question process behavior charts were created to address.

To illustrate the Cusum approach and compare it with an average chart, I’ll use the example from page 20 of Shewhart’s first book, Economic Control of Quality of Manufactured Product (Martino Fine books, 2015 reprint).These data consist of 204 measurements of electrical resistivity for an insulator. Shewhart organized them into 51 subgroups of size four, based upon the time order in which the measurements were obtained. Figure 1 gives the averages and ranges for these 51 subgroups.

Many people have been taught that capability indexes only apply to “normally distributed data.” This article will consider the various components of this idea to shed some light on what has, all too often, been based on superstition.

Capability indexes are statistics

Capability and performance indexes are arithmetic functions of the data. They are no different from an average or a range, just slightly more complex. The four basic indexes are the following:

The capability ratio, C_p, is an index number that compares the [space available within the specifications] with the [generic space required for any predictable process].

The performance ratio, P_p, is another index number that compares the [space available within specifications] with the [estimated space used by process in the past].

As municipalities clamor for a slice of President Biden’s $1.2 trillion infrastructure spending bill, one Johns Hopkins scientist is re-examining one of the basic elements of road-building: Determining the width of road lanes. But determining the width that provides the highest level of safety, access, and comfort for every road user—drivers, cyclists, and pedestrians—is complex, says Shima Hamidi, an assistant professor in Johns Hopkins’ Department of Environmental Health and Engineering, which is shared by the Whiting School of Engineering and the Bloomberg School of Public Health.

It’s a data problem, she says, and she wants to help cities solve it.

Hamidi is undertaking a massive collection of data on urban streets across the United States to answer one question: How low can cities go on street width to make room for bike lanes and wider sidewalks?

All too often the topic of fixing dirty data is neglected in the plethora of online media covering artificial intelligence (AI), data science, and analytics. This is wrong for many reasons.

To highlight just one, confidence in the quality of data is the vital foundation of all analysis. This topic remains relevant for all levels of complexity, from spreadsheets to complex machine-learning models.

So, I was delighted to review Susan Walsh’s book, Between the Spreadsheets: Classifying and Fixing Dirty Data (Facet Publishing, 2021). Here are some highlights from her book, and my own advice on who should read it.

Do you ever feel like you’re spending money like crazy on marketing and getting little or nothing in return? If so, you might be tempted to pull the plug on marketing altogether. That would be a big mistake.

An effective marketing strategy can mean the difference between your organization’s success and failure. To maximize your strategy, there are eight common marketing mistakes you should avoid at all costs.

#1 Focusing solely on data

Most marketers firmly believe the old saying, “What doesn’t get measured doesn’t get improved.” They track various metrics, hoping the data will show them how to improve customer engagement.

The problem is some of the most important elements of customer engagement—like emotional response—can’t be tracked easily. How do you measure whether or not you’re tugging at their heartstrings?

The real power of marketing comes from synergy of both the left brain (data) and the right brain (emotion). Focusing solely on the data will never lead to optimal results.