When I started dealing with data analytics, the focus was on learning the “sexy” statistics to help drawing business-relevant conclusions out of data. Graphical displays were always part of the…
Data analysts have not only the job to turn organisational data into information. They also need to master analytical storytelling.…
Many companies spend considerable amounts of money on customer and employee surveys every year. The survey results are used to amend strategies, design new products and services, focus improvement activities, target staff development activities and … to celebrate success.
The question is: Can we always rely on what we see?…
At MyInsurance, survey results have been collected in 2017, 2018 and 2019. The rating was done on a 10-point Likert ranging from 1 … very poor to 10 … outstanding. As always, the upper 3 points, i.e. 8, 9 and 10, are seen as customer is satisfied. All other ratings are undesirable. How did MyInsurance do over the years?…
Many companies spend considerable amounts of money on customer surveys every year. Customer survey results are being used to amend strategies, design new products and services, focus improvement activities and…
Every blood donor of a large blood bank has to go through five process steps. These steps are Registration, Screening, HB Test, Donation and Refreshment. At the end of the process, that often takes around an hour, feedback forms are available for the donors. In one week, 210 donors have returned these forms with their satisfaction score for each process step.…
Three teams compete in our CHL business simulation. After completing Day One, it looks like the teams show a very different performance. Although the means look very similar, the variation is strikingly different. To test this assumption of different variation among the teams, the Test for Equal Variances is deployed.…
Nearly all medium-size and large companies spend hundreds of thousands or even millions on customer surveys every year. Customer survey results serve to amend strategies, design new products and services and focus improvement activities. Gathering customer survey data is only the first step.
Consider a production process that produced 10,000 widgets in January and experienced a total of 112 rejected widgets after a quality control inspection (i.e., failure rate = 1.12%). A Six Sigma project was deployed to fix this problem and by March the improvement plan was in place. In April, the process produced 8,000 widgets and experienced a total of 63 rejects (failure rate = 0.79%). Did the process indeed improve?
Linear regression is one of the most commonly used hypothesis tests in Lean Six Sigma work. Linear regression offers the statistics for testing whether two or more sets of continuous data correlate with each other, i.e. whether one drives another one.…
Three methods for dissolving a powder in water show a different time (in minutes) it takes until the powder dissolves fully. The results are summarised in Figure 1.
There is an assumption that the population means of the three methods Method 1, Method 2 and Method 3 are not all equal (i.e., at least one method is different from the others). How can we test this?…
In some situations, Lean Six Sigma practitioners find a Y that is discrete and Xs that are continuous. How can we apply regression in these cases? Black Belt training indicated that the correct technique is something called b inary logistic regression or binary regression. But this tool is often not well understood.…
The two-sample t-test is one of the most commonly used hypothesis tests in Data Analytics or Lean Six Sigma work. The two-sample t-test offers the statistics for comparing average of two groups and identify whether the groups are really significantly different or if the difference is due instead to random chance.…
Measurement error is unavoidable. There will always be some measurement variation that is due to the measurement system itself.
Most problematic measurement system issues come from measuring attribute data in terms that rely on human judgment such as good/bad, pass/fail, etc. This is because it is very difficult for all testers to apply the same operational definition of what is “good” and what is “bad.”…
Architect of High-Performing Organisations
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