Nowadays, plenty of data is available in all parts of any organisation. However, data is of no use to the firm if it is not turned into information and business relevant insights. Therefore, the demand for Data Analytics (Data Science) has grown dramatically. Only with that, data has the power to support tactical and strategic […]

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 … to celebrate success. Because the impact of customer service results can be quite hefty the data driving important decisions shall be trust-worthy. And, the […]

The Chi-Squared test is used to check whether there is a significant difference between observed frequencies (discrete data) and expected frequencies for two or more groups (discrete data). For small sample sizes, Fisher’s Exact test is to be used instead. Both test are performed in a similar way. See for yourself.

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.

Survey data should be analysed with different tools at the same time in order to find the most appropriate method to show “patterns in data” that lead to conclusions. The Kano analysis or the Jaccard index offer additional insights into survey data.
Remember: Attaining the data is expensive, analysing them is cheap.

Data analytics, or data analysis, is the process of screening, cleaning, transforming, and modeling data with the objective of discovering useful information, suggesting conclusions, and supporting problem solving as well as decision making. Researchers use multiple approaches, including a variety of techniques and tools. Data analytics finds applications in many different environments. It usually covers two steps, graphical analysis and […]

Consider a production process that produced 10,000 widgets in January and experienced a total of 112 rejected widgets after a quality control inspection. A project was deployed to fix this problem. In April, the process produced 8,000 widgets and experienced a total of 63 rejects. Did the process really improve? Read..

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. Additionally, it shows the influence of discrete variables, too.

ANOVA (analysis of variances) is a statistical technique for determining the existence of differences among several population means. The technique requires the analysis of different forms of variances – hence the name. It is testing whether means are different. Read about an application example here.

In some situations, Six Sigma practitioners find a Y that is discrete and Xs that are continuous. How can a regression equation be developed in these cases? Black Belt training indicated that the correct technique is something called logistic regression or binary regression. But this tool is often not well understood.