Tag: Data sets

In 1920, Sir Ronald A. Fisher invented a statistical way to compare data sets. Fisher called his method the analysis of variance, which was later dubbed an ANOVA. This method eventually evolved into Six Sigma data set comparisons. The F ratio is the probability information produced by an ANOVA. It was named for […]

What is Continuous? Continuous data refers to numerical data with any value within a certain range. The values have infinite possibilities, but they all fall within a range. These can be whole numbers or decimals measured using data analysis like skews and line graphs. This kind of data can change over time and […]

When it comes to Six Sigma, data is your lifeblood. The ability to interpret what the data is saying is how you know whether you are on the right path. Further, it shows how you’re achieving your goals and objectives and avoiding roadblocks on your journey toward success. Accurately collected and analyzed data […]

Big Data vs. Small Data Big data vs. small data, which should you be paying attention to? Data is generated throughout an organization. You’re gathering it when selling products, producing things, and even just interacting with customers. However, when we start talking about data, the notion of big data and small data enter […]

Throughout any project, you’re going to gather up quite a bit of data. Now, this can be broken down into two categories: variable and attribute data. Understanding the difference and learning how it is applicable during the analysis stage can be crucial for future decision-making. So, with that in mind, let’s take a […]

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 […]

Let’s face it – without meaningful data there cannot be meaningful data analysis. Data is typically collected as a basis for measuring success and, ultimately, taking action. However, unless data is viewed realistically – separating opportune signals from probable noise – the actions taken may be inconsistent with the data collected in the first place. […]