Careful data analysis is always important, but sometimes we need to quickly get a sense of the relationship between variables or factors. It’s also true that pictures speak louder than raw data – you may have analyzed every last scrap of your data and run every possible test to confirm your analysis, but an effective graph shows people what your data mean in much less time than a collection of numbers.
This can be particularly important when you’re presenting the results of continuous quality improvement projects, whether it’s lean six sigma or some other data-driven process improvement methodology.
You can use graphs to quickly see and show relationships such as those between:
- Soil pH and the growth of plants
- Viscosity, age, and temperature of oil and acceleration and wear in car engines
- Weather and car accidents
Statistical software such as Minitab makes it easy to see relationships between one or more pairs of variables by creating graphs of your data.
Here are three types of graphs you can use to quickly get a “feel” for the relationships between variables:
Use a scatterplot to assess the relationship between two variables. The values of the two variables serve as the x- and y-coordinates for plotting each observation.
Use a matrix plot to assess relationships among several pairs of variables at once. A matrix plot is just an array of individual scatterplots.
Use a marginal plot to assess the distributions of two variables as well as the relationship between them. A marginal plot is a scatterplot with histograms, boxplots, or dotplots in the margins.
Other Ways to Show Your Data with Graphs
Of course there are many other ways to use graphs to show the results of a careful data analysis. Minitab Statistical Software provides a flexible suite of graphs to support a variety of analysis needs. Minitab also makes it easy to perform all of the data analysis necessary for Six Sigma and other data-driven quality improvement methods. To see what options might suit what you want to show, check out Minitab Help.