# Example Library

This power-interest matrix example illustrates three characteristics of stakeholders: power, interest, and support.

This power-interest matrix example illustrates four characteristics of stakeholders: power, interest, awareness, and support.

You may select one of the four prioritization matrix template formats for your prioritization analysis.

In this example, each team member has distributed 100 points between the selected criteria to weight them before conducting the prioritization exercise.

In this example, the team has used the prioritization matrix to select an equipment among 5 alternatives.

This example illustrates a prioritization analysis that was conducted to select the most efficient data collection method at a workplace. Note that high score of cost doesn’t mean that the cost is high, but low or “cost-effective”.

A manufacturing company needs to select two projects to be implemented this year. It was agreed that ‘savings’ should be given a weight of 3 as it is relatively more important than the other two criteria.

In this example, a team has used the prioritization matrix to select the most profitable among five candidate projects.

A four field matrix can be used to prioritize and present the alternatives if you have only two evaluation criteria.

Histograms allow to visually assess the shape of the distribution, the central tendency, the amount of variation in the data, as well as the presence of gaps, outliers or unusual data points.

A histogram can be drawn either horizontally or vertically.

Histograms can be helpful to identify whether variability is within specification limits, whether the process is capable, and whether there is a shift in the process over time.

Histograms are ideal to represent moderate to large amount of data. A histogram may not accurately display the distribution shape if the data size is too small.

In this histogram, the distribution looks symmetric around the cable diameter mean and appears to fit the Normal Distribution fairly well.

In this histogram, the distribution of the data is skewed to the right.

A box plot can be drawn either horizontally or vertically.

A box plot is also referred to as a Box-and-Whisker Plot as it displays the data in a box-and-whiskers format.

A box plot may show key statistics such as the median of the data, maximum and minimum values, as well as the lower and upper quartiles.

The data is plotted in a box plot in such away that the bottom 25% and the top 25% of the data points are represented by the two whiskers, whereas the middle 50% of the data points are represented by the box.

Box plots are most useful when comparing between several data sets. They allow you to compare the central tendency as well as the variability of multiple data sets.