How to read oprofile call graph generator

Phrases and 6 Analysis Steps to interpret a graph

Watch this 8-minute video to learn more about vocabulary to use for interpreting graphs.

Getting to know the 6 Analysis Steps to interpret a graph

Let's continue with our example of mice and kestrels from the previous chapter.

In our example Roy counted how many kestrels and how many field mice are in a field. For many years he notes the numbers in his diary. He produced this line chart.

Let's try to interpret this example carefully.

Analysis 1: Reading basics

First you have to read the labels and the legend of the diagram. What does it visualize?

In our example ...

  • x-axis: You can read what years the animals have been sighted.
  • y-axis: You can read the numbers of sightings.
  • Blue line: The number of sighted kestrels.
  • Green line: The number of sighted field mice.

So this diagram visualises how many kestrels and field mice have been sighted over the years by Roy.

Analysis 2: Reading important numbers

First we have to read the most important points. Important points are peaks, lows, turning points and intersection points.

In our example ...

  • 1952: A peak of the mice line and a low of the kestrel’s line. A turning point for both lines.
  • 1954: An intersection point between the kestrel’s line and mice line.
  • 1962: A low point of the mice line and a high point for the kestrel’s line. A turning point for both lines.

Analysis 3: Define trends

Now it is important to define all significant trends.

In our example ...

Sightings of kestrels:

  • From 1950 to 1952 they drop.
  • Since 1952 they rise steadily.
  • Since 1962 they drop slightly again.

Sightings of field mice:

  • From 1950 to 1952 they rise significantly.
  • Since 1952 they drop significantly.
  • Since 1954 they drop much slower.
  • Since 1962 they rise again slowly.

Analysis 4: Compare trends

Knowing the trends, we can compare them, to find out differences and relations.
Are there common trends?
Is there a pattern?

In our example ...

  • When there are many sightings of field mice, there are fewer sightings of kestrels.
  • When there are many sightings of kestrels, there are fewer sightings of field mice

Analysis 5: Analysis trends

Finally we can establish hypotheses how the data is related. These hypotheses have to be questioned and assessed.

In our example ...

A)“Mice eat kestrels. Therefore there are many kestrels when there are less mice. "

  • According to our diagram this is possible. But: We know that mice do not eat kestrels.

B)“The kestrels hunt the mice. Therefore there can only be a lot of mice when there are fewer kestrels. "

  • Mice are typical food for kestrels. This hypothesis could be correct.

C)“The Mice hide from the kestrels. When there are many kestrels to see, we cannot see many mice. "

  • Prey animals often shelter from their hunters. Also this hypothesis could be correct.

D)“The relation between sightings of kestrels and mice is only a translucent connection. The numbers of sightings have very different reasons. "

  • Very often there are only translucent connections. There can be many reasons why Mr. Varney sights a certain number of animals each year. Also this hypothesis could be correct.

Analysis 6: Predict a development

Based on the development of the diagram and the established hypothesis we can predict future developments of the diagram.
But be careful: Predictions are always only speculations!

In our example ...

  • Towards the end of the lines become closer again. If they continue like that there will be an intersection at some point.
  • In the coming years there might be more sightings of mice than kestrels.


A diagram helps to draft a hypothesis. To check a hypothesis very often you need to do an experiment. Based on a diagram, graph or chart we can predict a development in the future. But we have to be aware that it is only a prediction.

This example about kestrels and mice has been published by courtesy of

Concept and graphs by author Martin Forster.
Note: Some words in the graphs have been deleted to make it suitable for international use. Arrows to explain graphs have been added.
Some text has been changed slightly to suit the audience of

You can find the original under:
Serlos work is under Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)