1. Graphing in science Compiled by Woodruff

2. Graphing in science Typically the x-axis represents the independent variable and the y-axis represents the dependent variable. This type of graph shows the direct link between the independent variable and the dependent variable.

4. Example: Weight of paper airplane VS. flight time

5. Correlation VS. Causation When two variables are put on a graph it shows how they are related (the correlation). But, this does not mean that one variable necessarily caused the other variable to change (causation).

6. Thus, we have a saying in science: correlation does not imply causation. In other words, just because two variables are linked mathematically, does not mean that one had an effect on the other. If the independent variable does not directly affect the dependent variable, then the graph is not scientific.

7. Correlation Correlation refers to how closely two sets of information or data are related.

8. Causation Causation is the action of causing something. In other words, causation is when one variable directly affects (changes) another variable.

9. Is this graph scientific? Why or why not?

10. Scientific? Why or why not? Not scientific because the variables do not affect each other. The number of lemons imported into the US has nothing to do with highway fatality rates.

11. Is this graph scientific? Why or why not? Number of Storks Birthrate

12. Scientific? Why or why not? Not scientific because storks have nothing to do with the birthrate of humans.

13. Is this graph scientific? Why or why not?

14. Scientific? Why or why not? Scientific: the x-axis is directly related to the y- axis: As time passes, CO2 levels increase.

15. Is this graph scientific? Why or why not?

16. Scientific? Why or why not? Scientific: x-axis directly impacts (changes) y- axis Independent Variable (type of fertilizer) impacts (changes) the dependent variable (plant growth).