Use a bar graph for plotting means or percentages for different values of a nominal variable, such as mean blood pressure for people on four different diets. Don't do this unless you're really sure that it's a strong tradition in your field. For example, oceanographers often put "distance below the surface of the ocean" on the \(Y\) axis, with the top of the ocean at the top of the graph, and the dependent variable (such as chlorophyll concentration, salinity, fish abundance, etc.) on the \(X\) axis. There are a few situations where it is common to put the independent variable on the \(Y\) axis. Finally, there are times when there is no cause-and-effect relationship, in which case you can plot either variable on the \(X\) axis an example would be a graph showing the correlation between arm length and leg length. For example, you might plot "height, in cm" on the \(X\) axis and "number of head-bumps per week" on the \(Y\) axis if you are investigating whether being tall causes people to bump their heads more often. In that case, if you are testing the hypothesis that changes in one variable cause changes in the other, put the variable that you think causes the changes on the \(X\) axis. Sometimes you don't really manipulate either variable, you observe them both. For example, you might manipulate salt content in the diet and observe the effect this has on blood pressure. The independent variable is the one that you manipulate, and the dependent variable is the one that you observe. Plot the independent variable on the \(X\) axis (the horizontal axis), and plot the dependent variable on the \(Y\) axis. These could be measurement variables, or they could be nominal variables summarized as percentages. Use a scatter graph (also known as an \(X-Y\) graph) for graphing data sets consisting of pairs of numbers. But by far the most common graphs in scientific publications are scatter graphs and bar graphs, so that's all that I'll talk about here. There are many kinds of graphs-bubble graphs, pie graphs, doughnut graphs, radar graphs-and each may be the best for some kinds of data. And don't use both red and green bars or symbols on the same graph from \(5\%\) to \(10\%\) of the men in your audience (and less than \(1\%\) of the women) have red-green colorblindness and can't distinguish red from green. But don't use red type on a blue background (or vice-versa), as the eye has a hard time focusing on both colors at once and it creates a distracting \(3-D\) effect. It's pretty, and it makes it easier to distinguish different categories of bars or symbols.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |