A plot graph, also called a scatterplot, has six main parts: a title, legend, source, data, x-axis and y-axis. The title explains what data and information is being presented in the graph. Titles of graphs tend to be longer, cite the year the data was gathered and even the correlation potential between the axes. The legend explains the points and what they represent. The source references where the data was taken from. The data is the information that informed your point placement. The x-axis runs horizontally, or along the bottom of the graph. The y-axis is perpendicular to the x-axis, running vertically, or along the side of the graph.
The x-axis holds labels for data that is steady and non-variable, such as time or money. These pieces of information tend to be more constant than the y-axis, which tends to show the variance in the data, such as the number of something compared to the constant. For example, if you are showing the number of transactions per month over the past year, you put time (in months) on the x-axis, because it is steady and unchanging. The number of transactions each month should be represented on the y-axis because that will be different each month.
When looking at your data, locate its place on the x-axis first. Because this data is consistent, you know where along the number line it will fall. After finding the x-axis point, run your pencil directly up from the x-axis until you reach the corresponding place for that piece of data on the y-axis. Keeping with the example, if there were 53 transactions in the month of June, find June along the x-axis first. Then locate 53 on the y-axis, which will probably not be labeled, but you can estimate it between 50 and 55. Find the place in the area of the graph where the two points converge and make a small dot.
Once you have plotted a significant amount of data onto your graph, you can find correlations between your x-axis and your y-axis. It is important to remember that these are correlations, not causations. It is unlikely to find causation in one study or in one graph conclusively. Draw lines where most of the data points are clustered to make a "line of best fit." You will see trends, such as the numbers of transactions increase toward the end of the year and the beginning of the summer, possibly due to holidays and increased vacationing.