Realistic Algebra

Lately I've learned a lot more about how to use algebra for practical data functioning. Correlations can go beyond y=x, y=mx+b and the similar simple parents; while a simple solid line can predict the direction in which a trend is moving, data presented in a scatterplot is often a much more logical basis for predictions. Because each set of information falls on a point which is not yet governed by a trend or equation, it can all be studied afterwards collectively. So, for example, while a parabola (or a function laid out precisely by the parent y=x^2) shows a perfectly curved line, the graph of a curvilinear scatterplot will show data as it actually falls into place, so that one can see where certain pieces stray from the viewer's expectations. Likewise, a scatterplot won't lie to you that a certain set of data will rise by the same degree over a certain period of time in each and every single instance, whereas a simple diagonal line will tell otherwise. A perfect positive line graph can, in this way, be misleading, while a scatter plot will tell you that the trend thusfar may be a strong positive, but has some pitfalls as far as predictions are to be made. Using this methodology businesses can be better prepared to determine how to handle deals and situations in which a customer reacts differently to a certain product than the norm.

And we artists certainly are not the norm (:! For those times you've just got a little too much math on the brain, have a good laugh at data in general with this miscellaneous website full of funny real-life and nonsensical examples.

GRAPH JAM!