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Misleading graphs are abound on the internet. Sometimes they are deliberately misleading, other times the people creating the graphs don't fully understand the data they are presenting. "Classic" cases of misleading graphs include leaving out data, not labeling data properly, or skipping numbers on the vertical axis.
I came across the following misleading graphic in a…Continue
Naming conventions are often quite different in statistics and data science, which causes quite a bit of confusion. Part of the problem with naming conventions is that "...data science is the child of statistics and computer science” (Blei & Symth, 2017) . In essence, data science then is the child of two parents who speak different languages. In one sense, this makes the job of the data scientist not only to apply the knowledge from both…Continue
Regression and classification are both supervised machine learning techniques that use known data to make predictions. Where they differ is in what type of question you want answer, and how your output data is structured. For example, do you want discrete, categorical answer choices, like yes/no, or a range of possible values from 0 to 100? This one picture shows the basic differences between the two methods.…Continue
Inference and prediction are two often confused terms, perhaps in part because they are not mutually exclusive. Both provide pieces of the "What is data telling me?" puzzle. In fact, many inferential questions are raised as a result of predictions: For example, you might predict how input variables X, Y, and Z affect an output variable B. Then you can…Continue