By Robert M. Leekley
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Suppose you had to guess the salary for each. If you had nothing else to go on, your best guess for each would be the overall average. It would be wrong—sometimes too low and sometimes too high—but with nothing else to go on, it would give you the smallest possible errors. A successful explanatory variable helps you tell the cases that are likely to be above or below average, and by how much. It allows you to tailor your guesses so that your errors are smaller. ” The explanatory variable is a predictor; it need not be the actual cause.
In this course, the training wheels are the worked examples, the answers in the back, the tutor, and the smart kid down the hall. They are great for getting started. Use them. But do not fool yourself. You have not yet mastered the material until you can get by without these aids. That means facing a brand new problem and a blank sheet of paper, and doing it on your own. You may fall a few times. But that is part of how you really learn. And the good news is, for many students, all of a sudden it is easy!
It contains 281 points, each one representing the sex and weight combination for one of the 281 individuals in the data. The advantage of this approach is that there is none of the distortion that inevitably occurs when you break a continuous variable into artificial groupings. In this case, it is impossible to identify all 281 cases since the points for two people of the same sex and weight will print over each other. Still, you can see fairly easily where the bulk of the individuals fall and where the outliers are.