ShmoopTube

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Here's your shmoop du jour, brought to you by Distraction.

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It's usually not a good thing,

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but sometimes—OOH, LOOK, A SQUIRREL!

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Some of us feel like we can work better with a little distraction.

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We gathered data to see if this hypothesis was true for everyone, and graphed a scatterplot data

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showing "hours spent watching Hulu after dinner" on the x-axis and "hours of homework completed

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after dinner" on the y-axis. We calculated a linear correlation coefficient

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of r = -0.27. This coefficient shows a relationship of... what?

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And here are the potential answers...

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All we have to do is interpret the r-value to figure out whether or not the hypothesis is true.

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Remember that r is the correlation coefficient of a set of data.

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In other words, it tells us how good of an approximation we have.

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In this case, we have a linear approximation, so r tells us how close to a line the data forms.

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R values range between -1 and 1. The closer

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r is to 0, the worse our approximation. However, as r approaches either end of its range, the

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better the approximation. How can the correlation get better by moving

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in either direction? Well, the more negative r is, the better a

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negative correlation we have. The more positive, the better the positive correlation.

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But what does THAT mean?

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A positive correlation means that the data has a positive linear

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slope, and a negative correlation means it has a negative linear slope.

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We have an r value of -.27, which means there is a negative correlation.

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Just from this knowledge, we can eliminate (A), (B), and (E).

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-.27 is closer to 0 than -1. In other words, it's closer to having no correlation

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than having a perfect correlation. Which means that the correlation is pretty weak.

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Since we have a weak negative correlation,

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our answer is (C). Even though the correlation is weak, there

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is definitely a negative correlation... ...which means that the more Hulu you watch,

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the less homework you'll get done.

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