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<p>The key takeaway from this lesson is that the value for the correlation coefficient can be used to determine the strength of the relationship between the two variables represented in the data.</p>
<p>Here are some questions for discussion:</p>
<ul>
  <li>&ldquo;What does it mean for two variables to have a weak, positive relationship?&rdquo; (When one of the variables increases, the other variable also tends to increase, but since the linear relationship is weak, the data do not follow a linear path.)</li>
  <li>&ldquo;If the \(r\)-value for a line of best fit in a scatter plot is 0.8, what would you expect the data in the scatter plot to look like?&rdquo; (I would expect the scatter plot to show the data generally increasing from left to right, and for the data to look somewhat like a line, but not perfectly like a line.)</li>
  <li>&ldquo;What does the correlation coefficient tell you about the relationship between two variables?&rdquo; (It tells you if the two variables are positively or negatively related. It also tells you how strong the relationship between the two variables is.)</li>
</ul>
<p>Tell students that when the variables increase together, we can say they have a <strong>positive relationship</strong>. If an increase in one variable&rsquo;s data tends to be paired with a decrease in the other variable&rsquo;s data, the variables have a <strong>negative relationship</strong>. When the data are tightly clustered around the best fit line, we say there is a <strong>strong relationship</strong>.When the data are loosely spread around the best fit line, we say there is a <strong>weak relationship</strong>.</p>