The data below shows the weight and length of 10 rats. Find the correlation coefficient for this relationship AND the equation for the regression line. Use weight as x (independent variable) and length as y (dependent variable). HINT – Use Statcrunch! Weight (ounces) Length (inches) 1.5 2.2 2.3 3.9 2.4 4.2 2.7 5.1 3.4 5.1 3.8 5.6 4.1 6.5 4.3 6.2 6.8 5.6 7.4 Correlation coefficient: Regression line:
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
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