1. Outliers Sometimes there is data that doesn’t fit the pattern of the rest. In the following graphs Circle the one that is unlike the trend A point that is far away from the trend line (line of best fit) of the data is considered an outlier. Sometimes these data points should be removed to give a better representation of the trend of the data. If they are removed, this fact should be mentioned.

2. Outliers Let’s see the effect outliers have on the line of best fit. With outlier: Without outlier: Equation: y = 0.000454x + 3.96 r 2 = 0.0016 r = 0.04 Equation: y = 0.0116x + 3.93 r 2 = 0.87 r = 0.93

3. Outliers Let’s see the effect outliers have on the line of best fit. With outlier: Without outlier: Equation: y = 0.0133x + 3.912 r 2 = 0.59 r = 0.77 Equation: y = 0.00829x + 3.926 r 2 = 0.85 r = 0.92

4. Outliers Let’s see the effect outliers have on the line of best fit. With outlier: Without outlier: Equation: y = 0.00895x + 3.95 r 2 = 0.46 r = 0.68 Equation: y = 0.00456x + 3.975 r 2 = 0.64 r = 0.80

5. Outliers Let’s see the effect outliers have on the line of best fit. With outlier: Without outlier: Equation: y = 0.00325x + 3.98 r 2 = 0.045 r = 0.21 Equation: y = 0.0118x + 3.93 r 2 = 0.83 r = 0.91

6. Outliers Compare the correlation before the outlier was removed to after it was removed. What effect does the outlier have on the line of best fit?. The outlier pulls the line of best fit towards it. The correlation becomes stronger when the outlier is removed. Before removal Equation: y = 0.00325x + 3.98 r = 0.22 After removal Equation: y = 0.0118x + 3.93 r = 0.91