To answer this question correctly, we need to recall what "outlier" means. An outlier is a value that is much smaller or larger than the rest of the values in a set of data. Also, a data point that does not follow the same pattern as the rest of the set could be described as an outlier.

In this case, we are looking for a number that doesn't match with the other numbers provided in a set:

 has a much larger value than the other number in the set; thus,  is an outlier. 

To answer this question correctly, we need to recall what "outlier" means. An outlier is a value that is much smaller or larger than the rest of the values in a set of data. Also, a data point that does not follow the same pattern as the rest of the set could be described as an outlier.

In this case, we are looking for a number that doesn't match with the other numbers provided in a set:

 has a much larger value than the other number in the set; thus,  is an outlier. 

To answer this question correctly, we need to recall what "outlier" means. An outlier is a value that is much smaller or larger than the rest of the values in a set of data. Also, a data point that does not follow the same pattern as the rest of the set could be described as an outlier.

In this case, we are looking for a number that doesn't match with the other numbers provided in a set:

 has a much smaller value than the other number in the set; thus,  is an outlier. 

To answer this question correctly, we need to recall what "outlier" means. An outlier is a value that is much smaller or larger than the rest of the values in a set of data. Also, a data point that does not follow the same pattern as the rest of the set could be described as an outlier.

In this case, we are looking for a number that doesn't match with the other numbers provided in a set:

 has a much smaller value than the other number in the set; thus,  is an outlier. 

The data points in the scatter plot move up the y-axis as the x-axis decreases; thus the data points show a negative association. Also, the data points do not curve, or go up and down, but gradually decreased; thus the scatter plot shows a linear association. We could even draw a "best fit" line:

In the provided scatter plot, we can pick out data points and organize them from least to greatest, based on test grade:

Based on the results, we can see that the number of missing assignments decreased, the test grade increased; thus, a negative association, a higher number of missing assignments  correlated to a lower test score.

The data points in the scatter plot move up the y-axis as the x-axis increases; thus the data points show a positive association. Also, the data points do not curve, or go up and down, but gradually increase; thus the scatter plot shows a linear association. We could even draw a "best fit" line:

This data is quadratic rather than linear - it increases much more sharply than the line . It is also a positive correlation, so the equation should be positive. It is not because the data starts very near the origin .

A line of best fit is a line on a graph that follows the general direction of the data points displayed on the graph. The line of best fit should be as close as possible to all of the data points graphed. There should be just as many dots above the line as below the line. It displays where the two variables are correlated to one another.

Based on the options, the graph below has the line of best fit because it is close to all of the data points, follows the same direction as the points, and there are just as many data points below the line as above the line.

A line of best fit is a line on a graph that follows the general direction of the data points displayed on the graph. The line of best fit should be as close as possible to all of the data points graphed. There should be just as many dots above the line as below the line. It displays where the two variables are correlated to one another.

Based on the options, the graph below has the line of best fit because it is close to all of the data points, follows the same direction as the points, and there are just as many data points below the line as above the line.