An outlier is defined as a point that does not fit within the general pattern of the data. Thus, we are looking for a height that is not within the normal range for an adult male, and shoe size which is outside of the range for an adult male. Typically, an adult male would be between 65 and 77 inches tall (5 feet 5 inches and 6 feet 5 inches). Typically, an adult male's shoe size would be around a 10. Thus, the outlier would have height and shoe size drastically different from these, $\displaystyle (50, 16)$. 

A positive association is defined as a scatterplot on which the best fit line has a positive slope.

This pattern is identified because on the graph, looking from left to right, the vast majority of the points goes up.

This can also be described by saying, "as $\displaystyle x$ increases, $\displaystyle y$ increases". 

When creating a scatterplot, data is collected. This data is formulated into ordered pairs. Each of these ordered pairs, which are later graphed, represent one person's data. Thus, this particular piece of data would represent one man's height of $\displaystyle 72$ inches and that same man's shoe size of $\displaystyle 13$. 

It has a positive correlation because the points all trend upward. In other words, as the independent variable on the x-axis increases, the dependent variable on the y-axis also increases. Therefore, the line of best fit that is drawn through the data represents a positive line as it has a positive slope. This verifies that our data has a positive correlation.

I. is a true statement about the scatter plot: as quality increases, price tends to increase.

II. is not true - the points under the line have a relatively low price compared to their quality.

III. is also not true - the points above the line have relatively low quality compared to their price.

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 follow the same pattern as the rest of the set could be described as an outlier. In this case, if we had a student that studied $\displaystyle 5$ hours, but received a test score of $\displaystyle 10\%$ that data point would be considered an outlier because it doesn't follow the same pattern as the rest of the set. However, there are no data points in this set that don't follow the pattern; thus, there are no outliers in this set. 

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

Based on the results, we can see that as the number of hours spent studying increased, the test grade also increased; thus, a positive association, a higher number of hours spent studying correlated to a higher test score.

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 follow the same pattern as the rest of the set could be described as an outlier. In this case, if we had a student that had $\displaystyle 10$ missing assignments, but received a test score of $\displaystyle 100\%$ that data point would be considered an outlier because it doesn't follow the same pattern as the rest of the set. However, there are no data points in this set that don't follow the pattern; thus, there are no outliers in this set. 

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. 

Let's look at our answer choices:

$\displaystyle (2,50)$

This point is showing that a student who studied for $\displaystyle 2$ hours received a $\displaystyle 50\%$ on the test. If we look at our graph, we can see that the two students that spent $\displaystyle 2$ hours studying received scores of $\displaystyle 45\%$ and $\displaystyle 55\%$. A score of $\displaystyle 50\%$ fits in with that data; thus, $\displaystyle (2,50)$ is not an outlier. 

$\displaystyle (1,47)$

This point is showing that a student who studied for $\displaystyle 1$ hour received a $\displaystyle 47\%$ on the test. If we look at our graph, we can see that the two students that spent $\displaystyle 1$ hour studying received scores of $\displaystyle 45\%$ and $\displaystyle 50\%$. A score of $\displaystyle 47\%$ fits in with that data; thus, $\displaystyle (1,47)$ is not an outlier. 

$\displaystyle (2,52)$

This point is showing that a student who studied for $\displaystyle 2$ hours received a $\displaystyle 52\%$ on the test. If we look at our graph, we can see that the two students that spent $\displaystyle 2$ hours studying received scores of $\displaystyle 45\%$ and $\displaystyle 55\%$. A score of $\displaystyle 50\%$ fits in with that data; thus, $\displaystyle (2,52)$ is not an outlier. 

$\displaystyle (5,10)$

This point is showing that a student who studied for $\displaystyle 5$ hours received a $\displaystyle 10\%$ on the test. If we look at our graph, we can see that the student who spent $\displaystyle 5$ hours studying received a score of $\displaystyle 90\%$. Also, if we look at the student who studied for $\displaystyle 4$ hours, that student received an $\displaystyle 80\%$. Based on this results,  $\displaystyle (5,10)$ would be 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. 

Let's look at our answer choices:

$\displaystyle (2,92)$

This point is showing that a student had $\displaystyle 2$  missing assignments received a $\displaystyle 92\%$ on the test. If we look at our graph, we can see that the student that had $\displaystyle 2$ missing assignments received a score of $\displaystyle 88\%$ . A score of $\displaystyle 92\%$ fits in with that data; thus, $\displaystyle (2,92)$ is not an outlier. 

$\displaystyle (3,80)$

This point is showing that a student who had $\displaystyle 3$ missing assignments received an $\displaystyle 80\%$ on the test. If we look at our graph, we can see that the student that had $\displaystyle 4$ missing assignments received score of $\displaystyle 75\%$. A score of $\displaystyle 80\%$ fits in with that data; thus, $\displaystyle (3,80)$ is not an outlier. 

$\displaystyle (7,55)$

This point is showing that a student who had  $\displaystyle 7$ missing assignments received a $\displaystyle 55\%$ on the test. If we look at our graph, we can see that the two students that had $\displaystyle 7$ missing assignments received scores of $\displaystyle 50\%$ and $\displaystyle 60\%$. A score of $\displaystyle 55\%$ fits in with that data; thus, $\displaystyle (7,55)$ is not an outlier. 

$\displaystyle (2,15)$

This point is showing that a student who had $\displaystyle 2$ assignments missing received a $\displaystyle 15\%$ on the test. If we look at our graph, we can see that the student who had $\displaystyle 2$ missing assignments received a score of $\displaystyle 88\%$. Also, if we look at the student who had $\displaystyle 4$ missing assignments, that student received a $\displaystyle 75\%$. Based on this results,  $\displaystyle (2,15)$ would be an outlier.