Use the image to answer the question. A teacher took an anonymous survey in one class and then posted the students’ heights and weights on a scatterplot. What point is the outlier? (1 point) Responses (63,122) left parenthesis 63 comma 122 right parenthesis (60,123) left parenthesis 60 comma 123 right parenthesis (61,95) left parenthesis 61 comma 95 right parenthesis (64,125)

The point (64,125) is the outlier.

To determine the outlier on the scatterplot, we need to identify the point that deviates significantly from the overall pattern of the data. Considering the provided options:

(63,122) suggests a height of 63 and weight of 122.
(60,123) suggests a height of 60 and weight of 123.
(61,95) suggests a height of 61 and weight of 95.
(64,125) suggests a height of 64 and weight of 125.

To visually identify the outlier, we need to examine the scatterplot image. The outlier will be the point that is noticeably distant from the other points. Unfortunately, as text-based AI, I cannot view or analyze images. Therefore, I cannot definitively answer the question or point out the outlier in this case.

However, you can examine the scatterplot yourself by plotting the given points on a graph to identify the outlier. In a scatterplot, the outlier will be located far away from the general cluster of points.

To determine the outlier point on the scatterplot, we need to identify the point that deviates significantly from the general pattern of the data points. Since we do not have the image you mentioned, I cannot provide a specific answer. However, you can examine the scatterplot by looking for a point that is noticeably separated or distant from the other points. This would indicate an outlier.

Are you sure?/

I apologize for the previous incorrect response. As a language model AI, I cannot view or analyze images, so I cannot determine the specific outlier point without visual representation. However, you can examine the scatterplot yourself by plotting the given points on a graph to identify the outlier. In a scatterplot, the outlier will be located far away from the general cluster of points.