Question

1. which of the scatter plots could likely represent the number of food items ordered and the total number of calories consumed at a meal? explain.
2. for each scatter plot, label the type of association as positive, negative or none. then record whether it is linear or non - linear.

i can describe relationships in scatter plots from graphical representations.

1. compare scatter plots a and c.

a. which has a stronger association? explain.
b. which scatter plot has an outlier?

Explanation:

Step1: Analyze scatter - plot A

The points in scatter - plot A show a general upward trend (positive direction), and the points are somewhat clustered around an imaginary line. So, it has a positive association. Also, as the \(x\) - values (number of food items ordered) increase, the \(y\) - values (total number of calories) tend to increase.

Step2: Analyze scatter - plot B

The points in scatter - plot B are randomly distributed. There is no clear pattern (either linear or non - linear) that can be used to predict the relationship between the two variables. So, it has no association.

Step3: Analyze scatter - plot C

The points in scatter - plot C show a general downward trend (negative direction). As the \(x\) - values increase, the \(y\) - values tend to decrease. So, it has a negative association.

Step4: Analyze scatter - plot D

The points in scatter - plot D show a general upward trend (positive direction), and the points are more tightly clustered around an imaginary line compared to plot A. So, it has a positive association.

For question 5:
The number of food items ordered and the total number of calories consumed at a meal. As more food items are ordered, we expect more calories (in general). So, a positive association (either scatter - plot A or D). But if we assume a more typical meal (not extreme outliers), scatter - plot A (positive) is a more likely representation.

For question 6a:
Scatter - plot D has a stronger association. In a scatter - plot, a stronger association means that the points are more closely clustered around an imaginary line (either positive or negative). In plot D, the points are more tightly grouped compared to plot A.

Step5: Analyze for outliers

An outlier is a point that is far away from the general pattern of the other points. In scatter - plot A, if we assume a general positive trend, there are no points that are extremely separated from the rest of the data points. In scatter - plot D, also no extreme outliers. But if we consider the context of the problem (not given specific numerical data, but just visual inspection), if we assume a more "normal" meal (not a single item with an extremely high - calorie count), scatter - plot A has no outlier. But if we consider a more strict visual inspection (assuming no data manipulation), neither A nor D has a clear outlier. But if we assume a wrong - data - entry - like situation (not in the spirit of the problem, but for the sake of answering), if we consider the left - most point in plot A (if we assume a very low number of items with a relatively high calorie count as an outlier, but it's a stretch). However, if we go by the strictest visual (no numerical values), we can't be sure. But if we assume the problem expects an answer:

Step6: Re - evaluate

Since the problem is about scatter - plots:
For 6b: If we assume that in plot A, there is a point that is relatively apart from the general cluster (a bit of a stretch, but compared to plot D which is more uniform), plot A could be considered to have an outlier (if we assume a non - uniform cluster).

Answer:

4.
A. Positive, linear (assuming the points follow a somewhat straight - line positive trend)
B. None, non - linear (random distribution)
C. Negative, linear (assuming a straight - line negative trend)
D. Positive, linear (more tightly clustered positive trend)

1. Scatter - plot A (positive association is likely as more food items usually mean more calories)

6a. Scatter - plot D has a stronger association (points are more tightly clustered)
6b. Scatter - plot A (assuming a relatively apart point in the cluster as an outlier)