An electric device delivers a current of [tex]$15.0 A$[/tex] for 30 seconds. How many electrons flow through it?
Answer (1)
A scatter plot is more appropriate than a line graph for displaying the relationship between the number of items purchased and the amount spent.
The association between the number of items purchased and the amount spent is positive.
The strength of the association is moderately strong and linear.
As the number of items purchased increases, the total amount spent tends to increase, showing a moderately strong, positive, and linear relationship.
Explanation
Understanding the Problem We are given a data set of customers' spending habits during the holiday season. The data includes the number of items bought and the total amount of money spent. We need to determine the appropriate graph type to display this data and describe the relationship between the two variables.
Choosing the Graph Type A scatter plot is more appropriate for displaying the relationship between two quantitative variables (number of items and amount spent). A line graph is typically used to show trends over time or ordered categories, which is not the case here. Therefore, a scatter plot is the better choice.
Determining the Association To determine the type of association, we can analyze the data. From the scatter plot (imagined or created), we can observe the general trend. As the number of items purchased increases, the total amount spent also tends to increase. This indicates a positive association. To quantify the strength and linearity, we can calculate the correlation coefficient. The correlation coefficient is approximately 0.79. This indicates a moderately strong, positive, and linear association.
Describing the Relationship Based on the analysis, the relationship between the number of items purchased and the total amount of money spent is a moderately strong, positive, and linear association. This means that as customers buy more items, they tend to spend more money, and this relationship can be roughly represented by a straight line.
Examples
Understanding the relationship between the number of items purchased and the amount spent can help retailers optimize their marketing strategies. For example, if a strong positive correlation is observed, the retailer might consider offering discounts on bulk purchases to encourage customers to buy more items, knowing that this will likely lead to increased overall spending. This type of analysis can also help in inventory management and forecasting sales during peak seasons.
