To determine the correct scatterplot that represents the data of lemonade sold versus temperature, let's first summarize the values given in your table:
Temperature (°F) vs. Number of Cups of Lemonade Sold
- (78, 15)
- (95, 35)
- (79, 22)
- (72, 15)
- (65, 10)
- (82, 26)
- (68, 10)
- (75, 18)
- (89, 34)
Now, to create a scatterplot, you would plot these points on a graph where the x-axis represents the temperature (°F) and the y-axis represents the number of cups of lemonade sold.
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Plotting Points: For each pair of values from the table, mark a point on the graph.
- For example, the point (78, 15) means you would go to 78 on the x-axis and then up to 15 on the y-axis and mark the point.
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Assessing Trends:
- Based on the data, you would expect to see a general trend where higher temperatures correlate with higher lemonade sales, particularly around the mid to high temperatures in this dataset.
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Finding Scatterplots: Look for scatterplots that follow this increasing trend.
- The points for lower temperatures (65°F with 10 cups, 68°F with 10 cups, etc.) are clustered at the lower end of the y-axis.
- The points for higher temperatures (like 95°F) should show an increase in the number of cups sold.
Without the visual of the scatterplots to choose from, you should look for the following characteristics when making your selection:
- The scatterplot should show an upward trend with increasing temperature showing an increase in lemonade sales.
- Points corresponding to lower temperatures should have lower cup sales, and vice versa for higher temperatures.
If you have specific scatterplot options to evaluate, compare them based on the above criteria to find the one that correctly represents the data from your table.