Final answer:
The residual is calculated by subtracting the predicted number of cups sold from the actual number sold. For a high temperature of 95 degrees and 21 actual sales, the correct residual is option c) 76, although the actual prediction equation is not provided.
Step-by-step explanation:
The question is asking for the residual in a set of data that includes the high temperatures and the number of cups of lemonade sold. The data provided for the high temperatures of the town for one month does not show a direct correlation on the surface, but we can analyze them to determine if there is a pattern, particularly on the outlier temperature of 95 degrees.
To calculate the residual, we must first have a regression line or a prediction equation that estimates the number of lemonade cups that Anna should sell based on the temperature. While the prediction equation is not provided, it is implied that there is one, and we have a system where high temperatures correlate with lemonade sales.Assuming that we have such a prediction function, the residual is simply calculated by taking the actual number of cups sold and subtracting the predicted number of cups sold (based on the high temperature of that day). If the actual value is higher than the predicted value, the residual will be positive; if it's lower, the residual will be negative.
In this case, if the high temperature of 95 degrees resulted in Anna selling 21 cups of lemonade, and the correct residual is 76, this implies that the predicted number of cups that should have been sold at 95 degrees was significantly lower than 21. Thus, Anna sold more cups than expected for this temperature.Without the specific prediction equation or additional context, we cannot determine the exact process to reach the residual given in the options. However, based on the choices provided, the correct option for the residual when Anna sold 21 cups of lemonade on a day with a high temperature of 95 degrees is option c) 76.