The managers of an outdoor coffee stand in Coast City are examining the relationship between coffee sales and daily temperature. They have bivariate data detailing the stand's coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of 35 randomly selected days during the past year. The least-squares regression equation computed from their data is y = 2516.72 - 10.96x. Tomorrow's forecasted high is 68 degrees Fahrenheit. The managers have used the regression equation to predict the stand's coffee sales for tomorrow. They now are interested in both a prediction interval for tomorrow's coffee sales and a confidence interval for the mean coffee sales on days on which the maximum temperature is 68 degrees Fahrenheit. They have computed the following for their data. . mean square error (MSE) ~ 2290.61 (68- 7) + 35 35 ~ 0.0657, where X1, X2, ..., X35 denote temperatures in the sample, and x denotes their mean i = 1 Based on this information, and assuming that the regression assumptions hold, answer the questions below. (If necessary, consult a list of formulas.) (a) What is the 99% prediction interval for an individual value for coffee sales (in dollars) when the maximum temperature is 68 degrees Fahrenheit? (Carry your intermediate computations to at least four decimal places, and round your answer X to at least one decimal place.) Lower limit: Upper limit