Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by X, in degrees Fahrenheit) for each of randomly selected days during the past year are given below. These data are plotted in the scatter plot below. Temperature, Coffee sales, (in degrees Fahrenheit) (in dollars) 57.7 1955.6 62.2 1814.7 2400- 71.7 1953.8 2200 74.6 1516.1 56.5 1592.9 2000 52.5 1812.2 1800- 2180.4 (In dollars) 46.8 Coffee sales, y 1600- 67.3 1712.3 83.9 1517.2 1400 75.8 2000.2 1200 51.6 2198.3 73.6 1675.4 40 60 70 39.2 2229.3 Temperature, x 44.9 1799.6 (in degrees Fahrenheit) 37.5 1965.3 18.8 1998.0 After deciding on the appropriateness of a linear model relating coffee sales and maximum temperature, the managers calculate the equation of the least- squares regression line to be y = 2460.75-10.01x. This is the line shown in the scatter plot above. Based on the sample data and the regression line, complete the following. (a) For these data, temperature values that are less than the mean of the temperature values tend to be paired with coffee sales values that are (Choose one) | the mean of the coffee sales values. X ? (b) According to the regression equation, for an increase of one degree in temperature, there is a corresponding (Choose one) v of 10.01 dollars in coffee sales. (c) What was the observed coffee sales value (in dollars) when the temperature was 44.9 degrees Fahrenheit? 0 (d) From the regression equation, what is the predicted coffee sales value (in dollars) when the temperature is 44.9 degrees Fahrenheit? (Round your answer to at least one decimal place.)