Managers of an outdoor coffee stand in Coast City are examing 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 fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1.

Temperature, x(in degrees Fahrenheit) Coffee sales, y(in dollars)

47.91 984.7

55.1 1566.5

57.9 1926.2

44.1 1760.8

76.2 1511.7

75.5 1958.6

81.8 1553.2

39.3 2234.4

52.0 2162.1

46.3 2198.7

40.7 1984.6

72.3 1988.6

62.2 1791.6

74.0 1658.6

67.7 1737.2

The value of the sample correlation coefficient r for these data is approximately 0.612. Answer the following. Carry your intermediate computations to at least four decimal places, and round your answers as specified below. (If necessary, consult a list of formulas.)

What is the value of the slope of the least-squares regression line for these data? Round your answer to at least two decimal places.

What is the value of the y-intercept of the least-squares regression line for these data? Round your answer to at least two decimal places.