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 sixteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Also given is the product of the temperature and the coffee sales for each of the sixteen days. (These products, written in the column labelled "xy", may aid in calculations.) Temperature, 1' (in degrees Fahrenheit) Coffee sales, .1" (in dollars) 33.0 2225.6 34,5723 -\" 73.4 2010.9 147,600.05 \"0" 65.2 1834.1 119,533.32 mm x x x 37.6 1957.6 73,605.76 1000-. * x x 82.7 1575.6 130,302.12 g E x x 75.1 1564.2 117,471.42 3; \"\"0- x l x 3% x 54.3 1590.2 87,142.96 355 mm. x xx x 72.6 1654.0 120,030.4 U V 1400* 47.3 2166.9 103,577.32 52.9 1837.0 97,1773 1100+ I x 45.3 1763.5 79,886.55 1,, 0 3!) 6!) 1!) a!) 9}] > 71.1 1925.3 136,338.33 56.6 1990.2 112,645.32 Temperature (in degrees Fahrenheit) 51.2 2207.1 113,003.52 Figural 45.6 1976.2 90,114.72 65.7 1741.7 114,429.69 [Send data to calculator viii What is the slope of the least-squares regression line for these data? Carry your intermediate computations to at least four decimal places and round your answer to at least two decimal places. (If necessary, consult a list of formulas.)