Reference no: EM132474232
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 fifteen randomly selected days during the past year are given below.
Temperature, x (in degrees Fahrenheit) Coffee sales, y (in dollars)
80.8 1549.0
43.9 1788.9
47.9 2118.1
75.8 1518.6
50.4 2195.4
56.3 1943.0
56.1 1562.9
73.0 1699.8
64.4 1838.1
66.8 1741.8
40.4 2243.4
72.0 1942.6
76.3 2023.2
39.1 2004.4
45.4 1979.4
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= 2445.11 - 9.60x.
1. For these data, coffee sales values that are greater than the mean of the coffee sales values tend to be paired with temperature values that are _____ the mean of the temperature values. (greater than or less than)
2. According to the regression equation, for an increase of one degree in temperature, there is a corresponding _____ of 9.60 dollars in coffee sales.
(increase or decrease)
3. What was the observed coffee sales value (in dollars) when the temperature was 40.4 degrees Fahrenheit?
4. From the regression equation, what is the predicted coffee sales value (in dollars) when the temperature is 40.4 degrees Fahrenheit?
(Round your answer to at least one decimal place.)