Question

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 maxim&m 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. Temperature, X (in degrees Fahrenhet) Coffee sales, y (in dollars) 71.2 1657.6 1604.6 1536.2 2203.3 56.6 76.8 49.1 46.0 482 39.3 81.5 1800.8 1567.5 48.9 2167.1 75.9 1942.4 54.2 38.6 2255.7 2004.0 1834.2 71.4 64.6 58.6 Send data to Excel 29

Answer 1

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2. For any eruatiou f he int tntiou of X he for ameunt one unt in oremenl in nerares by 4h amouut- b . regression 2qunkion is - 25a 8 o auation 7erion Acconding t h dag ne n tdmpercobwre of inereare one a n da rare a Corusp ou o.49 doo ffe sales Ce gine b : -l0. 491 Con den «huu inenomse S o as derease , o Te obs Lrvid coffce value of coffeu Sales (y) uoueu temp. ir £56. 6 daqreer Fareukit i 1Co 4. 6 dellours (2nd ou predictsd Coffee sne valu (dollans) -from ern'on aguaeion in 2 500-o.4 x (56.6) 19o6 3 predicted co fefae The i1906 3 Saaler dol an