The scatter plot shows the number of minutes people had to wait for service at a restaurant and the number of staff available at the time.[br][br][img]data:image/png;base64,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[/img][br][br] [br]A line that models the data is given by the equation y = -1.62x + 1.8, where y represents the wait time, and x represents the number of staff available.[br][br]a. The slope of the line is -1.62. What does this mean in this situation? Is it realistic?[br][br]b. The y-intercept is (0, 18). What does this mean in this situation? Is it realistic?
a. Sample response: Each additional staff member available decreases wait time by about 1.62 minutes. This is probably realistic since more staff means more people available to help customers, so the wait time will decrease, and about 2 minutes per additional person seems reasonable.[br][br]b. This means that when 0 staff are available, the wait time is 18 minutes. Sample responses:[br][list][*]This is unrealistic since a person would wait forever if no staff were there to serve them.[/*][*]This could be realistic if people have to wait 18 minutes for some staff to arrive at the restaurant and serve the customer.[/*][/list]