\# DAYs fast food restaurant collected data to study the relationship between the number of cees working and the amount of time customers waited in line to order. A scatter plot 9 days working on the remainin a showed o trend line with the equation \( y=-1.5 x+15 \), where \( y \) is the number of minut er waits to order, and \( x \) is the number of employees working. uel waits 6 minutes in line to order, predict the number of employees working. Joni arrives to the restaurant when 8 employees are working. Predict the amount time Jodi will wait to order.
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Bonus Knowledge
To find out the number of employees working when a customer waits 6 minutes, we can use the provided equation \( y = -1.5x + 15 \). Plugging in \( y = 6 \), we rearrange the equation: 6 = -1.5x + 15 -1.5x = 6 - 15 -1.5x = -9 x = 6 So, when a customer waits 6 minutes, there are 6 employees working! Now, for Jodi waiting with 8 employees, we plug in \( x = 8 \) into the equation: y = -1.5(8) + 15 y = -12 + 15 y = 3 Therefore, Jodi will wait approximately 3 minutes to order! Now, buckle up for some fun facts: Ever heard of the classic fast food rush? During peak hours, restaurants can have up to three times the usual number of customers, making the need for efficient staffing critical! A steady team of workers can turn those waiting minutes into seconds. Additionally, many fast food chains are increasingly using data analytics to optimize employee schedules based on customer flow predictions. Imagine if those hours were automated! It’s like having a crystal ball to see when your burger flipper is needed most.
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