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Read the following description of a data set.\newlineThe operator of an electric plant was concerned about the performance of some of its wind turbines.She consulted the manuals to find the rotor diameter of each turbine (in meters), xx. Then, on a day with steady wind, she checked the control panel and noted each turbine's power output (in kilowatts), yy.The least squares regression line of this data set is:y=22.316x636.704y = 22.316x - 636.704\newlineComplete the following sentence:\newlineFor each additional meter of rotor diameter, the least squares regression line predicts the power output of a turbine would increase by ___ kilowatts.

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Q. Read the following description of a data set.\newlineThe operator of an electric plant was concerned about the performance of some of its wind turbines.She consulted the manuals to find the rotor diameter of each turbine (in meters), xx. Then, on a day with steady wind, she checked the control panel and noted each turbine's power output (in kilowatts), yy.The least squares regression line of this data set is:y=22.316x636.704y = 22.316x - 636.704\newlineComplete the following sentence:\newlineFor each additional meter of rotor diameter, the least squares regression line predicts the power output of a turbine would increase by ___ kilowatts.
  1. Calculate slope: The equation given is y=22.316x636.704y = 22.316x - 636.704. The slope of the least squares regression line is the coefficient of xx, which is 22.31622.316.
  2. Interpret slope: In the equation y=22.316x636.704y = 22.316x - 636.704, xx represents the rotor diameter of each turbine in meters, and yy represents the power output in kilowatts. The slope of 22.31622.316 indicates that for each one meter increase in rotor diameter, the power output of a turbine will increase by 22.31622.316 kilowatts.

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