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Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (°F), as the explanatory variable and the number of ice bags sold that day as the response variable. The least squares regression (LSR) line for the data is Yˆ = −114.05+2.17X. On one of the observed days, the temperature was 82 °F and 66 bags of ice were sold.
1. Determine the number of bags of ice predicted to be sold by the LSR line, Yˆ, when the temperature is 82 °F.
2. Compute the residual at this temperature.

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Answer:

Kindly check explanation

Step-by-step explanation:

Given the following :

Equation of regression line :

Yˆ = −114.05+2.17X

X = Temperature in degrees Fahrenheit (°F)

Y = Number of bags of ice sold

On one of the observed days, the temperature was 82 °F and 66 bags of ice were sold.

X = 82°F ; Y = 66 bags of ice sold

1. Determine the number of bags of ice predicted to be sold by the LSR line, Yˆ, when the temperature is 82 °F.

X = 82°F

Yˆ = −114.05+2.17(82)

Y = - 114.05 + 177.94

Y = 63.89

Y = 64 bags

2. Compute the residual at this temperature.

Residual = Actual value - predicted value

Residual = 66 - 64 = 2 bags of ice