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Suppose the Price, Y, Per Ton of Copper Can Be

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Suppose the price, y, per ton of copper can be modeled by Suppose the price, y, per ton of copper can be modeled by where the two predictor variables x<sub>1</sub> and x<sub>2</sub> are the prices per ton of zinc and lead, respectively. The least squares estimates, based on monthly observations in a five-year period, are: Assuming that the residual sum of squares (SSE) is 1424.48 and the SS due to regression is 5150.68. A) Estimate the error standard deviation. B) State the degrees of freedom used in part A. C) Find R<sup>2</sup>.
where the two predictor variables x1 and x2 are the prices per ton of zinc and lead, respectively. The least squares estimates, based on monthly observations in a five-year period, are: Suppose the price, y, per ton of copper can be modeled by where the two predictor variables x<sub>1</sub> and x<sub>2</sub> are the prices per ton of zinc and lead, respectively. The least squares estimates, based on monthly observations in a five-year period, are: Assuming that the residual sum of squares (SSE) is 1424.48 and the SS due to regression is 5150.68. A) Estimate the error standard deviation. B) State the degrees of freedom used in part A. C) Find R<sup>2</sup>.
Assuming that the residual sum of squares (SSE) is 1424.48 and the SS due to regression is 5150.68.
A) Estimate the error standard deviation.
B) State the degrees of freedom used in part A.
C) Find R2.

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