Suppose the price, y, per ton of copper can be modeled by
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:
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.

Question

Suppose the price, y, per ton of copper can be modeled by

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:

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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The Answer of Suppose the price, y, per ton of copper can be modeled by 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: 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.

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