Question 1

Find a linearizing transformation of y=1/(a + b x)

Accepted Answer

y' = 1/y, x' = x

Question 2

Consider the data set
  
A) Obtain the best fitting straight line with 
B) What proportion of the y'variability is explained by the fitted line?
Round your answers to three decimal places.

Accepted Answer

Part A:  = .180 - 1.940x
Part B: .993

Question 3

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 are: Predict the response for x₁= 2389 and x₂ = 2366.

Accepted Answer

$7131.07

Question 4

Consider the multiple linear regression model where , and the normal random variable e has standard deviation 6. What is the mean of the response Y when x₁ = 1 and x₂ = 4? A) 34 B) 19 C) 40 D) 25

Accepted Answer

The answer of Consider the multiple linear regression model

where...

Question 5

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.

Accepted Answer

The answer of Suppose the price, y, per ton of...

Quiz 12: Regression Analysis II Multiple Linear Regression and Other Topics

Find a linearizing transformation of y=1/(a + b x)

Consider the data set A) Obtain the best fitting straight line with B) What proportion of the y'variability is explained by the fitted line? Round your answers to three decimal places.

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 are: Predict the response for x1= 2389 and x2 = 2366.

Consider the multiple linear regression model where , and the normal random variable e has standard deviation 6. What is the mean of the response Y when x1 = 1 and x2 = 4?

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 are: Predict the response for x₁= 2389 and x₂ = 2366.

Consider the multiple linear regression model where , and the normal random variable e has standard deviation 6. What is the mean of the response Y when x₁ = 1 and x₂ = 4?