Give a practical interpretation of the estimate of the  slope of the least squares line.

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An oil exploration company wants to develop a statistical model to predict the cost of drilling a new well. One of the many variables thought to be an important predictor of the cost is the number of feet in depth that the must be drilled to create the well. Consequently, the company decided to fit the simple linear regression model,   where y = cost of drilling the new well (in $thousands) and x = number of feet drilled to create the well. Using data collected for a sample of  n=83 wells, the following results were obtained:

    = 10.5 + 16.20x
   
Give a practical interpretation of the estimate of the  slope of the least squares line.

An oil exploration company wants to develop a statistical model to predict the cost of drilling a new well. One of the many variables thought to be an important predictor of the cost is the number of feet in depth that the must be drilled to create the well. Consequently, the company decided to fit the simple linear regression model,   where y = cost of drilling the new well (in $thousands) and x = number of feet drilled to create the well. Using data collected for a sample of  n=83 wells, the following results were obtained:

    = 10.5 + 16.20x
   
Give a practical interpretation of the estimate of the  slope of the least squares line.

a. For every one dollar cost increase, we expect the depth to increase by 16.2 feet.

b. For a well with a depth of 0 feet, we estimate the cost to be $10,500.

c. For each additional foot of depth drilled, we estimate the cost to increase by $10,500.

d. For each additional foot of depth drilled, we estimate the cost to increase by $16,200.

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An oil exploration company wants to develop a statistical model to predict the cost of drilling a new well. One of the many variables thought to be an important predictor of the cost is the number of feet in depth that the must be drilled to create the well. Consequently, the company decided to fit the simple linear regression model,  

where y = cost of drilling the new well (in $thousands)

and x = number of feet drilled to create the well.

Using data collected for a sample of  n=83 wells, the following results were obtained:

    = 10.5 + 16.20x

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