A real estate agent wants to predict the selling price of single-family homes from the size of each house. A scatterplot created from a sample of houses shows an exponential relationship between price, in thousands of dollars, and size, in 100 square feet. To create a linear model, the natural logarithm of price was taken and the least-squares regression line was given as ln( price ˆ )=2.08+0.11(size) ln⁡(price^)=2.08+0.11(size) . Based on the model, which of the following is closest to the predicted selling price for a house with a size of 3,200 square feet?

Respuesta :

Answer:

[tex]\text{Selling price} = \$1000e^{5.6}[/tex]

The selling price of house is approximately 270.4264 thousand dollars.

Step-by-step explanation:

We are given the following in the question:

A linear model gives the relation between  natural logarithm of price, in thousands of dollars, and size, in 100 square feet.

[tex]\ln( \text{price})=2.08+0.11(\text{size})[/tex]

Let p be the price and s be the size.

[tex]\ln(p) = 2.08 + 0.11(s)[/tex]

We have to approximate the selling price for a house with a size of 3,200 square feet.

Thus, we put s = 32

[tex]\ln(p) = 2.08 + 0.11(32)\\\ln(p) = 5.6\\p = e^{5.6}\\p = 270.4264\\\text{Selling price} = \$1000e^{5.6}[/tex]

Thus, the selling price of house is approximately 270.4264 thousand dollars.

Using the regression line, it is found that the predicted selling price for a house with a size of 3,200 square feet is of $270,430.

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The regression line states that the price, in thousands of dollars, for a house of size s, in hundreds square feet, it given by:

[tex]\ln{p} = 2.08 + 0.11s[/tex]

  • Size of 3,200 square feet, thus [tex]s = \frac{3200}{100} = 32[/tex].

Then, the price is:

[tex]\ln{p} = 2.08 + 0.11(32)[/tex]

[tex]\ln{p} = 5.6[/tex]

[tex]e^{\ln{p}} = e^{5.6}[/tex]

[tex]p = 270.43[/tex]

The predicted selling price for a house with a size of 3,200 square feet is of $270,430.

A similar problem is given at https://brainly.com/question/22992800