Question 1 The Excel file contains data on 135 recent sales of single family hom

Question

Question 1

The Excel file contains data on 135 recent sales of single family homes in Center City.  

For each sale, the file shows the following variables:

1. Neighborhood (1, 2 or 3) in which the house is located

2. Number of offers made on the house

3. Square footage

4. Whether the house is made primarily of brick

5. Number of bathrooms

6. Number of bedrooms

7. Selling price

Neighborhoods 1 and 2 are more traditional older neighborhoods, whereas neighborhood 3 is a newer, more prestigious neighborhood.

Use regression analysis to come up with a "final" model and to estimate and interpret the factors affecting the selling price of houses in Center City.  

Here are some considerations:

(1) Do buyers pay a premium for brick houses, everything else being the same?

(2) Is there a premium for a house in neighborhood 3, everything else being the same?

(3) For purposes of estimation and prediction, could neighborhoods 1 and 2 be collapsed into a single "older" neighborhood variable?

Here is the link to the excel file... https://drive.google.com/open?id=1k3snpSFh23yiS0xgKMn3AggeueBg-mEPIkdHMi2JiHc

Explanation / Answer

For these analyzes, a multiple regression must be done in which one has several independent data and one result that is dependent, for the resolution of this exercise the Excel tool is used, through which data analysis can be done by means of a table, for your use just go in the toolbar to the section called data, there to the right above the window that says data analysis a window is displayed in which we can choose the type of data analysis to to do, the values ??values ??are chosen and the data is resolved, for the solution exercise the following was assumed:

• One in which the non-numeric variable is to say if the house had brick or was not translated into a numeric value where, if the house had brick we put a numerical value of 1, in case it did not have brick the numerical value is of 0, this is because the estimates and multiple regression are made based on numerical values

In addition to evaluate if it was possible to unite the neighborhood variable and if there is any incidence in the neighborhood was newer, the analysis model in two ways which are the following:

1. Leave the neighborhood column quiet, unaltered and do the multiple regression

2. Alter the neighborhood column as follows, if the neighborhood is "1" or "2" assign the element a value of 0 that would correspond to an older neighborhood, if the neighborhood is "3" assign a value to the element of 1 that would correspond to the newest neighborhood

To evaluate which is the closest case to reality, we can compare the value of R2, which is one of the results that the Excel tool gives me, and it is a measure of the degree of reliability or goodness of fit of the model adjusted to a data set, if it is closer to 1, the values ??present a greater fit with the model.

1. For the solution of model 1, he gave us the following result

Y = 4929.85548 + 18950.745 x1 -15128.8003 x2 +90.6159371 x3 + 31213.9433 x4 + 11242.4024 x5 + 12890.7273 x6

With a value of r2 of = 0.77512843

Where
x1 = Neighborhood
x2 = Number of offers
x3 = Sq Ft
x4 = Brick
x5 = Bedrooms
x6 = Bathrooms
2. For the solution of model 2, he gave us the following result

Y = 32770.4451 + 42686.27846x1 - 14530.74505x2 + 93.358424x3 + 34891.73097 x4 + 8365.703164 x5 + 11512.1304 x6

With a value of r2 of = 0.820888639

Where
x1 = Neighborhood
x2 = Number of offers
x3 = Sq Ft
x4 = Brick
x5 = Bedrooms
x6 = Bathrooms
As we see the models that best fit the data comparing the value of r2 in the second, on which basis we answer the questions:

(1) Do buyers pay a premium for brick houses, everything else is the same?

In the case that the house does not have bricks the reduced price, reason why it could be affirmed that a premium must be paid in the case of the house of the sea of ??brick

(2) Is there a premium for a house in neighborhood 3, everything else is the same?

In case the house is from neighborhood 3, a higher cost must be paid

(3) For estimation and prediction purposes, could neighborhoods 1 and 2 collapse into a single "older" neighborhood variable?

For the model to present a better estimate and prediction, it is due to neighborhoods 1 2 within the "old" variable that is equal to 0 for this given solution.

Related Questions

Navigate

• Browse (All)
• Browse Q
• Subjects

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.