Question

LA Real Estate Data. On a particular day in the spring, there were several properties for sale in Los Angeles. The dataset LARealEstate.xlsx on Blackboard contains the data used for this analysis (See Exhibit 1 for output). The relevant variables for this analysis are: 1. List Price: Saft Price the property is currently listed for Square footage of the living space To create the output yourself: .Excel: Data - Data Analysis- Regression, select the Y and X columns, including variable names, check the "Labels in First Row" option, and hit OK JMP: Analyze- Fit Model, select the Y column, put the X variable in the Construct Model Effects area and click "Run." . What is the predicted change in List Price related to an increase of one SQFT in size? Find the predicted List Price and residual (or error) values for the first property; i.е., 3182 SQFT and an actual List Price of $3,695,000. Interpret both of these values What is the intercept? What is its meaning? What percent of fluctuation in List Price can be explained when including the SQFT variable? What is the standard deviation around the regression line? a) b) c) d) e)

Answer 1

(a) For increase of 1 SQFT in size, predicted change in List Price = $1305.827.

(b) For x = 3182, predicted list price = -765663 + (1305.827 * 3182) = $3,389,479.
residual = $(3,695,000 - 3,389,479) = $305,521.
For 3182 SQFT, the predicted list price of the property is $3,389,479 and considering the actual list price, we can conclude that we have under-predicted (since the predicted value is less than the actual value) by $305,521.

(c) The intercept here is the predicted list price of the property if the SQFT value becomes 0. Its value here is -$765,663. In most cases, the intercept has no practical meaning.

(d) Percent of fluctuation = R-square * 100% = 78.1%.

(e) Standard deviation = Root mean square error = 3167283.