a. Use a regression program to estimate a linear demand equation. If
price is cut by $.10, by how much will the volume of sales increase?
b. Plot the 12 data points and the estimated regression line on a
quantity-price graph. Does the scatter of points look linear?
c. Use a log-linear regression to estimate a demand curve of the form:
Q kP. What is the price elasticity of demand? Does this equation
fit the data better than the linear equation in part (b)? Explain.
S3. You live in a neighborhood development of very similar homes
(roughly the same floor plans and lot sizes). You are considering
possible home improvements, not only for their immediate value to
you but also for the purpose of raising your home’s value over the next
five years (when you are likely to sell it to move into a larger house as
your family grows). The table shows the prices (in thousands of dollars)
of nine homes that have sold during the last 10 months, featuring
various improvements.^13 The dummy variable “one” indicates the
presence of an improvement.Selling New New Landscaping Central Air
Price Bathroom Kitchen and Patio Pool Conditioning
272 1 1 1
283 1 1 1
294 1 1 1
253 1 1
246 1 1
279 1 1
319 1 1 1
230 1
300 1 1a. Compute a multiple-regression equation estimating a typical home’s
sales price based on the various improvements. (Hint: Begin by
including all five improvements as explanatory variables, coded as
dummy variables: a “one” to denote an improvement, a “zero” if the
house lacks a given improvement.)
b. Which improvements make a significant positive difference in the
value of the typical home in the development?
c. In exploring the costs of making the various improvements, you have
found that a new bathroom will cost about $19,000, a new kitchen
$55,000, landscaping and patio $14,000, a pool $38,000, and central176 Chapter 4 Estimating and Forecasting Demand(^13) This problem was inspired by J. Toczek, “Home Improvement,” ORMS Today(October 2010);
p. 12.
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