Demonstrating the tools and techniques of market structure analysis is made difficult by
the fact that a firm’s competitive strategy is largely based upon proprietary data. Firms jealously
guard price, market share, and profit information for individual markets. No one should expect
Target, for example, to disclose profit-and-loss statements for various regional markets or on a
store-by-store basis. Competitors like Wal-Mart would love to have such information available.
It would provide a guide for their own profitable market entry and store expansion decisions.
To see the process that might be undertaken to develop a better understanding of product
demand conditions, consider the hypothetical example of Columbia Drugstores, Inc., based in
Seattle, Washington. Assume Columbia operates a chain of 30 drugstores in the Pacific
Northwest. During recent years, the company has become increasingly concerned with the longrun implications of competition from a new type of competitor, the so-called superstore.
To measure the effects of superstore competition on current profitability, Columbia asked
you to conduct a statistical analysis of the company’s profitability in its various markets. To net
out size-related influences, profitability was measured by Columbia’s gross profit margin, or
earnings before interest and taxes divided by sales. Columbia provided you with proprietary
company profit, advertising, and sales data covering the last year for its 30 stores, along with
public trade association and Census Bureau data concerning the number and relative size
distribution of competitors in each market, among other market characteristics.
You have decided to conduct a regression-based analysis of the various factors thought to
affect Columbia’s profitability. To aid you in this process, Columbia created the accompanying
spreadsheet entitled “Case_Data.xlsx.” The data contained in this spreadsheet are described as
follows, where the variable name (as it appears in the spreadsheet) is in italics.
The variable Store Number identifies a particular Columbia drugstore. The dependent
variable is Profit Margin, which as stated before, is Columbia’s gross profit margin. The
following independent variables are thought to affect Columbia’s profitability. The variable
Market Share is the relative size of leading competitors in a store’s market, measured at the
Standard Metropolitan Statistical Area (SMSA) level. Columbia’s market share in each area is
expected to have a positive effect on profitability. The Market Concentration Ratio, measured as
the combined market share of the four largest competitors in any given market, is expected to
have a negative effect on Columbia’s profitability given the stiff competition from large, well-

financed rivals. Both Capital Intensity, measured by the ratio of the book value of assets to
sales, and Advertising Intensity, measured by the advertising-to-sales ratio, are expected to exert
positive influences on profitability. Growth, measured by the geometric mean rate of change in
total disposable income in each market, is expected to have a positive influence on Columbia’s
profitability, because some disequilibrium in industry demand and supply conditions is often
observed in rapidly growing areas. Finally, to gauge the profit implications of superstore
competition, the variable Superstore Dummy takes the value of ‘1’ if Columbia faced superstore
competition in a particular store’s market and ‘0’ otherwise.
In five-to-seven pages of double-spaced writing in a Word document, answer the following
1. Based on the text above, build a multiple linear regression population model to analyze
the impact of the preceding determinants on Columbia’s profitability. What is the
multiple linear regression population equation? What are the assumptions underlying the
2. Using Excel and the accompanying dataset, estimate the population model. Copy and
paste your Excel output into your Word document.
3. Based on the Excel output, what is the estimated regression equation?
4. Interpret all coefficient estimates. Identify the significance level for all of these
estimates. Are any of the independent variables likely to actually influence Columbia’s
profitability? Are your estimates consistent or inconsistent with the a priori conjunctures
found in the article? (E.g., advertising intensity is thought, a priori, to increase profit
margin. Does your coefficient on advertising intensity and its associated p-value suggest
that it is directly correlated with profit margin?)
5. What portion of the variability in profit margin is explained by variability in the
independent variables? Is the estimated regression equation a good fit for explaining
profit margin?
6. Based on the estimate of the coefficient on Superstore Dummy and its associated p-value,
do you believe that superstores pose a threat to Columbia’s profitability? Expand on the
theoretical foundation for this conclusion, i.e., why would the existence of competitor
superstores affect Columbia’s profitability?…

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