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Definition: Regression is one of the methods used in business forecasting. The regression analysis is based on developing a definite model with the help of which it is possible to forecast the changes in revenues. The conclusions are made with references to the relationship reflected in the developed statistical model which is the result of the regression analysis. The statistical model depends on the correlation between a single dependent variable and independent variables (Collier & Evans, 2011, p. 223).
Regression models can be simple linear and multiple linear. Simple regression models examine the value of time to forecast the possible costs or revenues. Thus, simple linear regression is based on finding the correlation between two different variables with the help of the method of least squares (Collier & Evans, 2011, p. 224). If the primary variable of a simple linear regression model is time, a multiple linear regression model depends on using several independent variables.
Role: Regression analysis is actively used in business forecasting to develop the necessary strategies to predict and regulate costs and revenues. It is essential for the manager to analyze to forecast the numbers for the next year and prepare a budget (Collier & Evans, 2011, p. 224).
It is sufficient to use a simple linear model as well as a multiple regression model, but the infinite regression guarantees more accurate forecasting and more realistic data because of using more than one causal variable (Collier & Evans, 2011, p. 225). Thus, managers can use the regression model not only to examine the correlation of time and costs but also examine the dependence between time, price, and expenses.
Applicability: It is possible to state that McDonald’s also uses the regression analysis to forecast further revenues to plan the budget and strategy for the next year. The focus on the mathematical relationship between variables is a rather easy way to examine the current tendency and predict further changes because the regression analysis provides information about demands.
According to annual reports, McDonald’s usually examines the correlation between the variables and demand to identify the relationship between these variables and determine the causes which affect the development of the tendency and the current numbers to forecast the changes and possible connections in the future.
To save the leading position in the world as the successful company in the sphere of quick service restaurants, McDonald’s should regularly examine the past tendencies and relationships between the variables to predict the future results (Collier & Evans, 2011, p. 78-79).
For instance, according to the annual report of 2011, the company’s revenues increased 12% with 8% in constant currencies, and standard operating income increased 14% with 10% in constant currencies (McDonald’s Corporation: Annual Report 2011, 2012, p. 11). The annual report also provides the numbers concerning all the spheres of the company’s financial development.
The conclusions about the revenues are made with references to the graphs in which the regression models are used. These regression models can be also successfully used to predict the possible numbers for the next year, and the outlook for the year of 2012 provided by the managers and economists of McDonald’s is based on the conclusions made with references to the forecasting methods.
From this point, regression models (simple linear or multiple models) are essential to examine the changes in revenues from year to year to make the necessary predictions about the further growth or decline in revenues with references to the crucial variables. Thus, McDonald’s, as any other company, uses such forecasting methods as regression to control the situation and make different financial predictions.
References
Collier, D. A., & Evans, J. R. (2011). OM 3. USA: Cengage Learning. McDonald’s Corporation: Annual Report 2011. (2012). Web.
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