Statistical data in a business - Essay Example

STATISTICAL BUSINESS DECISION MAKING One important aspect of business is making choices among several alternative projects when the available funds for investment are limited. The idea is to arrive at a reasonable estimate of the most probable return on investment of the several projects, to rank the projects based on their probable returns, and to choose the most successful projects as far as the available funds may support them. For instance: given the following data, we decide among three projects: Initial Investment Project A Project B Project C 50,000 50,000 100,000             Probability Distribution of Rates of Return Project A Project B Project C P ROI P ROI P ROI 5% -10% 25% -15% 10% 5% 10% -5% 50% 15% 15% 7% 20% 5% 25% 20% 45% 9% 30% 7% 20% 11% 20% 10% 10% 13% 10% 15% 5% 20% In solving for the expected return on investment, the theory of probability distributions should be applied – that is, the probability P should be multiplied by its corresponding value ROI, then all the products should be added for each project to obtain the weighted average ROI, which is the ROI expected for the project (Rachev, et al., 2010). The projects are thus compared against each other.

The resulting expected ROIs are therefore: A: 0.05(-10) + 0.10(-5) + 0.20(5) + 0.30(7) + 0.20(10) + 0.10(15) + 0.05(20) = 7% B: 0.25(-15) + 0.25(20) + 0.50(15) = 10% C: 0.10(5) + 0.15(7) + 0.45(9) + 0.20(11) + 0.10(13) = 9% The possible outcomes of the decision will be thus: the lowest investment needed for any of the projects is $50,000. Assuming that the company has only $50,000 a choice will have to be made between Projects A and B. The decision would then be to adopt Project B because of its higher expected ROI.

However, assuming that the firm has $100,000 to invest, then the choice would be between undertaking both Projects A and B together, and Project C alone. Project A and Project B share the $100,000 in equal proportion, therefore their combined expected ROI is: 0.50 x ROI of Project A + 0.50 x ROI of Project B = (0.50) (7%) + (0.50) (10%) = 8.5% Comparing, therefore, the alternatives – Project A and B together to earn 8.5%, or Project C to earn 9%, the decision should be for Project C because it promises the higher ROI.

It will be noticed that the data contained in the table above may be achieved only to this precision with possibly complex information systems that may be aided by computer applications – otherwise, there will have to be trade-offs in estimating, firstly, the possible returns on investment, and secondly, the probabilities that they will occur, in order to come up with a probability distribution as detailed as these. More often, precise data are difficult to arrive at in business (Powell & Baker, 2010), because there are so many variables in the business setting.

In many instances, there is a need to sacrifice precision in order to enhance accuracy, where there are subjective considerations (Browne, 1995) or where confidence level is low (Sekaran & Bougie, 2009). For instance, in the example given here, subjective elements enter into the estimation of both the ROIs and the probabilities, because business is dependent upon social, political, and economic forecasts which may be optimistic or pessimistic, depending on the forecaster. Also, seasoned analysts may be more confident with their estimations than others, but there is still an element of uncertainty.

Based on these factors, the decision maker may sacrifice some measure of precision in order to arrive at a decision he feels is more correct. References: Browne, J.; O’Sullivan, D.; International Federation for Information Processing (1995) Reengineering the Enterprise. London: Chapman & Hall. Powell, S.G. & Baker, K.R. (2009) Management Science: The Art of Modeling with Spreadsheets, 3rd edition. Hoboken, New Jersey: John Wiley & Sons, Inc. Rachev, S. T.; Hoechstoetter, M.; Fabozzi, F.J.; & Focardi, S.M. (2010) Probability and Statistics for Finance.

Hoboken, New Jersey: John Wiley & Sons, Inc. Sekaran, U. & Bougie, R. (2009) Research Methods for Business: A Skill Building Approach, 5th edition. Hoboken, New Jersey: John Wiley & Sons, Inc.