Research statistical data in a business context that requires a decision – Assignment Example

Best Bet for Qantas Airways Ltd. Background of the Study Qantas Airways Ltd has an extra A billion to invest in their different business segments, International airline operations, Domestic airline operations, and Subsidiary operations. Typically, the company would want to maximize their earnings for the coming year. It is in this regards that time series forecasting using probability theory comes into play. Time series forecasting aids business in increasing profit while at the same time reducing profits. It is a process that aids businesses and investors in estimating a number of values from required capital to stock exchange position (Armstrong, 2001). Moreover, it allows analysts to make reliable and logical management decisions that are crucial to the organization. Although these methods cannot guarantee to make the uncertain future completely reliable, the use of statistical and mathematical methods allows businesses to properly plan business activities (Anderson, Sweeney, & Williams, 2009). In this particular case, probability theory and forecasting will be used to help Qantas Airways Ltd. decide on where to invest A$ 1 billion for their 2011 services. This is a particularly crucial decision because investing in a profitable business segment would entail larger profit returns while investing in a poorly-performing segment could possible yield major losses for the company. Methodology As a business strategist, I researched the earnings of Qantas Airways Ltd. from 2004 to 2010 and categorized them according to the three business segments. In order to predict which business segment would yield the most profitable investment, the probability of earnings of each segment will be computed using Exponential Smoothing, a process that smooths data based on a given trend (Kazmier, 2009). E1 = event that International airline operations would yield earnings E2 = event that Domestic airline operations would yield earnings E3 = event that Subsidiary operations would yield earnings These percentage earnings that will be used as probability values are given in Table 1. Qantas Airways must properly decide how to invest their money among their three business segments in order to maximize their returns. Presentation of Data Table 1. Qantas Airways Ltd. Earnings by Business Segment.   Earnings (%) Business segment 2004 2005 2006 2007 2008 2009 2010 International airline operations 53 50 49 38 41 64 28 Domestic airline operations 30 35 34 35 32 20 47 Subsidiary operations 17 15 17 27 27 16 25 Source: (Bloomberg Businessweek and Qantas Airways websites) (Eqn. 1) Forecast Value = Last year’s value* .90 + Forecast value * .10. With the given values in Table 1 and the formula for the forecast value in Eqn. 1, the forecast value for each business segment for 2011 was generated. As such, the following probability values shall apply: P(E1) = probability that International airline operations would yield earnings in 2011 P(E2) = probability that Domestic airline operations would yield earnings in 2011 P(E3) = probability that Subsidiary operations would yield earnings in 2011 Data from Table 1 together with the corresponding formula were inputted in Excel and yielded the following results. Table 2. Probability of Earnings Forecast for 2011 by business segment. Business segment International airline operations Domestic airline operations Subsidiary operations Actual Forecast Actual Forecast Actual Forecast 2004 53 50 30 30 17 20 2005 50 53 35 30 15 17 2006 49 50 34 35 17 15 2007 38 49 35 34 27 17 2008 41 39 32 35 27 26 2009 64 41 20 32 16 27 2010 28 62 47 21 25 17 2011   31   44   24 Interpretation of Data In order to interpret the data, I decided to use the Probability Theory around Forecasting because this would provide the proper data interpretation for the needed analysis. Results from Table 2 suggest that in 2011, the probability that International airline operations will yield earnings is 31%, the probability that Domestic airline operations will yield earnings is 44% and the probability that Subsidiary operations will yield earnings is 24%. This means that Qantas Airways may greatly profit by putting majority of their investments in their Domestic airlines operations because this would yield the highest earnings for 2011. Moreover, Qantas Airways should take caution in investing too much on their Subsidiary operations as this will most likely yield less earnings compared to the two other business segments. As with any major decisions, these decisions likewise entail a certain amount of risk and going with such decisions would also depend on the decision maker’s tolerance for risk (Stonehouse & Campbell, 2004). Conclusion and Recommendation Many business decisions are made on a regular basis. While the variability of real-world situations may provide business with an infinite number of possible scenarios, statistics allows managers and other top-level executives with a decision-making tool based on sound, quantifiable data. In this case, Qantas Airways Ltd. benefited from the assistance of Probability Theory and Forecasting in deciding how to invest A$ 1 billion. Of course, it should be remembered that such methodologies may also have its weaknesses. For example, there is no way to simultaneously satisfy the different underlying assumptions of forecasting and thus, may result to misleading results (Wang & Jain, 2003). As with any inferential technique that only works perfectly on paper, probability theory and forecasting both cannot guarantee the absence of mistakes and errors. However, using such methodologies are far better options when making managerial decisions rather than simply pulling a decision out of a hat, and hoping that it would work. References Anderson, D. R., Sweeney, D., & Williams, T. (2009). Statistics for Business and Economics. Mason, OH: Thomas Higher Education. Armstrong, J. S. (2001). Principles of forecasting: A handbook for researchers and practitioners. New York: NY: Springer Science + Business Media, Inc. Kazmier, L. (2009). Schaums outline of business statistics. McGraw-Hill Professional. Stonehouse, G., & Campbell, D. (2004). Global and transnational business: strategy and management. John Wiley & Sons. Wang, G., & Jain, C. (2003). Regression analysis: Modeling and forecasting. Institute of Business Forec.