First and foremost, it is essential to identify the main elements of the statistics analysis. In the framework of this paper, the key statistical data that needs to be analyzed is the return and the standard deviation values for two stocks. Experts point out that the relevant variables are critical for performing consistent business decision making and generating recommendations regarding the stock investment (Sharpe, De Veaux, & Velleman, 2016).
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In the meantime, there are some other elements, apart from the return and the standard deviation, which could be useful to a financial advisor in order to carry out an effective decision making. Hence, for instance, some experts note that it is the probability variable that plays an important role in the statistics analysis (Siegel, 2011). This element offers insights on the chances at which a particular event can be realized.
Integration and Application
The next step in the business statistics analysis resides in interpreting the relevant data elements and integrating them in the decision making process. Generally speaking, the return variable signifies the ability of a firm, or stocks, in this particular case, to generate profit from the contributed investments. Therefore, a higher return coefficient will naturally add some advantage to the stock from the competitive perspective. Nevertheless, it would be irrational to rely on the return variable exclusively, neglecting other critical coefficients, such as standard deviation.
Standard deviation signifies the potential risk rate. Thus, Siegel (2011) describes this variable as “the risk in terms of approximately how far from the mean you can expect to be” (p.179). Therefore, while trying to carry out a consistent statistics analysis, it is highly important that the return variable is considered together with the deviation coefficient. Otherwise stated, a high return variable does not offer good prospects, in case it is accompanied by a high deviation coefficient – it is safer to give preference to those stocks that show relatively insignificant risks even if the expected return is not very high (Downing & Clark, 2010).
The stocks under analysis show relatively equal return variables. Hence, it is stated that Stock A offers 15% return, and Stock B is expected to bring 14% return. From this perspective, Stock A has an insignificant competitive advantage over Stock B. In the meantime, the discrepancy between the standard deviation coefficients is rather considerable. Hence, the deviation for Stock A is 8.3%, while Stock B shows a lower deviation coefficient – 2.1%. Therefore, it is considered rational to recommend Stock B – in this case, the client is more likely to receive the expected return. As long as the difference between the expected returns is only 1%, the risk that choosing Stock A implies is not worth the benefit.
The offered recommendation is likely to have a positive impact on the client’s decision making. In case the client decides to follow the recommended scenario and choose Stock B, he or she is less exposed to the risk of losing profits. Even if the deviation of 2.1% is taken into account, the minimal revenue that the client might account for is 11.9%, which is only 3.1% lower than the maximal revenue he or she can receive making a riskier decision and choosing Stock A.
Meanwhile, it should be pointed out that the recommendation has been generated in the context of the suggested choice. Thus, the recommended stock is beneficial only in terms of its comparison to the other stock. In fact, the revenues that both stocks offer are rather low.
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Downing, D., & Clark, J. (2010). Business Statistics. New York, New York: Barron’s Educational Series.
Sharpe, N.R., De Veaux, R.D., & Velleman, P. (2016). Business Statistics. New York, New York: Pearson Education.
Siegel, A. (2011). Practical Business Statistics. Burlington, Massachusetts: Academic Press.