Quantitative Analysis & Decision Making
Conditional probability is the likelihood of an event to occur. As a marketing manager I would evaluate discrete data distribution by using statistical techniques. This will allow me to reduce uncertainty and summarize available knowledge for making right prediction. Point and interval estimates will be used for making true predictability CITATION Mar128 \l 1033 (LopesI, SilvaII, & Araujo, 2012). Discrete data is a good quality data in healthcare because it is measurable and reportable. This data will provide accurate estimation of the buyer base and will be appropriate for fulfilling the purpose of key business decision. One of the most important skills required by the manager is to understand the database. Statistical descriptors will be used for understanding the buyers preferences and the demand for the breakfast cereals. Although its is not possible to predict the exact demand but use of statistics will allow manager to find conditional probability. This will be adequate for determining if investing in the loyalty program is a rational decision or not.
Conditional probability depicts the chances for the acceptance of the loyalty program. The manager must bee able to analyze the data that will provide clear estimation of the future event. Repurchase is a popular method used for distributing cash and offer long-run benefits.
Other discrete data are not appropriate because they are difficult to compute and cannot be presented in the form of tables. This reflects the weakness of thee other discrete data. It would be difficult to produce meaningful results by relying on this data. The manager will rely on the knowledge of database that will predict the possible outcomes of the loyalty program. There are no limits to discrete data so it will provide accurate estimation.
BIBLIOGRAPHY Kok, R. M., & Reynolds, C. F. (2017). Management of Depression in Older Adults. JAMA, 317 (20), 2114-2122.
LopesI, M. V., SilvaII, V. M., & Araujo, T. L. (2012). Nursing diagnoses analysis under the bayesian perspective. Revista da Escola de Enfermagem da US , 46 (4).
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