Essay Sample on Basic Concepts of Probability

Probability distribution in business is critical as it helps the business management in forecasting the future sales trends and levels (Franke et al., 2019). Scenario analysis which is based upon probability is essential in aiding companies to formulate their strategies on levels of sales based on worst-case and best-case scenarios. It is critical to the reflection of the chances that something will happen in a particular pattern (Franke et al., 2019). This paper discusses the importance of the application of basic probability concepts by analyzing the application of the concepts in two peer-reviewed articles.

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Both internal and external factors are in a position to influence property value. Through the use of the hedonic model, various explanatory variables are applied to control observable different property values. Features that can be seen are standing higher chances of influencing market value pricing and the buying trends. The probability will depend on statistical publications because in the cases where something is not right with the surroundings of the property being sold and the public is not aware of the problems, the price implication will be very minimal. The implications that are most publicized will have a significant impact on the value of a property by above 10% (Franke et al., 2019).

According to Franke et al., (2019), the coefficient of variation and quartile analysis are usable in the simple numerical analysis of data to come up with a probability distribution. It is possible to apply fundamental statistical analysis on numerical data in any field of practice to establish probability distribution of economic performance. The combination of these two concepts can add objectivity to data analysis. In other words, the coefficient of variation and quartile analysis combined forms a summation of pattern analysis of data (Li, 2017). This pattern, however, is challenged by the problem of subjectivity and paucity of research outcomes. Compared to the use of standard deviation alone to assess variability, CV and QA do not have the limitations of mean non-accountability (Li, 2017).

From the sampled articles, there is an incorporation of sample spaces, events and their probabilities. There has been test space of an arbitrary experiment in the process of collection of all predictable results. When we have an occasion which can be related to an arbitrary test, then it becomes a subgroup of the test space (Grami, 2019). Therefore, any possible outcome will have a probability of between 0 and 1. The summation of all the probable outcomes is 1. The likelihood of the event equals the synopsis of the probability of the result (Grami, 2019).

Conditional probability and independent events have also been incorporated in the sampled articles. In conditional probability, the chance is that an event has already taken place, putting into considerations external information about the experiment's results. (Li, 2017) This kind of probability is computable through discarding a portion of the test space. These two occasions are indipendend variables (Li, 2017).

By interpretation, sampling is one of the ways to come up with probability distribution in business forecasting. The outcome of a conditional probability depends on the behaviour of targeted variables. Fundamental data analysis is useful in understanding and formulation of likelihood in the formulation of a business probability distribution. The measure of uncertainty of any probability distribution of identifiable random variables is dependable upon the corresponding distributions of entropy.

References

Franke, J., Härdle, W. K., & Hafner, C. M. (2019). Basic concepts of probability theory. In Statistics of Financial Markets (pp. 37-47). Springer, Cham.

Grami, A. (2019). Basic Concepts of Probability Theory.

Li, X. R. (2017). Basic Concepts in Probability. In Probability, Random Signals, and Statistics (pp. 9-64). CRC Press.