Probability and Statistics: Decision of Uncertainty

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Introduction

This assignment shall evaluate research statistical data in a business contest that requires decisions to be done. It shall mainly concentrate on the use of probability concepts to make decisions required in the business I shall explain the research processes and methods which I use to limit uncertainty in decision making. According to Bradshow, (2006) probability is a mathematical concept that is used to draw a conclusion based on the likelihood of potential events.

Application of Probability concept in decision formulation

I applied concepts of probability as a statistician to obtain both differences and similarities in the aspects which are being reviewed for decision making. By obtaining both of them I am better placed to base my arguments on making decisions. I use computational probability to develop measures of data relationship and properties as well this finding enables an easier understanding of the relationship of data that is being analyzed for decision making.

There are two types of arguments that are being used in research which include induction and deduction. A deduction is a form of argument that seems to be conclusive and it must have followed from the reason provided. The conclusion being done from the research must posses’ two aspects which include the validity of the research being done while the other one is truth based on the reasons which are done.

Application & Probability concepts and its to limit uncertainty

Probabilistic &Statistical methods of analysis and decision making have been more powerful because of the use of computer applications for their delivery. The use of these applications reduces uncertainty indecision-making. According to Arsham, (2009) good decision-making in a contest of business statistics is achieved when it is done in the face of uncertainty.

Its application is diversified in different business decision making which is done on the daily operations of such businesses. The decisions being done are based on financial analysis, operation, and production, auditing, and market research.

Rationale and statistical analysis/outcome for each

Probability is used as a measuring tool in statistics and it’s being expressed by probabilistic statements which are called inferential statistics. It is also used to deal with inferential statistics and uncertainties. One of the main aspects of statistical inference is to estimate the value of the population from the sample of data provided.

The events being evaluated by the use of probability would either be mutually exclusive, joint, independent and conditional, or complement. Two events would be mutually exclusive this occurs in a scenario in which when one event happens the other one does not occur. A good example of such a scenario in a business is that a business would not be profitable, breakeven while at the same time it is bankrupt it can only be in one of the states.

The events which are joint are when two or more events than two happen at the same time. While the third one on the independent event is when the occurrence of one event would not affect the occurrence of another event

Tradeoffs between precision and accuracy effects on data and probability concepts

Accuracy and precision in probability are essential as it’s the one that determines the usefulness of the data obtained. When the research is being done problems do arise in need of concept inventiveness and precision which is used to design hypotheses concepts. There are measurement concepts that are used to test hypothetical statements and gather data using measurement concepts Spiegel Et al. (2002). Therefore the success of the research will mainly depend on how well others are able to understand the concepts being used, how those who are analyzing the data conceptualize it, and the probability procedures being used to ensure that it will be accurate and precise.

Use of probability theory in decision making

Event	Product selection	Method Used	Market Reaction	Development Probability
Introduce new product	0.5	0.25	0.4	1.15
			0.4	1.15
			0.2	0.95
		0.25	0.1	0.85
			0.2	0.95
			0.7	1.45
Consolidate the products	0.5	0.25	0.3	1.05
			0.4	1.15
			0.3	1.05
		0.25	0.6	1.35
		0.25	0.4	1.15

The probability of likelihood that either the business will determine to make a new product or consolidate the product is analyzed in the table above. Whose market reactions are being evaluated on whether it would be good, moderate, or poor in that order but the last one in the table evaluates on the market reaction of either being good or poor.

Decisions made based on statistical data

There are different decisions that are made based on statistical data, financial analysis, operation and production, auditing, and market research. The decisions are the ones that are commonly used in a business environment and need to be done appropriately for the success of the business. Therefore, appropriate data analysis methods and application is required to ease decision-making.

It is therefore paramount to use of the decision tools such as probability to ensure it is accurate. Data that is being analyzed is the main factor on the quality of statistical analysis generated for decision making which would be done by use of decision trees (Mind Tools, 2010). Therefore, one would make decisions in confidence that the data used is accurate and reliable to support the decisions which they make.

Conclusion

Complexity indecision-makingg in a business environment has been increasing over the years, which has mainly contributed to the development of better methods of making decisions by use of probability and statistics. The use of probability has proved to be an effective tool of making decisions in most of business environments/applications. There has also increased need for accuracy and being able to make many decisions within a short time. This has been achieved by the application of probability and statistics being computerized hence automating the decisions procedures. Automation has not only increased on a number of decisions made but also ensured that it reduced uncertainty of the findings as it has been characterized by the use of manual decision-making processes.

References

Arsham, H. (2009). Statistical Thinking for Managerial Decisions. Web.

Bradshow, J. (2006). Cooper−Schindler: Business Research Methods, 9th Ed. New York: McGraw-Hill

Mind Tools, (2010). Decision Tree Analysis. Web.

Spiegel, J., Srinivasan, S & Murray R. (2002). Probability and statistics New York: McGraw-Hill

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