Statistics in Business Analysis
Statistics plays an important role in effective business analysis. It can help analyze both past and future business strategies. Through utilizing the power of probability, business analysis can make the most effective predictions.
Within analyzing business strategy, using binomial distribution can be useful to provide predictions backed up by strong mathematical concepts. It breaks down the potential of probability to help list what strategies if implemented would provide the most favorable results. According to research, “it is appropriate to summarize a group of independent observations by the number of observations in the group that represent one of two outcomes,” (Lacey 2010). This system can be used when there are a fixed number of observations within the probability context. Thus, businesses should use binomial distribution only when they have a set number of observations to be used within the equation strategy. Additionally, each of those observations used within the equation itself must be independent and represent “one of two outcomes,” meaning the “success or failure” of the probability of the prediction. Therefore, business can harness the powerful predictability of the binomial distribution.
The Poisson distribution is also and beneficial tool within business analysis. The method helps predict them probability of the size and range of event frequency. By taking into account independent observations within a fixed period of time, it can help show strong correlations of success or failure through an increased or decrease frequency of those independent observations. Thus, the actions with the highest frequency levels then become the actions pertaining to the highest levels of probability. This method of determining probability can be very effective in business analyses of measuring new customer growth. According to research, one business in Florida used the Poisson distribution method based on frequency of customer visits. In order to efficiently predict customer levels based on frequency, “data on customer arrivals must be collected and a statistical test conducted to see whether an assumption of a Poisson distribution for arrivals is reasonable,” (Anderson 2008:472). Business such as liquor marts can use such a process to determine if customer frequency is sufficient enough for them to stay open later. For instance, if a store of such nature where to collect data for customer arrival after 2 am in a one week span, they can then use that number to make a strong prediction whether it is worth staying open based on the cost of operation during that debatable time period. Thus, it is clear that frequency can become useful in business predictions in certain occasions.
Hypergeometric distribution can also be used to compile viable business predictions. Unlike the binomial distribution, hypergeometric distribution provides its analysis without the allowance for replacement variable. Thus, without the availability of replacements, there is a limited pool of variables in which to use within the context of the analysis. This makes the combinations within observations the most important element in prediction analysis. According to the research, the hypergeometric distribution “formula computes all the possible ways of getting x successes from the A successes available and n — x failures from the N — A available failures in the population,” (Black 2009:166). Therefore, when dealing with multiple variables, more efficient analysis of combinations can help predict business situations.
Anderson, David Ray. (2008). Statistics for Business and Economics. Thomson South-Western.
Black, Ken. (2009). Business Statistics: Contemporary Decision Making. Wiley Publishing.
Lacey, Michelle. (2010). The binomial distribution. Statistics Topics. Yale University. Retrieved January 25, 2010 from http://www.stat.yale.edu/Courses/1997-98/101/binom.htm.
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