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Free Custom Essay Sample - Statistics in business decisions
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This is a free essay sample about statistics in business decision-making process. This essay briefly tells how statistics can be useful in the above described process, which tools and methods of statistics can be applied; essay is supported with samples.
Statistics in business decisions
Statistics (from Italian ‘stato’ – state) is the science of data. Statistics is concerned with collection of data, its classification, organization, processing and analysis. The data used in statistics is a sort of periodical information characterizing quantitative and (or) qualitative patterns of processes and phenomena. In other words, statistics deals with samples of data, collected on a periodical basis, about certain event or process, which represent some characteristics of the whole set of studied units (population). The primary objective of inferential statistics (the one applied in decision making) is to draw a conclusion or an inference relying on the analysis of the collected data sets.
Statistics includes many methods and tools for data analysis which allow to forecast the future state of a process with a certain probability. Moreover, statistics closely interacts with other economic sciences and many other sciences apply statistical methods.
Forecasting is indispensable component in making a well-grounded business decisions which often impact companies’ further successes or failures in considerable way. For example, good business decision can result in increased sales volume and profit; wrong decision can cause the opposite consequences and bring company to bankruptcy. In this paper we will summarize how statistics with its tools and methods can be used in decision making process.
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The easiest way to answer the above question is to cite an instance. Let’s imagine a company which produces 3 types of beverage (A, B and C). This is quite young company which operates a little more than 3 years. Within this period the department of statistics along with accounts department collected and stored information about total production volume, sales volume, invested capital, promotion expenses, age of customers that prefer each type of beverage, gross income of the customer groups and so on. The whole set of data is a population, which can be used for many calculations, estimations and forecasts. Company’s analysts may build regression and correlation models, which can describe the dependencies between various financials or variable of the company with its products and customers.
There may be a certain relationship or even dependency (correlation) between two or more variables like the amount of sugar contained in the beverage and the age of customer that likes this beverage. The inference from such analysis can be the following: beverage A is preferred by children of 6-9 years old, beverage B is preferred by youth of 13-19 years old, C – by people of older ages. At the same time A contains more sugar than B and C. That does not mean adults do not drink beverage A or B, however, it describes age characteristics of the customers and company may improve the efficiency of its marketing campaign and save money. Similar analysis can be used to distinguish the tastes of people of different income level – which beverage is preferred by people with gross income of $1,000-2,500 per month, $5,000-7,000 per month etc.
Regression models can be used to forecast the future state of one variable depending on the state of another one – e.g. between production volume of each beverage and fixed costs, between fixed and variable costs, between marketing expenditures and sales volume. Regression can be used to forecast the future cash flows of the company, which can be used for the purpose of valuation of a company before a merger or acquisition, or initial public offering. Of course such forecast will have a stochastic nature but analysts can evaluate the measure of reliability of this forecast and adjust their calculations in accordance to the degree of uncertainty of the forecast or conclusion.
Theoretically any variables can be connected with each other and this connection can be strong, weak or be absent at all (for example production volume can be correlated with the amount of workers, down-time, labor productivity etc and it can not be correlated with promotion expenses). Absent correlation between certain variables is a result too. E.g. if promotion expenses do not affect the sales volume or affect it insignificantly it is clear that company should find some other ways to increase its sales, inasmuch as increased promotion expenses will give no desired result. Moreover, company may decrease its promotion expenses to the optimal level. Further statistical analysis may reveal that other variables (such as quality of the product, price, package etc) can affect sales volume. So the task of the manager will be to increase sales by adjusting variables that can affect sales. Incautious manager may not pay attention to statistics and increase promotion expenses directly because from the first sight it seems to be logically. But indeed it can give no desirable effect and just increase company’s expenditures.
The general framework for applying statistics in business decisions is the following:
1. setting the objective and formulating the problem, which requires a decision
2. setting the questions related to the problem and formulating it in terms of statistics
3. conducting statistical analysis and getting the answers to the formulated questions
4. getting new questions of getting the problem solved.
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Of course there are many other statistical tools which can be useful for analysis of business activities in order to increase company’s overall performance, effectiveness, profitability and decrease unnecessary expenses. In order to apply statistics in business decisions there is no need in costly software or large analysis department – everyone can make different researches and calculations in Microsoft Excel which supports major statistical tools and methods, and is very easy to use. However, professional statisticians use more sophisticated software like SPSS which is able to process huge volumes of data.
Obviously, statistics can be very useful in making well-grounded business decisions (by researching correlations between different business variables, building regression models, forecasting sales, adjusting qualitative features of the products etc) and can prevent from making ineffective decisions if used in timely and appropriate way.
Statistics:
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