Business world faces many situations, when decisions play major role in the future of the business. Some decision can make dramatic profit, another may drive into debt. While making decision a decision-maker collects some data, then analyses it, then develops possible problem solutions and then chooses the best solution. Sometimes collected data can be unreliable or inaccurate and then decision can be destructive. It can be easily illustrated on the process of purchasing of something. Let’s take purchasing of communication facilities. If a company needs some, they may purchase them from different manufacturers. Of course the company strives to buy as cheap as possible, but not to the prejudice of quality. So the company needs to collect information about different manufacturers – their history, assortment, and quality, amount of sells, customers’ judgments and other features. After this data is collected, it must be analyzed and correctly interpreted (especially important), then company may choose the best manufacturer from all the available accordingly to the selected priorities – in our case they are low price and high quality. If the company makes decisions in this way, it is more probably that it would successfully operate on the market.

Another example of how statistical tools can be useful in business I would like to get in touch with correlation. Correlation (from Latin “correlatio" – ratio) – means interrelation, interdependency between different objects or subjects. But let's get back to the subject at hand – correlation of the sales and the color of the product, weight, novelty, age of targeted customers, education and awareness of customers, types of advertisement applied etc. All these indicators may be interrelated (significantly or insignificantly) or non-connected at all. In order to increase sales, a company may provide regression analysis of the above indicators, and make corresponding changes in the correlated indicators and may be forget non-correlated (for example disposal of ineffective advertisement).  

How can a company offer an incentive to the usage of credit cards? In order to answer this question, a company needs to collect, group, analyze and correctly interpret the data according to the following list of question: - how people may use the credit card (comfort, ease, simplicity and accessibility etc); - credit card features (size, color, quality of the material, endurance, security level etc); - people’s attitude and confidence to credit cards; - available advantages and discounts for credit card owners; - reasons of admission and rejection of credit cards and so on. But the preference must be given to the indicators, which can be governed by the company.    

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