Statistical Techniques in Business and Economics With Global Data Sets
"Big data" has become one of the biggest buzzwords in modern business. Companies are storing larger amounts of data, increasing the need for people with the statistical knowledge and skills to analyze and synthesize raw data, transforming them into actionable information. Although large corporations often employ data analysts to help them make sense of their data, small business operators, especially those with an Internet presence that enables them to reach a global customer base, can benefit from an understanding of statistical techniques.
Factor analysis, strongly associated with survey research, is a data reduction technique that strives to identify unobserved explanations or factors that account for observed patterns of relationships among measured variables. For example, underlying factors related to customer preferences or socioeconomic status may help explain patterns of responses to questions in customer satisfaction surveys administered by many small businesses. Knowledge of factor analysis can help small businesses identify and enhance the factors driving customer satisfaction.
Regression analysis is one of the chief analytical techniques used by economists and quantitative financial analysts. Regression analysis uses statistical methods to estimate the impact of one or more predictor variables on a particular outcome, according to Damodar Gujarati, author of "Basic Econometrics." For example, regression analysis can help estimate the extent to which education and experience influence annual incomes. Small business owners with statistical knowledge can use regression analysis to estimate the extent to which longer business hours or sales promotions increase company revenues or raise profit margins.
Some research questions in business in economics involve yes and no questions, such as whether a particular person will shop at a certain retailer, eat in a certain restaurant or buy a specific type of product. Binary variables, often coded a "1" for yes and zero for no, do not lend themselves to traditional linear regression techniques. Logistic regression techniques, however, can identify and assess the extent to which certain variables raise or lower the probability that a certain quality or behavior is present in a population. For business owners, logistic regression can identify factors driving customers to their physical or online locations. Armed with this knowledge, businesses can work to enhance customer satisfaction, ensuring repeat business.
This technique, commonly used in operations research, helps companies identify ways to maximize a particular outcome, such as profits, subject to certain constraints, according to Barry Render and Ralph Stair, authors of "Quantitative Analysis for Management." Because small businesses have limited resources, including facilities and labor, linear programming techniques can provide managers and owners with a way to maximize productivity and profit or minimize overhead through the appropriate combination of inputs.