This volume begins with an essay by Ruth A. Wienclaw that discusses the different elements involved in the study of statistics and data analysis and how they impact business efforts and management processes. Data analysis can offer organizations many benefits, including decision facilitation, profit maximization, and hypothesis testing. To assist in decision making, managers will often employ descriptive and inferential statistics. Descriptive statistics typically take the form of graphs and other simple means of summarizing data, whereas inferential statistics generally deal with convoluted and comprehensive business situations. To develop complex analyses, individuals will often make use of statistical computing. This method, which is a cross between the statistics and computer science disciplines, blends large packages of analysis to create collections of statistical algorithms. Mathematical tools can also be used in conducting dynamic optimization. According to Wienclaw, “these tools help the analyst to maximize the effectiveness, performance, or functionality of a system or minimize undesirable characteristics through the use of advanced mathematical techniques.” Two additional forms of mathematical and statistical modeling at the disposal of organizations are regression analysis and process analysis. Regression analysis is primarily used in business as a means of predicting the value of one variable from another. Process analysis, on the other hand, pertains mostly to manufacturing and is best applied when considering the consistencies and variations in a production process.