Fiona McNeill – författare

Poor women do not fit easily into the household in Shakespeare. They shift in and out of marriages, households, and employments, carrying messages, tallying bills, and making things happen; never the main character but always evoking the ever-present problem of female poverty in early modern England. Like the illegal farthings that carried their likenesses, poor women both did and did not fit into the household and marriage market. They were both essential to and excluded from the economy. They are both present and absent on the early modern stage. In the drama, they circulate between plots, essential because they are so mobile, but largely unnoticed because of their mobility. These female characters represent an exploration of gender and economic roles at the bottom, as England shifted from feudalism to empire in the span of Shakespeare's lifetime. We find their dramas played out in the plays of Shakespeare and his contemporaries.

Poor women do not fit easily into the household in Shakespeare. They shift in and out of marriages, households, and employments, carrying messages, tallying bills, and making things happen; never the main character but always evoking the ever-present problem of female poverty in early modern England. Like the illegal farthings that carried their likenesses, poor women both did and did not fit into the household and marriage market. They were both essential to and excluded from the economy. They are both present and absent on the early modern stage. In the drama, they circulate between plots, essential because they are so mobile, but largely unnoticed because of their mobility. These female characters represent an exploration of gender and economic roles at the bottom, as England shifted from feudalism to empire in the span of Shakespeare's lifetime. We find their dramas played out in the plays of Shakespeare and his contemporaries.

Employ heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more.Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to: Realize just how random the world is, and how unplanned events can affect analysisIntegrate heuristic and analytical approaches to modeling and problem solvingDiscover how graph analysis is applied in real-world scenarios around the globeApply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and moreUnderstand how text analytics can be applied to increase the business knowledgeEvery single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.

Employ heuristic adjustments for truly accurate analysis

Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more.

Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to:

Realize just how random the world is, and how unplanned events can affect analysis Integrate heuristic and analytical approaches to modeling and problem solving Discover how graph analysis is applied in real-world scenarios around the globe Apply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and more Understand how text analytics can be applied to increase the business knowledge

Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.