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Foundations of Quantitative Finance Book I: Measure Spaces and Measurable Functions

Foundations of Quantitative Finance Book I: Measure Spaces and Measurable Functions

This is the first in a set of 10 books written for professionals in quantitative finance. These books fill the gap between informal mathematical developments found in introductory materials and more advanced treatments that summarize without formally developing the important foundational results professionals need. Book I in the Foundations in Quantitative Finance Series develops topics in measure spaces and measurable functions and lays the foundation for subsequent volumes. Lebesgue and then Borel measure theory are developed on ℝ motivating the general extension theory of measure spaces that follows. This general theory is applied to finite product measure spaces Borel measures on ℝn and infinite dimensional product probability spaces. The overriding goal of these books is a complete and detailed development of the many mathematical theories and results one finds in popular resources in finance and quantitative finance. Each book is dedicated to a specific area of mathematics or probability theory with applications to finance that are relevant to the needs of professionals. Practitioners academic researchers and students will find these books valuable to their career development. All ten volumes are extensively self-referenced. The reader can enter the collection at any point or topic of interest and then work backward to identify and fill in needed details. This approach also works for a course or self-study on a given volume with earlier books used for reference. Advanced quantitative finance books typically develop materials with an eye to comprehensiveness in the given subject matter yet not with an eye toward efficiently curating and developing the theories needed for applications in quantitative finance. This book and series of volumes fill this need. | Foundations of Quantitative Finance Book I: Measure Spaces and Measurable Functions

GBP 68.99
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Evaluating What Works An Intuitive Guide to Intervention Research for Practitioners

Evaluating What Works An Intuitive Guide to Intervention Research for Practitioners

Those who work in allied health professions and education aim to make people’s lives better. Often however it is hard to know how effective this work has been: would change have occurred if there was no intervention? Is it possible we are doing more harm than good? To answer these questions and develop a body of knowledge about what works we need to evaluate interventions. Objective intervention research is vital to improve outcomes but this is a complex area where it is all too easy to misinterpret evidence. This book uses practical examples to increase awareness of the numerous sources of bias that can lead to mistaken conclusions when evaluating interventions. The focus is on quantitative research methods and exploration of the reasons why those both receiving and implementing intervention behave in the ways they do. Evaluating What Works: Intuitive Guide to Intervention Research for Practitioners illustrates how different research designs can overcome these issues and points the reader to sources with more in-depth information. This book is intended for those with little or no background in statistics to give them the confidence to approach statistics in published literature with a more critical eye recognise when more specialist advice is needed and give them the ability to communicate more effectively with statisticians. Key Features: Strong focus on quantitative research methods Complements more technical introductions to statistics Provides a good explanation of how quantitative studies are designed and what biases and pitfalls they can involve | Evaluating What Works An Intuitive Guide to Intervention Research for Practitioners

GBP 44.99
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Introduction to NFL Analytics with R

Introduction to NFL Analytics with R

It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans and commentators throughout broadcasts regularly use terms such as air yards CPOE and EPA on a casual basis. This rapid growth combined with an increasing accessibility to NFL data has helped create a burgeoning amateur analytics movement highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavor Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to opting to provide step-by-step instructions in a structured jargon-free manner. Key Coverage: • Installing R RStudio and necessary packages • Working and becoming fluent in the tidyverse • Finding meaning in NFL data with examples from all the functions in the nflverse family of packages • Using NFL data to create eye-catching data visualizations • Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient Boosting The book is written for novices of R programming all the way to more experienced coders as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists bloggers and arm-chair quarterbacks alike will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.

GBP 52.99
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