Statistical Analysis Of Empirical Data
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Statistical Analysis of Empirical Data
Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method. Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.
Statistical Analysis Demystified
Unlock the mysteries of data with "Statistical Analysis Demystified," your essential guide to navigating the intriguing world of statistics. This comprehensive eBook empowers you with the knowledge and confidence needed to interpret data and make informed decisions in both personal and professional contexts. Start your journey with an overview of why statistics matter and how they impact our daily lives. You'll learn how to conquer math anxiety and build a solid foundation in descriptive statistics, diving into measures of central tendency, variability, and data visualization. Venture further into data distributions, exploring the nuances of normality, skewness, and outliers. Gain a solid grasp of inferential statistics, from sampling methods to hypothesis testing, equipping you with the tools to make predictions and insights about broader populations. Regression analysis takes center stage as you explore simple linear models, interpret outputs, and understand their practical limitations. Delve into the intricacies of correlation, distinguishing between covariance and causation, and uncover the profound implications of hypothesis testing, ANOVA, and non-parametric tests. For those ready to dive deeper, advanced chapters introduce multiple regression, Bayesian statistics, and multivariate methods. Discover the art of time series analysis and learn how to leverage statistical software like R, Python, and SPSS to visualize and interpret data like a pro. Beyond technical skills, this eBook emphasizes ethical considerations and the statistician's responsibility in ensuring data integrity and avoiding misleading conclusions. As you absorb these robust concepts, witness how data-driven decisions shape industries, demonstrated through compelling case studies. Finally, embrace a statistical mindset that fosters critical thinking and continuous learning. Whether you're a beginner or seeking to enhance your expertise, "Statistical Analysis Demystified" is your gateway to mastering the language of data. Prepare to transform abstract numbers into meaningful insights and make your data work for you.
Statistical Analysis of Financial Data
Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet. Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data. Features * Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions. * Describes both the basics of R and advanced techniques useful in financial data analysis. * Driven by real, current financial data, not just stale data deposited on some static website. * Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.