Who knew Laine Crosby, former marketing executive at The Weather Channel, had the gift of being an Investigative Medium? Not even Laine, that is, until she moved from Atlanta to the property of an eighteenth century Maryland plantation and woke up talking to a former slave buried in her backyard! Investigative Medium - the Awakening, examines the gift that was thrust upon Laine who was at first surprised, and then upset, and over time and through experience, embracing of this new way of life. Laine’s husband, Chris, twins Annie and Caleb, and even her Jack Russell terrier “Steve” have peripheral ghostly involvement, so it is a family affair, although at first an unsettling one.Along Laine’s journey, she meets an unlikely friend, the spirit of Jannette, once nanny to the children on the plantation where she lives. Laine becomes Jannette's voice, bringing her back from the antebellum South to tell the incredible story of her life as a slave, and a tender romance is revealed. Interwoven with Laine’s personal story, are the first hand accounts of former slaves Jannette and Bill, and others.Laine finds similarities in her own life and Jannette's, and with Jannette's friendship, Laine begins her journey down a path of self discovery. Once Laine accepts her gift, it is her mother who, from beyond the grave, helps Laine find a way to surrender, and at long last give up on the life she dreamed of having, in order to have the life she was meant to live. Investigative Medium - the Awakening is first in the Investigative Medium series, a preface to future books about Laine's adventures with law enforcement, historians and archaeologists. It is a fast read and an uplifting and hopeful book for anyone who has lost a loved one. But, put on your seat belts; It's a thriller along the way!Now available as an ebook on Amazon, B&N, and Smashwords. Paperback available July 2013 (ISBN 978-1-940261-00-3)on LaineCrosby.com among other stores.

    It can be freely downloaded and it works on multiple computer platforms. This book provides an elementary introduction to R. In each chapter, brief introductory sections are followed by code examples and comments from the computational and statistical viewpoint. A supplementary R package containing the datasets can be downloaded from the web.

    

    Pattie’s Personal Narrative is a prime source for the history of the Southwest during the 1820s. He, and a group of fur trappers, set out on a journey from St. Louis to California and back. After Jed Smith’s trip this journey, which began in 1824, is the second known expedition to California. This remarkable book records an eyewitness account of what the West was like before the great swathes of migration occurred. Pattie’s book fully explores the dangers of life as a trapper in the wilderness of the far west, including during one episode after Pattie and a group of French trappers were attacked and only three of them survived. Personal Narrative provides fascinating insight into the earliest clashes that were beginning to occur between citizens of the travelers from the east, Native Americans and Mexicans as United States began its great westward expansion. Yet, Pattie also demonstrates how there was great cooperation between groups, for example when he aided Mexicans, Native Americans, missionaries and settlers with smallpox vaccinations. It is essential reading for anyone interested in finding out more about the Old West and life of this fascinating American frontiersman. James O. Pattie first published his account The Personal Narrative of James O. Pattie of Kentucky in 1831 and he passed away in 1851.

    While such visualizations can better inform us, they can also deceive by displaying incomplete or inaccurate data, suggesting misleading patterns—or simply misinform us by being poorly designed, such as the confusing “eye of the storm” maps shown on TV every hurricane season.Many of us are ill equipped to interpret the visuals that politicians, journalists, advertisers, and even employers present each day, enabling bad actors to easily manipulate visuals to promote their own agendas. Public conversations are increasingly driven by numbers, and to make sense of them we must be able to decode and use visual information. By examining contemporary examples ranging from election-result infographics to global GDP maps and box-office record charts, How Charts Lie teaches us how to do just that.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    R is packed with powerful programming capabilities, but learning to use R in the real world can be overwhelming for even the most seasoned statisticians. This easy-to-follow guide explains how to use R for data processing and statistical analysis, and then, shows you how to present your data using compelling and informative graphics. You'll gain practical experience using R in a variety of settings and delve deeper into R's feature-rich toolset.Includes tips for the initial installation of RDemonstrates how to easily perform calculations on vectors, arrays, and lists of dataShows how to effectively visualize data using R's powerful graphics packagesGives pointers on how to find, install, and use add-on packages created by the R communityProvides tips on getting additional help from R mailing lists and websitesWhether you're just starting out with statistical analysis or are a procedural programming pro, "R For Dummies" is the book you need to get the most out of R.

    This book has two underlying themes that are more or less independent of the statistical hypothesis tests that are the main content of the book. The first theme is the importance of looking at the data before formulating a hypothesis. With this in mind, the author discusses, in detail, plotting data, looking for outliers, and checking assumptions (Graphical displays are used extensively). The second theme is the importance of the relationship between the statistical test to be employed and the theoretical questions being posed by the experiment. To emphasize this relationship, the author uses real examples to help the student understand the purpose behind the experiment and the predictions made by the theory. Although this book is designed for students at the intermediate level or above, it does not assume that students have had either a previous course in statistics or a course in math beyond high-school algebra.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    Having the same class in college, curiosity got the better of me when she told me her dad was an ex-MMA fighter.I searched him online and found that he was a ripped, huge fighter in his youth. But he looks even better today, forty-two years old and just as muscular as he used to be. Even better, his hair has turned silver and he looks even more rugged and manly. The truth is I let myself fantasize about him sometimes.But it has to stay a fantasy. A man like Liam Larson – rich, successful, handsome – would never be interested in a naïve virgin like me.But then Kayla asks me to come home with her for Valentine’s weekend everything changes.At first, I think she’s just trying to rope me into singing at her Dad’s club. She’s always saying I need to be more confident with my music and get over my stage fright. I know that’s not going to happen any time soon.Then I meet Liam and something crazy happens. It turns out he wants me just as badly as I want him. I don’t know if we’ve been shot by Cupid’s arrow or if we’re both just crazily attracted to each other. All I know is we shouldn’t be doing this. If Kayley ever found out, it would be World War three.Yet we can’t stop. I try to fight it. I try to focus on my songwriting. He tries to focus on his work, his businesses. But this tall, muscular alpha is a man I just can’t quit, even if I’m constantly surprised that he wants me to.He says he loves my curves. He says he loves my body. He says he loves my singing voice.But that’s not all.He says I belong to him, and that on Valentine’s night he’s going to claim me in the most possessive way a man can.What if Kayley finds out? What if he discovers that I’m a virgin? What if this all blows up in our faces?I never knew Valentine’s Day could be so complicated.*Valentine’s with My Best Friend’s Dad is an insta-everything standalone instalove romance with a HEA, no cheating, and no cliffhanger.

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    Over the next 3 centuries, it would grow to become one of the mightiest empires in the world, the Vijayanagara Empire. An empire dazzling in it's achievements, in it's riches, in it's arts. From it's founding, to it's fall after the Battle of Tallikota to the heights it achieved under Sri Krishna Deva Raya, City of Victory aims to recreate the splendor and glory of one of the most magnificent empires ev

    Arthur Thompson proved them all wrong. For forty years Thompson ruled Glasgow's mean streets, always devising new terror.

    Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress." Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under the "laws of the medium numbers"-which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. A few examples: - The sample mean is rarely in line with the population mean, with effect on "na�ve empiricism," but can be sometimes be estimated via parametric methods. - The "empirical distribution" is rarely empirical. - Parameter uncertainty has compounding effects on statistical metrics. - Dimension reduction (principal components) fails. - Inequality estimators (Gini or quantile contributions) are not additive and produce wrong results. - Many "biases" found in psychology become entirely rational under more sophisticated probability distributions. - Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions. This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.

    In this e-book you will learn what exactly is needed to run a successful Design Thinking Workshop which is fun and at the same time generates great results. In this e-book Pauline Tonhauser, founder of designthinkingcoach.de, shares her best practices.