This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    This "devastating rebuttal to Fatal Vision" (Boston Phoenix) demonstrates that the jury was not privy to crucial evidence in the case of Jeffrey MacDonald, the Green Beret Captain convicted of the murders of his wife and two young daughters.

    Increasingly, the decisions that affect our lives--where we go to school, whether we can get a job or a loan, how much we pay for health insurance--are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.But as mathematician and data scientist Cathy O'Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination--propping up the lucky, punishing the downtrodden, and undermining our democracy in the process.

    As the suffering of the people intensifies so does the resentment against the ruling elite. As Raavan, the king of Lanka, grows increasingly powerful, the citizens of the Sapt Sindhu cry out for a leader to lead them out of this morass.The Malayaputras and the Vayuputras — two powerful tribes and the protectors of the divine land of India — decide that enough is enough. A saviour is needed. They begin their search.Who will fulfil the destiny of the Vishnu? Will a leader, who can restore the past glory of the Sapt Sindhu, emerge? Will Ram, the law abiding prince of Ayodhya rise above the taint that others heap on him? Will Sita, the warrior princess of Mithila, be able to prove her worth?Start on an epic journey with Amish’s Ram Chandra Series.

    Sambhaji had a tough childhood. His father was too busy to look after him and his mother died when he was only two. The young Sambhaji's main support came from his grandmother. When she died, he was bereft of love and care. To make matters worse, his step-mother was campaigning to make her own son the next ruler, trying to poison Shivaji's mind against Sambhaji. This Amar Chitra Katha traces the events that led up to the coronation of this wise and just Maratha ruler.

    Bayesian statistics is taught in most first-year statistics classes across the nation, but there is one major problem that many students (and others who are interested in the theorem) face. The theorem is not intuitive for most people, and understanding how it works can be a challenge, especially because it is often taught without visual aids. In this guide, we unpack the various components of the theorem and provide a basic overview of how it works - and with illustrations to help. Three scenarios - the flu, breathalyzer tests, and peacekeeping - are used throughout the booklet to teach how problems involving Bayes Theorem can be approached and solved. Over 60 hand-drawn visuals are included throughout to help you work through each problem as you learn by example. The illustrations are simple, hand-drawn, and in black and white. For those interested, we have also included sections typically not found in other beginner guides to Bayes Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios.A few examples of how to think like a Bayesian in everyday life. Bayes Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. It is a great tool that can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes Rule. Bayes Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700’s to its being used to break the German’s Enigma Code during World War 2, its tale is quite phenomenal.Fascinating real-life stories on how Bayes formula is used in everyday life.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. We have summed up 3 examples for you and provided an example of how Bayes could be used.An expanded definitions, notations, and proof section.We have included an expanded definitions and notations sections at the end of the booklet. In this section we define core terms more concretely, and also cover additional terms you might be confused about. A recommended readings section.From The Theory That Would Not Die to a few other books, there are a number of recommendations we have for further reading. Take a look! If you are a visual learner and like to learn by example, this intuitive booklet might be a good fit for you. Bayesian statistics is an incredibly fascinating topic and likely touches your life every single day. It is a very important tool that is used in data analysis throughout a wide-range of industries - so take an easy dive into the theorem for yourself with a visual approach!If you are looking for a short beginners guide packed with visual examples, this booklet is for you.

    This social transformation didn't happen by accident. We’ve built a country where we can all choose the neighborhood -- and religion and news show -- most compatible with our lifestyle and beliefs. And we are living with the consequences of this way-of-life segregation. Our country has become so polarized, so ideologically inbred, that people don’t know and can’t understand those who live just a few miles away. The reason for this situation, and the dire implications for our country, is the subject of this groundbreaking work.In 2004, the journalist Bill Bishop, armed with original and startling demographic data, made national news in a series of articles showing how Americans have been sorting themselves over the past three decades into alarmingly homogeneous communities -- not by region or by red state or blue state, but by city and even neighborhood. In The Big Sort, Bishop deepens his analysis in a brilliantly reported book that makes its case from the ground up, starting with stories about how we live today and then drawing on history, economics, and our changing political landscape to create one of the most compelling big-picture accounts of America in recent memory.The Big Sort will draw comparisons to Robert Putnam's Bowling Alone and Richard Florida's The Rise of the Creative Class and will redefine the way Americans think about themselves for decades to come.

    Human beings have long been both fascinated and appalled by randomness. On the one hand, we love the thrill of a surprise party, the unpredictability of a budding romance, or the freedom of not knowing what tomorrow will bring. We are inexplicably delighted by strange coincidences and striking similarities. But we also hate uncertainty's dark side. From cancer to SARS, diseases strike with no apparent pattern. Terrorists attack, airplanes crash, bridges collapse, and we never know if we'll be that one in a million statistic. We are all constantly faced with situations and choices that involve randomness and uncertainty. A basic understanding of the rules of probability theory, applied to real-life circumstances, can help us to make sense of these situations, to avoid unnecessary fear, to seize the opportunities that randomness presents to us, and to actually enjoy the uncertainties we face. The reality is that when it comes to randomness, you can run, but you can't hide. So many aspects of our lives are governed by events that are simply not in our control. In this entertaining yet sophisticated look at the world of probabilities, author Jeffrey Rosenthal--an improbably talented math professor--explains the mechanics of randomness and teaches us how to develop an informed perspective on probability.

    These are stories of ordinary people whom you have met - at work, during the daily commute, in your friend circles, or on social media. However, the stories have a twist or an element of thrill to them. They rip open the sheath of mundane lives and present you with raw, poignant, and profound vignettes of urban life. These stories attempt to capture the dramatic flipside of the banal existence of everyday people. As the editor defines them… The stories are the perfect mix of sensationalism out of the mundane, exhibitionism of what has been undercover, and the simple refinement of human thought perspectives. The words sway and dance, tantalizingly just out of reach, trying to entice the reader into that false lull of security until a twist comes that makes them wonder, ‘What did I just read?’

    But before numerical information can be useful it must be analyzed, interpreted, and assimilated. Unfortunately, teaching the techniques for making sense of data has been neglected at all levels of our educational system. As a result, through our culture there is little appreciation of how to effectively use the volumes of data generated by both business and government. This book can remedy that situation. Readers report that this book as changed both the way they look a data and the very form their monthly reports. It has turned arguments about the numbers into a common understanding of what needs to be done about them. These techniques and benefits have been thoroughly proven in a wide variety of settings. Read this book and use the techniques to gain the benefits for your company.

    Along the way we’ll learn how selection bias can explain why your boss doesn’t know he sucks (even when everyone else does); how to use Bayes’ Theorem to decide if your partner is cheating on you; and why Mark Zuckerberg should never be used as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    In 1988, they decided to carry out one final heist that would ensure they could retire forever. It did indeed turn out to be their last heist - but not for the reasons they planned. Their ship was seized, and the key Ring members all scattered all over the world.

    This book describes his descent. Read about how he moved quickly from level to level, seeking power. While he thought he was moving up, he was really being dragged down.