Matematisk statistik

Denna bok är en omarbetning av Kerstin Vännmans tidigare bok Matematisk statistik från 2002. Boken syftar till att träna det statistiska tänkandet så att man kan förstå och använda några ofta förekommande statistiska metoder. Den är uppbyggd kring ett stort antal exempel och nya begrepp introduceras och motiveras med hjälp av inledande exempel.I boken behandlas bland annat enkla sannolikhetsresonemang, några vanligt förekommande fördelningar, till exempel binomial-, Poisson-, normal- och exponential-fördelningen, samt olika läges- och spridningsmått. Vidare behandlas punktskattningar, konfidensintervall (även jämförande situationer) och test. Den största förändringen jämfört med den tidigare boken är att regressionsanalys (både enkel och multipel) och flerdimensionella stokastiska variabler behandlas i denna bok. I regressionsanalysen ligger fokus på tillämpning med hjälp av statistisk programvara och tolkning av resultat.Boken vänder sig i första hand till studenter inom utbildningarna för civil- och högskoleingenjörer, men den är även lämplig för annan högskoleutbildning eller för självstudier. Till varje kapitel finns ett stort antal övningsuppgifter med svar. Dessutom finns ett separat kapitel med blandade övningar av varierande svårighetsgrad.

Business Statistics: Communicating with Numbers takes a comprehensive and contemporary approach that aims to bridge the gap between how statistics is taught and applied in the business world. This title not only prepares students in basic statistics but also gets them excited about further exploration of data analytics. The authors emphasize communicating with numbers rather than number crunching, through relatable case studies with relevant statistical methods and takeaways. This title incorporates timely examples from various fields, reinforces core features from previous editions, uses Excel and R to analyze data and solve problems, and includes new improvements, such as a revised chapter on data visualization, an exclusive chapter on logistic regression, and digital enhancements such as a big data capstone project with algorithmic exercises that span across multiple chapters.

Kan statistik verkligen vara intressant och lätt att förstå? Svaret är ja! Denna bok bryter ned de komplexa principerna för statistisk analys och gör dem tillgängliga för alla. Så oavsett om du är student, forskare eller bara nyfiken på statistik, kommer du att finna värdefull vägledning här.Kan jag lita på statistiken? Hur stor är den statistiska felmarginalen? Är förändringen statistiskt säkerställd? Vad innebär statistisk signifikans? Svaren på dessa och många andra frågor finns i den här boken. Med praktiska tillämpningar och utan krånglig matematik förklaras grunderna för statistisk beskrivning, kritisk granskning och statistisk analys. Det finns många exempel med fullständiga lösningar och övningsuppgifter med utförliga svar i boken. Därför bör den fungera bra även för den som vill lära sig statistik på egen hand. I slutet på varje kapitel finns avsnittet Med datorns hjälp. Där visas hur man med statistikprogrammet SPSS kan låta datorn göra det siffermässiga grovarbetet. Statistik upplevs av många som svårt och tråkigt. Med denna bok visar författarna ännu en gång att statistik både kan vara lätt och roligt.

'A necessary book for our times. But also just great fun' Saul Perlmutter, Nobel LaureateThe world is awash in bullshit, and we're drowning in it. Politicians are unconstrained by facts. Science is conducted by press release. Start-up culture elevates hype to high art. These days, calling bullshit is a noble act.Based on a popular course at the University of Washington, Calling Bullshit gives us the tools to see through the obfuscations, deliberate and careless, that dominate every realm of our lives. In this lively guide, biologist Carl Bergstrom and statistician Jevin West show that calling bullshit is crucial to a properly functioning social group, whether it be a circle of friends, a community of researchers, or the citizens of a nation. Through six rules of thumb, they help us recognize bullshit whenever and wherever we encounter it - even within ourselves - and explain it to a crystal-loving aunt or casually racist grandfather.

Vad är kvantitativa metoder? Hur kan de användas? Hur genomför man och planerar en undersökning? Hur kan och bör man tolka olika resultat och vilka osäkerhetsfaktorer finns det att ta hänsyn till vid resultattolkningar? Hur hjälper oss slumpen i att förstå och tolka rimligheten i olika resultat?Grunderna i statistisk dataanalys går igenom frågorna ovan och syftar till att ge kunskap om hur man planerar och genomför enklare undersökningar och hur man ska förhålla sig kritiskt till avancerade studier och forskningsresultat.Boken avslutas med ett kapitel om matematik för den som behöver repetera grundläggande kunskaper. Alla data som används i bokens olika exempel finns samlade i ett Excelark som kan hämtas från bokens webbplats. På så sätt kan läsaren upprepa det som görs i boken och även experimentera ytterligare.Boken riktar sig till blivande sjuksköterskor, fysio- och arbetsterapeuter, tandhygienister samt folkhälsovetare och även till övriga hälso- och sjukvårdsutbildningar.

Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called "sexy." From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

‘Probably the UK’s greatest living statistician’ TelegraphFrom the UK’s ‘statistical national treasure’, a clever and data-driven guide to how we can live with risk and uncertaintyWe live in a world where uncertainty is inevitable. How should we deal with what we don’t know? And what role do chance, luck and coincidence play in our lives?David Spiegelhalter has spent his career dissecting data in order to understand risks and assess the chances of what might happen in the future. In The Art of Uncertainty, he gives readers a window onto how we can all do this better.In engaging, crystal-clear prose, he takes us through the principles of probability, showing how it can help us think more analytically about everything from medical advice to pandemics and climate change forecasts, and explores how we can update our beliefs about the future in the face of constantly changing experience. Along the way, he explains why roughly 40% of football results come down to luck rather than talent, how the National Risk Register assesses near-term risks to the United Kingdom, and why we can be so confident that two properly shuffled packs of cards have never, ever been in the exact same order.Drawing on a wide range of captivating real-world examples, this is an essential guide to navigating uncertainty while also having the humility to admit what we do not know

Winner of the 2023 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.FeaturesIntegrates working code into the main text. Illustrates concepts through worked data analysis examples. Emphasizes understanding assumptions and how assumptions are reflected in code. Offers more detailed explanations of the mathematics in optional sections. Presents examples of using the dagitty R package to analyze causal graphs. Provides the rethinking R package on the author's website and on GitHub.

Engineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering, Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments.The first eight chapters cover probability and probability distributions, graphical displays of data and descriptive statistics, combinations of random variables and propagation of error, statistical inference, bivariate distributions and correlation, linear regression on a single predictor variable, and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies.Key features: All examples based on work in industry, consulting to industry, and research for industry Examples and case studies include all engineering disciplinesEmphasis on probabilistic modeling including decision trees, Markov chains and processes, and structure functionsIntuitive explanations are followed by succinct mathematical justificationsEmphasis on random number generation that is used for stochastic simulations of engineering systems, demonstration of key concepts, and implementation of bootstrap methods for inferenceUse of MATLAB and the open source software R, both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applicationsUse of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooksExperiments designed to show fundamental concepts that have been tested with large classes working in small groupsWebsite with additional materials that is regularly updated

Credit Risk Management: Basic Concepts is the first book of a series of three with the objective of providing an overview of all aspects, steps, and issues that should be considered when undertaking credit risk management, including the Basel II Capital Accord, which all major banks must comply with in 2008. The introduction of the recently suggested Basel II Capital Accord has raised many issues and concerns about how to appropriately manage credit risk. Managing credit risk is one of the next big challenges facing financial institutions. The importance and relevance of efficiently managing credit risk is evident from the huge investments that many financial institutions are making in this area, the booming credit industry in emerging economies (e.g. Brazil, China, India, ...), the many events (courses, seminars, workshops, ...) that are being organised on this topic, and the emergence of new academic journals and magazines in the field (e.g. Journal of Credit Risk, Journal of Risk Model Validation, Journal of Risk Management in Financial Institutions, ...). Basic Concepts provides the introduction to the concepts, techniques, and practical examples to guide both young and experienced practitioners and academics in the fascinating, but complex world of risk modelling. Financial risk management, an area of increasing importance with the recent Basel II developments, is discussed in terms of practical business impact and the increasing profitability competition, laying the foundation for books II and III.

Shortlisted for the William Hill Sports Book of the Year Award 2022 Football has always measured success by what you win, but only in the last twenty years have clubs started to think about how you win. Data has now suffused almost every aspect of how football is played, coached, scouted and consumed. But it’s not the algorithms or new metrics that have made this change, it’s the people behind them.This is the story of modern football’s great data revolution and the group of curious, entrepreneurial personalities who zealously believed in its potential to transform the game. Central to this cast is Chris Anderson, an academic with no experience in football, who saw data as an opportunity to fundamentally change a sport that did not think it could be changed. His aim: to infiltrate the strange, insular world of professional football by establishing a club whose entire DNA could be built around data.Expected Goals charts his remarkable journey into the heart of the modern game and reveals how clubs across the world, from Liverpool to Leipzig and Brentford to Bayern Munich, began to see how data could help them unearth new players, define radical tactics and plot their path to glory.

An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences  “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC)Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.

Leonard Mlodinow's The Drunkard's Walk: How Randomness Rules Our Lives is an exhilarating, eye-opening guide to understanding our random world.Randomness and uncertainty surround everything we do. So why are we so bad at understanding them? The same tools that help us understand the random paths of molecules can be applied to the randomness that governs so many aspects of our everyday lives, from winning the lottery to road safety, and reveals the truth about the success of sporting heroes and film stars, and even how to make sense of a blood test.The Drunkard's Walk reveals the psychological illusions that prevent us understanding everything from stock-picking to wine-tasting - read it, or risk becoming another victim of chance.'A wonderfully readable guide to how the mathematical laws of randomness affect our lives' Stephen Hawking, author of A Brief History of Time

'A great introduction to a crucial topic' Bill Gates'Perhaps the most popular book on statistics ever published ... It's a marvel ... gave me a peek behind the curtain of statistical manipulation, showing me how the swindling was done so that I would not be fooled again' Tim HarfordIn 1954, Darrell Huff decided enough was enough. Fed up with politicians, advertisers and journalists using statistics to sensationalise, inflate, confuse, oversimplify and - on occasion - downright lie, he decided to shed light on their ill-informed and sneaky ways. How to Lie with Statistics is the result - the definitive and hilarious primer in the ways statistics are used to deceive.With over one and half million copies sold around the world, it has delighted generations of readers with its cheeky takes on the ins and outs of samples, averages, errors, graphs and indexes. And in the modern world of big data and misinformation, Huff remains the perfect guide through the maze of facts and figures that are designed to make us believe anything.'A hilarious exploration of mathematical mendacity.... Every time you pick it up, what happens? Bang goes another illusion!' The New York Times'A pleasantly subversive little book guaranteed to undermine your faith in the almighty statistic' Atlantic

This greatly expanded third edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as statisticians in academia who teach introductory and second-level courses on survival analysis. The third edition continues to use the unique "lecture-book" format of the first two editions with one new chapter, additional sections and clarifications to several chapters, and a revised computer appendix. The Computer Appendix, with step-by-step instructions for using the computer packages STATA, SAS, and SPSS, is expanded this third edition to include the software package R.

The Sunday Times Bestseller'Tim Harford is one of my favourite writers in the world. His storytelling is gripping but never overdone, his intellectual honesty is rare and inspiring, and his ability to make complex things simple - but not simplistic - is exceptional. How to Make the World Add Up is another one of his gems. If you're looking for an addictive pageturner that will make you smarter, this is your book' Rutger Bregman, author of Humankind'Tim Harford could well be Britain's Malcolm Gladwell'Alex Bellos, author of Alex's Adventures in Numberland'If you aren't in love with stats before reading this book, you will be by the time you're done. Powerful, persuasive, and in these truth-defying times, indispensable'Caroline Criado Perez, author of Invisible Women In How to Make the World Add Up, Tim Harford draws on his experience as both an economist and presenter of the BBC's radio show 'More or Less' to take us deep into the world of disinformation and obfuscation, bad research and misplaced motivation to find those priceless jewels of data and analysis that make communicating with numbers so rewarding. Through vivid storytelling he reveals how we can evaluate the claims that surround us with confidence, curiosity and a healthy level of scepticism. It is a must-read for anyone who cares about understanding the world around them.'Tim Harford is our most likeable champion of reason and rigour . . . clear, clever and always highly readable'The Times, Books of the Year'Fascinating and enjoyable'Bill Bryson'Now more than ever we need a book like this'Stephen Fry'Wise, humane and, above all, illuminating. Nobody is better on statistics and numbers - and how to make sense of them'Matthew Syed'One of the most wonderful collections of stories that I have read in a long time . . . fascinating.'Steven Levitt, co-author of Freakonomics'Wise and useful . . . such a delight' Financial Times'What should we do when someone makes a claim that they say is based on data? This wise book, distilled from years of experience, gives us the ten commandments, from first examining our feelings, to finally having the humility to admit we may be wrong. Priceless'Professor Sir David SpiegelhalterAnnounced as a top ten Sunday Times bestseller in paperback on 16 May 2021

Bayesian statistics and statistical practice have evolved over the years, driven by advancements in theory, methods, and computational tools. Bayesian Workflow explores the intricate workflows of applied Bayesian statistics, aiming to uncover the tacit knowledge often overlooked in published papers and textbooks. By systematizing the process of Bayesian model development, the book seeks to improve applied analyses and inspire future innovations in theory, methods, and software. It emphasizes the importance of iterative model building, model checking, computational troubleshooting, and simulated-data experimentation, offering a comprehensive perspective on statistical analysis.Through detailed examples and practical guidance, the book bridges the gap between theory and application, empowering practitioners and researchers to navigate the complexities of Bayesian inference. It is not a checklist or cookbook but a flexible framework for understanding and resolving challenges in statistical modeling and decision-making under uncertainty.FeaturesCovers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understandingDemonstrates iterative model development and computational problem-solving through real-world case studiesExplores computational challenges, calibration checking, and connections between modeling and computationHighlights the importance of checking models under diverse conditions to understand their limitations and improve their robustnessDiscusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learningIncludes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and JuliaThis book is designed for practitioners of applied Bayesian statistics, particularly users of probabilistic programming languages such as Stan, as well as developers of methods and software tailored to these users. It also targets researchers in Bayesian theory and methods, offering insights into understudied aspects of statistical workflows. Instructors and students will find adaptable exercises and case studies to enhance their learning experience. Beyond Bayesian inference, the book’s principles are relevant to users of non-Bayesian methods, making it a valuable resource for statisticians, data scientists, and machine learning professionals seeking to improve their modeling and decision-making processes.

This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology. Firstly, it discusses the importance of statistical models in applied quantitative research and the central role of the likelihood function, describing likelihood-based inference from a frequentist viewpoint, and exploring the properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book “Applied Statistical Inference” has been expanded to include new material on Markov models for time series analysis. It also features a comprehensive appendix covering the prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis, and each chapter is complemented by exercises. The text is primarily intended for graduate statistics and biostatistics students with an interest in applications.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and  astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Gain the statistics skills you need for the social sciences with this accessible introductory guide

Statistical Methods for the Social Sciences, 5th Edition, Global Edition, by Alan Agresti, introduces you to statistical methods used in social science disciplines with no previous knowledge of statistics necessary. With an emphasis on concepts and applications, the book requires only a minimal mathematical background, maintaining a low technical level throughout to make it accessible to beginners.

The 5th edition has a strong focus on real examples to help you learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. This approach also helps you understand how to apply your learning to the real world. This edition also emphasises the interpretation of software output rather than the formulas for performing analysis, reflecting advances in statistical software - which are more frequently used by social scientists to analyse data today.

Other updates include:

Numerous homework exercises included in each chapter. Updated data in most exercises. New sections, such as that on maximum likelihood estimation in chapter 5 New examples ask students to use applets to help them learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. The text also relies more on applets for finding tail probabilities from distributions such as the Normal, t, and chi-squared.

With a wide array of learning features and the latest available information, this text will equip you with the knowledge you need to succeed in your course - an ideal companion for students majoring in social science disciplines.

Dig deep to discover the truth about numbers and statistics in the mediaWrong Number: How to Extract Truth From a Blizzard of Quantitative Disinformation shows readers how to diagnose statistical errors in journal articles and media accounts of prominent public issues. In addition to the mistakes or tricks with numbers other people use to lose the truth, this book also offers more positive material, showing how to use numbers to find truth.Written by Aaron Brown, prominent media figure, former professor, sports bettor, and poker player, and host of the popular ReasonTV series Wrong Number, this book answers questions including: Did California’s minimum wage hike really create more jobs?Do studies show that weed causes strokes and heart attacks?Are poor geniuses being shut out of science?What is the real health risk of vaping?Does gun control work?Wrong Number: How to Extract Truth From a Blizzard of Quantitative Disinformation is an entertaining and eye-opening read for students in quantitative programs of study, along with all individuals curious as to whether or not they’re being told the truth from their favorite media organizations.

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

'My favourite author has done it again. Numbers Don't Lie is by far his most accessible book to date, and I highly recommend it to anyone who is curious about the world. I unabashedly recommend this book to anyone who loves learning' Bill GatesIs flying dangerous? How much do the world's cows weigh? And what makes people happy?From Earth's nations and inhabitants, through the fuels and foods that energize them, to the transportation and inventions of our modern world - and how all of this affects the planet itself - in Numbers Don't Lie, Professor Vaclav Smil takes us on a fact-finding adventure, using surprising statistics and illuminating graphs to challenge lazy thinking. Smil is on a mission to make facts matter, because after all, numbers may not lie, but which truth do they convey?'Smil's title says it all: to understand the world, you need to follow the trendlines, not the headlines. This is a compelling, fascinating, and most important, realistic portrait of the world and where it's going' Steven Pinker'The best book to read to better understand our world. It should be on every bookshelf!' Linda Yueh'There is perhaps no other academic who paints pictures with numbers like Smil' GuardianVaclav Smil is Distinguished Professor Emeritus at the University of Manitoba. He is the author of over forty books on topics including energy, environmental and population change, food production and nutrition, technical innovation, risk assessment and public policy. No other living scientist has had more books (on a wide variety of topics) reviewed in Nature. A Fellow of the Royal Society of Canada, in 2010 he was named by Foreign Policy as one of the Top 100 Global Thinkers. This is his first book for a more general readership.

From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff’s lively and engaging primer clarifies the basic principles of statistics and explains how they’re used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.

“Boken är en utmärkt resurs för alla som vill lära sig regressionsanalys från grunden och hur man kan tillämpa det med R eller Python. Den balanserar teori och praktik på ett föredömligt sätt och är välstrukturerad. Progressionen från grundläggande till mer avancerade ämnen är tydlig. En extra stjärna i kanten är att det sista kapitlet behandlar metoder för att studera orsakssamband – ett viktigt och unikt bidrag till den svenskspråkiga statistiklitteraturen.”Xavier de luna, professor i statistik, Umeå universitet“Vi lever i en tid av data där det är väsentligt att vi förstår statistiska metoder för att kunna använda dessa data meningsfullt. Denna bok är ett måste för både data- och statistikintresserade studenter som vill förstå tillämpad analys samt samhällsvetenskapligt intresserade studenter som vill ha metoder för att svara på viktiga samhällsvetenskapliga frågor.”Anna Dreber, professor i nationalekonomi, Handelshögskolan I StockholmBoken är avsedd för studenter som läser statistik på kandidatnivå och för studenter som använder regression för empiriska analyser på master- eller forskarutbildningsnivå inom medicin och samhällsvetenskap. Boken kan även användas för den tillämpade forskaren, då fokus ligger på hur regression kan användas och hur resultaten ska tolkas.