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.

'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

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.

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.

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

“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.

'A statistical national treasure' Jeremy Vine, BBC Radio 2'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular ScienceDo busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever. In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial. 'Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world' Nature

Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics. You'll find advice on: Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan; How to think about p values, significance, insignificance, confidence intervals, and regression Choosing the right sample size and avoiding false positives; Reporting your analysis and publishing your data and source code; Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know. The first step toward statistics done right is Statistics Done Wrong.

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.

"The level is appropriate for an upper-level undergraduate or graduate-level statistics major. Sampling: Design and Analysis (SDA) will also benefit a non-statistics major with a desire to understand the concepts of sampling from a finite population. A student with patience to delve into the rigor of survey statistics will gain even more from the content that SDA offers. The updates to SDA have potential to enrich traditional survey sampling classes at both the undergraduate and graduate levels. The new discussions of low response rates, non-probability surveys, and internet as a data collection mode hold particular value, as these statistical issues have become increasingly important in survey practice in recent years… I would eagerly adopt the new edition of SDA as the required textbook." (Emily Berg, Iowa State University)What is the unemployment rate? What is the total area of land planted with soybeans? How many persons have antibodies to the virus causing COVID-19? Sampling: Design and Analysis, Third Edition shows you how to design and analyze surveys to answer these and other questions. This authoritative text, used as a standard reference by numerous survey organizations, teaches the principles of sampling with examples from social sciences, public opinion research, public health, business, agriculture, and ecology. Readers should be familiar with concepts from an introductory statistics class including probability and linear regression; optional sections contain statistical theory for readers familiar with mathematical statistics.Key Features:Has been thoroughly revised to incorporate recent research and applications.Includes a new chapter on nonprobability samples, and more than 200 new examples and exercises have been added.Teaches the principles of sampling with examples from social sciences, public opinion research, public health, business, agriculture, and ecology.SDA’s companion website contains data sets, computer code, and links to two free downloadable supplementary books (also available in paperback) that provide step-by-step guides—with code, annotated output, and helpful tips—for working through the SDA examples. Instructors can use either R or SAS® software.SAS® Software Companion for Sampling: Design and Analysis, Third Edition by Sharon L. Lohr (2022, CRC Press)R Companion for Sampling: Design and Analysis, Third Edition by Yan Lu and Sharon L. Lohr (2022, CRC Press)

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

This is the only book that gives a rigorous and comprehensive treatment with lots of examples, exercises, remarks on this particular level between the standard first undergraduate course and the first graduate course based on measure theory. There is no competitor to this book. The book can be used in classrooms as well as for self-study.

Investeringskalkylering är arbetet med att bedöma investeringars lönsamhet, det är sålunda ett hjälpmedel vid beslutsfattandet. Det kan vara förhållandevis små, men viktiga, privatekonomiska beslut eller enorma samhällsekonomiska beslut. Den här boken ger på ett handgripligt sätt en introduktion till arbetsgången vid investeringskalkylering - hur gör man?För att kunna besvara den frågan får man i boken lära sig: vilka investeringsmetoder (beräkningssätt) som finns hur antaganden om grundinvestering, in- och utbetalningar, avkastningskrav med mera ska göras hur skatt, lån, inflation och osäkerhet bör beaktas hur investeringskalkylering kan göras i kalkylprogram.Investeringskalkylering - Ett hjälpmedel vid beslutsfattande innehåller instuderingsfrågor med facit och tips på litteratur för den som vill förkovra sig ytterligare. Boken vänder sig till universitetsstuderande, främst inom företagsekonomi och industriell ekonomi, men även andra ingenjörsutbildningar kan vara betjänta av denna bok. Den kan också fungera som ett stöd för den som arbetar ute på företag och behöver utföra investeringskalkyler.

Den här boken handlar om sannolikhetslära, statistisk slutledning med mera. Statistik kan presenteras på olika sätt. Här sker det med exempel och praktiska tillämpningar och utan onödig krånglig matematik. De övningsuppgifter som finns i varje kapitel är välstrukturerade. För den som vill ha lite extra tips hur man löser bokens övningsuppgifter finns sådana att ladda ner från fliken Extramaterial. Därför fungerar boken bra även för den som vill lära sig statistik på egen hand. Statistik upplevs av många som svårt och tråkigt. Med denna bok som blivit en modern klassiker visar författarna ännu en gång att statistik kan både vara lätt och roligt.

For junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science.

This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.

Understanding maths has never been easier.Combining bold, elegant graphics with easy-to-understand text, Simply Maths is the perfect introduction to the subject for those who are short of time but hungry for knowledge.Covering more than 90 key mathematical concepts from prime numbers and fractions to quadratic equations and probability experiments, each pared-back, single-page entry explains the concept more clearly than ever before.Organized by major themes - number theory and systems; calculations; geometry; algebra; graphs; ratio and proportion; measurement; probability and statistics; and calculus - entries explain the essentials of each key mathematical theory with simple clarity and for ease of understanding.Whether you are studying maths at school or college, or simply want a jargon-free overview of the subject, this indispensable guide is packed with everything you need to understand the basics quickly and easily.

Now in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis.

This famous little book was first published in German in 1933 and in Russian a few years later, setting forth the axiomatic foundations of modern probability theory and cementing the author's reputation as a leading authority in the field. It remains a foundational text for the understanding of probability theory, important both to students beginning a serious study of the topic and to historians of modern mathematics.

In this edition two new chapters, 9 and 10, on mathematical finance are added. They are written by Dr. Farid AitSahlia, ancien eleve, who has taught such a course and worked on the research staff of several industrial and financial institutions. The new text begins with a meticulous account of the uncommon vocab­ ulary and syntax of the financial world; its manifold options and actions, with consequent expectations and variations, in the marketplace. These are then expounded in clear, precise mathematical terms and treated by the methods of probability developed in the earlier chapters. Numerous graded and motivated examples and exercises are supplied to illustrate the appli­ cability of the fundamental concepts and techniques to concrete financial problems. For the reader whose main interest is in finance, only a portion of the first eight chapters is a "prerequisite" for the study of the last two chapters. Further specific references may be scanned from the topics listed in the Index, then pursued in more detail.

An essential guide to the ways data can improve decision making.  Statistics are everywhere: in news reports, at the doctor’s office, and in every sort of forecast, from the stock market to the weather. Blogger, teacher, and computer scientist Allen B. Downey knows well that people have an innate ability both to understand statistics and to be fooled by them. As he makes clear in this accessible introduction to statistical thinking, the stakes are big. Simple misunderstandings have led to incorrect medical prognoses, underestimated the likelihood of large earthquakes, hindered social justice efforts, and resulted in dubious policy decisions. There are right and wrong ways to look at numbers, and Downey will help you see which are which.  Probably Overthinking It uses real data to delve into real examples with real consequences, drawing on cases from health campaigns, political movements, chess rankings, and more. He lays out common pitfalls—like the base rate fallacy, length-biased sampling, and Simpson’s paradox—and shines a light on what we learn when we interpret data correctly, and what goes wrong when we don’t. Using data visualizations instead of equations, he builds understanding from the basics to help you recognize errors, whether in your own thinking or in media reports. Even if you have never studied statistics—or if you have and forgot everything you learned—this book will offer new insight into the methods and measurements that help us understand the world.

Neil J. Salkind’s best-selling Statistics for People Who (Think They) Hate Statistics has been helping ease student anxiety around an often intimidating subject since it first published in 2000. Now the bestselling SPSS and Excel versions are joined by a first edition of the text for use with the R software. New co-author Leslie A. Shaw carries forward Neil’s signature humorous, personable, and informative approach. The text guides students through various statistical procedures, beginning with descriptive statistics, correlation, and graphical representation of data, and ending with inferential techniques and analysis of variance.

Features and benefits:

·         Lots of support for getting started with R: Included are two introductory chapters on R and on R Studio, plus an appendix on other R packages and resource sites.

·         Step-by-step demonstrations of each statistical procedure in R: The authors show how to import the dataset, enter the syntax to run the test, and understand the output.

·         Additional resources make it easy to transition to this text, and to R:  Code and datasets are available on an accompanying website, which also includes screencast R tutorial videos for students, and PowerPoint slides and additional test questions for instructors.

Statistisk kvalitetsstyrning handlar om kvalitetskontroller och kapabilitet.Att tillverka produkter exakt enligt de fastställda måtten som finns i en specifikation eller ritning är en omöjlighet. Det finns alltid en slumpmässig variation runt målvärdet, i produktionen måste man kontrollera och skapa toleranser för denna variation (kapabilitetsmått). Kvalitetskontroll görs under tillverkningsprocessen där man mäter avvikelser för att kunna moderera och justera processen för att minska dessa avvikelser.Det är viktigt att ha ett statistiskt synsätt för att få förståelse för begreppet variation. På så sätt kan man bygga in kvalitet i produkterna redan under tillverkningen. En tumregel är att kostnaden för att hitta och rätta till ett fel ökar ju senare det upptäcks.Statistisk kvalitetsstyrning är en introducerande bok i kvalitetsstyrning med utgångspunkt i statistiska metoder. Boken vänder sig till ingenjörsutbildningar med inriktning mot t.ex. maskin, design och produktutveckling.

This highly popular introduction to confidence intervals has been thoroughly updated and expanded. It includes methods for using confidence intervals, with illustrative worked examples and extensive guidelines and checklists to help the novice.