Matematisk statistik

Perfected over three editions and more than forty years, this field- and classroom-tested reference:* Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures.* Treats all the basic and important topics in multivariate statistics.* Adds two new chapters, along with a number of new sections.* Provides the most methodical, up-to-date information on MV statistics available.

Which scientific results can we trust? This question has been brought to the forefront of research in the social sciences in recent years with the movement towards open science practices and preregistration. Systematic replication studies of laboratory experiments in the social sciences have found that only about half of the “statistically significant” results published in top journals can be replicated in the sense that similar results are achieved with new data. This low replicability may be even lower in studies based on observational data as such studies have more degrees of freedom in the analysis of the data leading to larger possibilities to selectively report more publishable findings.In this book, the authors provide a framework for evaluating reproducibility, replicability and generalizability of empirical research in the social sciences. They define different types of reproducibility and replicability and show how they can be measured to evaluate the credibility of published findings. Different approaches to improving the credibility of published findings, such as preregistration with detailed pre-analysis plans, Registered Report publications, and preregistered prospective meta-analysis are also outlined and discussed. Even if published results are not systematically biased, the variation in results across populations, research designs, and analyses decreases the reliability and generalizability of published findings. The book shows how such heterogeneity in results can be measured and incorporated in the analysis to more accurately represent the uncertainty and thereby generalizability of reported results.

"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)

This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation.FeaturesThe classic graduate-level textbook on statistical inferenceDevelops elements of statistical theory from first principles of probabilityWritten in a lucid style accessible to anyone with some background in calculusCovers all key topics of a standard course in inferenceHundreds of examples throughout to aid understandingEach chapter includes an extensive set of graduated exercisesStatistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background. It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations.This is a reprint of the second edition originally published by Cengage Learning, Inc. in 2001.

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.

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.

This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of research in asymptotic statistics.

Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.

The fourth edition of this successful text provides an introduction to probability and random processes, with many practical applications. It is aimed at mathematics undergraduates and postgraduates, and has four main aims.US BL To provide a thorough but straightforward account of basic probability theory, giving the reader a natural feel for the subject unburdened by oppressive technicalities. BE BL To discuss important random processes in depth with many examples.BE BL To cover a range of topics that are significant and interesting but less routine.BE BL To impart to the beginner some flavour of advanced work.BE UE OP The book begins with the basic ideas common to most undergraduate courses in mathematics, statistics, and science. It ends with material usually found at graduate level, for example, Markov processes, (including Markov chain Monte Carlo), martingales, queues, diffusions, (including stochastic calculus with Itô's formula), renewals, stationary processes (including the ergodic theorem), and option pricing in mathematical finance using the Black-Scholes formula. Further, in this new revised fourth edition, there are sections on coupling from the past, Lévy processes, self-similarity and stability, time changes, and the holding-time/jump-chain construction of continuous-time Markov chains. Finally, the number of exercises and problems has been increased by around 300 to a total of about 1300, and many of the existing exercises have been refreshed by additional parts. The solutions to these exercises and problems can be found in the companion volume, One Thousand Exercises in Probability, third edition, (OUP 2020).CP

Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods.

Percolation theory is the study of an idealized random medium in two or more dimensions. The mathematical theory is mature, and continues to give rise to problems of special beauty and difficulty. Percolation is pivotal for studying more complex physical systems exhibiting phase transitions. The emphasis of this book is upon core mathematical material and the presentation of the shortest and most accessible proofs. The book is intended for graduate students and researchers in probability and mathematical physics. Almost no specialist knowledge is assumed. Much new material appears in this second edition, including: dynamic and static renormalization, strict inequalities between critical points, a sketch of the lace expansion, and several essays on related fields and applications.

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.

This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critical re-examination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics

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.

This 5th edition of this popular graduate textbook presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. It includes numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The R package ‘astsa’ has had major updates and the text will reflect those updates. In general, the graphics have been improved. New topics include random number generation, modeling and fitting predator-prey interactions, more emphasis on structural models, testing for linearity, discussion of EM algorithm is more extensive, Bayesian analysis of state space models and MCMC is more extensive (including new scripts in astsa), particle methods are introduced, stochastic volatility coverage is expanded, changepoint detection is introduced (new topic).The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, and Markov chain Monte Carlo integration methods.This edition includes R code for each numerical example.

Morphometrical differentiation 63 Prediction of breeding origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 Body mass 69 Primary moult 69 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 The taxonomy of the Eurasian Golden Plover 69 The existence of a partial breast-feather moult during breeding 69 Morphometrical differentiation between both sexes . . . . . . . . . . . . . . . . . . . . . . . . 70 The occurrence of primary moult on the breeding grounds 70 Summary 70 6 Grey Plover - Pluvialis squatarola 71 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Morphometrical differentiation 77 Prediction of breeding origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Accuracy 81 Exclusivity 81 Contact zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Body mass 85 Primary moult 85 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 The taxonomy of the Grey Plover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Small size at northern latitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Morphometrical variation in the Palearctic 87 Summary 87 7 Red Knot - Calidris canutus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . 91 Morphometrical differentiation 93 Prediction of breeding origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Accuracy 99 Exclusivity 101 Contact zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Body mass 102 Primary moult 103 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 of the Knot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 The taxonomy Is the knot breeding in Yakutia? 106 Different degrees of geographical variation in the d'd' and !f!f 106 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 8 Sanderling - Calidris alba 109 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Morphometrical differentiation 111 Prediction of breeding origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Accuracy 116 Exclusivity 118 Contact zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Body mass 118 Primary moult 118 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 The taxonomy of the Sanderling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Sexing Sanderlings 119 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 119 VI 9 Curlew Sandpiper - Calidris ferruginea 121 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Morphometrical differentiation 123 Prediction of a bird's sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Accuracy 127 Exclusivity 127 Body mass 127 Primary moult 127 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 The taxonomy of the Curlew Sandpiper 127 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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. Distinctive features include: All examples based on work in industry, consulting to industry, and research for industry Examples and case studies include all engineering disciplines Emphasis on probabilistic modeling including decision trees, Markov chains and processes, and structure functions Intuitive explanations are followed by succinct mathematical justifications Emphasis on random number generation that is used for stochastic simulations of engineering systems, demonstration of key concepts, and implementation of bootstrap methods for inference Use 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 applications Use 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 textbooks Experiments designed to show fundamental concepts that have been tested with large classes working in small groups Website with additional materials that is regularly updated Andrew Metcalfe, David Green, Andrew Smith, and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering, mining, and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health, University of South Australia. Tony Greenfield, formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association, is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for

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.

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.

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.

Winner of the 2024 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.

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.

Mathematical modeling is a powerful craft that requires practice. The more practice the better one will become in executing the art. The authors wrote this book to develop the craft of mathematical modeling and to foster a desire for lifelong learning, habits of mind and develop competent and confident problem solvers and decision makers for the 21st century. This book offers a problem-solving approach. The authors introduce a problem to help motivate the learning of a particular mathematical modeling topic. The problem provides the issue or what is needed to solve using an appropriate modeling technique. Then principles are applied to the problem and present the steps in obtaining an appropriate model to solve the problem. Modeling Change and Uncertainty: Covers both linear and nonlinear models of discrete dynamical systems. Introduces statistics and probability modeling. Introduces critical statistical concepts to handle univariate and multivariate data. Establishes a foundation in probability modeling. Uses ordinary differential equations (ODEs) to develop a more robust solution to problems. Uses linear programming and machine learning to support decision making. Introduces the reality of uncertainty and randomness that is all around us. Discusses the use of linear programing to solve common problems in modern industry. Discusses he power and limitations of simulations.Introduces the methods and formulas used in businesses and financial organizations. Introduces valuable techniques using Excel, MAPLE, and R. Mathematical modeling offers a framework for decision makers in all fields. This framework consists of four key components: the formulation process, the solution process, interpretation of the solution in the context of the actual problem, and sensitivity analysis.Modeling Change and Uncertainty will be of interest to mathematics departments offering advanced mathematical modeling courses focused on decision making or discrete mathematical modeling and by undergraduate, graduate students and practitioners looking for an opportunity to develop, practice, and apply the craft of mathematical modeling.

Mathematical modeling is both a skill and an art and must be practiced in order to maintain and enhance the ability to use those skills. Though the topics covered in this book are the typical topics of most mathematical modeling courses, this book is best used for individuals or groups who have already taken an introductory mathematical modeling course. Advanced Mathematical Modeling with Technology will be of interest to instructors and students offering courses focused on discrete modeling or modeling for decision making.Each chapter begins with a problem to motivate the reader. The problem tells "what" the issue is or problem that needs to be solved. In each chapter, the authors apply the principles of mathematical modeling to that problem and present the steps in obtaining a model. The key focus is the mathematical model and the technology is presented as a method to solve that model or perform sensitivity analysis. We have selected , where applicable to the content because of their wide accessibility. The authors utilize technology to build, compute, or implement the model and then analyze the it. Features: MAPLE©, Excel©, and R© to support the mathematical modeling process. Excel templates, macros, and programs are available upon request from authors. Maple templates and example solution are also available. Includes coverage of mathematical programming. The power and limitations of simulations is covered. Introduces multi-attribute decision making (MADM) and game theory for solving problems.The book provides an overview to the decision maker of the wide range of applications of quantitative approaches to aid in the decision making process, and present a framework for decision making.

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

Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with purely technical matters with insufficient emphasis given to the technique's practical value. Furthermore, the subject has been rather inaccessible to the general statistician.The account presented in this book places emphasis on topics of methodological importance, in the hope that this will facilitate broader practical application of density estimation and also encourage research into relevant theoretical work. The book also provides an introduction to the subject for those with general interests in statistics. The important role of density estimation as a graphical technique is reflected by the inclusion of more than 50 graphs and figures throughout the text.Several contexts in which density estimation can be used are discussed, including the exploration and presentation of data, nonparametric discriminant analysis, cluster analysis, simulation and the bootstrap, bump hunting, projection pursuit, and the estimation of hazard rates and other quantities that depend on the density. This book includes general survey of methods available for density estimation. The Kernel method, both for univariate and multivariate data, is discussed in detail, with particular emphasis on ways of deciding how much to smooth and on computation aspects. Attention is also given to adaptive methods, which smooth to a greater degree in the tails of the distribution, and to methods based on the idea of penalized likelihood.

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.

“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.