Products related to Inference:
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Understanding Political Science Research Methods : The Challenge of Inference
This text starts by explaining the fundamental goal of good political science research—the ability to answer interesting and important questions by generating valid inferences about political phenomena.Before the text even discusses the process of developing a research question, the authors introduce the reader to what it means to make an inference and the different challenges that social scientists face when confronting this task.Only with this ultimate goal in mind will students be able to ask appropriate questions, conduct fruitful literature reviews, select and execute the proper research design, and critically evaluate the work of others.The authors' primary goal is to teach students to critically evaluate their own research designs and others’ and analyze the extent to which they overcome the classic challenges to making inference: internal and external validity concerns, omitted variable bias, endogeneity, measurement, sampling, and case selection errors, and poor research questions or theory.As such, students will not only be better able to conduct political science research, but they will also be more savvy consumers of the constant flow of causal assertions that they confront in scholarship, in the media, and in conversations with others. Three themes run through Barakso, Sabet, and Schaffner’s text: minimizing classic research problems to making valid inferences, effective presentation of research results, and the nonlinear nature of the research process.Throughout their academic years and later in their professional careers, students will need to effectively convey various bits of information.Presentation skills gleaned from this text will benefit students for a lifetime, whether they continue in academia or in a professional career.Several distinctive features make this book noteworthy:A common set of examples threaded throughout the text give students a common ground across chapters and expose them to a broad range of subfields in the discipline.Box features throughout the book illustrate the nonlinear, "non-textbook" reality of research, demonstrate the often false inferences and poor social science in the way the popular press covers politics, and encourage students to think about ethical issues at various stages of the research process.
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Causal Inference for Data Science
When you know the cause of an event, you can affect its outcome.This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning. In Causal Inference for Data Science you will learn how to: Model reality using causal graphsEstimate causal effects using statistical and machine learning techniquesDetermine when to use A/B tests, causal inference, and machine learningExplain and assess objectives, assumptions, risks, and limitationsDetermine if you have enough variables for your analysis It's possible to predict events without knowing what causes them.Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes.Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events.You'll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.
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Causal Inference
A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy. Which of two antiviral drugs does the most to save people infected with Ebola virus?Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt?How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce?Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices.Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.
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Statistical Inference
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. Features The 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.
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What exactly is a mathematical inference in mathematics and computer science?
A mathematical inference in mathematics and computer science is the process of drawing conclusions or making predictions based on existing information or data. In mathematics, this often involves using logical reasoning and mathematical principles to make deductions or prove the validity of a statement. In computer science, mathematical inference can be used in areas such as artificial intelligence and machine learning to make predictions or decisions based on patterns and data. Overall, mathematical inference is a fundamental concept in both fields that allows for the application of logic and reasoning to solve problems and make decisions.
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What is inference in linear regression?
Inference in linear regression refers to the process of drawing conclusions about the relationships between variables based on the estimated coefficients of the regression model. It involves testing hypotheses about the significance of these coefficients and making predictions about the dependent variable. Inference helps us understand the strength and direction of the relationships between the independent and dependent variables, as well as the overall fit of the model to the data. It is an important aspect of linear regression analysis that allows us to make informed decisions and interpretations based on the statistical results.
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How are logical inference, the Gentzen calculus, and De Morgan's laws correctly derived?
Logical inference is the process of deriving new information from existing knowledge using valid reasoning. The Gentzen calculus is a formal system for representing and manipulating logical inference in a rigorous way. De Morgan's laws, which describe the relationships between logical conjunction and disjunction, can be correctly derived using the rules of the Gentzen calculus, which ensures that the inference process is sound and valid. By following the rules of the Gentzen calculus, one can systematically derive De Morgan's laws and other logical principles in a mathematically rigorous manner.
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Does market research hinder innovation in business administration?
Market research does not necessarily hinder innovation in business administration. In fact, it can provide valuable insights into consumer needs and preferences, helping businesses to develop innovative products and services that meet market demands. By understanding market trends and customer behavior, businesses can identify opportunities for innovation and stay ahead of competitors. However, relying too heavily on market research without allowing room for creativity and risk-taking can limit the potential for groundbreaking innovations. It is important for businesses to strike a balance between leveraging market research and fostering a culture of innovation to drive success in business administration.
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Inference and Learning from Data: Volume 2 : Inference
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference.This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning.A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code.Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
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Social Inquiry and Bayesian Inference : Rethinking Qualitative Research
Fairfield and Charman provide a modern, rigorous and intuitive methodology for case-study research to help social scientists and analysts make better inferences from qualitative evidence.The book develops concrete guidelines for conducting inference to best explanation given incomplete information; no previous exposure to Bayesian analysis or specialized mathematical skills are needed.Topics covered include constructing rival hypotheses that are neither too simple nor overly complex, assessing the inferential weight of evidence, counteracting cognitive biases, selecting cases, and iterating between theory development, data collection, and analysis.Extensive worked examples apply Bayesian guidelines, showcasing both exemplars of intuitive Bayesian reasoning and departures from Bayesian principles in published case studies drawn from process-tracing, comparative, and multimethod research.Beyond improving inference and analytic transparency, an overarching goal of this book is to revalue qualitative research and place it on more equal footing with respect to quantitative and experimental traditions by illustrating that Bayesianism provides a universally applicable inferential framework.
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Statistical Foundations, Reasoning and Inference : For Science and Data Science
This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science.The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty.Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design.The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory.It will also be useful for data science practitioners who want to strengthen their statistics skills.
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Inference and Representation : A Study in Modeling Science
The first comprehensive defense of an inferential conception of scientific representation with applications to art and epistemology. Mauricio Suárez develops a conception of representation that delivers a compelling account of modeling practice.He begins by discussing the history and methodology of model building, charting the emergence of what he calls the modeling attitude, a nineteenth-century and fin de siècle development.Prominent cases of models, both historical and contemporary, are used as benchmarks for the accounts of representation considered throughout the book.After arguing against reductive naturalist theories of scientific representation, Suárez sets out his own account: a case for pluralism regarding the means of representation and minimalism regarding its constituents.He shows that scientists employ a variety of modeling relations in their representational practice—which helps them to assess the accuracy of their representations—while demonstrating that there is nothing metaphysically deep about the constituent relation that encompasses all these diverse means. The book also probes the broad implications of Suárez’s inferential conception outside scientific modeling itself, covering analogies with debates about artistic representation and philosophical thought over the past several decades.
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What is the difference between electrical engineering, computer science, and network technology?
Electrical engineering focuses on the study and application of electricity, electromagnetism, and electronics to design and develop electrical systems and devices. Computer science, on the other hand, deals with the theory, design, development, and application of computer systems and software. Network technology involves the design, implementation, and management of computer networks to facilitate communication and data exchange between devices. While electrical engineering and computer science have some overlap in areas like digital systems and hardware design, network technology specifically focuses on the networking aspects of computer systems.
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What is the difference between electrical engineering, electronics, information technology, and technical computer science?
Electrical engineering focuses on the generation, transmission, and distribution of electrical power, as well as the design of electrical systems. Electronics deals with the study of electronic components, circuits, and systems, including semiconductors and transistors. Information technology involves the use of computer systems to store, retrieve, transmit, and manipulate data. Technical computer science focuses on the design and development of software and hardware systems, including programming languages and algorithms. Each field has its own specialized focus within the broader realm of technology and engineering.
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What is the research question for a mechanical engineering dual study program with a focus on vehicle system engineering or production technology?
The research question for a mechanical engineering dual study program with a focus on vehicle system engineering or production technology could be: "How can advancements in vehicle system engineering improve the efficiency and performance of modern vehicles?" This question would involve exploring innovative technologies and design principles to enhance the functionality and sustainability of vehicles. Additionally, the research could investigate the integration of production technology to streamline manufacturing processes and optimize the production of vehicles.
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Will the development of technology ever stop?
It is unlikely that the development of technology will ever stop. As long as there are new problems to solve and new opportunities to explore, there will be a need for technological innovation. Additionally, the pace of technological advancement has been accelerating in recent years, with new breakthroughs and discoveries constantly pushing the boundaries of what is possible. While the specific direction and focus of technological development may change over time, it is likely that the overall trend of progress will continue.
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