ISBN: 9789811520372 - quantitative economics with r: a data science approach (14 Ergebnisse)

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9789811520372.

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Taschenbuch. Zustand: Neu. Quantitative Economics with R | A Data Science Approach | Vikram Dayal | Taschenbuch | xv | Englisch | 2021 | Springer | EAN 9789811520372 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Zustand: Brand New. 341 pages. 9.25x6.10x0.75 inches. In Stock.

PF. Zustand: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a contemporary treatment of quantitative economics, with a focus ondata science. The book introduces the readerto Rand RStudio, and uses expert Hadley Wickham's tidyverse packagefor different parts of the data analysis workflow. Aftera gentleintroductionto R code, the reader'sR skills are gradually honed, with the help of 'your turn' exercises.At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the readerwill beginusing the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operationsisalsocovered.The book uses Monte Carlo simulation tounderstandprobability and statistical inference, and the bootstrap isintroduced. Causal inferenceis illuminated using simulation, data graphs,and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models ispresented, before the book introduces the reader to time series data analysis with graphs, simulation,andexamples.Lastly, twocomputationally intensivemethods-generalized additive models andrandom forests (an important and versatile machine learning method)-are introduced intuitively with applications.The book will beof great interest to economists-students, teachers,and researchersalike-who want to learn R.It will help economics students gainan intuitive appreciation of applied economicsand enjoy engagingwith the material actively, while also equipping them with key data science skills. 344 pp. Englisch.…

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Employs a popular data science approach while discussing concepts and applications related to economicsExplains causal inferences with the aid of simulations, data graphs,&nbspand sample applicationsIntroduces readers to two versati.…

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a contemporary treatment of quantitative economics, with a focus ondata science. The book introduces the readerto Rand RStudio, and uses expert Hadley Wickham's tidyverse packagefor different parts of the data analysis workflow. Aftera gentleintroductionto R code, the reader'sR skills are gradually honed, with the help of 'your turn' exercises.At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the readerwill beginusing the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operationsisalsocovered.The book uses Monte Carlo simulation tounderstandprobability and statistical inference, and the bootstrap isintroduced. Causal inferenceis illuminated using simulation, data graphs,and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models ispresented, before the book introduces the reader to time series data analysis with graphs, simulation,andexamples.Lastly, twocomputationally intensivemethods-generalized additive models andrandom forests (an important and versatile machine learning method)-are introduced intuitively with applications.The book will beof great interest to economists-students, teachers,and researchersalike-who want to learn R.It will help economics students gainan intuitive appreciation of applied economicsand enjoy engagingwith the material actively, while also equipping them with key data science skills.…

Zustand: New. Print on Demand.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham's tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader's R skills are gradually honed, with the help of 'your turn' exercises.At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inferenceis illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods-generalized additive models and random forests (an important and versatile machine learning method)-are introduced intuitively with applications.The book will be of great interest to economists-students, teachers, and researchers alike-who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 344 pp. Englisch.…

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