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Titlebook: An Introduction to R for Quantitative Economics; Graphing, Simulating Vikram Dayal Book 2015 The Author(s) 2015 Applied Econometrics.Quanti

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发表于 2025-3-21 16:31:45 | 显示全部楼层 |阅读模式
期刊全称An Introduction to R for Quantitative Economics
期刊简称Graphing, Simulating
影响因子2023Vikram Dayal
视频video
发行地址Introduces the free software R, using its cutting edge interface, R Studio, aimed at economists.Starts from simpler topics such as supply and demand and elasticity and goes on to more advanced ones su
学科分类SpringerBriefs in Economics
图书封面Titlebook: An Introduction to R for Quantitative Economics; Graphing, Simulating Vikram Dayal Book 2015 The Author(s) 2015 Applied Econometrics.Quanti
影响因子This book gives an introduction to R to build up graphing, simulating and computing skills to enable one to see theoretical and statistical models in economics in a unified way. The great advantage of R is that it is free, extremely flexible and extensible. The book addresses the specific needs of economists, and helps them move up the R learning curve. It covers some mathematical topics such as, graphing the Cobb-Douglas function, using R to study the Solow growth model, in addition to statistical topics, from drawing statistical graphs to doing linear and logistic regression. It uses data that can be downloaded from the internet, and which is also available in different R packages. With some treatment of basic econometrics, the book discusses quantitative economics broadly and simply, looking at models in the light of data. Students of economics or economists keen to learn how to use R would find this book very useful.
Pindex Book 2015
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发表于 2025-3-21 20:17:28 | 显示全部楼层
Vikram DayalIntroduces the free software R, using its cutting edge interface, R Studio, aimed at economists.Starts from simpler topics such as supply and demand and elasticity and goes on to more advanced ones su
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An Introduction to R for Quantitative Economics978-81-322-2340-5Series ISSN 2191-5504 Series E-ISSN 2191-5512
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Rißbildung in Kesselblechen uswere we use parentheses and brackets). We use four types of objects frequently—vectors, matrices, data frames and lists. We often act on the whole or part of an object, so we need to refer to the whole or part of the object precisely.
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