東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Economics- Data processing." ]

切換: 標籤 | MARC模式 | ISBD

Quantitative economics with R = a da...

Dayal, Vikram.

FindBook

Google Book

Amazon

博客來

Quantitative economics with R = a data science approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantitative economics with R/ by Vikram Dayal.
其他題名:	a data science approach /
作者:	Dayal, Vikram.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xv, 326 p. :ill. (some col.), digital ;24 cm.
內容註:	Ch 1 Introduction -- Ch 2 R and RStudio -- Ch 3 Getting data into R -- Ch 4 Wrangling and graphing data -- Ch 5 Functions -- Ch 6 Matrices -- Ch 7 Probability and statistical inference -- Ch 8 Causal inference -- Ch 9 Solow model and basic facts of growth -- Ch 10 Causal inference for growth -- Ch 11 Graphing and simulating basic time series -- Ch 12 Simple examples: forecasting and causal inference -- Ch 13 Generalized additive models -- Ch 14 Tree models.
Contained By:	Springer eBooks
標題:	Economics - Data processing. -
電子資源:	https://doi.org/10.1007/978-981-15-2035-8
ISBN:	9789811520358

Quantitative economics with R = a data science approach /
Dayal, Vikram.

Quantitative economics with Ra data science approach /[electronic resource] :by Vikram Dayal. - Singapore :Springer Singapore :2020. - xv, 326 p. :ill. (some col.), digital ;24 cm.

Ch 1 Introduction -- Ch 2 R and RStudio -- Ch 3 Getting data into R -- Ch 4 Wrangling and graphing data -- Ch 5 Functions -- Ch 6 Matrices -- Ch 7 Probability and statistical inference -- Ch 8 Causal inference -- Ch 9 Solow model and basic facts of growth -- Ch 10 Causal inference for growth -- Ch 11 Graphing and simulating basic time series -- Ch 12 Simple examples: forecasting and causal inference -- Ch 13 Generalized additive models -- Ch 14 Tree models.

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 bootstrapis introduced. Causal inference is 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 appliedeconomics and enjoy engaging with the material actively, while also equipping them with key data science skills.

ISBN: 9789811520358

Standard No.: 10.1007/978-981-15-2035-8doiSubjects--Topical Terms:

782280
Economics
--Data processing.

LC Class. No.: HB143.5 / .D393 2020

Dewey Class. No.: 330.0151

Quantitative economics with R = a data science approach /
LDR:03203nmm a2200325 a 4500 001 2216445
003 DE-He213
005 20200717105440.0
006 m d
007 cr nn 008maaau
008 201120s2020 si s 0 eng d
020 $a 9789811520358 $q (electronic bk.)
020 $a 9789811520341 $q (paper)
024 7 $a 10.1007/978-981-15-2035-8 $2 doi
035 $a 978-981-15-2035-8
040 $a GP $c GP
041 0 $a eng
050 4 $a HB143.5 $b .D393 2020
072 7 $a PBUD $2 bicssc
072 7 $a MAT011000 $2 bisacsh
072 7 $a PBUD $2 thema
082 0 4 $a 330.0151 $2 23
090 $a HB143.5 $b .D275 2020
100 1 $a Dayal, Vikram. $3 2056601
245 1 0 $a Quantitative economics with R $h [electronic resource] : $b a data science approach / $c by Vikram Dayal.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xv, 326 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Ch 1 Introduction -- Ch 2 R and RStudio -- Ch 3 Getting data into R -- Ch 4 Wrangling and graphing data -- Ch 5 Functions -- Ch 6 Matrices -- Ch 7 Probability and statistical inference -- Ch 8 Causal inference -- Ch 9 Solow model and basic facts of growth -- Ch 10 Causal inference for growth -- Ch 11 Graphing and simulating basic time series -- Ch 12 Simple examples: forecasting and causal inference -- Ch 13 Generalized additive models -- Ch 14 Tree models.
520 $a 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 bootstrapis introduced. Causal inference is 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 appliedeconomics and enjoy engaging with the material actively, while also equipping them with key data science skills.
650 0 $a Economics $x Data processing. $3 782280
650 0 $a R (Computer program language) $3 784593
650 0 $a Game theory. $3 532607
650 0 $a Economics. $3 517137
650 0 $a Statistics. $3 517247
650 0 $a Computer simulation. $3 525602
650 0 $a Sociology $x Research. $3 522299
650 1 4 $a Game Theory, Economics, Social and Behav. Sciences. $3 891103
650 2 4 $a Economic Theory/Quantitative Economics/Mathematical Methods. $3 2162305
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 3382132
650 2 4 $a Simulation and Modeling. $3 890873
650 2 4 $a Research Methodology. $3 519201
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-15-2035-8
950 $a Mathematics and Statistics (Springer-11649)