Name: Handbook of Regression Modeling in People Analytics: With Examples in r and Python (en Inglés)
Price: 94860.00 CLP
Availability: InStock
Author: Keith Mcnulty
ISBN: 9781032041742

portada Handbook of Regression Modeling in People Analytics: With Examples in r and Python (en Inglés)

Formato

Libro Físico

Autor

Keith Mcnulty

Año

2021

Idioma

Inglés

N° páginas

272

Encuadernación

Tapa Dura

ISBN13

9781032041742

N° edición

Categorías

Econometría

Handbook of Regression Modeling in People Analytics: With Examples in r and Python (en Inglés)

Keith Mcnulty (Autor) · · Tapa Dura

Libro Nuevo

$ 94.860

$ 131.750

Ahorras: $ 36.890

28% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Lunes 13 de Mayo y el Viernes 24 de Mayo.

Lo recibirás en cualquier lugar de Chile entre 1 y 3 días hábiles luego del envío.

Reseña del libro "Handbook of Regression Modeling in People Analytics: With Examples in r and Python (en Inglés)"

Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features:• 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) • Clear step-by-step instructions on executing the analyses. • Clear guidance on how to interpret results. • Primary instruction in R but added sections for Python coders. • Discussion exercises and data exercises for each of the main chapters.• Final chapter of practice material and datasets ideal for class homework or project work.