"Handbook of Regression Modeling in People Analytics" - Information and Links:

Handbook of Regression Modeling in People Analytics - Info and Reading Options

With Examples in R and Python

Book's cover
The cover of “Handbook of Regression Modeling in People Analytics” - Google Books.

"Handbook of Regression Modeling in People Analytics" was published by Taylor & Francis Group in 2021, the book is classified in Business & Economics genre, it has 256 pages and the language of the book is English.


“Handbook of Regression Modeling in People Analytics” Metadata:

  • Title: ➤  Handbook of Regression Modeling in People Analytics
  • Author:
  • Language: English
  • Number of Pages: 256
  • Is Family Friendly: Yes - No Mature Content
  • Publisher: Taylor & Francis Group
  • Publish Date:
  • Genres: Business & Economics

“Handbook of Regression Modeling in People Analytics” Subjects and Themes:

Edition Identifiers:

AI-generated Review of “Handbook of Regression Modeling in People Analytics”:


Snippets and Summary:

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.

"Handbook of Regression Modeling in People Analytics" Description:

Google Books:

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.

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  • Public Domain: No
  • Availability Status: Partially available
  • Availability Status for country: US.
  • Available Formats: Text is not avialbe, image copy is available.
  • Google Books Link: Google Books

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