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Handbook of Regression Modeling in People Analytics : With Examples in R and Python

By: Keith McNulty (Author)

2 in stock

Ksh 11,650.00

Format: Paperback or Softback

ISBN-10: 1032046635

ISBN-13: 9781032046631

Publisher: Taylor & Francis Ltd

Imprint: Chapman & Hall/CRC

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Aug 26th, 2024

Publication Status: Active

Product extent: 256 Pages

Weight: 470.00 grams

Dimensions (height x width x thickness): 15.50 x 23.60 x 1.60 cms

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  • Description

  • Reviews

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.

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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