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Handbook of Regression Modeling in People Analytics

Keith McNulty
€61.50
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Author_Keith McNulty
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Binomial Logistic Regression
Binomial Logistic Regression Model
Category1=Non-Fiction
Category=KCH
Category=KCHS
Category=PBT
COP=United Kingdom
Cox Proportional Hazard Model
Cox Proportional Hazard Regression Model
Data Set
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Dummy Variables
employee retention predictive modeling
eq_bestseller
eq_business-finance-law
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Frailty Models
hierarchical data structure
hypothesis testing
inferential statistics
Language_English
Multinomial Logistic Regression
Multinomial Logistic Regression Model
Multiple Linear Regression
Ordinal Logistic Regression Model
PA=Available
Price_€50 to €100
Proportional Hazard Assumptions
Proportional Odds Assumption
Proportional Odds Logistic Regression
Proportional Odds Logistic Regression Model
Proportional Odds Model
Proportional Odds Regression Models
PS=Active
Regression Model
Regression Modeling
Relative Odds
RStudio Ide
Satisfactory Measurement Model
softlaunch
statistical modeling
Stratified Cox Proportional Hazard Model
Structural Equation Modeling
survival analysis
talent analytics
Undergraduate GPA
Vice Versa

Product details

  • ISBN 9781032046631
  • Weight: 460g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Aug 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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Description

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.
About Keith McNulty

Keith McNulty, PhD is a leading practitioner of applied statistics, psychometrics and people analytics. He is currently Global Director of Talent Science and Analytics at McKinsey and Company.

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