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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
1. The Importance of Regression in People Analytics. 2. The Basics of the R Programming Language. 3. Statistics Foundations. 4. Linear Regression for Continuous Outcomes. 5. Binomial Logistic Regression for Binary Outcomes. 6. Multinomial Logistic Regression for Nominal Category Outcomes. 7. Ordinal Logistic Regression for Ordered Category Outcomes. 8. Modeling Explicit and Latent Hierarchical Structure in Data. 9. Survival Analysis for Modeling the Occurrence of Singular Events Over Time. 10. Alternative Technical Approaches in R and Python. 11. Power Analysis to Estimate Required Sample Sizes for Inferential Modeling. 12. Further Exercises for Practice.
1. The Importance of Regression in People Analytics. 2. The Basics of the R Programming Language. 3. Statistics Foundations. 4. Linear Regression for Continuous Outcomes. 5. Binomial Logistic Regression for Binary Outcomes. 6. Multinomial Logistic Regression for Nominal Category Outcomes. 7. Ordinal Logistic Regression for Ordered Category Outcomes. 8. Modeling Explicit and Latent Hierarchical Structure in Data. 9. Survival Analysis for Modeling the Occurrence of Singular Events Over Time. 10. Alternative Technical Approaches in R and Python. 11. Power Analysis to Estimate Required Sample Sizes for Inferential Modeling. 12. Further Exercises for Practice.
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