Data science for business and decision making
Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its e...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom ; San Diego, CA :
Academic Press
[2019]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630530006719 |
Tabla de Contenidos:
- Part 1: Foundations of Business Data Analysis
- 1. Introduction to Data Analysis and Decision Making
- 2. Type of Variables and Mensuration Scales
- Part 2: Descriptive Statistics
- 3. Univariate Descriptive Statistics
- 4. Bivariate Descriptive Statistics
- Part 3: Probabilistic Statistics
- 5. Introduction of Probability
- 6. Random Variables and Probability Distributions
- Part 4: Statistical Inference
- 7. Sampling
- 8. Estimation
- 9. Hypothesis Tests
- 10. Non-parametric Tests
- Part 5: Multivariate Exploratory Data Analysis
- 11. Cluster Analysis
- 12. Principal Components Analysis and Factorial Analysis
- Part 6: Generalized Linear Models
- 13. Simple and Multiple Regression Models
- 14. Binary and Multinomial Logistics Regression Models
- 15. Regression Models for Count Data: Poisson and Negative Binomial
- Part 7: Optimization Models and Simulation
- 16. Introduction to Optimization Models: Business Problems Formulations and Modeling
- 17. Solution of Linear Programming Problems
- 18. Network Programming
- 19. Integer Programming
- 20. Simulation and Risk Analysis Part 8: Other Topics
- 21. Design and Experimental Analysis
- 22. Statistical Process Control
- 23. Data Mining and Multilevel Modeling.
