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...
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| Main Authors: | , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
London, United Kingdom :
Academic Press, an imprint of Elsevier,
2019. ©
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| Subjects: | |
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Table of Contents:
- 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.