電子書籍詳細

電子書籍詳細


洋書 kinoppy

ビジネスと意思決定のためのデータサイエンス

Data Science for Business and Decision Making

Fávero, Luiz Paulo   Belfiore, Patrícia

Academic Press 2019/04
1240p.
出版国: US
ISBN: 9780128112168
eISBN: 9780128112175
KNPID: EY00342629
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Full Description

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 emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®.

  • Combines statistics and operations research modeling to teach the principles of business analytics
  • Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business
  • Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs

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