Tangible competitive advantages can emerge from vast amounts of complex data translated into clear and manageable information. 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, it more clearly defines the principles of Business Analytics for those with backgrounds in business 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 of its chapters uses the same didactic format followed by exercises with answers at the back of the book. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, IBM SPSS Statistics Software®, and R. Combines statistics and operations research modeling to teach the principles of business analyticsWritten for students who want to apply statistics, optimization, and multivariate modeling to gain competitive advantages in businessShows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs, focusing on in-depth interpretation of the results, sensitivity analysis, and alternative modeling approaches INDICE: 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
- ISBN: 978-0-12-811216-8
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 1000
- Fecha Publicación: 01/08/2018
- Nº Volúmenes: 1
- Idioma: Inglés