Business Analytics Data Analysis Decision Making 6th Edition by Christian Albright, Wayne Winston ISBN 1305947541 9781305947542

Business Analytics Data Analysis Decision Making 6th Edition by S. Christian Albright, Wayne Winston – Ebook PDF Instant Download/Delivery: 1305947541, 978- 1305947542

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ISBN 10:   1305947541   

ISBN 13: 978-1305947542

Author:      S. Christian Albright, Wayne Winston

Business Analytics Data Analysis Decision Making 6th  Table of contents:

Part 1: Data Analysis

1. Introduction to Business Analytics – Overview of the role and importance of business analytics, including methods and software used.
2. Describing the Distribution of a Variable – Techniques for summarizing and analyzing data distributions (e.g., central tendency, dispersion, and graphical methods).
3. Finding Relationships Among Variables – Methods for identifying correlations, associations, and interactions between variables.
4. Business Intelligence (BI) Tools for Data Analysis – Using BI tools and software (e.g., Excel, Tableau) to analyze and visualize business data.

Part 2: Probability and Decision Making under Uncertainty

5. Probability and Probability Distributions – Basic probability concepts, and how different probability distributions (normal, binomial, etc.) model uncertain outcomes.
6. Decision Making under Uncertainty – Techniques like decision trees and EMV (Expected Monetary Value) to help businesses make decisions in uncertain environments.

Part 3: Statistical Inference

7. Sampling and Sampling Distributions – Methods for selecting random samples and understanding sampling distributions.
8. Confidence Interval Estimation – How to estimate parameters like population means and proportions with a range of values (confidence intervals).
9. Hypothesis Testing – Techniques for making inferences about populations and testing business hypotheses, including significance levels and p-values.

Part 4: Regression Analysis and Time Series Forecasting

10. Regression Analysis: Estimating Relationships – Techniques for modeling the relationship between dependent and independent variables (linear regression).
11. Regression Analysis: Statistical Inference – Drawing inferences from regression models, including hypothesis testing and interpretation of coefficients.
12. Time Series Analysis and Forecasting – Methods to model and forecast business data over time (e.g., ARIMA, moving averages).

Part 5: Optimization and Simulation Modeling

13. Introduction to Optimization Modeling – Introduction to optimization techniques used for business decision-making.
14. Optimization Models – Using linear, integer, and nonlinear programming models for practical optimization in various business areas.
15. Introduction to Simulation Modeling – Basics of simulation, including Monte Carlo methods, to model complex business scenarios.
16. Simulation Models – Advanced simulation techniques to model real-world business processes and predict outcomes.

Part 6: Advanced Data Analysis

17. Data Mining – Techniques for extracting valuable patterns and insights from large datasets, including classification and clustering.

Part 7: Bonus Online Material

18. Analysis of Variance (ANOVA) and Experimental Design – Methods for comparing group means and understanding experimental designs in business.
19. Statistical Process Control – Techniques for monitoring and improving business processes through statistical methods, including control charts and process capability analysis.

Appendix A: Statistical Reporting

• Guidelines for reporting statistical findings effectively, including interpreting results and presenting them clearly for decision-making.

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