Statistics, data analysis, and decision modeling
Author(s)
Bibliographic Information
Statistics, data analysis, and decision modeling
Prentice Hall, c2000
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
For a brief or modular course covering business statistics and introductory topics in management science. Designed specifically for today's shorter courses, often found in MBA programs.
This text covers the basic concepts of business statistics, data analysis, and management science in a contemporary spreadsheet environment. The authors emphasize practical applications of the approaches to business decision making.
Table of Contents
1. Data and Business Decisions.
The Importance of Data for Decision Making. Types and Sources of Business Data. Measurement and Statistics. Decision Models. Using Microsoft Excel. Summary.Questions and Problems.
2. Displaying and Summarizing Data.
Displaying Data with Charts and Graphs. Descriptive Statistics. Visual Display of Statistical Measures. Statistical Relationships. Case Study: Using Descriptive Statistics for the Malcolm Baldrige National Quality Award. Summary. Questions and Problems.
3. Random Variables and Probability Distributions.
Basic Concepts. Discrete Probability Distributions. Continuous Probability Distributions. Random Sampling From Probability Distributions. Summary. Questions and Problems. Appendix: Introduction to Crystal Ball.
4. Sampling and Statistical Analysis for Decision-Making.
Statistical Sampling. Statistical Analysis of Sample Data. Estimation. Hypothesis Testing. ANOVA: Testing Differences of Several Means. Chi-Square Test for Independence. Summary. Questions and Problems. Appendix: Distribution Fitting.
5. Statistical Quality Control.
The Role of Statistics and Data Analysis in Quality Control. Statistical Process Control. Control Charts for Attributes. Statistical Issues in the Design of Control Charts. Process Capability Analysis. Summary. Questions and Problems.
6. Regression.
Simple Linear Regression. Measuring Variation About the Regression Line. Regression as Analysis of Variance. Assumptions of Regression Analysis. Applications of Regression Analysis to Investment Risk. Multiple Linear Regression. Building Good Regression Models. Regression with Ordinal and Nominal Independent Variables. Regression Models with Nonlinear Terms. Summary. Questions and Problems.
7. Forecasting.
Qualitative and Judgmental Methods. Statistical Forecasting Models. Regression Models. The Practice of Forecasting. Summary. Questions and Problems. Appendix: CB Predictor.
8. Selection Models and Risk Analysis.
Decision Criteria and Selection. Monte-Carlo Simulation for Risk Analysis. Applications of Monte-Carlo Simulation. Case Study: Simulation and Risk Analysis in New Product Screening at Cinergy Corporation. Summary. Questions and Problems. Appendix: Additional Crystal Ball Options.
9. Introduction to Optimization.
Constrained Optimization. Types of Optimization Problems. Spreadsheet Optimization. Solving Linear Optimization Models. Solving Integer Optimization Models. Solving Nonlinear Optimization Models. Risk Analysis of Optimization Results. Combining Optimization and Simulation. Summary. Questions and Problems.
Appendix.
The Standardized Normal Distribution. The Cumulative Standard Normal Distribution. Critical Values of t. Critical Values of F.
Index.
by "Nielsen BookData"
