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For undergraduat Features + Benefits
For undergraduate and graduate level courses that combines introductory statistics with data analysis or decision modeling.
A pragmatic approach to statistics, data analysis and decision modeling.
Statistics, Data Analysis & Decision Modeling focuses on the practical understanding of its topics, allowing readers to develop conceptual insight on fundamental techniques and theories. Evans dedication to present material in a simple and straightforward fashion is ideal for student comprehension.
Excel 2007 focus with accompanying Excel Add-Ins: This feature allows students to focus more on the interpretation of results as well as the managerial implications of those results.
Excel and Add-In explanation place in Notes: By separating this feature from the text, the explanation does not get in the way of the narrative and conceptual understanding yet still enhances comprehension.
Material re-organization:
Theory and extensive computational formulas moved to end of chapter appendices: Separating the formulas from the chapter material provides professors with instructing flexibility and encourages students to focus on the concepts first and the application second.
The end of chapter material now includes three different types of problems: The incorporation of three different types of problems gives professors flexibility on what concepts they want students to practice on.
Completely re-written Instructors Manual created by the book author.
PART I: STATISTICS AND DATA ANALYSIS
Chapter 1 Data and Business Decisions
Chapter 2 Displaying and Summarizing Data
Chapter 3 Probability Distributions and Applications
Chapter 4 Sampling and Estimation
Chapter 5 Hypothesis Testing and Statistical Inference
Chapter 6 Regression Analysis
Chapter 7 Forecasting
Chapter 8 Statistical Quality Control
PART II: DECISION MODELING AND ANALYSIS
Chapter 9 Building and Using Decision Models
Chapter 10 Risk Analysis and Monte Carlo Simulation
Chapter 11 Decisions, Uncertainty, and Risk
Chapter 12 Queues and Process Simulation Modeling
Chapter 13 Linear Optimization
Chapter 14 Integer and Nonlinear Optimization
Appendix
For undergraduate and graduate level courses that combines introductory statistics with data analysis or decision modeling.
A pragmatic approach to statistics, data analysis and decision modeling.
Statistics, Data Analysis & Decision Modeling focuses on the practical understanding of its topics, allowing readers to develop conceptual insight on fundamental techniques and theories. Evans dedication to present material in a simple and straightforward fashion is ideal for student comprehension.
Excel 2007 focus with accompanying Excel Add-Ins: This feature allows students to focus more on the interpretation of results as well as the managerial implications of those results.
Excel and Add-In explanation place in Notes: By separating this feature from the text, the explanation does not get in the way of the narrative and conceptual understanding yet still enhances comprehension.
Material re-organization:
Theory and extensive computational formulas moved to end of chapter appendices: Separating the formulas from the chapter material provides professors with instructing flexibility and encourages students to focus on the concepts first and the application second.
The end of chapter material now includes three different types of problems: The incorporation of three different types of problems gives professors flexibility on what concepts they want students to practice on.
Completely re-written Instructors Manual created by the book author.
PART I: STATISTICS AND DATA ANALYSIS
Chapter 1 Data and Business Decisions
Chapter 2 Displaying and Summarizing Data
Chapter 3 Probability Distributions and Applications
Chapter 4 Sampling and Estimation
Chapter 5 Hypothesis Testing and Statistical Inference
Chapter 6 Regression Analysis
Chapter 7 Forecasting
Chapter 8 Statistical Quality Control
PART II: DECISION MODELING AND ANALYSIS
Chapter 9 Building and Using Decision Models
Chapter 10 Risk Analysis and Monte Carlo Simulation
Chapter 11 Decisions, Uncertainty, and Risk
Chapter 12 Queues and Process Simulation Modeling
Chapter 13 Linear Optimization
Chapter 14 Integer and Nonlinear Optimization
Appendix