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Statistics, Data Analysis, and Decision Modeling,9780130675538
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Statistics, Data Analysis, and Decision Modeling



Pub. Date:
Prentice Hall
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  • Statistics, Data Analysis, and Decision Modeling
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  • Outlines and Highlights for Statistics, Data Analysis and Decision Modeling by James R Evans, Isbn : 9780136066002
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This book covers basic concepts of business statistics, data analysis, and management science in a spreadsheet environment. Practical applications are emphasized throughout the book for business decision-making; a comprehensive database is developed, with marketing, financial, and production data already formatted on Excel worksheets. This shows how real data is used and decisions are made. Using Excel as the basic software, and including such add-ins as PHStat2, Crystal Ball, and TreePlan, this book covers a wide variety of topics related to business statistics: statistical thinking in business; displaying and summarizing data; random variables; sampling; regression analysis; forecasting; statistical quality control; risk analysis and Monte-Carlo simulation; systems simulation modeling and analysis; selection models and decision analysis; optimization modeling; and solving and analyzing optimization models. For those employed in the fields of quality control, management science, operations management, statistical science, and those who need to interpret data to make informed business decisions.

Table of Contents

Data and Business Decisions
Statistical Thinking in Business
Data in the Business Environment
Sources and Types of Data
Populations and Samples
Decision Models
Using Microsoft Excel
Working with Data in Excel
Case: The Tracway Balanced Scorecard
Displaying and Summarizing Data
Displaying Data with Charts and Graphs
Contingency Tables and Cross-Tabulations
Descriptive Statistics
Measures of Dispersion
Calculations for Grouped Data
Coefficient of Variation
Measures of Shape
Excel's Descriptive Statistics Tool
Data Profiles and Proportions
Visual Display of Statistical Measures
Statistical Relationships
Case: Descriptive Statistical Analysis of Tracway Data
Random Variables and Probability Distributions
Basic Concepts
Random Variables
Probability Distributions
Expected Value and Variance of a Random Variable
Discrete Probability Distributions
Continuous Probability Distributions
Other Useful Distributions
Monte-Carlo Methods in Statistics
Sampling Distributions and Sampling Error
Case: Probability Modeling for Tracway Quality Measurements
Sampling and Statistical Inference
Statistical Sampling
Statistical Analysis of Sample Data
Interval Estimates
Confidence Intervals for the Mean
Confidence Intervals for Proportions
Confidence Intervals and Sample Size
Additional Types of Confidence Intervals
Hypothesis Testing
One Sample Hypothesis Tests
Two-Sample Hypothesis Tests
ANOVA: Testing Differences of Several Means
Non Parametric Hypothesis Tests
Distribution Fitting: An Application of Hypothesis Testing
Case: Statistical Inference for Tracway
Regression Analysis
Simple Linear Regression
Measuring Variation About the Regression Line
Regression as Analysis of Variance
Assumptions of Regression Analysis
Application of Regression Analysis to Investment Risk
Multiple Linear Regression
Building Good Regression Models
Regression with Categorical Independent Variables
Regression Models with Nonlinear Terms
Case: Regression Analysis for Tracway
Qualitative and Judgmental Methods
Indicators and Indexes
Statistical Forecasting Models
Forecasting Models for Stationary Time Series
Forecasting Models with Linear Trends
Models for Time Series with Trend and Seasonal Components
CB Predictor
Regression Models
The Practice of Forecasting
Statistical Quality Control
The Role of Statistics and Data Analysis in Quality Control
Statistical Process Control
Analyzing Control Charts
Control Charts for Attributes
Statistical Issues in the Design of Control Charts
Process Capability Analysis
Case: Quality Control at Tracway
Risk Analysis and Monte-Carlo Simulation
Risk Analysis
Monte-Carlo Simulation withCrystal Ball
AdditionalCrystal BallModeling and Analysis Options
Applications of Monte-Carlo Simulation
Systems Simulation Modeling and Analysis
System Simulating Modeling Approaches
Simulating Inventory Systems Using Activity Scanning
Simulating Waiting Line Systems Using Process-Driven Models
Event-Driven Simulation Models
An Event-Driven Inventory Simulation Model
Entities and Attributes
Continuous Simulation Modeling
Case: Tracway Production/Inventory Planning
Selection Models and Decision Analysis

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