Statistical Thinking for Managers

J.A. John, David Whitaker, David G. Johnson

336 pages, parution le 01/09/2001

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Résumé

All business activities are subject to variability. As a consequence, managers and business students need the ability to think statistically about how to deal with the resulting uncertainty, and its effect on decision-making in management and commerce. To give them that ability, there is a growing recognition that we must change the way business statistics is taught. Traditional texts tend to focus on probability, mathematical detail, and heavy computation, and thus fail to meet the real needs of future business managers. Statistical Thinking for Managers takes a very different, very practical, approach that presents even sophisticated statistics concepts with a minimum of mathematics. It focuses on statistical thinking and discusses a range of topics that specifically apply to managers in business. Its scenario-based, interactive format and integrated use of Excel facilitate and reinforce the learning experience. Through this innovative treatment, readers will gain the ability to:

Appreciate basic statistical ideas
Use a scientific approach to problem solving
Understand the nature of variability
Use meaningful information to make informed decisions
Think in terms of processes and systems and develop strategies for process improvementDesigned as an introductory text in business statistics, Statistical Thinking for Managers challenges the way students look at business problems and issues.

It shows them the importance of statistics in all aspects of business and equips them with the skills they need to make informed and effective decisions.

Contents

Variation
Airport Immigration
Debt Recovery
Fire Insurance
Budget Deviations
Red Beads Experiment
Some Conclusions
Data for the Beads Experiment
Exercises
Problem Solving
A Scientific Approach to Problem Solving
The Need for Data
Deciding What to Measure
Mapping the Process
Cause Analysis
Collecting Data
Evaluating the Results
Standardising and Planning for Future Improvements
Exercises
Looking at Data
Types of Data
Presentation of Data
Rounding Data
Effective Tables
Graphs
Bar Charts
Pie Charts
Pareto Diagrams
Run Charts
Stem and Leaf Diagrams
Median, Range and Quartiles
Box Plots
Stratification
Exercises
Modelling Data
Introduction
Distributions
Arithmetic Mean
Standard Deviation
Calculating the Mean and Standard Deviation
Interpreting the Standard Deviation
Descriptive Statistics
Modelling Julia's Diner
Distribution Shapes
The Normal Distribution
Normal Tables
Exercises
Attribute Data
Analysing Attribute Data
Looking at Margins
Two-way Tables
Binary Data
Working with Proportions
Working with Counts
Exercises
Sampling
Collecting Data
Sampling
Sampling Concepts
Choosing the Sample
Sampling in Practice
Random Sampling
Non-random Sampling
The Accuracy of Sample Estimates
Data for the Wood Experiment
Exercises
Estimation
Sampling Error
Estimating a Proportion
Sample size for Proportions
Estimating a Mean
Sample size for Means
Unknown Population Standard Deviation
Finite Populations
What Affects the Margin of Error?
Tests of Hypotheses
Exercises
Regression Analysis
Introduction
Scatterplots
Relationships Between Variables
Regression Analysis
Least Squares Method
Correlation
Regression Assumptions
Analysis of Residuals
Unusual Observations
Prediction from Regression
Margin of Error
Exercises
Multiple Regression
Introduction
Multiple Correlation Coefficient
Multiple Regression
Polynomial Regression
Using Dummy Variables
Exercises
Forecasting
Introduction
Time Series
Identifying the Trend
Estimating Seasonal Effects
Exponential Smoothing
Differencing
Residual Analysis
Forecasting Accuracy
Exercises
Statistical Process Control
Introduction
Processes
Benefits of Reducing Variation
Process Adjustment
Common Cause Highway
Three Sigma Limits
Analysis of Patterns
Stability and Predictability
The 85/15 Rule
Implementing SPC
Data for the Dice Experiment
Data for the Quincunx Experiment
Exercises
Control Charts
Introduction
Control Charts for Process Variables
Interpreting the X-Bar and R Charts
Individuals Chart
Attribute Charts
Exercises
Improvement Strategies
Monitoring the Process
Sensitivity of Control Charts
Specifications
Specifications and Control Limits
Process Capability
Improvement Strategies
Special Cause Strategies
Common Cause Strategies
Experimentation
Data for the Helicopter Experiment
Exercises
Postscript
Decisions Based on Data
Thinking Statistically: the Key Principles
What We have not Done
Statistical Computing
Bibliography and Further Reading
Appendix A: Introduction to Excel
Introduction
An Excel Spreadsheet
Entering and Editing Data
Entering Formulae
Absolute Cell References
Inserting Rows and Columns & Formatting Cells
Standard Functions
Excel Add-ins
Graphs and Charts
Printing and Saving a Workbook
Appendix B: Statistical Tables
Normal Distribution
Percentage Points of the t-Distribution
Percentage Points of the X 2-Distribution
Random Numbers
Constants for Control Charts

Caractéristiques techniques

	PAPIER
Éditeur(s)	Chapman and Hall / CRC
Auteur(s)	J.A. John, David Whitaker, David G. Johnson
Parution	01/09/2001
Nb. de pages	336
Format	17,6 x 25,3
Couverture	Broché
Poids	617g
Intérieur	Noir et Blanc
EAN13	9781584882480

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Statistical Thinking for Managers

Résumé

Caractéristiques techniques

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