Statistical Thinking for Managers

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 improvement Designed 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.

Table of Contents:
VARIATION 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

About the Author :
John; J.A. University of Waikato, Hamilton, New Zealand,Whitaker; David University of Waikato, Hamilton, New Zealand,Johnson; D.G. University of Loughborough, Leicestershire, UK,

Review :
'A realistic book that challenges the way students look at business problems and issues...the text equips the reader with the skills and techniques required to make informed decisions. The authors demonstrate the techniques with a wealth of practical examples drawn from a variety of real life applications. One of the more innovative features of this book is the inclusion of a number of 'hands on' exercises and experiments...The book is extremely interesting and would be useful to aid managers in their decision-making process.' - "Short Book Reviews of the ISI, Vol. 22, No. 2 Aug. 2002" 'This introductory text in business statistics takes a practical approach that presents even sophisticated concepts with a minimum of mathematics. It focuses on statistical thinking and discusses a range of topics that apply to managers in business, incorporating a scenario-based, interactive format and integrated use of Excel.' - "SciTech Book News"