Quantitative methods for decision makers

Appealing both to students on introductory courses for quantitative methods and MBA students, this well-respected text provides an accessible introduction to an area that students often find difficult. As a manager, developing a good understanding of the business analysis techniques at your disposal is crucial. Knowing how and when to use them and what their results really mean can be the difference between making a good or bad decision and, ultimately, between business success and failure. Quantitative Methods for Decision Makers helps students to understand the relevance of quantitative methods of analysis to manager's decision-making by relating techniques directly to real-life business decisions in public and private sector organisations and focusing on developing appropriate skills and understanding of how the techniques fit into the wider management process. Key features: * Student Activities with a solutions Appendix * Fully worked examples and exercises supported by Excel data sets *"QMDM in Action" case studies illustrating how real-life organisations benefit from the use of quantitative techniques * Chapter on financial decision-making "Wisniewski makes numerical and statistical concepts understandable and brings them to life using excellent scenarios and case studies. This book was a valuable resource during my MBA studies and I am encouraging all my non-statistical colleagues and anyone who works with statistics or performance measurement data to read this book!" Brian J Pickett, Assistant Director, Local Government Data Unit, Wales Mik Wisniewski is Senior Research Fellow at Strathclyde Business School in Scotland. He also works as a freelance management consultant with clients including PriceWaterhouseCoopers, ScottishPower and Shell, and a variety of public sector organisations in the UK and internationally

Contents List of 'QMDM in Action' case studies Preface Acknowledgements 1 Introduction The Use of Quantitative Techniques by Business The Role of Quantitative Techniques in Business Models in Quantitative Decision Making Use of Computers Using the Text Summary 2 Tools of the Trade Learning objectives Some Basic Terminology Fractions, Proportions,

Percentages Rounding and Significant Figures Common Notation Powers and Roots Logarithms Summation and Factorials Equations and Mathematical Models Graphs Real and Money Terms Worked Example Summary Exercises 3 Presenting Management Information Learning objectives A Business Example Bar Charts Pie Charts Frequency Distributions Percentage and Cumulative Frequencies Histograms Frequency Polygons Ogives Lorenz Curves Time-Series Graphs Z Charts Scatter Diagrams General Principles of Graphical Presentation Worked Example Summary Exercises 4 Management Statistics Learning objectives A Business Example Why Are Statistics Needed? Measures of Average Measures of Variability Using the Statistics Calculating Statistics for Aggregated Data Index Numbers Worked Example Summary Exercises 5 Probability and Probability Distributions Learning objectives Terminology The Multiplication Rule The Addition Rule A Business Application

Probability Distributions The Binomial Distribution The Normal Distribution Worked Example Summary Exercises 6 Decision Making Under Uncertainty Learning objectives The Decision Problem The Maximax Criterion The Maximin Criterion The Minimax Regret Criterion Decision Making Using Probability Information Risk Decision Trees The Value of Perfect Information Worked Example Summary Exercises 7 Market Research and Statistical Inference Learning objectives Populations and Samples Sampling Distributions The Central Limit Theorem Characteristics of the Sampling Distribution Confidence Intervals Other Confidence Intervals Confidence Intervals for Proportions Interpreting Confidence Intervals Hypothesis Tests Tests on a Sample Mean Tests on the Difference Between Two Means Tests on Two Proportions or Percentages Tests on Small Samples Inferential Statistics Using a Computer Package p Values in Hypothesis Tests x2 Tests Worked Example Summary

Exercises 8 Quality Control and Quality Management Learning objectives The Importance of Quality Techniques in Quality Management Statistical Process Control Control Charts Control Charts for Attribute Variables Pareto Charts Ishikawa Diagrams Six Sigma Worked Example Summary Exercises 9 Forecasting I: Moving Averages and Time Series Learning objectives The Need for Forecasting Approaches to Forecasting Trend Projections Time-Series Models Worked Example Summary Exercises 10 Forecasting II: Regression Learning objectives The Principles of Simple Linear Regression The Correlation Coefficient The Line of Best Fit Using the Regression Equation Further Statistical Evaluation of the Regression Equation Non-linear Regression Multiple Regression The Forecasting Process Worked Example Summary Exercises 11 Linear Programming Learning objectives The Business Problem Formulating the Problem Graphical Solution to the LP Formulation

Sensitivity Analysis Computer Solutions Assumptions of the Basic Model Dealing with More than Two Variables Extensions to the Basic LP Model Worked Example Summary Exercises 12 Stock Control Learning objectives The Stock-Control Problem Costs Involved in Stock Control The Stock-Control Decision The Economic Order Quantity Model The Reorder Cycle Assumptions of the EOQ Model Incorporating Lead Time Classification of Stock Items MRP and JIT Worked Example Summary Exercises 13 Project Management Learning objectives Characteristics of a Project Project Management Business Exam