Quantitative Analysis for Decision Makers, 8th edition

Published by Pearson (11 September 2025) © 2025

  • Mik Wisniewski
  • Farhad Shafti University of Glasgow
  • Wee Meng Yeo University of Glasgow

Quantitative Analysis for Decision Makers

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Title overview

Discover how quantitative analysis techniques can enhance decision-making with the Pearson eTextbook.

Quantitative Analysis for Decision Makers, 8th Edition, by Wisniewski, Shafti and Yeo provides an accessible introduction to quantitative methods of analysis to management decision-making, focusing on the practical application of the techniques rather than on mechanical calculation.

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Table of contents

  1. 1 Introduction
  • Decisions, decisions and more decisions
  • Data, information and analysis
  • So where does quantitative analysis fit in?
  • So who uses quantitative analysis?
  • What’s quantitative analysis got to do with managers and with me?
  • Models in quantitative decision making
  • Using the text
  • Summary
  1. 2 Tools of the Trade
  • Some basic terminology
  • Fractions, proportions, percentages
  • Rounding and significant figures
  • Common notation
  • Powers and roots
  • Logarithms
  • Summation and factorials
  • Equations and mathematical models
  • Graphs
  • Log graphs
  • Real and money terms
  • Worked example
  • Summary
  • Exercises
  1. 3 Presenting Management Information
  • A business example
  • Bar charts
  • Pie charts
  • Frequency distributions
  • Percentage and cumulative frequencies
  • Histograms
  • Frequency polygons
  • Ogives
  • Lorenz curves (Pareto diagrams)
  • Time-series graphs (line charts)
  • Z charts
  • Scatter diagrams
  • Radar charts
  • Which chart to use
  • General principles of graphical presentation
  • Worked example
  • Summary
  • Exercises
  1. 4 Management Statistics
  • A business example
  • Why are management statistics needed?
  • Measures of average
  • Measures of variability
  • Using the statistics
  • Calculating statistics for aggregated data
  • Index numbers
  • Worked example
  • Summary
  • Exercises
  1. 5 Probability and Probability Distributions
  • Terminology
  • The multiplication rule
  • The addition rule
  • A business application
  • Probability distributions
  • The Binomial distribution
  • The Normal distribution
  • Worked example
  • Summary
  • Exercises
  1. 6 Decision Making Under Uncertainty
  • 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
  1. 7 Statistical Inference: Making Sense of Sample Information
  • Populations and samples
  • Sampling distributions
  • The Central Limit Theorem
  • Characteristics of the sampling distribution
  • Confidence intervals
  • Other confidence intervals
  • Confidence intervals for percentages and 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
  • c2 tests
  • Worked example
  • Summary
  • Exercises
  1. 8 Quality Control and Quality Management
  • The importance of quality
  • Techniques in quality management
  • Statistical process control
  • Control charts
  • Control charts for attribute variables
  • Specification limits versus control limits
  • Pareto charts
  • Ishikawa diagrams
  • Six sigma
  • Worked example
  • Summary
  • Exercises
  1. 9 Forecasting I: Moving Averages and Time Series
  • The need for forecasting
  • Approaches to forecasting
  • Trend projections
  • Time-series models
  • Worked example
  • Summary
  • Exercises
  1. 10 Forecasting II: Regression
  • 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
  1. 11 Linear Programming
  • 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
  1. 12 Inventory Control
  • The inventory control problem
  • Costs involved in inventory control
  • The inventory control decision
  • The economic order quantity model
  • The reorder cycle
  • Robustness of EOQ decision
  • Assumptions of the EOQ model
  • Incorporating lead time
  • Some technical insights
  • Classification of inventory
  • MRP and JIT
  • AI and inventory management
  • Worked example
  • Summary
  • Exercises
  1. 13 Project Management
  • Characteristics of a project
  • Project management
  • Business example
  • Network diagrams
  • Developing the network diagram
  • Using the network diagram
  • A technical point
  • Gantt charts
  • Uncertainty
  • Project costs and crashing
  • Worked example
  • Summary
  • Exercises
  1. 14 Simulation
  • The principles of simulation
  • Business example
  • Developing the simulation model
  • A simulation flowchart
  • Using the model
  • Worked example
  • Summary
  • Exercises
  1. 15 Financial Decision Making
  • Interest
  • Nominal and effective interest
  • Present value
  • Investment appraisal
  • Replacing equipment
  • Worked example
  • Summary
  • Exercises

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