Quantitative Methods for Decision Makers




600 pages
23 MAR 2016


Were you looking for the book with access to MyMathLab Global? This product is the book alone and does NOT come with access to MyMathLab Global. Buy Quantitative Methods for Decision Makers, 6th edition with MyMathLab Global access card (ISBN 9780273770763) if you need access to MyMathLab Global as well, and save money on this resource. You will also need a course ID from your instructor to access MyMathLab Global. 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 BusinessThe Role of Quantitative Techniques in BusinessModels in Quantitative Decision MakingUse of ComputersUsing the TextSummary 2 Tools of the Trade Learning objectivesSome Basic TerminologyFractions, Proportions, PercentagesRounding and Significant FiguresCommon NotationPowers and RootsLogarithmsSummation and FactorialsEquations and Mathematical ModelsGraphsReal and Money TermsWorked ExampleSummaryExercises 3 Presenting Management Information Learning objectivesA Business ExampleBar ChartsPie ChartsFrequency DistributionsPercentage and Cumulative FrequenciesHistogramsFrequency PolygonsOgivesLorenz CurvesTime-Series GraphsZ ChartsScatter DiagramsGeneral Principles of Graphical PresentationWorked ExampleSummaryExercises 4 Management Statistics Learning objectivesA Business ExampleWhy Are Statistics Needed?Measures of AverageMeasures of VariabilityUsing the StatisticsCalculating Statistics for Aggregated DataIndex NumbersWorked ExampleSummaryExercises 5 Probability and Probability Distributions Learning objectivesTerminologyThe Multiplication RuleThe Addition RuleA Business ApplicationProbability DistributionsThe Binomial DistributionThe Normal DistributionWorked ExampleSummaryExercises 6 Decision Making Under Uncertainty Learning objectivesThe Decision ProblemThe Maximax CriterionThe Maximin CriterionThe Minimax Regret CriterionDecision Making Using Probability InformationRiskDecision TreesThe Value of Perfect InformationWorked ExampleSummaryExercises 7 Market Research and Statistical Inference Learning objectivesPopulations and SamplesSampling DistributionsThe Central Limit TheoremCharacteristics of the Sampling DistributionConfidence IntervalsOther Confidence IntervalsConfidence Intervals for ProportionsInterpreting Confidence IntervalsHypothesis TestsTests on a Sample MeanTests on the Difference Between Two MeansTests on Two Proportions or PercentagesTests on Small SamplesInferential Statistics Using a Computer Packagep Values in Hypothesis Testsx2 TestsWorked ExampleSummaryExercises 8 Quality Control and Quality Management Learning objectivesThe Importance of QualityTechniques in Quality ManagementStatistical Process ControlControl ChartsControl Charts for Attribute VariablesPareto ChartsIshikawa DiagramsSix SigmaWorked ExampleSummaryExercises 9 Forecasting I: Moving Averages and Time Series Learning objectivesThe Need for ForecastingApproaches to ForecastingTrend ProjectionsTime-Series ModelsWorked ExampleSummaryExercises 10 Forecasting II: Regression Learning objectivesThe Principles of Simple Linear RegressionThe Correlation CoefficientThe Line of Best FitUsing the Regression EquationFurther Statistical Evaluation of the Regression EquationNon-linear RegressionMultiple RegressionThe Forecasting ProcessWorked ExampleSummaryExercises 11 Linear Programming Learning objectivesThe Business ProblemFormulating the ProblemGraphical Solution to the LP FormulationSensitivity AnalysisComputer SolutionsAssumptions of the Basic ModelDealing with More than Two VariablesExtensions to the Basic LP ModelWorked ExampleSummaryExercises 12 Stock Control Learning objectivesThe Stock-Control ProblemCosts Involved in Stock ControlThe Stock-Control DecisionThe Economic Order Quantity ModelThe Reorder CycleAssumptions of the EOQ ModelIncorporating Lead TimeClassification of Stock ItemsMRP and JITWorked ExampleSummaryExercises 13 Project Management Learning objectivesCharacteristics of a ProjectProject ManagementBusiness ExampleNetwork DiagramsDeveloping the Network DiagramUsing the Network DiagramPrecedence DiagramsGantt ChartsUncertainty Project Costs and CrashingWorked ExampleSummaryExercises 14 Simulation Learning objectivesThe Principles of SimulationBusiness ExampleDeveloping the Simulation ModelA Simulation FlowchartUsing the ModelWorked ExampleSummaryExercises 15 Financial Decision Making Learning objectivesInterestNominal and Effective InterestPresent ValueInvestment AppraisalReplacing EquipmentWorked ExampleSummaryExercisesConclusion AppendicesA Binomial DistributionB Areas in the Tail of the Normal DistributionC Areas in the Tail of the t DistributionD Areas in the Tail of the x2 DistributionE Areas in the Tail of the F Distribution, 0.05 LevelF Solutions to Progress Check QuestionsIndex