Quantitative Analysis for Decision Makers (formerly known as Quantitative Methods for Decision Makers)
Lýsing:
There's no doubt that a manager's job is getting tougher. Do it better, do it faster, do it cheaper are the pressures every manager faces. And at the heart of every manager's job is decision-making: deciding what to do and how to do it. This well-respected text looks at how quantitative analysis techniques can be used effectively to support such decision making. As a manager, developing a good understanding of the quantitative 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. Appealing both to students on introductory-level courses and to MBA and postgraduate students, this internationally successful text provides an accessible introduction to a subject area that students often find difficult.
Quantitative Analysis for Decision Makers (formerly known as Quantitative Methods for Decision Makers) helps students to understand the relevance of quantitative methods of analysis to management decision-making by relating techniques directly to real-life business decisions in public and private sector organisations and focuses on developing appropriate skills and understanding of how the techniques fit into the wider management process.
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Annað
- Höfundur: Mik Wisniewski
- Útgáfa:7
- Útgáfudagur: 2019-11-27
- Hægt að prenta út 2 bls.
- Hægt að afrita 2 bls.
- Format:Page Fidelity
- ISBN 13: 9781292276632
- Print ISBN: 9781292276618
- ISBN 10: 1292276630
Efnisyfirlit
- Title Page
- Copyright Page
- Contents
- List of 'QADM in action' case studies
- Preface
- Publisher's acknowledgements
- 1 Introduction
- The use of quantitative techniques by business
- The role of quantitative analysis 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
- Radar charts
- Which chart to use
- General principles of graphical presentation
- Worked example
- Summary
- Exercises
- 4 Management Statistics
- Learning objectives
- 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
- 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
- Specification limits versus control limits
- 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
- Appendix: Solving LP problems with excel
- 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
- Some technical insights
- Classification of stock items
- Worked example
- Summary
- Exercises
- 13 Project Management
- Learning objectives
- Characteristics of a project
- Project management
- Business example
- Network diagrams
- Developing the network diagram
- Using the network diagram
- Technical point
- Gantt charts
- Uncertainty
- Project costs and crashing
- Worked example
- Summary
- Exercises
- 14 Simulation
- Learning objectives
- The principles of simulation
- Business example
- Developing the simulation model
- A simulation flowchart
- Using the model
- Worked example
- Summary
- Exercises
- Appendix: Simulation with excel
- 15 Financial Decision Making
- Learning objectives
- Interest
- Nominal and effective interest
- Present value
- Investment appraisal
- Replacing equipment
- Worked example
- Summary
- Exercises
- Postscript: A quick look at recent developments in QADM
- Big Data and data/business analytics
- Artificial Intelligence
- Agent-based simulation
- Data visualisation
- Summary
- Appendices
- A Binomial Distribution
- B Areas in the Tail of the Normal Distribution
- C Areas in the Tail of the t Distribution
- D Areas in the Tail of the x2 Distribution
- E Areas in the Tail of the F Distribution, 0.05 Level
- F Solutions to Chapter Progress Check Questions
- Index
- Back Cover
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- Gerð : 208
- Höfundur : 8183
- Útgáfuár : 2016
- Leyfi : 379