Quantitative Analysis for Decision Makers
Pearson Education Limited (Verlag)
978-1-292-46985-0 (ISBN)
Quantitative Analysis for Decision Makers, 8th Edition, by Wisniewski, Shafti and Yeo provides an accessible introduction to the quantitative methods of analysis that are routinely used across the public and private sectors to support effective management decision making.
Adopting a learning-by-doing approach throughout, the text provides a clear explanation of each technique illustrating their practical application with the use of real data sets and with examples from government bodies and prominent businesses, including Google, Marks & Spencer, Tesla, Netflix, WWF and many more.
The text is suitable for business and management undergraduate and postgraduate students and for MBA students taking a quantitative course as part of their studies.
The new edition offers:
Updated case illustrations from global business organisations like Tesla, Netflix, WWF, Google, Unilever
Extended discussion of how artificial intelligence (AI) impacts on quantitative analysis and decision making.
Updated ‘QADM in action’ case studies illustrating how organisations benefit from the use of analytical techniques in the real world.
Coverage of recent developments such as Big Data and business analytics; multi-criteria decision analysis and data mining; agent-based simulation; data visualisation.
Examples and cases have been updated to reflect global events such as the Covid pandemic and geopolitical uncertainties.
Fully worked examples and exercises supported by Excel data sets to show how to approach a particular problem using the techniques
About the authors:
Mik Wisniewski has almost five decades of experience in Quantitative Analysis. He has taught at a number of leading universities, worked in both industry and government and has extensive consultancy experience in the UK and across Europe, Africa, the Middle East and Asia.
Dr Farhad Shafti is a senior academic with expertise in Management Science. He has extensive experience teaching in highly ranked universities, covering undergraduate, postgraduate and MBA studies in the UK and overseas.
Dr Wee Meng Yeo is a senior lecturer at Adam Smith Business School, Glasgow. He has considerable expertise in the areas of operations management, forecasting and inventory control and has worked in the UK and the Far East.
Pearson, the world's learning company.
Mik Wisniewski has almost five decades of experience in Quantitative Analysis. He has taught at a number of leading universities, worked in both industry and government and has extensive consultancy experience in the UK and across Europe, Africa, the Middle East and Asia. Dr Farhad Shafti is a senior academic with expertise in Management Science. He has extensive experience teaching in highly ranked universities, covering undergraduate, postgraduate and MBA studies in the UK and overseas. Dr Wee Meng Yeo is a senior lecturer at Adam Smith Business School, Glasgow. He has considerable expertise in the areas of operations management, forecasting and inventory control and has worked in the UK and the Far East.
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
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
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
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
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
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
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
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
9 Forecasting I: Moving Averages and Time Series
The need for forecasting
Approaches to forecasting
Trend projections
Time-series models
Worked example
Summary
Exercises
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
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
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
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
14 Simulation
The principles of simulation
Business example
Developing the simulation model
A simulation flowchart
Using the model
Worked example
Summary
Exercises
15 Financial Decision Making
Interest
Nominal and effective interest
Present value
Investment appraisal
Replacing equipment
Worked example
Summary
Exercises
| Erscheinungsdatum | 17.06.2025 |
|---|---|
| Verlagsort | Harlow |
| Sprache | englisch |
| Gewicht | 1153 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| ISBN-10 | 1-292-46985-4 / 1292469854 |
| ISBN-13 | 978-1-292-46985-0 / 9781292469850 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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