Quantitative Analysis for Decision Makers 7th Edition by Mik Wisniewski, Dr Farhad Shafti ISBN 1292276614 978-1292276618

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ISBN 10: 1292276614
ISBN 13: 978-1292276618
Author: Mik Wisniewski, Dr Farhad Shafti 

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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.

Quantitative Analysis for Decision Makers 7th Table of contents:

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

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Tags: Mik Wisniewski, Dr Farhad Shafti, Quantitative Analysis, Decision Makers