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1 A spreadsheet Approach to Business Quantitative Methods by John Flaherty Ric Lombardo Paul Morgan Basil desilva David Wilson with contributions by: William McCluskey Richard Borst Lloyd Williams Hugh Williams For further information contact: John Flaherty School of Marketing, RMIT 239 Bourke Street, Melbourne, 3000 Australia Phone FAX:

2 A Spreadsheet Approach to Business Quantitative Methods This textbook represents a major revision of the previous textbook "Quantitative Methods in Property" by the same authors. Several new chapters have been added and much of the existing material has been refined and improved upon. A significant improvement with the new edition is the explicit use of the Excel spreadsheet throughout. Some of the new chapters added include: Selected Features and Functions in Microsoft Excel The key Excel features used throughout the text and presented in this chapter to focus attention of a number of some of the more recent enhancements to the spreadsheet. A brief introduction to the VBA programming language is included to suggest ways of further harnessing the power of the spreadsheet. Simulation The use of simulation as a decision and planning tool is becoming more prevalent. Spreadsheets are ideal to introduce many simulation concepts and to demonstrate their usefulness in a wide variety of areas. When more specialised simulation tools are required users can move to Excel plug-ins such (not discussed in the text). Geographic Information Systems GIS is now a widely used tool in the property profession and in the wider business community. Applications to department

3 store location are discussed in the text. Supplementary resources Solutions manual for all exercises Web site containing examples and applications using Excel

4 Part I Building the Foundations 1. Mathematical Preliminaries 2. Selected Features and Functions in Microsoft Excel 3. Matrix Algebra 4. Introduction to Statistics Part II Basic Statistical Concepts 5. Probability and Mathematical Expectation 6. Probability Distributions 7. Elements of Hypotheses Testing 8. Nonparametric Statistics 9. Analysis of Variance Part III Regression and Time Series Models 10. Introduction to Regression Analysis 11. Multiple Regression 12. Data Problems and Residual Analysis in Regression 13. Time Series Forecasting 14. Advanced Time Series Models PART IV Multivariate Analysis 15. Principal Component Analysis 16. Factor Analysis

5 Part V Heuristics and Optimisation 17. Simulation 18. Linear Programming 19. Transportation, Assignment, and Transshipment Problems 20. Network Analysis / Project Management 21. Dynamic Programming 22. Decision Theory and Expected Utility 23. Markov Chains and Input Output Analysis 24. Inventory Models 25. Queuing Theory 26. Artificial Neural Networks 27. Geographic Information Systems Appendix - Answers to Selected Problems - Statistical Tables Index

6 A Spreadsheet Approach to Business Quantitative Methods PART I Building the Foundations Mathematical Preliminaries 1.1 Review of Basic Algebra 1.2 Functions 1.3 Exponents and Logarithms 1.4 The Mathematics of Finance 1.5 Introduction to Calculus 1.6 Maximization and Minimization of Functions 1.7 Summation Notation 2. Selected Features and Functions in Microsoft Excel 2.1 Introduction 2.2 Using Excelto Chart Data 2.3 The Analysis ToolPak 2.4 Lookup and Reference Functions 2.5 Hyperlinks 2.6 Offset Function 2.7 Data Tables and Array Formulae 2.8 Using the Trend Function 2.9 Introductiont o Solver 2.10 GoalSeek 2.11 Recording a Macro 2.12 Creating Your Own Functions 2.12 Some Common Limitations in Excel

7 3. Matrix Algebra 3.1 Elements of Matrix Algebra 3.2 Matrix Operations 3.3 Matrix Algebra Using Excel 3.4 The Determinant 3.5 Matrix Inversion 3.6 Rank, Traceand Orthogonality 3.7 Eigenvalues and Eigenvectors 3.8 Examples of Matrix Algebra Using Excel 4.Introduction to Statistics 4.1 The Role of Statistics 4.2 Descriptive Statistics 4.3 Measures of Central Tendency 4.4 Dispersion of the Data Around the Mean 4.5 Measures of Relative Standing 4.6 Skewness and Kurtosis

8 Part II Basic Statistical Concepts 5. Probability and Mathematical Expectation 5.1 Elementary Set Theory 5.2 Basic Counting Methods 5.3 Basic Concepts in Probability 5.4 Conditional Probability and Bayes' Theorem 5.5 Jointly Distributed Random Variables 5.6 The Expected Value of a Random Variable 5.7 Coefficient of Correlation 5.8 Elements of Portfolio Theory Appendix - Mathematical Expectation Formulae 6. Probability Distributions 6.1. Random Variable 6.2. Probability Distribution 6.3. Discrete Distribution - Binomial & Poission The Normal Distribution 6.5 The Exponential Distribution 6.6 The Erlang Distribution 6.7 The Chi-square Distribution 6.8 The t-distribution 6.9 The F-distribution 6.10 Sampling and the CLT

9 7. Elements of Hypotheses Testing and Interval Estimation 7.1 Tests of Hypotheses for the Population Mean 7.2 Tests of Hypotheses for the Population Proportion 7.3 The P-value approach to hypotheses testing 7.4 Confidence Interval Estimation 7.5 Other Approaches to Testing Hypotheses 7.6 Quality Control (include Acceptance Sampling) 8. Nonparametric Statistics 8.1 Introduction 8.2 DataTypes 8.3 Measurement Scales 8.4 Parametric Methods 8.5 NonParametric vs Parametric Methods 8.6 Contingency Tables 8.7 Wilcoxon Signed -Rank Test 8.8 Mann-Whitney Test 9. Analysis of Variance 9.1 Introduction 9.2 ANOVA - Single Factor 9.3 ANOVA - Two Factor without Replication

10 9.4 ANOVA - Two Factor with Replication 9.5 Conclusion ANOVA EXERCISES Part III Regression and Yime Series Models 10. Regression Analysis 10.1 Introduction 10.2 Bivariate Regression 10.3 The Gauss-Markov Theorem and OLS Assumptions 10.4 Simple Regression_An Illustrative Example 10.5 Regression using Matrix Algebra 10.6 The Capital Asset Pricing Model 10.7 Estimating the Regression Equation Using Excel 11. Multiple Regression 11.1 The Multiple Regression Model 11.2 An Example of Multiple Regression 11.3 Validation of the Equation 11.4 Analysis of Variance for the Regression 11.5 Model Selection Criteria 11.6 Dummy Variables In Regression 11.7 Comparing Two Regressions 11.8 Non Linear Regression Models

11 Appendix - Properties of the General Linear Model 12. Data Problems and Residual Analysis in Regression 12.1 Multicollinearity 12.2 Autocorrelation 12 3 Heteroscedasticity 12.4 Residual Analysis to Detect Outliers 12.5 The Logit Model 12.6 Carrying out a Regression Project 13. Time Series Forecasting 13.1 Introduction 13.2 Decomposition of a Time Series 13.3 Calculating a Seasonal Index Using Dummy Variables 13.4 Moving Average Smoothing Methods 13.5 ExponentialSmoothingMethods 13.6 Model Validation

12 14. Advanced Time Series Models 14.1 Autocorrelation Analysis 14.2 Unit Roots and Cointegration 14.3 Box-Jenkins ARIMA Models Part IV Multivariate Analysis 1 5. Principal Component Analysis 15.1 Population Principal Components 15.2 Principal Components and Standardised Variables 15.3 Sample Principal Components 15.4 Application of Principal Components to Linear Regression 16. Factor Analysis 16.1 The Orthogonal Factor Model 16.2 MethodsofEstimation 16.3 Factor Rotation

13 Part V Heuristics and Optimisation 1 7. Simulation 17.1 Introduction 17.2 Simulating The Price of Residential Units 17.3 Freezing Randomly Generated Numbers 17.4 Using The Spreadsheet to Generate Distributions Creating Other Distributions Using Excel 17.5 Simulating Demand Using a Data Table 17.6 Monte Carlo Simulation of the Least Squares Model 18. Linear Programming 18.1 Introduction 18.2 The General LP Problem 18.3 Maximisation, Minimisation and the Duel 18.4 TheSimplexMethod 18.5 The Dual Simplex Algorithm 18.6 LP Applications in Property 1 9. Transportation, Assignment, and Transshipment Problems 20. Network Analysis / Project Management 20.1 Basic Terminology 20.2 Shortest Route Problem 20.3 CPM and PERT 20.4 Earliest Time, Latest Time and Activity Slack 20.5 Activity Time Statistics and Project Completion Times

14 20.6 Time/Coat Tradeoff and Project Crashing 21. Dynamic Programming 21.1 Dynamic Programming for a Multistage Problem 21.2 Recursion and Bellman's Principle of Optimality 21.3 Formal Notation of Dynamic Programming 21.4 A DP Problem with a Multiplicative Recursive Relation 21.5 Other Types of DP Problems 22. Decision Theory and Expected Utility 22.1 Basic Elements of The Decision Making Process 22.2 Maximin Criterion 22.3 Minimax Regret Criterion 22.4 Expected Monetary Value 22.5 Expected Utility Analysis 22.6 Prospect theory 22.7 Conclusion & 23. Markov Chains and Input Output Analysis

15 23.1 Input-Output Analysis 23.2 Markov Chain Analysis 23.3 The Use of Markov Chains for Optimal Decision Making 23.4 The Application of Markov Chains to Input Output Analysis 23.5 Using Excel and Minitab & 2 4. Inventory Models 24.1 Introduction 24.2 Derivation of the EOQ Formula 24.3 Quantity Discounts 24.4 When to Place an Order and Safety Stock 24.5 Lead Time Variation 24.6 Some Illustrative Examples & 2 5. Queuing Theory 26. Artificial Neural Networks and Genetic Algorithms (McCloskey/Borst) 26.1 Introduction 26.2 Main Elements of a Neural Network System 26.3 Case Study 2 7 Geographic Information Systems (Lloyd & Hugh Williams)

16 Appendix - Answers to Selected Problems Index - Statistical Tables

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