with a contribution by
William McCluskey, University of Ulster, Northern Ireland, UK.
and
Richard Borst, Senior Vice President, Cole Layer & Trumble, Pennsylvania, USA.
© 1996, Flaherty, Lombardo, Morgan, de Silva
ISBN 0 86444 499 0 690 pages
Property Studies Education Unit
Department of Marketing Logistics and Property
Faculty of Business RMIT
GPO Box 2476V Melbourne 3000
Printer: New Generation Print & Copy
2. Matrix Algebra
2.1 Elements of Matrix Algebra
2.2 Matrix Operations
2.3 The Determinant
2.4 Rank, Trace and Orthogonality
2.5 Matrix Inversion
2.6 Eigenvalues and Eigenvectors
2.7 Matrix Algebra using Excel and Minitab
APPENDIX 2 Summary of Vectors and Matrices
EXERCISES
3. Basic Concepts in Statistics
3.1 The Role of Statistics
3.2 Descriptive Statistics
3.3 Measures of Central Tendency
3.4 Dispersion of the Data Around the Mean
3.5 Measures of Relative Standing
3.6. The Normal Distribution
3.7 Skewness and Kurtosis
Appendix 3 Estimators of the Mean and Variance
EXERCISES
5. Elements of Hypotheses Testing
5.1 Tests of Hypotheses for the Population Mean
5.2 Tests of Hypotheses for the Population Proportion
5.3 Other Approaches to Testing Hypotheses
5.4 The P-value approach to hypotheses testing
EXERCISES
6. Decision Theory and Expected Utility
6.1 Basic Elements of The Decision Making Process
6.2 Maximin Criterion
6.4 Minimax Regret Criterion
6.5 Expected Monetary Value
6.6 Expected Utility Analysis
6.7 Conclusion
EXERCISES
7. Nonparametric Statistics
7.1 Introduction
7.2 Data Types
7.3 Measurement Scales
7.4 Parametric Methods
7.5 Nonparametric vs Parametric Methods
7.6 Contingency Tables
7.7 Wilcoxon Signed-Rank Test
7.8 Mann-Whitney Test
EXERCISES
8. Markov Chains and Input Output Analysis
8.1 Input-Output Analysis
8.2 Markov Chain Analysis
8.3 The Use of Markov Chains for Optimal Decision Making
8.4 The Application of Markov Chains to Input Output Analysis
8.5 Using Excel and Minitab
EXERCISES
10. Multiple Regression
10.1 The Multiple Regression Model
10.2 An Example of Multiple Regression
10.3 Validation of the Equation
10.4 Analysis of Variance for the Regression
10.5 Model Selection Criteria
10.6 Dummy Variables In Regression
10.7 Comparing Two Regressions
10.8 Non Linear Regression Models
Appendix 10.1 Covariance Matrix
Appendix 10.2 Residential Units Data Set
EXERCISES
11. Data Problems and Residual Analysis
11.1 Multicollinearity
11.2 Autocorrelation
11.3 Heteroscedasticity
11.4 Residual Analysis to Detect Outliers
11.5 The Logit Model
EXERCISES
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 Exponential Smoothing Methods
13.6 Model Validation
EXERCISES
14. Advanced Time Series Models
14.1 Autocorrelation Analysis
14.2 Unit Roots and Cointegration
14.3 Box-Jenkins ARIMA Models
EXERCISES
16. Factor Analysis
16.1 The Orthogonal Factor Model
16.2 Methods of Estimation
16.3 Factor Rotation
EXERCISES
18. Network Analysis
18.1 Basic Terminology
18.2 Shortest Route Problem
18.3 CPM and PERT
18.4 Earliest Time, Latest Time and Activity Slack
18.5 Activity Time Statistics and Project Completion Times
18.6 Time/Coat Tradeoff and Project Crashing
Appendix 18 - Glossary of Terms
EXERCISES
19. Dynamic Programming
19.1 Dynamic Programming for a Multistage Problem
19.2 Recursion and Bellman's Principle of Optimality
19.3 Formal Notation of Dynamic Programming
19.4 A DP Problem with a Multiplicative Recursive Relation
19.5 Other Types of DP Problems
EXERCISES
20. Artificial Neural Networks
20.1 Introduction
20.2 Main Elements of a Neural Network System
20.3 Case Study
Selected Readings
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