Syllabus: Syllabus: Introduction, The Marketing Research Process, Defining the problem with exploratory research, Survey research: Methods of communication with respondents, Test marketing, Measurements and Attitude scaling, Questionnaire design, Sampling procedures, Data analysis, report writing and presentation: Stochastic models of brand choice, Applications of General Linear Models in marketing, Conjoint analysis, Correspondence analysis, Advertising media models, Marketing response models.

Evaluation Criteria: End-of-semester examination and assignments

Suggested Readings: Marketing Research (D.R. Lehmann, S. Gupta, J.H. Steckel), Marketing Research, (Melvin Crask, Richard J. Fox, Roy Stout), Marketing Research (David A. Aakar)

Dependencies: AM 2003, AM 2004

Syllabus: Integer programming and solution techniques, Zero-one programming and solution techniques, Transportation models, Assignment models and their solution techniques. Project planning and evaluation techniques, Deterministic inventory models with shortages and without shortages, Queuing models, Different queuing systems and disciplines.

Evaluation Criteria: End-of-semester examination and Assignments

Suggested Readings: Operational Research an Introduction (Hamdy A. Taha), Operational research (Harvey M. Wagner)

Dependencies: AM 1001

Syllabus: Characteristic function, Sampling from Normal population, sampling distributions of sample mean and sample variance (S2), independence of sample mean and S2, Estimation Criterion: Mean-squared error, Unbiasedness, Consistency, Sufficiency, Completeness, Efficiency, Factorization criterion. Variance Reduction: Cramer Rao Lower Bound, Rao-Blackwell Theorem, Lehmann- Scheffe’ Theorem. Methods of Estimation: Method of moments, Maximum Likelihood and Its Properties, Least Squares. Interval Estimation: Pivotal Method, General Method.

Evaluation Criteria: Examinations and assignments

Suggested Readings: Introduction to Mathematical Statistics (Hogg and Craig), Statistical Theory (Lindgren), Statistical Inference (Casella and Berger)

Syllabus: Elementary linear and matrix algebra; Generalized and conditional inverses; Solutions of linear equations, idempotent matrices, trace of matrices; Derivatives of quadratic forms, expectation of random matrices; Multivariate normal distribution and its properties, distribution of quadratic forms; General linear model; optimal estimation and hypothesis testing, applications to regression model; Continued application of optimal inference; Design models, estimability, solving normal equations; Components of variance models and mixed models.

Evaluation Criteria: End-of-semester examination and Assignments

Suggested Readings: Theory and applications of the linear model (Graybill, F. A.), Matrices with applications in Statistics(Graybill, F. A.).

Dependencies: ST1004, ST2004

Syllabus: Introduction to regression, Correlation, Uses of regression, Simple linear regression model, Parameter estimation, inferences about the model, model diagnostics and prediction, Multiple regressions, Qualitative variables as predictors, Model building, Comparison of Regressions using dummy variables, Analysis of collinear data, Transformation of Variables, Polynomial Regression

Evaluation Criteria: End-of-semester examination and Assignments

Suggested Readings: Applied Regression Analysis (Draper & Smith), Applied Linear Regression (Sanford Weisberg), Introduction to Linear Regression Analysis (Montgomery, D.C. & Peck, E.A), Applied Regression Analysis (Draper, N.R. & Smith H.)

Syllabus: Fundamentals of probability sampling and estimation, Simple Random Sampling: Theory involved in estimation procedures, Estimating population mean, variance, total & proportion, Estimating a ratio & its variance, Estimation using Ratio and Regression methods and their properties, Sample size determination. Stratified Random Sampling: Proportional and optimal cost allocations to strata, Estimating population mean, variance, total & proportion, Overview of advanced topics in stratified random sampling, Estimating a ratio & its variance, Regression estimators, Sample size determination. Post-stratification, Quota sampling, Cluster Sampling: Overview of cluster sampling, clustering with equal and unequal probabilities, Sample size determination, Design effect and intra-cluster correlation. Multi-stage sampling: Complex surveys and related problems, Sources of errors in surveys.

Evaluation Criteria: End-of-semester examination and Assignments

Suggested Readings: Elementary Sampling Theory (Vic Barnet), Survey Sampling (Leslie Kish), Sampling Techniques (William G. Cochran)

Syllabus: Introduction: Definition, Types of time series, Components of time series, Time plot, Time series decomposition, Transformation, Differencing, Autocorrelation, Stationarity: Stationary & non-stationary time series, Tests for stationary, Modelling time series: Time series models, Model identification, Parameter estimation, Diagnostic checks, Forecasting, Spectral analysis

Evaluation Criteria: End-of-semester examination and Assignments

Suggested Readings: Forecasting Methods and Applications (Makridakis, S. Weelwright, S. C. and Hyndman, R. J.), The analysis of Time Series: An Introduction (Chatfield, C), Forecasting and Control (Box, G. E. P., Jenkins, G. M. and Reinsell)

Syllabus: Principles of planning and designing comparative experiments; Review of ANOVA and related topics; Basic designs: completely randomized design (C.R.D), randomized complete block design (R. C. B. D), Latin squares/multiple Latin squares, treatment contrasts and mean comparisons; Factorial experiments (2k and others); confounding and partial confounding in 2k experiments; split-plot designs; analysis of covariance

Evaluation Criteria: End-of-semester examination and Assignments

Suggested Readings: Design and analysis of experiments (Montgomery, D. C.), Statistics for experiments: An introduction to design, data analysis and model building (Box, Hunter, and Hunter), The design of experiments (Mead, R).

Syllabus: Reliability concepts and Reliability data, Models, censoring and likelihood for failure time data, Non-parametric estimation, Location-Scale based parametric distributions, Probability plotting, Parametric likelihood fitting concepts, Maximum likelihood estimates for the exponential mean based on the density approximation. Failure time regression analysis; failure time regression models, accelerated failure time models, regression diagnostics, proportional hazards model, weibull proportional hazards model. Accelerated test models: Different types of acceleration, accelerated life tests, methods of acceleration, acceleration models. Planning accelerated life tests: planning information, evaluation of test plans, planning single variable ALT experiments.

Evaluation Criteria: End-of-semester examination and Assignments

Suggested Readings: Statistical methods for reliability data (Meeker, M.Q. and Escobar, L.A.), Reliability Modeling: A Statistical approach (Wolstenholme L.C.)

Conduct by University of Colombo School of Computing

Conduct by University of Colombo School of Computing

Conduct by University of Colombo School of Computing

Conduct by University of Colombo School of Computing

Conduct by University of Colombo School of Computing

Syllabus: Read and discuss text/papers for a general sense of what research is/are about, how one thinks when doing research, and what the major research activities are, Identify research articles from different areas of statistics/computer science, involving different methodologies of research, and abstract them, Select an area related to statistics, which is of particular interest to you. Write a professional quality literature review for a problem of your choice.

Evaluation Criteria: examinations

Dependencies: ST1006

Syllabus: Sampling plans of attribute type; OC curve scheme, Dodge and Romig approach, Decision theory approach, Double sampling plans, Sampling inspection by variables, Sequential sampling plans, Control charts; Variable type control charts, Attribute type control charts.

Evaluation Criteria: Examinations and assignments

Suggested Readings: Statistical Quality Control (Mahajan, M), Statistical Quality Control, (Gupta, R.C.), Quality Control and Industrial Statistics (Duncan, A.J.)

Syllabus: Selected topics depending on the availability of teaching staff.

Evaluation Criteria: Examinations and assignments

Syllabus: Introduction, Epidemiology: Basic designs for epidemiological studies, relative risk and odds ratio, confounding and interaction. Analysis of data from cohort and case control studies, Matched case control studies, logistic regression. Clinical trials: Introduction, protocols for clinical trials, cross-over designs, allocation to treatment, sample size determination, Phase I and Phase II studies. Survival Analysis: Analysis of survival data, the survival and hazard functions. Non-parametric procedures: Kaplan-Meier estimate of survivor functions, log-rank test for comparing two survival times. Parametric modeling: Proportional hazards model, Cox’s proportional hazards model.

Evaluation Criteria: End-of-semester examination and Assignments

Suggested Readings: Statistical methods in medical research (Armitage, P.), Case-control studies ( Schlesselman, J.J.), Clinical trials (Pocock, S.J.), Modeling survival data in medical research (Collett, D.)

Syllabus: Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.

Evaluation Criteria: Continuous Assignments

Suggested Readings:

Syllabus: Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.

Evaluation Criteria: Continuous Assignments

Suggested Readings:

Syllabus: Students will work as a team to achieve a common goal: finding a solution to a given problem. This will be done under the supervision of a suitable academic staff member.

Evaluation Criteria: Report, Viva and Supervisor’s marks

Suggested Readings:

Syllabus: Students will work as a team to achieve a common goal: finding a solution to a given problem. This will be done under the supervision of a suitable academic staff member.

Evaluation Criteria: Report, Viva and Supervisor’s marks

Suggested Readings:

Conduct by Department of Mathematics

Conduct by Department of Mathematics

Contact Dean's office

Contact Dean's office

Syllabus: Train students in analysing real/simulated data using latest statistical software: SPSS, SAS, MINTAB, STATISTICA, etc. The emphasis will be on applications of theory covered in the other subject areas as well as on presentation and report writing.

Evaluation Criteria: Examinations