## Level III Course Units

Semester | Pre Req | Course Unit | Title | Credit | Hours | PS | IS |
---|---|---|---|---|---|---|---|

SI | ST 3007 | Operational Research | 3 | 45L | o | ||

SI | ST 2010 | ST 3008 | Applied Statistical Models | 3 | 30L 30P | x | x |

SI | ST 3009 | Applied Time Series | 2 | 30L | x | x | |

SI | ST 3010 | Introduction to Health Statistics | 2 | 15L 30P | o | o | |

SI | IS 1009/ ST 1011 | IS 3001 | Sampling Techniques | 2 | 30L | x | x |

SI | CS 3008 | Introduction to Data Structures & Algorithms | 3 | 30L 30P | x | x | |

SI | MS 3009 | Operational Research II | 3 | 30L 30P | o | ||

SI | CS 1001 | IT 3003 | Advanced Programming Techniques | 3 | 30L 30P | x | x |

SII | ST 3011 | Statistical Programming | 2 | 60P | x | x | |

SII | ST 2008/ MS2001 | ST 3012 | Statistical Process Control | 2 | 30L | o | o |

SII | ST 3013 | Essential Mathematics for Statistics | 3 | 45L | x | x | |

SII | IS 3004 | Applied Multivariate Methods | 2 | 15L 30P | x | x | |

SII | IS 3005 | Statistics in Practice I | 3 | 90P | x | x | |

SII | MS 3004 | Quality Management/Project Management | 2 | 30L | o | o | |

SII | IT 3002 | Database Systems | 3 | 30L 30p | x | x |

**Note:** Students may take a maximum of 33 credites.Abbreviations : x – core courses, o – electives, L – lectures, P – practicals, C – credits

## Level IV Course Units

Semester | Pre Req | Course Unit | Title | Credit | Hours | P1 | P2 | P3 | P4 | P5 | P6 |
---|---|---|---|---|---|---|---|---|---|---|---|

SI | ST 2006 | ST 3006 | Regression Analysis | 2 | 30L | o | x | o | x | o | x |

SI | ST 3007 | Operational Research | 3 | 45L | o | x | o | x | o | x | |

SI | ST 2006 | ST 3009 | Applied Time Series | 2 | 30L | o | x | o | x | o | x |

SI | ST 1011, | IS 3001 | Sampling Techniques | 2 | 30L | o | o | o | o | o | o |

ST 2006 | |||||||||||

SII | ST 2008 | ST 3012 | Statistical Process Control | 2 | 30L | o | o | o | o | o | o |

**Note:** Abbreviations : x – core courses, o – electives, L – lectures, P – practicals, C – credits

## Level IV Course Units

Semester | Pre Req | Course Unit | Title | Credit | Hours | IS |
---|---|---|---|---|---|---|

SI | ST 3006 | Regression Analysis | 2 | 30L | x | |

SI | ST 3009 | Applied Time Series | 2 | 30L | o | |

SI | IS 1009 | IS 3001 | Sampling Techniques | 2 | 30L | x |

SI | MS 3009 | Operational Research II | 3 | 30L 30P | x | |

SII | IS 3004 | Applied Multivariate Methods | 2 | 15L 30 P | o | |

SII | IS 3005 | Statistics in Practice I | 3 | 60P | x | |

SII | MS 3004 | Quality Management/Project Management | 2 | 30L | o |

## Level IV Course Units

Semester | Pre Req | Course Unit | Title | Credit | Hours | PS | IS |
---|---|---|---|---|---|---|---|

SI | ST 4011 | Econometrics | 2 | 30L | x | x | |

SI | CS 3008 | ST 4035 | Data Science | 3 | 30L 30P | x | x |

SI | ST 4036 | Time to Event Analysis | 2 | 30L | x | x | |

SI | ST 3010 | ST 4037 | Epidemiology | 2 | 30L | o | o |

SI | IS 4007 | Statistics in Practice II | 3 | 90P | x | x | |

SI | MS 4007 | Risk Management | 2 | 30L | o | o | |

SI | MS 4008 | Industrial Psychology | 2 | 30L | x | x | |

SI | IT 4004 | Advance Database Systems | 3 | 30L 30P | x | x | |

SI | CS 2002 | IT 4005 | Advanced Software Engineering | 3 | 30L 30P | o | o |

SII | IS 4008 | Internship/ Industrial Project | 12 | 360P | x | x |

**ST 3007 – Operational Research (45L, 3C)**

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.

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

**ST 3008 –Applied Statistical Models (3C, 30L 30P)**

**Prerequisites: **ST 2010

**Intended learning outcomes:**

After successful completion of the course the student should be able to analyze and interpret categorical and continuous data using appropriate linear and non-linear models using SAS/R. The student should also be able to use appropriate model diagnostic tools to validate the fitted models.

**Course content**:

Introduction to modeling. Continuous models with fixed effects: Simple Linear Regression, Multiple Linear Regression, Non Linear Regression. Data categorization. Contingency table analysis. Categorical models with fixed effects: log linear models, logistic models, Polytomous regression, Ordinal response models, Nominal response models, Analysis of categorical data using a SAS/R, Interpreting parameter estimates, Goodness of fit test. Introduction to random effects and mixed models.

**Method/s of evaluation: **End of semester examination (70%), Continuous assessment (20%) and Case studies/ Group project (10%)

**References:**

- Regression analysis by example (Chatterjee S., Price B.)
- Categorical data analysis (Agresti A.)
- An introduction to generalized linear models (Dobson A.J., Barnett A.G.)
- Applied mixed models in medicine (Brown H., Precott R.)
- Modeling binary data (Collet D.)

**ST 3009 –Applied Time Series (2C, 30L)**

**Prerequisites: **ST 2006

**Intended learning outcomes:**

After successful completion of the course the student should be able to use appropriate univariate time series models for forecasting.

**Course content**:

Introduction: Areas of application, Objectives of time series analysis, Components of time series, Descriptive analysis. Distributional properties: Independence, Autocorrelation, Stationary. Probability models to time series: Random walk, Autoregressive model. Moving Average model, mixed models, parameter estimation, Diagnostics. Forecasting: Optimal forecasts, Forecasts for ARMA models, Exponential Smoothing forecasting method.

**Method/s of evaluation: **End of semester examination (80%) and Continuous assessment (20%)

**References:**

- Forecasting Methods and Applications -3rd Edition (Makridakis, S. Weelwright, S. C. and Hyndman, R. J.)
- The analysis of Time Series: An Introduction – 6th Edition (Chatfield, C )
- Forecasting and Control – 4th Edition (Box, G. E. P., Jenkins, G. M. and Reinsell, G. C)

**ST 3010 –Introduction to Health Statistics (2C, 15L 30P)**

**Course code: **ST 3010

**Prerequisites: **

**Intended learning outcomes:**

At the end of the course the students should be able to define and compute official health statistics and construct life tables. Compute suitable descriptive statistics. Construct confidence intervals. Carryout hypothesis tests, calculate sample sizes. Identify Data Science approaches to health data. Analyze and interpret health data using statistical package/s.

**Course content**:

Introduction to official health Statistics: Mortality, Crude death rate, Standardization, Morbidity, Incidence and prevalence. Introduction to Life tables and applications. Descriptive statistical methods for health data (Summary statistics), Inferential methods (confidence intervals, hypothesis testing) for health data, Sample size calculation. Introduction to Data science for health statistics. Health data analysis using statistical packages.** **

**Method/s of evaluation: **End of semester examination (70%) and Continuous assessment [minimum of 2 In-class assignments and Case studies] (30%)

**References:**

- Statistical methods in medical research – 4
^{th}Edition (Armitage P.,Berry G., Mathews J.) - Practical Statistics for medical research (Altman D.G)
- An Introduction to medical statistics – 3
^{rd}edition (Bland J.M) - SAS for Data Analysis (
*Marasinghe**G.**)*

**ST 3011 –Statistical Programming (2C, 60P)**

**Prerequisites: **

**Intended learning outcomes:**

After a successful completion a student should be able to plot 2D and 3D graphs using Python /R; write Python/R functions to solve statistical problems ; perform data analysis using Python /R

**Course content**:

Introduction to Python. Built-in data types, Arrays and Matrices, Basic Math using Python. Basic functions and Numerical indexing, Special arrays. Advanced selection and Assignment, Flow control, loops and exception handling. Graphics using Python. Introduction to R; Data Management, Descriptive Analysis, Writing functions in R; Statistical Inference.

**Method/s of evaluation: **Continuous assessments [At least 5 lab assignments] (100%)

**References:**

- An Introduction to R – Version 3.1.2 (2014-10-31) (W.N.Venables, D.M. Smith and the R Core Team)

**Prerequisites: **ST 2008/ MS2001

**Intended learning outcomes:**

Upon successful completion of the course the student should be able to investigate and analyze process capability, advanced charts and control charts for correlated data. The student should also be able to recognize the statistical quality control methods using acceptance sampling, response surface approach for optimizing the process.

**Course content**:

Capability analysis; Cumulative Sum (CUSUM) control charts; Exponentially Weighted Moving Average (EWMA) Charts; Acceptance sampling: double, sequential, multiple; Decision theory approach; Multivariate control charts; Process optimization with design experiment.

**Method/s of evaluation: **End of semester examination (80%) and Continuous assessment (20%)

**References:**

- Introduction to Statistical Quality Control – 6
^{th}Edition (Douglas C. Montgomery) - Quality Control and Industrial Statistics (A. J. Duncan)

**ST 3075 –Essential Mathematics for Statistics (3C, 45L)**

**Prerequisites: **

**Intended learning outcomes:**

Upon the successful completion of the course the students should be able to apply basic mathematical tools in solving theoretical and practical problems in Statistics.

**Course content**:

Linear algebra: Linear dependence, rank and the solution of homogeneous equations, characteristic polynomials, eigenvalues , eigenvectors, spectral theorem for symmetric matrices, idempotent matrices and properties, orthogonal projections, trace of a matrix and properties, positive definite/semi definite matrices, quadratic forms, differential calculus in matrix notation, direct product (kronecker) of any two matrices, generalized inverse /conditional inverse; Caculus1: Concepts of functions, limits and continuity, L’Hopital’s rule, the fundamental theorem of calculus, approximation of definite integrals, Improper integrals; Series and Sequences: sequences and their convergence, series and convergence of series, power series and their convergence of radius, Taylor series and their application; Several variable calculus: functions of several variables, continuity, differentiability, derivatives, multiple integrals, change of variables

**Method/s of evaluation: **End of semester examination (70%) and Continuous assessment (30%)

**References:**

- Calculus and Linear Algebra. Vol. 1: Vectors in the Plane and One-Variable Calculus (Wilfred Kaplan; Donald J. Lewis)
- Linear Algebra Done Right [Undergraduate Texts in Mathematics] (Sheldon Axler)
- Calculus (Ron Larson and Bruce H. Edwards)

**ST 3076 –Sampling Techniques (2C, 30L)**

**Prerequisites: **IS 1009/ST 1011, ST 2006

**Intended learning outcomes:**

Upon the successful completion of the course the students should be able to identify and effectively use the theory behind sampling techniques that are commonly used in statistics.**Course content**:

Simple Random Sampling (SRS), Sample size determination, Ratio and Regression estimators under SRS, Stratified, Systematic, and Quota sampling. Separate and combined estimators for stratified sampling. Cluster sampling, Multi-stage sampling, Complex sample designs and related issues.

**Method/s of evaluation: **End of semester examination (70%) and Continuous assessments & group projects (30%)

**References:**

- Sampling – 2
^{nd}Edition (Steven K. Thompson) - Practical sampling Techniques (Ranjan Kumar Som)
- Hand book of Sampling Techniques and Analysis (Poduri S.R.S. Rao, Myron J. Katzoff)

**IS 3004 –Applied Multivariate Methods (2C, 15L 30P)**

**Prerequisites: **ST 2006

**Intended learning outcomes:**

Upon successful completion of this course the students should be able to apply the related multivariate techniques to data with multiple measurements satisfying the underlying theories and assumptions. The students should also be able to demonstrate basic computer skills in analyzing such data with the help of appropriate statistical packages.

**Course content**:

Overview, examples and introduction. multivariate normal distribution ; mean vector and variance covariance matrix, correlation matrix; bivariate normal distribution and density, missing values and outliers, summary statistics, standardized data; sample correlations; multivariate data plots, checking for multivariate normality; eigen-values and eigenvectors; geometric descriptions; principal components analysis, factor analysis, discriminant analysis, cluster analysis; multivariate inference; inference for one and two independent samples, profile analysis, repeated measurements, manova, canonical variates analysis, canonical correlations.

**Method/s of evaluation: **End of semester examination (70%) and Continuous assessment (30%)

**References:**

- Applied Multivariate Methods for Data Analysts (1998) (D.E. Johnson)
- Applied Multivariate Statistical Analysis – 3rd Edition (1992) (R.A. Johnson and D.W. Wichern)
- Computer Aided Multivariate Analysis (Afifi, Clark, and May)

**IS 3005 –Statistics in Practice I (3C, 90P)**

**Prerequisites: **

**Intended learning outcomes:**

**A**fter the successful completion of this course, a student should be able toemploy the complex process of problem-solving a massing various areas in the field of statistics. Students should be able to formulate the problems, improve on report-writing and research skills, their communication, personnel and business skills.** **

**Course content**:

This course deals with general principles involved with statistical methods covered levels I, II in solving real-life statistical problems.** **

**Method/s of evaluation: **100% based reports on a minimum of 2 Case studies/mini projects, presentations based on seminars, in-class assignments, attendance

**References:**

- Problem Solving: A Statistician’s Guide – 2nd edition (Chris Chatfield)

**MS 3004 –Quality Management/Project Management (2C, 30L)**

**Prerequisites: **

**Intended learning outcomes:**

Upon successful completion of the course students should be able to apply key theoretical concepts on quality control and project management practiced by the corporate world.

**Course content**:

Quality Management: Macro and Micro organizational Environment (PESTEL, Resource Based View)Market Analysis(Porter’s Five Forces Analysis, SWOT Analysis) Project Feasibility Analysis ( Johnson & Schole’s SFA Framework) Stakeholder Analysis, Organizational Change Management; Project Management: Project Selection, Approach Selection, The Work Breakdown Structure, The Network Diagram, Cost Effort Estimation, Optimizing the Network, Gantt Chart, Risk Management, Cost Estimation, Contract Management, Productivity Improvement, Project Management Steps, Making the Budget, Project Monitoring and Control, Human Resource Management, Project Termination.

**Method/s of evaluation: **End of semester examination (70%) and Case studies[minimum of 2]and presentations (30%)

**References:**

- Fundamentals of Project Management (Joseph Heagney)

##### **MS 3009 – Operational Research II (3C, 30L 30P)**

**Prerequisites: **MS 1003

**Intended learning outcomes:**

Upon successful completion of the course, the students should be able to describe the fundamental concepts of real world applications in operational research, model decision making problems, obtain solution/s for the formulated model/s using appropriate techniques and software packages.

**Course content**:

Network Models (Minimal Spanning Tree algorithm, algorithms for Shortest-Route problem, Maximal Flow model), Project Planning (Critical Path Method, Programming Evaluation and Review Technique), Inventory Models (Deterministic inventory models with shortages and without shortages, Probabilistic Inventory models), Queuing models (Elements of a queuing model, steady-state measures of performance, single-server models, multiple-server models), Solution techniques using suitable OR packages.

**Method/s of evaluation: **End of semester examination (70%) and Continuous assessment (30%)

**References:**

- Operational Research : An Introduction – 6
^{th}Edition (Hamdy Taha) - Operational Research – 3
^{rd}Edition (A. P. Verma) - Operational Research (R. Panneerselvam)
- Operational Research (Harvey M. Wagner)
- Operational Research (Hillier and Liebermann)

**ST 4011 –Econometrics (2C, 30L)**

**Prerequisites: **ST 3008, ST 3009

**Intended learning outcomes:**

Upon successful completion the students will be able to apply statistical methods in the context of economics and carry out a successful econometric analysis.

**Course content**:

The application of linear regression model and the interpretations of properties of least squares estimates in the context of economic theory, an introduction to violations of OLS assumptions in economics, Simultaneous equations, Time Series Econometrics, Case studies.

**Method/s of evaluation: **End of semester examination (70%) and Continuous assessment [minimum of 3 ] (30%)

**References:**

- Basic Econometrics (Damodar N. Gujarati)

**ST 4035–Data Science (3C, 30L 15P)**

**Course code: **ST 4035

**Prerequisites: **ST 3011, ST 3013, CS 3008

**Intended learning outcomes:**

After a successful completion a student should be able to apply basic techniques of Data Science for decision making.

**Course content**:

Introduction; Ethics; Data Wrangling & Pre-processing; How to deal with large data sets: Parallel computing, Map reduce framework – Hadoop; Data Communication & Visualization; Statistical Methods: Regression, Logistic Regression, Random Forest, Support Vector Machines; Machine Learning Algorithms.

**Method/s of evaluation: **Minimum of 3 continuous assessments(Inclass) and 1 group project

**References:**

- An Introduction to Statistical Learning: With Applications in R – 2013 (James, G., Witten, D., Hastie, T., Tibshirani, R.)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction – 2nd edition ( Hastie, T., Tibshirani, R., Friedman, J.)
- Mining Massive Data Sets – 2014 (Leskovec, J., Rajaraman, A., Ullman, J.)
- Data Science for Business: What you need to know about data mining and data-analytic thinking – 2nd edition (Provost, F., Fawcett, T.)

**ST 4036 –Time to Event Analysis (2C, 30L)**

**Prerequisites: **ST 2006

**Intended learning outcomes:**

At the end of the course the students should be able to explain characteristics of time to event data. Identify suitable distributions for time to event data. Describe time to event data using suitable parametric and non-parametric measures. Analyze time to event data using parametric models and non-parametric regression models. Calculate sample sizes. Analyze and interpret time to event data using statistical package/s.

**Course content**:

Characteristics of time to event data, Distributions for time to event data, Non-parametric methods, Parametric regression, Hazard regression, Power analysis and sample size calculation, fitting parametric and semi parametric models, analysis of time to event data using SAS/R.

**Method/s of evaluation: **End of semester examination (70%) and Continuous assessment (30%)

**References:**

- Regression modeling of time to event data – 2nd Edition (D.W. Hosmer, S. Lemeshow, and Susanne May) Wiley
- Modeling Survival Data in Medical Research – 2nd Edition (D. Collet) Chapman & Hall
- Survival Analysis (D.R. Cox and D. Oakes) Chapman & Hall

**ST 4037 – Epidemiology (2C, 30L)**

**Prerequisites: **ST 3010

**Intended learning outcomes:**

At the end of the course the students should be able to describe basic designs for epidemiological studies. Compute relative risk and odds ratio. Identify confounding and interaction. Fit suitable models for epidemiological data using SAS/R. Perform bioassay. Plot and interpret ROC curve for epidemiological data. Identify ethics in health data analysis.

**Course content**:

Introduction to epidemiology. Basic Epidemiological designs: surveys, cohort studies, case control studies. Relative risk and odds ratio. Confounding and interaction. Modeling epidemiological data using SAS/R: logistic regression and other models. Bioassay. ROC analysis. Ethics in health data analysis.

**Method/s of evaluation: **End of semester examination (80%) and Continuous assessment (20%)

**References:**

- Case control studies (Schlesselman J.J.)
- Modeling binary data (Collet D.) Chapman & Hall
- Epidemiology (Woodward M.) Chapman & Hall

**IS 4007 –Statistics in Practice II (3C, 90P)**

**Prerequisites: **

**Intended learning outcomes:**

**A**fter the successful completion of this course, a student should be able solve real-world problems currently faced by the industry by using various areas in the field of statistics. Students should also be able to communicate the findings to the industry in both oral and written form.

**Course content**:

This course deals with general principles involved with statistical methods covered levels I, II and III in solving real-life statistical problems. It is aimed at students who have exposure to these areas but are unsure what to do when faced with real data, especially if the data are ‘messy’ or the objectives are unclear.

**Method/s of evaluation: **100% based on minimum of 3 written reports, presentations based on seminars, in-class activities, attendance

**References:**

- Problem Solving: A Statistician’s Guide – 2nd edition (Chris Chatfield)

**Prerequisites: **

**Intended learning outcomes:**

**Upon completion of Industrial Training, the stud**ent should be able to integrate classroom theory with workplace practice, develop greater clarity about academic and career goals, recognize administrative functions and company culture, appreciate the ethical basis of professional practice in relevant industry, display a capacity for critical reasoning and independent learning, explore options in career plans and goals.

**Course content**:

Industrial training provides students to understand and appreciate real-life working experiences. Students may realize their ambition and ascertain their career path from the experience gained during industrial training. The training provides students the opportunity to meet and network with people in the industry, and for the industry the opportunity to identify talents and potential skilled employees.

**Method/s of evaluation: **100% based on

- Daily Work logs maintained by the student.
- Minimum one written report every two weeksfrom the student.
- The supervisor to monitor performance of the student by visiting the industrial training company at least once during the period of training.
- The industry to evaluate the performance of the student trainee.
- Final Report and a presentation on the industrial training at the end of the industrial training period.

**References:**

**MS 4007 –Risk Management (2C, 30L)**

**Prerequisites: **

**Intended learning outcomes:**

**Upon successful completion students will be up-to-date with curre**nt trends capital markets and risk management and will be able to easily adapt to the corporate environment.

**Course content**:

Introduction to Capital Markets; Types of financial markets: (debt, equity and derivatives); Introduction to Financial Instruments: (debt, equity and derivatives), Introduction to Time Value of money and interest rates, Risk and Risk aversion, Financial ratios, Portfolio risk, Capital allocation, Market Risk Management, Operational risk, Financial crisis Business case studies and presentations.

**Method/s of evaluation: **End-Semester Examination ( 70%) and Continuous Assessments, Business Case Studies and Presentation (30%)

**References:**

- Financial markets + Institutions (Fredric S. Mishkin and Stanley G. Eakins)
- Options, Futures, and Other Derivatives (John C. Hull)

**MS 4008 – Industrial Psychology (2C, 30L)**

**Prerequisites: **

**Intended learning outcomes:**

Upon successful completion students will be able to apply concepts of psychology in an industrial context to further their career goals and successful work-life balance.

**Course content**:

Introduction to Psychology, Organizational Behavior, Leadership & Group Behavior, Psychological Assessments, Work Motivation and Job Designing, Diversity and Issues in Organizations, Psychology of HRM & Ergonomics, Conflicts at Work & Stress, Career Development/Work –Life Interface.

**Method/s of evaluation: **End of semester examination (70%) and Continuous Assessments, Case Studies and Presentations (30%)

**References:**

- Industrial/Organizational Psychology (Paul Levy)

**ST 4002 – Generalized Linear Models (30L, 2C)**

Generalized linear models: Continuous models, logit models, probit models, Model diagnostics, Biological assay, Over-dispersion, Quasi Likelihood models.

Categorical Data Analysis (Alan Agresti), Modelling Binary Data (D. Collett) Generalised Linear Models (McCullah and Nelder)

**ST 4011 – Econometrics (30L, 2C)**

Linear regression model and properties of least squares estimates; Autocorrelation; Heteroscadasity; Multicollinearity; Model specification; Simultaneous equations; Unit roots, Non stationary and Cointegration.

Econometric Model and Economic Forecasts (Robert Pindyck and Daniel Rubinfeld), Introductory Econometrics: A Modern Approach (Jeffrey Wooldridge), Econometric Analysis (Greene, W.G.), Econometric Methods (Jack Johnston and John DiNardo)

**ST 4016 – Catergorical Data Analysis (45L, 3C)**

Categorical response data, description and inference of two dimensional contingency tables, models for binary response variables, logistic regression, model diagnostics, Polytomous response variables, Log-linear models: Log-linear models for two dimensions, log-linear models for three or more dimensions, testing goodness of fit, estimation model parameters, iterative MLEs, hierarchical model fitting, diagnostics, partitioning chi-square to compare models, Strategies in model selection, Analysis of deviance, Testing conditional independence. Ordinal data: Log-linear models for ordinal variables, Models for ordinal variables in multidimensional tables.

Categorical Data Analysis (Alan Agresti)

Analysis Ordinal Categorical Data (Alan Agresti)

Applied Logistic Regression (Hosmer and Lemeshow)

**ST 4030 – Multivariate Data Analysis (45L, 3C)**

Review of matrix algebra; Mean and variance- covariance of a random vector, correlation matrix; Properties of multivariate normal distribution and applications, checking for multivariate normality; Hypothesis testing using multivariate tests, tests on covariance matrices, tests of independence; Principal components analysis; Factor analysis; Discriminant analysis; Cluster analysis; Multivariate regression; MAOVA

Applied multivariate statistical analysis (Johnson and Wichern), Multivariate statistical methods (Morrison), Applied multivariate methods for data analysts (Johnson).

**ST 4031 – Stochastic Processes and Applications (3C, 45L)**

Generating functions, Convolution, Compounding, Random walks, Recurrent events, Discrete parameter Markov Chains, Continuous parameter Markov Chains, Birth and Death processes, Queuing processes.

The Elements of Stochastic Processes(Bailey), An Introduction to Probability Theory and Applications (Feller), Stochastic Processes(Cox & Miller), Probability and Statistics with Reliability Queues and Computer Science Applications(Kishor S. Trivedi)

**ST 4040/ST 4050 – Individual Project in ST/ST+CS (180P(60P+120P), 6C)**

The project topic could be selected from any area in the third and fourth year Statistics and/or Computer Science subject units. The selection of the project is done at the beginning of the year. The project will be done throughout the year and consists of six (6) progress reports (3 per each semester). Students are supposed to collect data for their individual projects from different Ministries/Research Institutes/Organizations, etc. They would be visiting these places during their fourth year for this purpose.

Statistical Theory (Lindgren)

**ST 4001 – Statistical Inference – II (30L, 2C)**

Parametric Inference: Introduction to Hypothesis Testing, Errors, Power, Neymann-Pearson Lemma, Most Powerful Tests, Uniformly Most Powerful Tests, Likelihood Ratio Tests: Sequential Tests; Sequential Probability Ratio Test (SPRT), Wald’s Identity, Average Sample Number (ASN). Distribution-free Inference: Tests of Randomness; Run Tests. One sample Location Tests for Median; Sign Test. Asymptotic Relative Efficiency (ARE), Two sample Location problem.

Introduction to Theory of Statistics (Mood. Graybill and Boes), Statistical Theory (Lindgren)

**ST 4012 – Special Topics for ST (30L, 2C)**

Selected topics depending on the availability of teaching staff.

Examinations and assignments

**ST 4013 – Special Topics For ST + CS (30L, 2C)**

**ST 4015 – Decision Theory (30L, 2C)**

Convex Combinations, Utility, Personal probability. No Data Problem: Loss and Regret, Mixed Actions, Minimax Principle, Bayes Actions, Admissibility. Data in Decisions: Risk function, Estimation and Testing as Special cases, Properties of Decision Rules. Bayes Theorem: Posterior Distribution, Solving the Decision Problem, Conjugate Families, Estimation and Testing. Limiting distributions, laws of large numbers.

**ST 4032 – Case Studies in ST (30P, 1C)**

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.

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.

**ST 4034 – Computational Statistics (3C, 45L)**

Introduction to software package R. Introduction to Random numbers: pseudo random numbers, properties of random numbers, testing for basic properties, Software for random number generation; Introduction to Simulation: Simulation of random variables, Monte Carlo simulation methods, Simulation of inventory models, Simulation of Queuing models; Data Re-sampling: Introduction to Bootstrap, Bootstrap estimation of Variance, Bootstrap Confidence Intervals, Non-parametric bootstrap algorithm, Introduction to EM algorithm, Markov Chain Monte Carlo Methods

Computational Statistics (Givens, G. H. and Hoeting, J. A.), Elements of Computational Statistics (Gentle, J. E), An introduction to the bootstrap (Efron and Tibshirani).

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