Course Code

: STAT 11613

Title

: Fundamentals of Statistics

Learning Outcomes:

At the completion of this course student will be able to:

• Describe a given set of data using graphical and numerical summaries in terms of measures of location, variability, and skewness.
• Describe the importance of natural variation in real-life problems.
• Recognize appropriate statistical models of given real-life situations.
• Demonstrate the importance of Statistics in making decisions on day-to-day life.

Course Content:
Introduction: Rationale for learning Statistics, Essence of Science, How the Statistics serves the scientists, Basic terminology, Descriptive and inferential studies, Types of measurements, Statistical approach and types of studies, Introduction to Data Collection and Sampling methods
Descriptive Statistics: Techniques of data presentation, Measures of Central Tendency, Measures of Dispersion, and Shapes of distributions.
Probability: Algebra of sets, Random or non-deterministic experiment, Sample space, Events, and event space, Classical, frequency and axiomatic definitions of probability, Conditional probability, Partition of a sample space, Law of total probability, and Bayes' Theorem.
Probability Distributions: The concept of Random variables, discrete and continuous random variables, probability distributions, probability mass function, probability density function, Expectation and variation of random variables,
Applications of Standard Probability Distributions: Bernoulli, Binomial, Poisson, uniform, Normal distribution.

Introduction to statistical inference:  Sampling Error, Sampling distributions, Sampling Distribution of the Sample Mean, Central Limit Theorem, Introduction to point estimates and interval estimates,Introduction to hypothesis testing
Decisions about relationships: Introduction to Correlation, Relationship between interval/ratio variables, Geometric appearance of relationship, Product-Moment Correlation.
Ethics for Statisticians

Method of Teaching and Learning:
A combination of lectures, group work, and assignments.

Assessment:
Assignments, End of Semester examination.

Recommended Reading:

1. Runyon Richard P., Harber Andrey, Pittenger David J., Coleman Kay, 8^th edition, (2002), ‘Fundamentals of Behavioral Sciences’, McGraw-Hill.
2. Anderson, D.R., Sweeney, D.J. and William, T.A, 3^nd Edition, (1994), ‘Introduction to Statistics Concepts and Applications’, West Publishing Company.
3. Erricker B. C., (1977), Reprint, ‘Advanced General Statistics’, Alden Press Oxford.
4. Ross, S.M., 5^nd Edition, (2014), ‘Introduction to Probability and Statistics for Engineers and Scientists’, Harcourt Academic Press.
5. Jessica, M. U. 3^rd Edition (2005), ‘Seeing through Statistics’, Thomson.

Course Code

: STAT 11621

Title

: Statistical Laboratory

Learning Outcomes:

At the completion of this course student will be able to:

• Apply tools provided in Statistical software packages to solve real-world problems.
• Present data in graphical and numerical form that conveys a reasoned analysis and conclusions.
• Construct statistical reports with clear interpretations to a diverse audience.

Course Content:

Introduction Statistical Software, Working with Spreadsheets
Exploratory data analysis: Applications of data presentation techniques, Uses of Central Tendency and Dispersion measures in real-world applications, Detection of outliers, Missing value imputation, Identifying relationships among variables
probability sampling: simple random sampling, sampling with and without replacement, identifying the sampling distribution
Probability distributions: Simulate standard probability distributions, Applications of standard probability distributions, checking normality assumption
Introduction to statistical programming
Statistical report writing.

Method of Teaching and Learning:
Lecture cum demonstration, Laboratory work.

Assessment:
End of course practical examination and assignments.

Recommended Reading:

1. Manuals relevant to statistical packages
2. Runyon Richard P., Harber Andrey, Pittenger David J., Coleman Kay, 8^th edition, (2002), ‘Fundamentals of Behavioural Sciences’, McGraw-Hill.
3. Horvath Theadore, (1985), ‘Basic Statistics for Behavioural Sciences’, LittleBrown & Company.

Course Code

: STAT 11632

Title

: Optimization I

Learning Outcomes:

At the completion of this course student will be able to:

• Translate real-life situations involving linear relationships into Linear Programming Models.
• Apply Optimization techniques in solving Linear Programming models.
• Validate the adequacy of the models using the appropriate techniques.

Course Content:

Overview of Operations Research Modelling Approach.
Linear Programming: Introduction to Linear Programming, Construction of Mathematical Models, 
Solution Techniques: Graphical Method, Simplex Method; Algebraic Approach, Tabulation Approach.
Duality and Sensitivity Analysis: Essence of duality theory, Economic interpretation of duality, Primal-dual relationships, Role of duality theory in sensitivity analysis, Essence of sensitivity analysis, and Applying sensitivity analysis.

Method of Teaching and Learning:
A combination of lectures and tutorials.

Assessment:
Assignments and End of semester examination.

Recommended Reading:

1. Fredrick S. Hiller, Gerald J. Lieberman, 8^th Edition,(2005), ‘Introduction to Operations Research’, Mc-Graw Hill.
2. Hamdy A.. Taha, 10^th Edition, (2016), ‘Operations Research’, Pearson.
3. Bazaraa M.S., Javis J.J., Sherali H. D., 2^nd Edition, (1990), ‘Linear programming and network flows’.

Course Code

: STAT 12643

Title

: Probability Distributions and Applications I 

Learning Outcomes:

At the completion of this course student will be able to:

• Define probability distributions and density functions for random variables in general and discuss their properties,
• Explain different types of probability distributions with their properties,
• Use probability distributions to calculate different probabilities to solve problems arising from a wide range of disciplines.
• Effectively communicate the underlying results

Course Content:

Axiomatic definitions of probability, and rigorously prove basic propositions of probability theory.
Random variables: Rationale for the introduction of random variables, Definition of a random variable, Types of random variables.
Distribution functions of random variables:  probability mass function and its properties, Probability density function and its properties, Cumulative probability distribution function and Properties.
Expectation of a function of a random variable: General definition, Properties of expectation, Mean, Variance and Standard deviation, Moments, Moment generating function, Probability generating function and Characteristic function, Tshebysheff’s Lemma or Chebychev’s theorem, Generalized form of Bienayme-Tchebycheff Inequality, Tchebycheff Inequality, Bernoulli’s Law of Large Numbers.
Transformation: find the distribution of transformed random variables;
Discrete Probability Distributions: Discrete uniform, Bernoulli, Binomial, Poisson, Geometric, Hypergeometric and Negative binomial distributions, Poisson approximation to Binomial distribution.
Continuous Probability Distributions: Uniform, Normal, Negative Exponential, Gamma, Beta and Log-normal distributions, Normal approximation to Binomial distribution, Normal approximation to Poisson distribution.
Probability integral transform.
Applications of probability in other disciplines.

Method of Teaching and Learning:
A combination of lectures and tutorials.

Assessment:
Assignments and End of semester examination.

Recommended Reading:

1. Alexander M. Mood., Franklin A. Graybill, Pittenger Duane C. Boes, 3^rd edition, Reprint (2005), ‘Introduction to the Theory of Statistics’, McGraw-Hill.
2. Ronald E. Walpole, Raymand H..Mayers, 9^th Edition (2012), ‘Probability and Statistics for Engineers and Scientits’, Prentice Hall.
3. Dennis Wackerly, William Mendenhall, Richard L. Scheaffer, 7^th edition (2008), ‘Mathematical Statistics with Applications’, Thomson Learning
4. George Casella and Roger L. Berger, 2^nd edition, (2001), ‘Statistical Inference’, Thomson Learning.

Course Code

: STAT 12652

Title

: Optimization I

Learning Outcomes:

At the completion of this course student will be able to:

• Translate real-life situations involving linear relationships into Linear Programming Models.
• Apply Optimization techniques in solving Linear Programming models.
• Validate the adequacy of the models using the appropriate techniques.

Course Content:

Overview of Operations Research Modelling Approach.
Linear Programming: Introduction to Linear Programming, Construction of Mathematical Models, 
Solution Techniques: Graphical Method, Simplex Method; Algebraic Approach, Tabulation Approach.
Duality and Sensitivity Analysis: Essence of duality theory, Economic interpretation of duality, Primal-dual relationships, Role of duality theory in sensitivity analysis, Essence of sensitivity analysis, and Applying sensitivity analysis.

Method of Teaching and Learning:
A combination of lectures and tutorials.

Assessment:
Assignments and End of semester examination.

Recommended Reading:

1. Fredrick S. Hiller, Gerald J. Lieberman, 8^th Edition,(2005), ‘Introduction to Operations Research’, Mc-Graw Hill.
2. Hamdy A.. Taha, 10^th Edition, (2016), ‘Operations Research’, Pearson.
3. Bazaraa M.S., Javis J.J., Sherali H. D., 2^nd Edition, (1990), ‘Linear programming and network flows’.

Course Code

: STAT 21613

Title

: Probability Distributions and Applications II

Learning Outcomes:

At the completion of this course student will be able to:

• Explain the properties of joint probability distribution, joint density function, and marginal density functions.
• Carry out probabilistic calculations to determine whether specified events or specified random variables are dependent or independent.
• Use joint probability distributions to calculate different probabilities to solve problems arising from a wide range of disciplines.

Course Content:

Two-dimensional Random variables: Introduction and characteristics of two-dimensional random variables
Joint distribution functions: Properties of the bivariate cumulative distribution function, Joint density functions, Marginal density functions, Conditional probability distributions, Independence, and related theorems.
Expectations: Expectation of a function of two-dimensional random variable, Covariance and correlation coefficient, Conditional expectation and related theorem, Conditional variance and related theorem, Joint moments, Joint moment generating function, Uncorrelated random variables, and Cauchy-Schwartz Inequality.
Distributions of functions of random variables and Expectations: Distributions of sum, difference, product, and quotient of two continuous random variables, Expectations, and related theorems.
Cumulative distribution function techniques: Probability distributions of maximum and minimum of a set of random variables.
Moment generating function techniques: Distribution of sum of independent random variables, Central limit theorem.
Transformations: The joint probability density function of random variables
Sampling and sampling distributions: Sampling, Distribution of a sample, Sample moments, Sample variance, Law of Large Numbers, Concept of Convergence of random variables, Central Limit Theorem, sampling from Normal distribution, Chi-square distribution, Student’s t -distribution and F - distribution.

Method of Teaching and Learning:
A combination of lectures and tutorial discussion.

Assessment:
Assignments and End of semester examination.

Recommended Reading:

1. Alexander M. Mood, Franklin A. Graybill, Pittenger Duane C. Boes, 3^rd Edition, Reprinted (2005), ’Introduction to the Theory of Statistics’, McGraw-Hill.
2. Ronald E. Walpole, Raymand H.Mayers, 9^th Edition (2012), ‘Probability and Statistics for Engineers and Scientists’, Prentice Hall.
3. Dennis Wackerly, William Mendenhall, Richard L. Scheaffer, 7^th edition (2008), ‘Mathematical Statistics with Applications’, Thomson Learning.

Course Code

: STAT 21623

Title

: Statistical Inference I

Learning Outcomes:

At the completion of this course student will be able to:

• Derive point estimators for given statistical models using the method of moments
• Perform likelihood-based point estimation for a variety of statistical models
• Recognize appropriate estimators of a statistical model using the key properties of the estimators
• Derive interval estimates of parameters in simple statistical models.
• Verify assumptions underlying statistical inference.

Course Content:

Point Estimation: Introduction to Point Estimation,
Methods of Finding Point Estimators: Methods of Moments, Maximum Likelihood Method.
Properties of Point Estimators: Closeness, Mean Squared Error, Unbiasedness, Consistency, Sufficiency.
Unbiased Estimators: Uniformly Minimum Variance Unbiased Estimator (UMVUE), Cramer-Rao inequality, Sufficiency and Completeness, Exponential family of distributions, Rao-Blackwell theorem, Lehmann-Scheffe theorem.
Interval Estimation: Introduction, Pivotal quantity method. Confidence Intervals: Confidence intervals for the mean, variance for the random samples from the normal distribution, confidence interval for proportion, Simultaneous confidence region for the mean and variance, Confidence interval for the difference in means, Approximate confidence intervals. 
Large sample confidence interval

Method of Teaching and Learning:
A combination of lectures and tutorials.

Assessment:
Assignments and End of Semester examination

Recommended Reading:

1. Alexander M. Mood, Franklin A..Graybill , Pittenger Duane C. Boes, 3^rd Edition, Reprinted( 2005 ), ’Introduction to the Theory of Statistics’, McGraw-Hill.
2. Ronald E. Walpole, Raymand H..Mayers, 9^th Edition (2012), ‘Probability and Statistics for Engineers and Scientists’.
3. George Casella and Roger L. Berger, Statistical Inference, 2^nd edition (2001), Cengage Learning

Course Code

: STAT 22632

Title

: Survey Methods and Sampling Techniques

Learning Outcomes:

At the completion of this course student will be able to:

• Identify the main variables to achieve the objectives of a given study and design a survey for a given scenario.
• Apply the most appropriate sampling techniques to obtain a representative sample.
• Conduct a survey for a given scenario and analyze the result.
• Communicate the findings of a survey to a diverse audience.

Course Content:

Survey Methods: General concepts of surveys, Introduction to different survey methods, Advantages and disadvantages of the methods,

Principal steps in a sample survey: Techniques of data collection, Questionnaire design, Validation and reliability, Selection of proper sample design, determination of sample size, Organization of fieldwork, Pilot survey, Analyze the results, and draw conclusions                                                

Sampling Techniques: Introduction and terminology, Probability vs Non-Probability Sampling, Probability Sampling Techniques: Simple Random Sampling, Stratified Random Sampling, Systematic sampling, Cluster sampling, and Multistage sampling. Sample Size Calculations

Workshop  with hands on experience.

Method of Teaching and Learning:
A combination of lectures, tutorials, Group project.

Assessment:
Group project, Assignments and End of Semester examination.

Recommended Reading:

1. Cochran, W.G., 3^rd Edition, (1977), ‘Sampling Techniques’ , John Wiley & Sons
2. Scheaffer, R.L., Mendenhall, W., Ott, R.L., Gerow, K.G.,  7^th Edition (2011), ‘Elementary Survey Sampling’,  Cengage Learning
3. Barnet, V., 3^rd Edition, (1974),‘Elements of Sampling Theory’ , London: The English University Press Ltd
4. Sharon Lohr, 2nd Edition, (2010), 'Sampling: Design and Analysis', Cengage.

Course Code

: STAT 22642

Title

: Statistical Inference II

Learning Outcomes:

At the completion of this course student will be able to:

• Describe the logical arguments underlying the theory of statistical hypothesis testing
• Perform fundamental hypothesis tests, with due regard to the underlying assumptions
• Carry out tests of goodness-of-fit, association, and independence
• Interpret the conclusions of the statistical hypothesis test to communicate the results of statistical inference effectively

Course Content:

Introduction and terminology to hypotheses: null hypothesis, alternative hypothesis, test statistic, rejection region, significance level, type I error, type II error, power, p-value; Simple null hypothesis versus simple alternative hypothesis, Simple Likelihood ratio test.

Most Powerful Test: Definition, Neyman-Pearson criteria, Neyman-Pearson Lemma.

Composite Hypotheses: Generalized Likelihood ratio test, Uniformly most powerful test.

Sampling from the Normal Distribution: Tests on the mean, Test on the variance, Tests on several variances, Pairwise comparisons, Tests on the several Means, Tests on Binomial proportion,

Power functions and sample size calculations

Chi-square Tests:  Contingency tables, tests of goodness-of-fit, association, and independence

Method of Teaching and Learning:
A combination of lectures and tutorials, Statistics Camp (Outbound training program).

Assessment:
Assignments and End of Semester examination, Activities conducted at the Statistics Camp.

Recommended Reading:

1. Alexander M. Mood, Franklin A..Graybill , Pittenger Duane C. Boes, 3^rd Edition, Reprinted ( 2005 ), ’Introduction to the Theory of Statistics’, McGraw-Hill.
2. Ronald E. Walpole, Raymand H. Mayers, 9^th Edition (2012), ‘Probability and Statistics for Engineers and Scientists’.
3. George Casella and Roger L. Berger, Statistical Inference, 2^nd edition (2001), Cengage Learning.

Course Code

: STAT 22651

Title

: Statistical Programming

Learning Outcomes:

At the completion of this course student will be able to:

• use techniques provided in statistical packages to solve real-world problems.
• design and write functions in R and implement simple iterative algorithms.
• create Documents using R Markdown
• present statistical analysis, in both written and oral form

Course Content:

Introduction to R, R language Syntax and Fundamentals, Special Functions,

Data Management: Data types and data structures, Entering and Importing Data, Convert between types and set the display formats for different variables, Changing the layout of a dataset and related functions, Reshape data between long and wide forms, Merging, Appending and Collapsing datasets, Stratification and perform operations separately for subgroups

Descriptive Statistics: Perform descriptive analysis using R, Create sophisticated figures and graphs

Statistical Inference: Fitting a suitable probability distribution for data, statistical inferences using R, power and sample size calculations using R

Functions in R: Control structures, Conditional statements, implement iterative algorithms.

Statistical communication: Documentation and reports, Dashboards
Workshop on Python

Method of Teaching and Learning:
Lecture cum demonstration, Laboratory work.

Assessment:
End of course practical examination and assignments.

Recommended Reading:

1. An Introduction to R, Notes on R: A Programming Environment for Data Analysis and Graphics Version 4.0.5 (2021-03-31) https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
2. Wickham, H., & Grolemund, G. 1st Edition, (2016). R for data science: import, tidy, transform, visualize, and model data. " O'Reilly Media, Inc.".
3. Moore, D. S., & Kirkland, S. (2007). The basic practice of statistics (Vol. 2). New York: WH Freeman.

Course Code

: STAT 31622

Title

: Design and Analysis of Experiments

Learning Outcomes:

At the completion of this course student will be able to:

• Identify the best possible experimental design for a given scenario.
• Develop an understanding of the concepts of analysis of variance. 
• Apply theory and methods to obtain objective conclusions of a variety of applications.
• Evaluate designs using common optimality criteria and use them to critically compare competing designs.

Course Content:

Introduction: Introduction to experimental design, the difference between an experiment and an observational study, Basic definitions, Principles of randomisation, replication and stratification, practical applications.

General theory of designs: Completely randomized design, randomized block design, lattin-square design, missing observations, Model adequacy checking.

Analysis of variance for one-way classification and two-way classification, Multiple comparisons.

Introduction to Factorial Designs:  fractional factorials

Method of Teaching and Learning:
A combination of lectures, and tutorials, field visits.

Assessment:
Assignments,  field visits  report, and End of course examination.

Recommended Reading:

1. Hicks C. R., Turner K. V., 5^th Edition, (1999),’Fundamental Concepts in Design of Experiments’, Oxford University Press.
2. Douglas C. Montgomery, 5^th Edition, (2001), ‘Design and Analysis of Experiments’, John Wiley & Sons, Inc.

Course Code:

: STAT 31613

Title

: Regression Analysis

Learning Outcomes:

At the completion of this course student will be able to:

• Identify the main variables of interest in a regression problem.
• Effectively apply regression techniques to build statistical models for real-life problems.
• Assess the assumptions underlying regression models.
• Use the model to make precise predictions.

Course Content:

Simple linear regression: Parameter estimation, Gauss-Markov Theorem, Statistical inferences, Prediction, Analysis of variance approach, Regression in matrix form, Model adequacy, Lack of fit. 

Multiple linear regression: Parameter estimation, Statistical inferences, Model adequacy, diagnostics for leverage and influential observations, 

Multicollinearity, Heteroscedasticity, Transformations, Prediction, Variable selection and model building procedures, categorical predictor variables, interaction terms.

Non-Linear Regression: Parameter Estimation in a Nonlinear System, Statistical Inference in Nonlinear Regression, logistic regression.

Method of Teaching and Learning:
A combination of lectures, tutorials, group activities.

Assessment:
Assignments, and End of Semester examination.

Recommended Reading:

1. Montgomery, D.C., Peck, E. A. and Vining, G. G. 5^th Edition, (2012), ‘Introduction to Linear Regression Analysis’, John Wiley & Sons.
2. Draper, N.R and Smith, H. 3^rd Edition, (1998), ‘Applied Regression Analysis’, John Wiley & Sons.
3. Chatterjee, S., Hadi, A. S. 5^th Edition, (2013), ‘Regression Analysis by Example’, John Wiley & Sons.
4. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, (2009), 2nd Edition, Springer Series in Statistics

Course Code

: STAT 31642

Title

: Applied Time Series Analysis  

Learning Outcomes:

At the completion of this course student will be able to:

• Recognize time series processes and their characteristics.
• Fit suitable models to the real world time series data 
• Use forecasting techniques to predict future values of the model under consideration. 
• Use statistical software for predictive inferences.

Course Content:
An introduction to Time Series, Stationary time series, Components of a time series,  Estimation and Elimination of Trend and Seasonal Components

Simple forecasting techniques: Moving averages, Exponential smoothing, Holt-Winters procedure, 

Introduction to Auto-Covariance function, Auto-Correlation function, Partial autocorrelation function

Models of time series: Autorgressive models, Moving Average models, Auto Regressive Moving Average models, and Autoregressive Integrated Moving Average models, Seasonal ARIMA models

Tentative identification of a model for a real-world time series and estimation of model parameters using R software, Model checking and forecasting, Case Studies. 

Method of Teaching and Learning:
A combination of lectures tutorials, practical assignments.

Assessment:
practical sessions using software, Assignments, and End of Semester examination

Recommended Reading:

1. Brockwell and Davis, 2^nd Edition, (1991), ‘Time Series- Method and Forecasting’, Springer.
2. Box and Jenkins, (1976), ‘Time Series Analysis’, John Willy.
3. DeLurgio, S.A., (1998), ‘Forecasting Principles and Applications’, McGraw Hill.
4. Chatfield, C., 2^nd Edition, (1980), ‘Analysis of Time Series’, Chapman-Hall. 
5. Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci, (2015) Introduction to Time Series Analysis and Forecasting, 2nd Edition, Wiley

Course Code

: STAT 31631

Title

: Statistical Modeling 

Learning Outcomes:

At the completion of this course student will be able to:

• Fit a suitable statistical model for a real world scenario using statistical software
• Draw conclusions about association and causation.
• Assess underlying assumptions of statistical models.
• Evaluate the validity and accuracy of fitted statistical models.

Course Content:

Introduction to statistical software,  

Preprocessing: missing values, extreme values, smoothing, standardizing

Simple linear regression, Multiple linear regression: Parameter estimation, Statistical inferences, Model adequacy, diagnostics for leverage and influential observations, Multicollinearity, Heteroscedasticity, Transformations, Prediction, Variable selection, and model building procedures, categorical predictor variables, interaction terms.

Design and Analysis of Experiments: Completely randomized design, randomized block design, lattin-square design, missing observations, Model adequacy checking, Analysis of variance for one-way classification and two-way classification, Multiple comparisons.

Numerical solution of nonlinear equations, Interpolation, Non-Linear Regression, Case studies

Method of Teaching and Learning:
Lecture cum demonstration, Laboratory work, project based learning.

Assessment:
End of course practical examination and assignments, group activities.

Recommended Reading:

1. Douglas C. Montgomery, 5^th Edition, (2001), ‘Design and Analysis of Experiments’, John Wiley & Sons, Inc.
2. Montgomery, D.C., Peck, E. A. and Vining, G. G. 5^th Edition, (2012), ‘Introduction to Linear Regression Analysis’, John Wiley & Sons.
3. Field, A. P. (2009). Discovering statistics using SPSS: (and sex and drugs and rock 'n' roll). Los Angeles, Thousand Oaks, Calif.: SAGE Publications.

Course Code

: STAT31653

Title

: Introduction to Economics

Learning Outcomes:

At the completion of this course student will be able to:

• Describe and explain how microeconomic models can be used to consider fundamental economic choices of households and firms.
• Describe and explain how macroeconomic models can be used to analyse the economy as a whole.
• Describe and explain how government policy influences microeconomic choices and macroeconomic outcomes.
• Formulate economic arguments and understand the role of argument and evidence in the
  policy-making process.
• Interpret and use economic models, diagrams and tables and use them to analyse economic situations.

Course Content:

Fundamentals of microeconomics: The nature, scope and methodology of economics, Supply and demand, Market equilibrium, Elasticity: price elasticity, income elasticity of demand, cross price elasticity; Consumer choice: Utility function, Consumer equilibrium; Production and cost: production function and cost function; Market models and optimal choice: Perfect competition, Monopoly, Monopolistic competition and Oligopoly; Factor market.

Fundamentals of macroeconomics: Introduction to macroeconomics: National accounting, Introduction to Classical, Keynesian, Neoclassical and Monetarist theories, Inflation, Unemployment, Balance-of-payments, Aggregate demand and Aggregate supply, Economic growth. Multiplier, Monetary and fiscal policies in both open and closed economy

Method of Teaching and Learning:
A combination of lectures, tutorials and quizzes.

Assessment:
Assignments, Midterm exam and End of Semester written exam
Midterm exam - Short Paper (maximum of 500 words)An empirical report discussing a current economic issue with quantitative descriptive analysis and, a brief forecast and evaluation of the economic consequences of that issue.

Recommended Reading:

1. Open Textbook Library, (2012). Principles of economics.
2. Kahneman, D., & Overdrive Inc. (2011). Thinking, Fast and Slow. S.I.: Farrar, Straus and Giroux.
3. Samuelson, Paul A.  (1973). Economics.  New York :  McGraw-Hill.
4. Dutta, S. (2006). Introductory economics (micro and macro): A textbook for class XII. New Delhi: New Age International (P) Ltd., Publishers.

Course Code

: STAT 32682

Title

: Statistical Simulation

Learning Outcomes:

At the completion of this course student will be able to:

• Apply methods for random number generation.
• Use simulation for statistical inferences.
• Design suitable simulation models using Monte Carlo simulation.
• Develop suitable simulation models for real-world scenarios.

Course Content:

Introduction: Introduction & overview of simulation,  

Modeling & estimating input processes

Random number generation-  Properties, pseudo-random numbers, Middle-Square Method, Linear Congruential Generators

Generation of discrete and continuous random variates-inverse transform method, acceptance-rejection method

Statistical analysis of simulation Output-Comparison, ranking, and selection of simulation models, Variance reduction techniques,

Designing simulation experiments, Monte Carlo Simulation, 

Discrete event simulation: Single server and two server queuing system, inventory models

Resampling Techniques: Introduction to Bootstrap, Bootstrap estimation of variance and confidence intervals.

Method of Teaching and Learning:
A combination of lectures and practical sessions using statistical software. 

Assessment:
Assignments and End of Semester examination.

Recommended Reading:

1. Ross, S. M. (2012). Simulation (5th edition). Elsevier.
2. Banks, J. (2005). Discrete event system simulation. Pearson Education India.
3. Kroese, D. P., Taimre, T., & Botev, Z. I. (2013). Handbook of Monte Carlo methods (Vol. 706). John Wiley & Sons.
4. Law, A. M., Kelton, W. D., & Kelton, W. D. (2000). Simulation modeling and analysis (Vol. 3). New York: McGraw-Hill.

Course Code

: STAT 32652

Title

: Statistical Process Control

Learning Outcomes:

At the completion of this course student will be able to:

• Describe the concepts underlying statistical quality control.
• Apply the techniques to design and improve the quality control processes in industries.

Course Content:

Definition and Terminology: Definitions of Quality, Dimensions of Quality, Quality Characteristics, Quality Costs, Quality Assurance, Philosophies

Concepts of Statistical Quality Control: Chance Causes and Assignable Causes, Magnificent seven, Sample size and Sampling frequency, Rational Subgrouping, 

Control Charts for Variables: Control charts for the mean and range, Control charts for mean and standard deviation, Changing sample size on control charts, Control Chart for individual measurements

Control Charts for Attributes: Control Chart for Fraction Nonconforming, Control Chart for Number Nonconforming

Further Aspects in Quality Control: Process Capability Ratios, Acceptance sampling, Average Run Lengths, OC-curves, Process curve, Methods of choosing sampling plans, Cumulative sum charts, Decision rules, Continuous sampling plans, Process troubleshooting. 

Method of Teaching and Learning:
A combination of lectures and tutorials, Field visit, Guest lectures from industry professionals, practical sessions using software.

Assessment:
Assignments, Field visit report, and End of Semester examination.

Recommended Reading:

1. Douglas C. Montgomery, 8^th Edition, (2019), Introduction to Statistical Quality Control’, John Wiley and Sons
2. Qiu, P. (2014), Introduction to statistical process control, Boca Raton, FL: CRC Press

Course Code

: STAT 32663

Title

: Corporate Capstone Project

Learning Outcomes:

At the completion of this course student will be able to:

• Practice the scientific research conduct and the associated ethics.    
• Plan work effectively by setting appropriate targets and monitoring progress. 
• Execute research projects collaboratively towards achieving common objectives timely. 
• Communicate findings to diverse audiences.
• Apply own learning and put strategies in place to improve own learning. 

Course Content:

Perform the steps in completing scientific research:

 Research ethics, Problem identification, literature survey, research proposal, relevant informal and formal methodologies, analysis and results interpretation, implementation, and validation.

Presentations and publications: Types of reports, referencing guidelines, presentation preparations, and skills.

Professional skills: Independent and collaborative work, leadership and interpersonal skills, teamwork, Working ethics

Workshop on Statistical Data Mining

Method of Teaching and Learning:

Combination of seminars and workshops,Professional training program: Statistical communication, consultancies, project management.

Assessment:
Continuous assessment based on presentations, reports , and skill development programs, Mini project.

Recommended Reading:

1. Janice R. Matthews, Robert W. Matthews, Successful Scientific Writing: A Step-by-Step Guide for the Biological and Medical Sciences (3rd Edition)
2. John Creswell, J. David Creswell, (2017) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, (5th Edition) Sage Publications

Course Code

: STAT 32672

Title

: Nonparametric Statistics

Learning Outcomes:

At the completion of this course student will be able to:

• Compare parametric and nonparametric statistical approaches
• Solve various real-life problems using appropriate nonparametric techniques

Course Content:

Introduction: Nonparametric statistics, Nonparametric estimation of distribution functions and quintiles, A confidence band for Confidence intervals for the distribution function at a fixed point, Confidence intervals for quantiles using order statistics,  

Goodness-of-Fit Tests: Simple Goodness-of-fits tests using the Empirical CDF, Chi-Square-type Goodness-of-fit tests, Probability Plotting, and Quantile-Quantile Plots,

Tests based on Signs, Runs and Ranks: Two sample test procedures, Paired sample procedures, Ranks, The Wilcoxon Signed-Rank Test, The General Two-Sample Problem, The Wald-Wolfowitz Runs Test, The Wilcoxon Rank-Sum Test, Median Test, Sign Test, Nonparametric Behrens-Fisher problem in paired data 

Nonparametric Behrens-Fisher Problem: Brunner-Munzel test,  

Measures of Association: Towards General Measures of Association, Kendall's Tau, Spearman correlation, Kruskal-Wallis test, and multiple testing procedures.

Method of Teaching and Learning:
A combination of lectures and practical sessions using statistical software. 

Assessment:
Assignments and End of Semester examination.

Recommended Reading:

1. Alexander M. Mood, Franklin A. Graybill, Pittenger Duane C. Boes, 3^rd Edition,  Reprinted ( 2005 ), ‘Introduction to the Theory of  Statistics’,  McGraw-Hill.
2. Higgins, J. (2003). ‘Introduction to Modern Nonparametric Statistics’, Duxbury.
3. Larry Wasserman, (2006), ‘All of Nonparametric Statistics’.
4. Ronald E. Walpole, Raymand H..Mayers, 6^th Edition,(1997), ‘Probability and Statistics for Engineers and Scientists’.
5. Brunner, E., Bathke, A. C., & Konietschke, F. (2018). Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs. Springer International Publishing 

Course Code

: STAT 41613

Title

: Stochastic Processes I

Learning Outcomes:

At the completion of this course student will be able to:

• demonstrate the importance of stochastic models in day-to-day life,
• Describe the Markov chains and their properties
• construct appropriate stochastic models for real-life situations giving the better representation, description, and specification

Course Content:
Introduction: Rationale for learning Stochastic Processes, Role of stochastic models in the study of Physical, Biological, Social and Economic systems encounter in day-to-day life,

Stochastic Processes in General: States and the State Space, Parameter Space and a realization of a Stochastic Process, Classification of Stochastic Processes, Probability Distribution of a Stochastic Process, Transition Probability Distributions, Markov Dependence of a Stochastic Process, Markov Processes and Chapman Kolmogorov Equation.

Markov Chain: Two-state Markov chains, Two-state Markov process as a limiting case of a Two-state Markov chain,

Classification of States: Classification of states according to the external nature of the states, Classification of states according to the internal nature of the states,

Limit theorem on Markov Chain, Periodicity, Limits of the Higher Probabilities,

Finite Markov Chains: One-step and n-step Transition Probability Distributions, Irreducible Aperiodic finite Markov chains, Finite Markov Chains with Transient and Recurrent States.

Limiting and stationary distributions of Markov chains, Applications of Markov chains

Method of Teaching and Learning:
A combination of lectures, group work, and tutorials. 

Assessment:
End of course examination and assignments.

Recommended Reading:

1. Narayan Bhat, U., (1972), ‘Elements of Applied Stochastic Processes’, John Wiley & Sons Inc.
2. Bailey, N. T. J., 1^st Edition 3^rd Reprint (1967), ‘The Elements of Stochastic Processes’, John Wiley & Sons Inc.
3. Medhi, J. 1^st Edition Reprint (1991), ‘Stochastic Processes’, Wiley Eastern Ltd.
4. Hoel, P. G., Port, S. C., Stone, C. J., (1994), ‘Introduction to Stochastic Processes’, Houghton Miffin Company.
5. Feller, W., 2^nd Edition Reprint (1966), Volume I, ‘An Introduction to Probability Theory and Its Applications’, John Wiley & Sons Inc.
6. Sheldon M. Ross (2019), Introduction to Probability Models 11th Edition by, Elsevier Inc.

Course Code

: STAT 44623

Title

: Advanced Optimization

Learning Outcomes:

At the completion of this course student will be able to:

• Solve linear programming problems with bounded constraints.
• Explain mathematical concepts of solving parametric linear programming problems.
• Find appropriate trade-off solutions for multi-objective decision-making problems in production systems, supply chain systems, and specific operational problems.
• Analyze the effectiveness of solution methods for continuous optimization problems.

Course Content:
Advanced Linear Programming: Simplex Method Fundamentals: From Extreme points to Basic Solutions, Generalized Simplex Tableau in Matrix Form;

Revised Simplex Method: Development of the Optimality and Feasibility Conditions, Revised Simplex Algorithm;

Bounded Variable Algorithm; Duality: Matrix Definition of the Dual Problem, Optimal Dual Solution; Parametric Linear Programming: Parametric Changes in Cost Vector C, Parametric Changes in Resources Vector b

Goal Programming: A Goal Programming Formulation, Goal Programming Algorithms;

Integer programming: Illustrative applications;

Integer Programming Algorithms: Branch and Bound Algorithm, Cutting Plane Algorithm;

Deterministic Dynamic Programming(DP): Recursive Nature of Comparison in Dynamic Programming; Forward and Backward Recursion; Selected DP Applications;

Non Linear Programming: Unconstrained Algorithms, Constrained Algorithms

Method of Teaching and Learning:
A combination of lectures, tutorials, and group work.

Assessment:
Assignments and End of Semester examination.                                   

1.  Bazaraa, M.S., Jarvis, J.J., Sherali, H.D., 3^rd   edition, (2004), ‘Linear Programming and Network Flows’, Wiley, New York.
2. Papaddimitriou, C.H., Steiglitz, K., Unabridged edition, (1998), ‘Combinatorial Optimization: Algorithm and Complexity’, Dover Publications.

Course Code

: STAT 44633

Title

: Bayesian Inference & Decision theory

Learning Outcomes:

At the completion of this course student will be able to:

• explain the basic concepts of Bayesian inference
• solve real-world problems related to Bayesian Inference and Decision Theory.
• simulate posterior distributions using statistical software

Course Content:

The Basics of Bayesian Statistics: Bayes' Rule, Discrete example of Bayes' Rule, Continuous example of Bayes' Rule, Bayesian vs. frequentist definitions of probability.

Introduction to Bayesian inference: Prior distribution, Posterior distribution, Summarizing the posterior.

Bayesian inference for discrete random variables with discrete priors: Binomial data, Poisson data

Bayesian inference for discrete random variables with continuous prior: Bayesian inference for parameters of binomial and Poisson distributions, Point estimation, credible intervals, highest posterior density interval, hypothesis testing, Comparing Bayesian and Frequentist Inferences

Choice of priors: Conjugate Priors, non-informative priors, Improper Priors, Jeffreys prior, Conjugate prior distributions with exponential families

Bayesian inference for normal mean:  inference with a discrete and continuous prior, Choosing your normal prior, Bayesian credible interval for the normal mean, Predictive density for next observation, Bayesian Inference for Difference between Means.

Sampling from the posterior distribution: Maximum a posteriori estimation, Markov Chain Monte Carlo (MCMC) methods, Posterior predictive checking

Method of Teaching and Learning:
A combination of lectures and tutorials.

Assessment:
Assignments and End of semester examination.                           

1. William M.B., 2^nd Edition, (2004), ‘Introduction to Bayesian Statistics’, John Wiley & Sons.
2. Reich B. J. and Ghosh S. K. (2019). Bayesian Statistical Methods, 1st Edition. Chapman & Hall/CRC Texts in Statistical Science (ISBN: 9780815378648)
3. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis. (3rd ed ed.).

Course Code

: STAT 42643

Title

: Advanced Topics in Time Series Analysis

Learning Outcomes:

At the completion of this course student will be able to:

• Derive the key theories of time series processes
• fit suitable models to the multivariate time series data,
• Apply forecasting techniques to predict future values of real-valued data
• Use statistical software for predictive inferences of multivariate time series

Course Content:
Stationary processes: Basic Properties of stationary processes, Linear Processes Introduction to ARMA Processes, Properties of the Sample Mean and Autocorrelation Function(ACF), Estimation of the sample mean, autocovariance(ACVF), and autocorrelation function

ARMA processes: Calculation of ACVF and ACF of ARMA processes, The Partial Autocorrelation Function,

Forecasting ARMA processes, parameter estimation, Diagnostic Checking, Order Selection,

Nonstationary and Seasonal Time Series Models: Regression with ARMA Errors

Multivariate Time Series: Vector Autoregressions, Testing for Joint Covariance Stationarity, Granger Causality. Introduction to Cointegration.

Co-integration: Introduction, The Relationship between Co-integration and Correlation, Implications of Co-integration, Tests for Co-integration, Models for dynamic relationships between returns in co-integrated systems.

Transfer Function Models and Intervention Models

Modern trends in time series analysis

Method of Teaching and Learning:
A combination of lectures, tutorials, quizzes

Assessment:
Assignments and End of Semester examination.

Recommended Reading:

1. Peter J. Brockwell Richard A. Davis, 2^nd Edition, (2002), ‘Introduction to Time Series and Forecasting’, Springer.
2. Box and Jenkins, (1976), ‘Time Series Analysis’, John Willy.
3. DeLurgio, S.A., (1998), ‘Forecasting Principles and Applications’, McGraw Hill.
4. Chatfield, C., 2^nd Edition, (1980), ‘Analysis of Time Series’, Chapman-Hall.
5. Gebhard Kirchgässner, Jürgen Wolters, Introduction to Modern Time Series Analysis
6. William W. S. Wei, Multivariate Time Series Analysis and Applications, ISBN: 978-1-119-50285-2

Course Code

: STAT 42653

Title

: Stochastic Processes II

Learning Outcomes:

At the completion of this course student will be able to:

• Explain the difference between a discrete-time and a continuous-time Markov Chain
• demonstrate the importance of birth and death processes and queuing systems in day-to-day life
• construct appropriate stochastic models for real-life situations giving the better representation, description, and specification

Course Content:

Infinite Markov Chains: Irreducible Aperiodic Infinite Markov Chains, Queuing Processes, Non-irreducible Infinite Markov Chains, Branching Processes.

Homogeneous and inhomogeneous linear difference equations and the standard procedure to solve them

The difference between a discrete-time and a continuous-time Markov chain, the concept of a rate matrix

Markov Processes with Discrete State Space: Exponential distribution and the concept of a homogeneous Poisson process, and derive the form of the distribution of the inter-arrival times, the expected length and waiting time of a Poisson process

Pure Birth Processes, Pure Death Processes and, Birth and Death Processes, stationary distribution

Brownian motion and its applications

Method of Teaching and Learning:
A combination of lectures, group work, and tutorials. 

Assessment:
Assignments and End of course examination.

Recommended Reading:

1. Narayan Bhat, U., (1972), ‘Elements of Applied Stochastic Processes’, John Wiley & Sons Inc.
2. Bailey, N. T. J., 1^st Edition 3^rd Reprint (1967), ‘The Elements of Stochastic Processes’, John Wiley & Sons Inc.
3. Medhi, J. 1^st Edition Reprint (1991), ‘Stochastic Processes’, Wiley Eastern Ltd.
4. Sheldon M. Ross (2019), Introduction to Probability Models 11th Edition
5. By, Elsevier Inc.
6. Resnick,S. I., (2002), ‘Adventures in Stochastic Processes’, Birkhäuser, Boston, MA

Course Code

: STAT 42663

Title

: Generalized linear models

Learning Outcomes:

At the completion of this course student will be able to:

• Explain the underlying assumptions of generalized linear models
• apply generalized linear modeling techniques to appropriate data analysis
• perform diagnostic checks whilst identifying potential problems
• Perform statistical analysis using statistical software, incorporating underlying theory and methodologies.

Course Content:
An overview of linear statistical models and their generalizations

models with various link functions and link distributions such as models for discrete data, Contingency tables,

Testing goodness of fit of a model, Association, and independence in multidimensional tables. Methods for two binomial variates

Logit models for categorical data

 Methods for log-linear models for multiway contingency tables, Fitting logit and log-linear models, Selection of a model,

Analysis of a given set of data using generalized linear models using a statistical software

Method of Teaching and Learning:
A combination of lectures, tutorials, and Practical sessions.

Assessment:
Assignments and End semester  examination including a practical component.                               

1. E. Fienberg, 2^nd Edition, (1980), ‘The analysis of cross- classified categorical data’, New York Springer
2. Aqresti, 3^rd Edition, (2013), ‘Categorical data analysis’, John Wiley& sons.
3. Collet, 2^nd Edition, (2003) ‘Modelling Binary data’, Chapman & Hall

Course Code

: STAT 44673

Title

: Multivariate Data Analysis

Learning Outcomes:

At the completion of this course student will be able to:

• recognize multivariate data and identify contexts where multivariate data analysis techniques should be used,
• use the tools of multivariate normal distribution for inference on population means
• analyze the correlation structure of multivariate data using the appropriate techniques.
• use statistical software packages for the analysis of multivariate data

Course Content:
Introduction to multivariate data: Display multivariate data in a variety of graphical ways(two dimensional scatter plots, box plots for groups ) and interpret such displays
Statistical distance measures, Random vectors, and matrices

Multivariate Statistical Inference: Multivariate normal distribution and its properties, Testing hypotheses on single population means

Inference about the mean vector

Comparison of two multivariate population means,

paired comparisons and repeated measure design

Multivariate Analysis of Variance (MANOVA),

Methods of dimension reduction: Principal component analysis, Factor analysis, canonical variates, Discriminant data analysis

Method of Teaching and Learning:
A combination of lectures and tutorials.

Assessment:
Assignments and End of course examination.

Recommended Reading:

1. Richard A. Johnson and Dean W. Wichern, 4^th edition, (1998), ‘Applied Multivariate Statistical Analysis’, Prentice Hall
2. Morrison, D. F., 4^th Edition, (2004), ‘Multivariate Statistical Methods’, Duxbury Press

Course Code

: STAT 44683

Title

: Advanced Design and Analysis of Experiments

Learning Outcomes:

At the completion of this course student will be able to:

• Construct optimal designs for a range of practical experiments
• apply statistical techniques to analyze the outcome of the experimental design.
• check diagnostics to compare the models 

Course Content:
Experiments with a Single Factor: The Analysis of Variance, Analysis of the Fixed Effects Model, Model Adequacy Checking, Practical Interpretation of Results, Determining Sample Size, The Regression Approach to the Analysis of Variance,

Randomized Blocks, Latin Square and Related Designs: The Randomized Complete Block Design, The Latin Square Design, repeated measures, Balanced Incomplete Block Design,

Factorial Designs: Basic Definitions and Principles, The Advantage of Factorials, The Two-Factor Factorial Design, The General Factorial Design, Blocking in a Factorial Design. The 2^k Factorial designs: the 2² Design, the 2³ Design, and The General 2^k Design.

Random Effects Models, Nested and Split-Plot Designs

Method of Teaching and Learning:
A combination of lectures and tutorials.

Assessment:
Assignments and End of course examination.

Recommended Reading:

1. Hicks C. R., Turner K. V., 5^th Edition, (1999),’Fundamental Concepts in Design of Experiments’, Oxford University Press.
2. Douglas C. Montgomery, 5^th Edition, (2001), ‘Design and Analysis of Experiments’, John Wiley & Sons, Inc.

Course Code

: STAT 44694

Title

: Industrial Training

Learning Outcomes:

At the completion of this course student will be able to:

• demonstrate an understanding of the industry/business environment in which the employing company or organization operates
• explain the purpose of his/her role within the context of the business and the contribution it makes to the organization as a whole
• Explain the role of the organization in the related disciplines of Statistics, Data Science, and Operations Research.
• Describe the structure of the organization and the purpose/role of each department and key function within the organization
• Identify the purpose and activities undertaken by key individual roles within the organization
• Demonstrate employability skills by working effectively and applying acquired knowledge to solve real-world problems
• communicate effectively both orally and in writing for a given audience
• Identify the primary policies in operation at the employing organization and evaluate their effectiveness

Course Content:
Industrial placement in an appropriate organization to achieve the aforementioned intended learning outcomes at least five months.

Method of Teaching and Learning:
Training under the supervision and guidance of a qualified professional in the industry and mentor from the University. Progress is monitored by the mentor biweekly.

Assessment:
Evaluation of the monthly progress reports and progress presentations, the evaluation report submitted by the industrial supervisor at the end of the training.                        

Recommended Reading:

• Reading and reference material recommended by the industrial supervisor and the mentor.

Course Code

: STAT 44713

Title

: Actuarial  Mathematics 

Learning Outcomes:

At the completion of this course student will be able to:

• describe the underlying principles of survival models
• apply the key concepts of survival models in contingency payments
• apply survival models in the formation of complex insurance policies

Course Content:
Principles of Survival models: Age-at-death random variable, Time-until-death random variables, Force of mortality, Parametric survival models such as De Moivre’s (Uniform), Exponential, Weibull, Makeham, Gompertz, Generalization of De Moivre’s, Curtate future lifetime.

Life Tables: tabulation of basic mortality functions, deriving probabilities/expectations from a life table, Relationships to survival functions, Assumptions for fractional (non-integral) ages, Select and ultimate tables.

Insurance Benefits: Basics of Life insurance, benefits payable contingent upon death, payment made to a designated, beneficiary, actuarial present values (APV), actuarial symbols and notation, Insurances payable at the moment of death and at the end of year of death for cases of discrete, continuous and varying benefits.

Life Annuities: Types of annuities, discrete - due or immediate, payable more frequently than once a year, continuous, varying payments, Current payment techniques, APV formulas.

Premium Calculation: contract premiums, net premiums, gross premiums, the present value of future loss random variable, premium principles, the actuarial equivalence principle, portfolio percentile premiums, return of premium policies.

Method of Teaching and Learning:
A combination of lectures, tutorials, and group work.

Assessment:
Assignments and End of Semester examination.

Recommended Reading:

1. D. Promislow, 1^st Edition, (2006), Fundamentals of Actuarial Mathematics, John Wiley and Sons.
2. Dickson, M. Hardy and H. Waters, Actuarial Mathematics for Life Contingent Risks, second edition, Cambridge University Press, 2013.
3. Camilli, S., Duncan, I. and R. London, 6th edition, Models for Quantifying Risk, ACTEX Publications, 2014
4. Hans U. Gerber, 3^rd Edition, (1997), ‘Life Insurance Mathematics’,
5. Bowers, N.L., Gerber, H.U., Hickman, J.C., Jones, D.A. and C.J. Nesbitt, 2nd edition, Actuarial Mathematics, Society of Actuaries, 1997.

Course Code

: STAT 44723

Title

: Econometrics

Learning Outcomes:

At the completion of this course student will be able to:

• Describe the concepts in econometrics models
• Apply the appropriate techniques in modeling economic data
• use statistical software packages in Econometrics
• Communicate the results and implications of econometric analysis.

Course Content:
Introduction to Econometrics models: Absolute Income Hypothesis (AIH), Improving AIH, Elasticity, Marginal Propensity to Save, Expenditure multiplier, Cobb-Douglas production function, and other new econometrics models.

Relaxing the assumptions of the classical model: Multicollinearity and micronumerosity, Heteroscedasticity, Auto-correlation,

Regression in Econometrics: Regression models on dummy independent variables, Regression models on dummy dependent variables, Dynamic econometric model, Simultaneous-Equation models.

Time-series Econometrics: Models of volatility, Multivariate time series models

Method of Teaching and Learning:
A combination of lectures tutorials and practical sessions, Group Project.

Assessment:
Assignments,  and End semester  examination including a practical component, Group project report, and presentation.

Recommended Reading:

1. Domodar N. Gujarat., 4^th Edition, (2002), ’Basic Econometrics’, McGraw-Hill.
2. Jeffrey M. Wooldridge, 6th Edition, (2016), ‘Introductory Econometrics: A Modern Approach’, Cengage Learning
3. William H. Greene., 5^th edition, (2002), ‘Econometric Analysis’, Prentice Hall.
4. G.S., 3^rd Edition, (2001), ‘Introduction to Econometrics’, Wiley

Course Code

: STAT 44733

Title

: Special Topics in Statistics

Learning Outcomes:

At the completion of this course student will be able to:

• describe the concepts and tools of the selected special topic in Statistics
• demonstrate applications of concepts associated with the topics studied, as discussed by the course convener.
• conduct seminar presentations on the selected special topic.

Course Content:
This course is designed to include specialized modern topics in Statistics and content therein.

* Hourly Breakdown and the assessments percentages are subject to change according to the selected topic. The expected minimum percentages are mentioned here.

Method of Teaching and Learning:
A combination of lectures/lectures cum practical, tutorials, and group activities or seminars by students.

Assessment:
End of semester examination/ peer observation of seminars, assignments, and group activities.
Note: Assessment strategy will be subjective to the selected topic and its content.                            

Recommended Reading:

• Required reading material will be recommended by the lecturer depending on the relevant topic.

Course Code

: STAT44743

Title

: Statistical Data Mining

Learning Outcomes:

At the completion of this course student will be able to:

• develop an understanding of wide variety of statistical learning algorithms
• apply appropriate data mining techniques to solve real-world problems
• evaluate the performance of different data-mining algorithms.
• recommend proper learning algorithms for a given application

Course Content:
Introduction: Introduction to data science and data mining, Misconception about data mining, Data Mining Process (Knowledge Discovery), Introduction to a suitable data mining software, applications of data mining.

Concepts of Statistical learning: Data splitting criteria, Performance measures, n-fold cross-validation, Challenges, and remedies: class imbalance problem, overfitting, undersampling, oversampling.

Supervised learning/predictive modeling: Classification (Classification Tree, Artificial Neural Network, Naïve Bayes Classifier, Support Vector Machine) and Regression (Artificial Neural Network, Regression Tree, Lasso regression, Ridge regression, Elastic Net)

Unsupervised learning: Classification (Self-Organizing-Maps), Clustering (k-means clustering, Hierarchical clustering).

Ensemble learning techniques: Introduction to ensemble methods, Random Forest, Bagging and Boosting.

Modern trends in data mining.

Method of Teaching and Learning:
A combination of lectures, tutorials, and practical sessions.

Assessment:
Assignments and End semester  examination including a practical component.                     

Recommended Reading:

• Han, J., Kamber, M., Pei, J., 3^rd Edition, (2012), ‘Data mining Concepts and Techniques’, Morgan Kaupmann-Elsevier.
• Witten, I. H., Frank, E., Hall, M. A. 3^rd Edition, (2011), ‘Data Mining Practical Machine Learning Tools and Techniques’, Morgan Kaupmann-Elsevier.
• Larose, D. T., Larose, C.D., 2^nd Edition, (2014), ‘Discovering Knowledge in Data: An Introduction to Data Mining, Wiley.
• Bishop, C. M. (1995), ‘Neural Networks for Pattern Recognition, Clarendon Press - Oxford.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning ( 2^nd) [PDF]. Springer.

Course Code

: STAT 44758

Title

: Research Project/ Independent Study

Learning Outcomes:

At the completion of this course student will be able to:

• describe and interpret real-world problems in quantitative measures towards finding solutions using statistical approaches.
• use appropriate statistical techniques for formulating and analyzing real-world problems
• conduct a proper literature survey to obtain the critical information relevant to the research problem
• develop theoretical/conceptual framework towards achieving the research objectives
• Working independently to critically analyze an extensive dataset
• communicate the information, ideas, issues, problems, and solutions to specialists as well as non-specialist audiences
• conduct the research project by practicing research ethics
• demonstrate awareness of current issues and developments related to Statistics.
• exercise initiative, personal responsibility, and accountability

Course Content:
Teaching /Learning Methods: A project/ study project under the supervision of a senior staff member/s of the department.

Method of Teaching and Learning:
A project/ study project under the supervision of a senior staff member/s of the department.

Assessment:
Minimum three interim presentations and thesis defense.
A dissertation should be submitted and the results should be presented at a seminar. The work will be assessed on the dissertation and the seminar.                              

Recommended Reading:

• Required reading material will be recommended by the supervisor depending on the relevant project/study