Dependencies: IS2002

Syllabus: 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.

Evaluation Criteria: End-of-semester examination (70%), Continuous assessments (30%)

Suggested Readings: Sampling (2e) - by Steven K. Thompson Practical sampling Techniques by Ranjan Kumar Som Hand book of Sampling Techniques and Analysis by Poduri Rao

Dependencies: None

Syllabus: Introduction, Relationship between two variables, Linear regression, Theory of Regression, Analysis of Variance, R-squared, Diagnostics, Matrix approach to regression, Multiple regression, Partial F-test, Lack of fit and pure error, Methods of model selection

Evaluation Criteria: End-of-semester examination (70%), Continuous assessments (30%)

Suggested Readings: Applied Regression Analysis by Draper & Smith Applied Linear Regression by Sanford Weisberg

Dependencies: None

Syllabus: Method of bootstrap : introduction, bootstrap standard error, parametric and non-parametric bootstrap estimates, applications to time series and regression models, bias estimation, improved estimate of bias through resampling vector; jackknife estimate of bias, adjustment for consistency; bootstrap confidence intervals; testing hypotheses using permutation tests and bootstrap methods; testing multi-modality. The EM algorithm. The Gibbs sampler and Metropolis algorithm.

Evaluation Criteria: End-of-semester examination (70%), Continuous assessments (30%)

Suggested Readings: “An introduction to the bootstrap (1993) by Efron andTibshirani. Tools for Statistical Inference by Martin, J. Tanner.

Dependencies: None

Syllabus: 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 ? and ?; two independent samples, profile analysis, repeated measurements, manova, canonical variates analysis, canonical correlations.

Evaluation Criteria: End-of-semester examination (70%),Continuous assessments (30%)

Suggested Readings: Applied Multivariate Methods for Data Analysts (1998) - D.E. Johnson (Duxbury). Applied Multivariate Statistical Analysis ( 3rd Edition) (1992) By R.A. Johnson and D.W. Wichern. Computer Aided Multivariate Analysis by Afifi, Clark, and May (Chapman and Hall).

Dependencies: None

Syllabus: Introduction to Generalised Linear models, Modelling Binary data, Modelling categorical data, Overdispersion.

Evaluation Criteria: End-of-semester examination (70%), Continuous assessments (30%)

Suggested Readings: An Introduction to Generalised Linear Models by A. J. Dobson Generalised Linear Models by McCullagh, P. and Nelder, J. A.

Dependencies: None

Syllabus: Traditional time series modelling methods; exponential smoothing (simple and double). Trends, Seasonality, Stationarity, Auto correlation function, Wald's representation of a linear discrete time process. Stationary Time series Models; Autoregressive, Moving Average, Auto regressive-moving average models. Non-stationary time series models, transformations (Box-Cox), Forecasting; Preliminary Model identification, Model Estimation, Diagnostic Checking. Seasonal time series model building.

Evaluation Criteria: End-of-semester examination (70%), Continuous assessments (30%)

Suggested Readings: The analysis of time series : An introduction (6th Ed.) by Chris Chatfield Time Series Analysis by Jonathan Cryer Time Series Analysis (Univariate and Multivariate) (3rd Ed.) by William Wei Introduction to Time Series Analysis (2nd Ed.), by Peter Brockwell and Roger Davies

Dependencies: None

Syllabus: Formulating integer programming problems related to finance. Using Lingo and Access databases to solve integer programming problems related to capital budgeting problem. Short term financial planning problem, Fixed charge problem, Risk insurance problem, Cutting stock problem, Problems related to cost curves, Traveling salesperson problem, Port folio optimization problem. The branch and bound method. The implicit enumeration method using dual simplex algorithm. The cutting plane algorithm. Formulating and solving Dynamic programming problems related to finance. The Wagner - Whitin algorithm. Forward recursion. Using spreadsheet to solve Dynamic programming problems in finance.

Evaluation Criteria: End of semester examination

Dependencies: None

Syllabus: The economics of insurance, Individual risk models for a short term, Collective risk models for a single period, Collective risk models over an extended period, Application of risk theory, Insurance models including expenses, Non-forfeiture benefits and dividends, Special annuities and insurance, Advance multiple theory.

Evaluation Criteria: End of semester examination

Dependencies: None

Syllabus: Plain curves : Conics, parametric equations, polar coordinates and graphs. Vector : Vectors in plane and space, Dot and Cross products, Lines, planes and surfaces in space, Cylindrical and Spherical coordinates. Vector valued functions : Differentiation and integration, Tangent and normal vectors, Arc length and curvature. Functions of several variables : Graphs of surfaces, Limits and continuity, Partial derivatives and differentiability, Linear approximation and error bounds, Chain rule, Directional derivatives and gradients, Tangent planes and normal lines, Extrema of functions of two variables and applications, Lagrange multipliers. Multiple integration : Iterated integrals, Double integrals and volumes, Change of variables and polar coordinates, Surface area, Triple integrals, Change of variables. Vector analysis : Vector fields, Line integrals, Conservative vector fields, Green's theorem, Surface integrals, Divergence theorem, Stoke's theorem.

Evaluation Criteria: End of semester examination

Dependencies: None

Syllabus: Expected learning outcomes: Understanding of advance topics in Numerical Methods Linear multi-step method of solving ordinary differential equations, consistency, stability and convergence, Numerical methods of solving a linear system of equations, sparse systems, direct and iterative methods, convergence, Interpolation techniques of higher order accuracy.

Evaluation Criteria: End of semester examination

Dependencies: None

Syllabus: Introduction to: Static and Dynamic games with complete information, Static games with incomplete information, Payoff matrix, applications. Static games with complete information : Standard games, zero-sum games, Prisoner's dilemma, battle of sexes, coordinate games, Chicken or Hark versus Dove. Basic Theory and Applications : Normal form games, Nash equilibrium, Iterated elimination of strictly dominated strategies. Cournot Model of Duopoly, Bertrand Model of Duopoly. Mixed strategies : Game theory applications in industry, politics, etc..

Evaluation Criteria: End of semester examination

Dependencies: MS2004

Syllabus: Multivariate Techniques including cluster, factor, conjoint, correspondence mapping, multiple regression, Data Fusion, Forecasting techniques including ARIMA Media Research: Definitions, specialized data collection methods, media terminology & measurement, media planning including introduction to proprietary software for pre & post evaluation of media schedules, Pricing Research: Types of pricing models & their application, Test Marketing & Simulated Test Marketing, Report Writing

Evaluation Criteria: End-of-semester examination (70%), Continuous assessments (30%)

Suggested Readings: Research for Marketing Decisions by Paul Green & Donald Tull “Marketing Research" by Harper Boyd, Ralph Westfall & Stanley Stasch “Essentials of Marketing Research" by V. Kumar, David Aaker & George Day

Dependencies: MS1003

Syllabus: Network Models: Minimal Spanning tree, shortest route problem, Project Planning and implementation, Inventory Models and OR packages

Evaluation Criteria: End-of-semester examination (70%), Continuous assessments (30%)

Suggested Readings: Operational Research an Introduction by Hamdy A. Taha, Introduction to Operations Research by Hillier and Lieberman

Dependencies: None

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

Evaluation Criteria: End-of-semester examination (70%), Continuous assessments (30%)

Suggested Readings: Econometric Model and Economic Forecasts by Robert Pindyck and Daniel Rubinfeld Introductory Econometrics: A Modern Approach by Jeffrey Wooldridge Econometric Analysis by Greene, W.G Applied Econometric time series (2nd Ed.) by Walter Enders

Dependencies: None

Syllabus: Accounting Theory and Financial Statements : Basic principles, Ledger accounting and Control accounts, Bank reconciliation, Intangibles, Suspense accounts, Trading, Profit and Loss accounts, Balance sheet, Trial balance, Income and Expenditure accounts, Incomplete records, Using Financial accounting packages. Cost Accounting Cost classification, Materials and Stocks control, Labour cost allocation and Overheads classification and analysis, Absorption and Marginal costing, Manufacturing and Departmental accounts, Budgets and budgetary control, Standard costing and variances, Integrated accounting systems and using Cost accounting packages.

Evaluation Criteria: End of semester examination