B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science




^ B – ELECTIVE COURSES :


Subject

Code

Title of the Course

L

T

P

C

MSI E101

Operations Research

3

0

0

3

MSI E102

Actuarial Statistics

3

0

0

3

MSI E103

Statistical Genetics

3

0

0

3

MSI E104

Markov Chain and its Applications

3

0

0

3

MSI E106

Statistical Methods for Epidemiology

3

0

0

3

MSI E107

Stochastic Modeling

3

0

0

3

MSI E108

Non B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science parametric inference

3

0

0

3

MSI E109

Data Mining Tools

3

0

0

3

MSI E110

Bayesian Inference

3

0

0

3

MSI S111 *

Statistics for Social Sciences

3

0

0

3

MSI S112 *

Bio-Statistics

3

0

0

3



* TO OTHER DEPARTMENTS ONLY



MSI C101

Real Analysis

C

3

1

0

4

Guest Faculty

Pre-requisite : Undergraduate level Mathematics.


Unit I : Recap B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science of elements of set theory; introduction to real numbers, introduction to n-dimensional Euclidian space; open and closed intervals (rectangles), compact sets, Bolzano – Weirstrass theorem, Heine – Borel theorem.


Unit II : Sequences B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science and series; their convergence. Real valued functions, continuous functions; uniform continuity, sequences of functions, uniform convergence ; power series and radius of convergence.


Unit III : Differentiation, maxima – minima of functions; functions of several B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science variables, constrained maxima – minima of functions.


Unit IV : Riemann integral & Riemann – Stieltjes integral with respect an increasing integrator – properties of R.S. integral –integrators of bounded variation.


Unit V : Multiple integrals and B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science their evaluation by repeated integration, change of variables in multiple integration. Uniform convergence in improper integrals, differentiation under the sign of integral – Leibnitz rule.


REFERENCES :


Apostol, T.M. (1985) : Mathematical Analysis, Narosa, Indian Ed B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science.

Bartle,R.G., Sherbert, D.R.(1982) : introduction to Real analysis.

Malik, S.C.(1985) : Mathematical analysis, Wiley Eastern Ltd.

Royden, H.L.(1995) : Real analysis, 3ed., Prentice Hall of India.

Rudin, Walter (1976) : Principles B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science of Mathematical Analysis, McGraw Hill.

Rangachari,M.S.(1996) : Real Analysis, Part 1, New Century Book House.



MSI C102

Linear Algebra

C

3

1

0

4

Ms. M.R. Sindhumol

Pre-requisite : Undergraduate level Mathematics.

Unit 1 : Vector spaces, Linear dependence, linear B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science independence, basis and diversion of vector space, inner product Gram Schmidt orthogonalization, linear transformations, projection operators, null space and nullity.


Unit II : Matrix algebra, rank and inverse of a matrix, determinants B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, characteristic roots, characteristic polynomial, Cayley Hamilton theorem, multiplicity of characteristic roots, idempotent matrix.


Unit III : Reduction of matrices, Echelon form, Hermite canonical form, diagonal reduction, rank factorization, triangular reduction Jordan form, pairs of symmetric matrices B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, singular value decomposition, spectral decomposition.

Unit IV : Kronecker product of matrices matrix differentiation, generalized inverse, Moore-Penrose inverse and properties of g-inverse, Application of g-inverse.


Unit V : Quadratic forms, classification B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, definiteness, index and signature, extremum of quadratic forms, reduction of quadratic form, transformation, applications of quadratic forms.


REFERENCES :


Bellman, R. (1970) : Introduction to Matrix Analysis, 2nd ed. McGraw Hill.

Biswas, S. (1984) : Topics B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science in Algebra of Matrices, Academic Publications.

David, A.Harville(1997) : Matrix algebra from a statistician’s perspective, Springer.

Hadley, G. (1987) : Linear Algebra, Narosa Publishing House.

Hoffman, K. and Kunze, R. (1971) : Linear Algebra B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, 2nd ed. Prentice Hall, Inc.

Graybill, F.A. (1983) : Matrices with application in Statistics, 2nd ed. Wadsworth.

Rao, C.R. & Bhimasankaran, P.(1992) : Linear algebra, Tata McGraw Hill Pub. Co. Ltd.

Searle, S B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science.R. (1982) : Matrix Algebra useful for Statistics, John Wiley and Sons, Inc.


MSI C103

Distribution Theory

C

3

1

0

4

Guest Faculty

Pre-requisite : Undergraduate level Mathematics.


Unit I : Brief review of distribution theory, functions of random variables and their distributions B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science using Jacobian of transformation, Laplace and Caushy distribution, lognormal distribution, gamma, logarithmic series.


Unit II : Bivariate normal, Bivariate exponential, Bivariate Poisson, Compound, truncated and mixture of distributions, concepts of convolution B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science.


Unit III : Sampling distributions, non-central chi-square distribution, t and F distributions and their properties, distributions of quadratic forms under normality and related distribution theory – Cochran’s and James theory.


Unit IV B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science : Order statistics their distributions and properties, Joint and marginal distributions of order statistics, extreme value and their asymptotic distributions, approximating distributions of sample moment, delta method.


Unit V : Kolmogorov Smirnov distributions, life distributions B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, exponential, Weibull and extreme value distributions Mills ratio, distributions classified by hazard rate.


REFERENCES :


Gibbons(1971) : Non-parametric inference, Tata McGraw Hill.

Rohatgi, V.K. and Md. Whsanes Saleh, A.K.(2002) : An introduction to B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science probability & Statistics, John Wiley and Sons.

Rao, C.R. (1973) : Linear statistical inference and its applications, 2ed, Wiley Eastern.

Mood,A.M. & Graybill, F.A. and Boes, D.C. : Introduction to the theory B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science of statistics, McGraw Hill.

Johnson,S. & Kotz,(1972): Distributions in Statistics, Vol. I, II & III, Hougton & Miffin.

Dudewicz, E.J., Mishra, S.N.(1988) : Modern mathematical statistics, John Wiley.

Searle, S.R.(1971) : Linear B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science models, John Wiley.



MSI C104

Measure Theory

C

3

1

0

4

Dr. G.Gopal/Guest Faculty

Pre-requisite : Undergraduate level Mathematics.


Unit I : Sets and set functions, Algebra of sets, limits of sequence of sets, classes of sets B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science : Ring, Field, Field and monotone classes, Generated classes.


Unit II : Measure functions, properties of measure functions, Outer measure, extension and completion of measures signed measures, Hahn Decomposion theorem.


Unit III : Lebesgue, Stieltjes B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science measures, examples, measurable functions, approximation theorems.


Unit IV : Measure integration, properties of measure integrals, Monotone convergence theorem and dominated convergence theorem, Fatou’s lemma.


Unit V : Absolute continuity, Radon Nikodymn theorem, singularity, Lebesgue B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science Decomposion theorem, Fubini’s theorem, convergence types for measurable functions (almost everywhere, in mean and their inter-relationships).


REFERENCES :


Munroe, M.E. (1971) : Measure and integration, 2nd ed. Addision Wesley B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science.

Ash, R.B. (1972) : Real analysis and probability, Academic press.

Kingman, J.F.C. and Taylor, J. (1973) : Introduction to measure and probability, Cambridge University Press.

Royden, H.L. (1968) : Real analysis, 2nd ed. Macmillan.

Loeve, M B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science. (1960) : Probability theory, Van Nostrand.

Halmos, P.R. (1974) : Measure theory, East-West.

De Barr, G. (1987) : Measure theory and integration, Wiley Eastern.



MSI C105

Probability Theory

C

3

1

0

4

Dr.G.Gopal/ Guest Faculty

Pre-requisite : Measure Theory B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science.


Unit I : Events, sample space, different approaches to probability, random variables and random vector, Distribution functions of random variables and random vector, Expectation and moments, basic, Markov, Chebyshev’s, Holder’s, Minkowski’s and B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science Jensen’s inequalities.


Unit II : Independence of sequence of events and random variables, conditional probability, conditional expectation, smoothing properties, Tail-sigma field, 0-1 law of Borel and Kolmogorov, Hew itt B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science-Savage 0-1 law.

Unit III : Characteristic functions and their properties, inversion formula, convergence of random variables, convergence in probability, almost surely, in the r-th mean and in distribution, their relationships, convergence of moments, Helly-Bray B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science theorem, continuity theorem and convolution of distributions.


Unit IV : Convergence of series of random variables, three-series theorem, Khintchines weak law of large numbers, Kolmogorov inequality, strong law of large numbers.


Unit B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science V : Central limit theorem, statement of CLT, Lindeberg, Levy and Liapounov forms with proof and Lindeberg Feller’s form examples.


REFERENCES :


Bhat, B.R. (1985) : Modern probability theory, 2nd ed B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science. Wiley Eastern.

Chow, Y.S. and Teicher, H. (1979) : Probability theory, Springer Verlag.

Ash Robert, B. (1972) : Real analysis and probability, Academic Press. 3rd ed.

Chung, K.L. et al : A course in probability theory B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, Academic press.

V.K.Rohatgi etal(2002) : An introduction to probability and statistics, John Wiley.

Parthasarthy, K.R. (1977) : Introduction to probability and measure, MacMillan Co., Breiman, L. (1968) : Probability, Addison Wesley.



MSI C106

Sampling B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science theory

C

3

1

0

4

Dr.M.R.Srinivasan

Pre-requisite : Undergraduate level Mathematics.


Unit I : Review of basic finite population sampling techniques SRS, Stratified, Systematic sampling, related results on estimation, allocation problem in stratification sampling, efficiency B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science of systematic over stratified and SRS.


Unit II : Varying probabilities, PPS WR/WOR ordered and un-ordered estimator, selection of samples Horowitz Thompson, Desraj, Rao Hartley-Cochran estimators.


Unit III : Sampling with supplementary information B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, Ratio and regression estimators and related results.


Unit IV : Multi stage and multiphase sampling, two stage sampling with equal number of second stage under-double sampling cluster sampling.


Unit V : Non sampling B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science errors, errors in surveys (Types of Errors), Observational errors (Measurement and related results, Incomplete samples (Non-response Politz and summary randomized response technique, Introduction to Jackknife and bootstrap techniques.


REFERENCES B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science :


Cochran, W.G. (1977) : Sampling Techniques 3rd ed., Wiley.

Des Raj and Chandak (1988) : Sampling Theory, Narosa.

Murthy, M.N. (1977) : Sampling theory and methods. Statistical publishing society, Calcutta.

Sukhatme & Sukhatme (1984) : Sampling theory of surveys B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science with applications. ISAS.

Singh, D. and Chaudhary, F.S. (1986) : Theory and Analysis of Sample Survey Designs, New Age International Publishers.



MSI C107

Statistical Estimation Theory

C

3

1

0

4

Dr.G.Gopal

Pre-requisite : Probability Theory.


Unit I B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science : Sufficient statistics, Neyman, Fisher Factorisation theorem, the existence and construction of minimal sufficient statistics, Minimal sufficient statistics and exponential family, sufficiency and completeness, sufficiency and invariance.


Unit II : Unbiased estimation : Minimum variance B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science unbiased estimation, locally minimum variance unbiased estimators, Rao Blackwell – theorem.Completeness- Lehmann Scheffe theorems, Necessary and sufficient condition for unbiased estimators

Unit III : Cramer- Rao lower bound, Bhattacharya system of lower bounds in the B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science 1-parameter regular case. Chapman -Robbins inequality.


Unit IV : Maximum likelihood estimation, computational routines, strong consistency of maximum likelihood estimators, Asymptotic Efficiency of maximum likelihood estimators, Best Asymptotically Normal estimators, Method of moments.


Unit V B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science : Bayes’ and minimax estimation : The structure of Bayes’ rules, Bayes’ estimators for quadratic and convex loss functions, minimax estimation, interval estimation.


REFERENCES :


V.K.Rohatgi etal(2002) : An introduction to probability and B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science statistics, John Wiley.

Lehmann, E.L. (1983) : Theory of point estimation, John Wiley.

Zacks, S. (1971) : The theory of statistical inference, John Wiley.

Rao, C.R. (1973) : Linear statistical inference and its applications, Wiley Eastern, 2nd B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science ed.

Ferguson, T.S. (1967) : Mathematical statistics, A decision theoretic approach, Academic press, New York and London.

Lindley, D.V. (1965) : Introduction to probability and statistics, Part 2, Inference, Cambridge University Press.



MSI C108

Practical B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science – I (Calculator Based)

C

2

0

0

2

All Faculty


Practical Exercises based on MSI C102, MSI C103, MSI C106 and MSI C107



MSI C109

Multivariate Analysis

C

3

1

0

4

Guest Faculty

Pre-requisite : Distribution theory.


Unit I : Random sampling from B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science a multivariate normal distribution. Maximum likelihood estimators of parameters. Distribution of sample mean vector. Wishart matrix – its distribution and properties. Distribution of sample generalized variance.


Unit II : Null and non-null distribution of B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science simple correlation coefficient. Null distribution of partial and multiple correlation coefficient. Distribution of sample regression coefficients. Application in testing and interval estimation. Distribution of sample intra – class correlation – coefficient in B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science a random sample from a symmetric multivariate normal distribution. Application in testing and interval estimation.


Unit III : Null distribution of Hotelling’s T2 statistics. Application in tests on mean vector for one B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science and more multivariate normal populations and also on equality of the components of a mean vector in a multivariate normal population.


Unit IV : Multivariate linear regression model – estimation of parameters, tests of linear hypotheses about B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science regression coefficients. Likelihood ratio test criterion. Multivariate Analysis of variance (MANOVA) of one-and two-way classified data.


Unit V : Classification and discrimination procedures for discrimination between two multivariate normal B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science populations – sample Discriminant function, tests associated with Discriminant functions, probabilities of misclassification and their estimation, classification into more than two multivariate normal populations.


Principal components, Dimension reduction, Canonical variables and canonical B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science correlation – definition, use, estimation and computation.


REFERENCES :


Anderson, T.W. (1983) : An introduction to multivariate statistical analysis. 2nd ed.Wiley. (study)

Giri, N.C. (1977) : Multivariate statistical inference, Academic press.

Kshirsagar, A.M. (1972) : Multivariate analysis, Marcel B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science Dekker.

Morrison, D.F. (1976) : Multivariate statistical methods, 2nd ed. McGraw Hill.(study)

Muirhead, R.J. (1982) : Aspects of multivariate statistical theory, Wiley.

Rao, C.R. (1973) : Linear Statistical Inference and its applications, 2nd B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science ed. Wiley.

Seber, G.A. (1984) : Multivariate observations, Wiley.

Sharma, S. (1996) : Applied multivariate techniques, Wiley.

Srivastava, M.S. and Khatri, C.G. (1979) : An introduction to multivariate statistics. North Holland.

Johnson,R.& Wichern(1992) : Applied multivariate B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science statistical analysis, Prentice Hall, 3ed.(study).



MSI C110

Testing Statistical Hypotheses

C

3

1

0

4

Dr.G.Gopal

Pre-requisite : Probability Theory .


^ Unit I : Uniformly most powerful tests, the Neyman-Pearson fundamental Lemma, Distributions with monotone likelihood ratio.Problems


Unit B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science II : Generalization of the fundamental lemma, two sided hypotheses, testing the mean and variance of a normal distribution.

Unit III : Unbiased ness for hypotheses testing, similarly and completeness, UMP B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science unbiased tests for multi parameter exponential families, comparing two Poisson or Binomial populations, testing the parameters of a normal distribution (unbiased tests), comparing the mean and variance of two normal distributions.


^ Unit B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science IV : Symmetry and invariance, maximal invariance, most powerful invariant tests.


Unit V : SPRT procedures, likelihood ratio tests, locally most powerful tests, the concept of confidence sets, non parametric tests.


REFERENCES :


V.K.Rohatgi etal(2002) : An B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science introduction to probability and statistics, John Wiley.

Lehmann, E.L. (1986) : Testing of statistical hypothesis, John Wiley.

Ferguson, T.S. (1967) : Mathematical statistics, A decision theoretic approach, Academic press.

Rao, C.R. (1973) : Linear B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science statistical inference and its applications, Wiley Eastern, 2nd ed.

Gibbons, J.D. (1971) : Non-parametric statistical inference, McGraw Hill.



MSI C111

Design and Analysis of Experiments

C

3

1

0

4

Dr.M.R.Srinivasan

Pre-requisite : Matrix algebra B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science & Linear models.


Unit I : Linear models, classification, linear estimators, Gauss-Markov theorem, BLUE, test of general linear hypothesis, fixed, mixed and random effects models.


Unit II : Review of basic designs: CRD, RBD, LSD B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, Orthogonal latin squares, Hyper Graeco Latin squares – analysis of variance – analysis of covariance – multiple comparisons – multiple range tests - Missing plot technique – general theory and applications.


Unit III : General factorial experiments B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, factorial effects; best estimates and testing the significance of factorial effects ; study of 2 and 3 factorial experiments in randomized blocks; complete and partial confounding. Fractional replication for symmetric factorials. Sprip plot and split B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science block experiments.


Unit IV : General block design and its information matrix (C), criteria for connectedness, balanced and orthogonality; intrablock analysis (estimability, best point estimates / interval estimates of estimable linear parametric functions and B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science testing of linear hypotheses) : BIBD – recovery of interblock information; Youden design – intrablock analysis.


Unit V : Response surface methodology - first order and second order rotatable designs, applications: clinical trials.


REFERENCES :


Das, M.N B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science. and Giri, N. (1979) : Design and analysis of experiments, Wiley Eastern.

John, P.W.M. (1971) : Statistical design and analysis of experiments, Macmillan.

Montgomery, C.D. (2001) : Design and analysis of experiments, John Wiley, New York.

Friedman B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, L.M., Furberg, C.D., Demets, D.L.(1998) : Fundamentals of clinical trials, Springer.

Robert, O., Kuelhl(2000) : Design of experiments. Statistical principles of research design and analysis, Duxbury.

Federer, W.T.(1963) : Experimental B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science design; Theory and application, Oxford & IBH publishing Co.

Doshi, D.D. (1987) : Linear estimation and design of experiments, Wiley Eastern Ltd.



MSI C112

Statistical Quality Management

C

3

1

0

4

Ms.M.R.Sindhumol

Pre-requisite : Undergraduate level Statistics B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science.


Unit I : Concept of quality – definition and standardization of quality – Functional elements of TQM, quality movements in India, quality circle, quality audit, Direct and indirect quality costs, measurement and analysis – Pareto B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science and Ishikawa diagrams, ISO 9000 series.


Unit II : General theory and review of control charts for attribute and variable data; O.C. and A.R.L. of control charts; Moving average and exponentially weighted B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science moving average charts; Cu-sum charts using V-masks and Economic design of X-bar chart.


Unit III : Acceptance sampling plans for attribute inspection ; single, double and sequential sampling plans and B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science their properties. Plans for inspection by variables for one-sided and two-sided specifications; Mil-Std and IS plans.


^ Unit IV : continuous sampling plans for Dodge type and Wald-Wolfiwitz type and their properties B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, chain sampling plan..


Unit V : Capability indices Cp, Cpk and Cpm; estimation, confidence intervals and tests of hypotheses relating to capability indices for Normally distributed characteristics. Use of Design of Experiments in B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science SPC, factorial experiments.


REFERENCES :


Montgomery, D.C. (2001) : Introduction to Statistical Quality Control, John Wiley.

Ott,E.R. (1975) : Process quality control, McGraw Hill.

Grant, L. and Leavenworth, S. (1996) : Statistical quality control B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science, McGraw Hill.

Murthy, M.N. (1989) : Excellence through quality & reliability, Applied statistical centre.

Thomas P.Ryan(2000) : Statistical methods for quality improvement 2ed., John Wiley.


MSI C113

Practical – II (Calculator Based)

C

0

0

2

2

Ms. M.R. Sindhumol


^ Practical B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science Exercises based on MSI C109, MSI C110, MSI C111, MSI C112 and MSI C113



MSI C114

Practical – III (Software Based)

C

0

0

2

2

Dr. M.R. Srinivasan


Use Statistical packages like SPSS, MINITAB / S-PLUS for solving B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science statistical problems in Core and Electives. Exercises will be prepared by the faculty in-charge.



MSI C115

Project Work / Dissertation

C

0

6

0

6

All Faculty




MSI C116

Reliability and Survival Analysis

C

3

1

0

4

Dr. G.Gopal

Pre-requisite : Probability Theory B – ELECTIVE COURSES - 10. school of mathematics, statistics and computer science.


Unit I : Introduction to Survival concepts, Survival functions and hazard rates, concepts of Type I, Type II, Random and other types of censoring, likelihood in these cases.

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