Syllabus
Syllabus mentioned in ERP
APPLIED MULTIVARIATE STATISTICAL MODELLING â I4-0-0 4Definitions and basic
concepts of multivariate modelling â variate, type of variables, measurement
scale, measurement error, multivariate measurement; types of multivariate
techniques, classification of multivariate techniques, guidelines for
multivariate analysis, structured approach to multivariate model building, and
cases for multivariate modelling.Multivariate basics â multivariate descriptive
statistics, statistical distance, multivariate normal distribution and its
properties, examining data and outliers detection, and multivariate sampling
distributions.Comparison of several multivariate means â paired
comparisons.Multivariate modelling of variance (MANOVA) â Univariate procedure,
objectives, design issues and assumptions, estimation of MANOVA model, goodness
of fit, interpretation of results, validations, and case examples. Multiple
linear regressions - Objectives, design and assumptions, estimating the
regression model and assessing overall model fit, interpreting the regressing
variate, validation of results, stepwise and hierarchical regression, and case
examples. Principal component analysis â Objectives, population principal
components, method of estimation, interpretation of components, and case
examples.Discriminant analysis â Objective, design and assumptions, estimating
discriminant model and assessing overall fit, results and validations, case
examples.Factor analysis - Objectives, design issues and assumptions,
orthogonal factor model, method of estimation, principal component analysis,
maximum likelihood method, factor rotation, factor scores, interpretation of
factors, and case examples.Path Analysis and Structural equation modelling â
Developing modelling strategy, confirmatory and competing models, stages in
structural equation modelling, developing a theoretically based model,
constructing path diagram, converting path diagram to structural equations,
input matrix, measurement model, LISREL, goodness of fit criteria, and case
examples.Hand on experience through EXCEL, MATLAB and SPSS.Textbooks and
Referencesa.Johnson R.A. and Wichern D.W., Applied Multivariate Statistical
Analysis, Pearson Education, Delhi, 2002, 767 pp. b.Hair J.F., Anderson R.E.,
Tatham R.L., Black W.C., Multivariate data analysis with readings, Prentice
Hall, Englewood Cliffs, New Jersey 07632, 1995, 745 pp. c.Agresti A. Analysis
of ordinal categorical data, John Wiley and Sons, New York, 1984, 287 pp.
d.Anderson S., Aquier A., Hauck W.W., Oakes D., Vandaele W., and Weisberg,
H.I., Statistical methods for comparative studies, John Wiley and Sons, New
York, 1980, 287 pp.
Concepts taught in class
Student Opinion
How to Crack the Paper
| Whatever he teaches in class are available in NPTEL lectures, go through them during the entire semester. Solve enough questions before going to the exam. Almost 60% of the paper are numerical in which you will get super confused if you haven't practiced. 40% paper is theory in which you will be easily able to score if you have followed his lectures.
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Classroom resources
Additional Resources
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