CN4011 Advanced Data Analysis Syllabus:
CN4011 Advanced Data Analysis Syllabus – Anna University PG Syllabus Regulation 2021
OBJECTIVE:
To learn concepts of data for construction management.
To learn concepts of various data analysis.
To learn concepts of regression and factor analysis.
To learn concepts of discriminant and cluster analysis.
To learn concepts of advanced multivariate data analysis techniques
UNIT I STATISTICAL DATA ANALYSIS
Data and Statistics- Review of Basic Statistical Measures-Probability Distributions-Testing of Hypotheses-Non-Parametric Tests.
UNIT II BASIC CONCEPTS
Introduction – Basic concepts – Uni-variate, Bi-variate and Multi-variate techniques – Types of multivariate techniques – Classification of multivariate techniques – Guidelines for multivariate analysis and interpretation – Approaches to multivariate model building.
UNIT III REGRESSION AND FACTOR ANALYSIS
Simple and Multiple Linear Regression Analysis – Introduction – Basic concepts – Multiple linear regression model – Least square estimation – Inferences from the estimated regression function – Validation of the model. Factor Analysis: Definition – Objectives – Approaches to factor analysis – methods of estimation – Factor rotation – Factor scores – Sum of variance explained – interpretation of results. Canonical Correlation Analysis – Objectives – Canonical variates and canonical correlation – Interpretation of variates and correlations.
UNIT IV DISCRIMINANT AND CLUSTER ANALYSIS
Discriminant Analysis – Basic concepts – Separation and classification of two populations – Evaluating classification functions – Validation of the model. Cluster Analysis – Definitions – Objectives – Similarity of measures – Hierarchical and Non – Hierarchical clustering methods – Interpretation and validation of the model.
UNIT V ADVANCED TECHNIQUES
Conjoint Analysis – Definitions – Basic concepts – Attributes – Preferences – Ranking of Preferences – Output of Conjoint measurements – Utility – Interpretation. Multi-Dimensional Scaling – Definitions – Objectives – Basic concepts – Scaling techniques – Attribute and Non-Attributes based MDS Techniques – Interpretation and Validation of models. Advanced Techniques – Structural Equation modeling
OUTCOME:
On completion of the course, the student is expected to be able to
CO1 Describe the different statistical analysis techniques.
CO2 Students will be able to formulate hypothesis
CO3 Explore the basic concepts of statistical analysis
CO4 Develop regression and factor analysis model and its interpretation
CO5 Create discriminant and cluster analysis model and its interpretation
REFERENCES:
1. Joseph F Hair, Rolph E Anderson, Ronald L. Tatham& William C. Black, Multivariate Data Analysis, Pearson Education, New Delhi, 2015.
2. Barbara G. Tabachnick, Linda S. Fidell, Using Multivariate Statistics, 6th Edition, Pearson, 2012.
3. Richard A Johnson and Dean W. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall, New Delhi, 2012.
4. David R Anderson, Dennis J Sweeney and Thomas A Williams, Statistics for Business and Economics, Thompson, Singapore, 2002.
5. Howard E.A. Tinsley & Steven D. Brown, Handbook of Applied Multivariate Statistics & Mathematical modeling, Academic Press, 2000.