ABOUT THE COURSE:Multivariate Procedures with R is designed to equip the learners with necessary theory and application of multivariate procedures in academic, research, and business domains with appropriate examples. The comprehensive content is designed to manually compute various multivariate statistics for learning and R-based computations for application. An important highlight of the course is reporting the results in formal documents such as research reports, journal articles, and class projects.INTENDED AUDIENCE: Students, research scholars, faculty, industry personnelPREREQUISITES: Basic knowledge of statistics. The course will provide a review of the basic concepts of statisticsINDUSTRY SUPPORT: The course will be useful to businesses and service industries
Overview
Syllabus
Week 1:Basic fundamentals, Introduction to working in R, Brief introduction to commands in R, data editing, R Studio, use of R as a calculator.
Week 2:Calculations with Data Vector, Built in function, Matrix operations, Univariate statistical measures for central tendency and variations.
Week 3:Handling bivariate data, Missing data handling, Measuring central tendency and variation with missing data.
Week 4:Introduction of coefficient of variation, Data frames, Box Plots, and Plots for discrete and continuous data.
Week 5:Two and three-dimensional plots for univariate, bivariate and multivariate data, univariate, bivariate and multivariate random variables, univariate normal distribution.
Week 6:Bivariate and multivariate normal distribution, Sampling distributions.
Week 7:Regression analysis, scaling of variables.
Week 8:Scaling of variables.
Week 9:Principle component analysis.
Week 10:Discriminant analysis.
Week 11:Canonical correlation analysis.
Week 12:Cluster analysis.
Lecturewise plan
Week 2:Calculations with Data Vector, Built in function, Matrix operations, Univariate statistical measures for central tendency and variations.
Week 3:Handling bivariate data, Missing data handling, Measuring central tendency and variation with missing data.
Week 4:Introduction of coefficient of variation, Data frames, Box Plots, and Plots for discrete and continuous data.
Week 5:Two and three-dimensional plots for univariate, bivariate and multivariate data, univariate, bivariate and multivariate random variables, univariate normal distribution.
Week 6:Bivariate and multivariate normal distribution, Sampling distributions.
Week 7:Regression analysis, scaling of variables.
Week 8:Scaling of variables.
Week 9:Principle component analysis.
Week 10:Discriminant analysis.
Week 11:Canonical correlation analysis.
Week 12:Cluster analysis.
Lecturewise plan
Taught by
Prof. Shalabh
