Welcome!
Syllabus
Basic Statistics: Frequency table, histogram, measures of location, measures of spread, skewness, kurtosis, percentiles/quantiles, box plot, correlation, probability distribution as a statistical model, fitting probability distributions, empirical distribution, checking goodness of fit through plots and tests
Basic Multivariate Statistics: Representation of multivariate data (covered via NA); conditional distribution, bivariate distribution, multivariate distribution; multinomial distribution, multivariate normal distribution, sample mean (vector) and sample variance-covariance matrix, basic tests, concepts of location and depth in multivariate data
Structures in multivariate data: Principal components analysis (PCA), factor analysis (FA), multidimensional scaling (MDS), multiple correlation (MC)
Rescheduled/NO lectures:
August 27 at 11:15am -> August 30 at 11:15am
September 18 at 4:05pm -> September 20 at 4:05pm
October 15 at 11:15am -> October 18 at 11:15am
November 12 at 11:15am -> NO lecture
November 19 at 11:15am -> November 19 at 4:05pm
December 3 at 11:15am -> NO lecture
PreReqs:
Quantiles - Sample and Population
Lecture material:
Standard probability distributions in R
Multinomial - 1, 2, 3 (exercise)
Multivariate Normal - 1, 2, 3, 4 (independence of sample mean and variance), 5 (for you to explore)
Sample Mean Vector and Sample Covariance Matrix
PCA - 1 (Jolliffe's book), 2 (issues with PCA), 3 (a demonstration), 4 (another demonstration), 5 (biplot for Iris data)
TA: Mr. Sayan (sayanranjan4@gmail.com)
If you send him an email, please include the word "PGDBA" in the subject of the email.