Topic: Exploratory Multivariate Data Analysis. Describing and visualizing data with principal component analysis (PCA) for continuous data, correspondence analysis (CA) for contingency tables, multiple correspondence analysis (MCA) for categorical data, factorial analysis for mixed data (FAMD) for both continuous and categorical data, and multiple factor analysis (MFA) for data structured into groups of variables. Studying and visualization of the correlation between groups of variables with the RV coefficient. Performing PCA with missing values, matrix completion of continuous and categorical data with principal components. Examples from sensory analysis, public health, genetics. All the analysis will be performed with R.
PhD Statistics students only
- Students can choose to take this course for 2 or 3 units. Those who enroll in the course for less than the maximum unit count will have reduced classwork proportional to the amount of units chosen.he amount of workload is monitored by the instructor, and it will increase or decrease relative to the number of units undertaken. Students are expected to attend all class/discussion sessions and perform work in proportion to the number of units in which they have enrolled (students often take only the number of units they need to fulfill a degree requirement).