Data Analysis 3: Pattern discovery and regression analysis
Timing: Full time, part time: Friday afternoon
Uncovering patterns in the data can be an important goal in itself, and it is the prerequisite to establishing cause and effect and carrying out predictions. The course starts with simple regression analysis, the method that compares expected y for different values of x to learn the patterns of association between the two variables. It discusses nonparametric regressions and focuses on the linear regression. It builds on simple linear regression and goes on to enriching it with nonlinear functional forms, generalizing from a particular dataset to other data it represents, adding more explanatory variables, etc. We also cover regression analysis for time series data, binary dependent variables, as well as nonlinear models such as logit and probit.