This course introduces students to widely used procedures for regression analysis for descriptive and causal inference, and provides intuitive, applied, and formal foundations for regression and more advanced methods treated later in the major course sequence.  The first half of the course covers descriptive inference with regression from a sampling perspective. The second half of the course covers causal inference with regression and standardization, also from a sampling and missing data perspective. The principles learned in this course provide a foundation for the future study of more advanced topics.  While the course will emphasize the mathematical foundations of these concepts, each topic will also cover the implementation of the relevant methods in the statistical computing program R.