Linear Regression Models

STAT5205 Sec02/STAT4205 Sec03, Fall 2021

Mon & Wed 1:10pm-2:25pm, 329 Pupin Laboratories

COVID-19 CORONAVIRUS: The policies set forth in this course are subject to change as we try to determine how best to keep you safe from the COVID-19 coronavirus while we provide the education we promised you.

Instructor: Xiaofei Shi xs2427[at]columbia(dot)edu
TAs: Navid Ardeshir na2844[at]columbia(dot)edu

Shouxuan Ji sj3071[at]columbia(dot)edu
Office Hours: Office hours will be on zoom: Meeting ID: 969 8687 3013

Course Description: This is a first course in regression analysis for graduates and undergraduates in Statistics and related majors. Topics covered include:~scatterplots and correlation, bivariate and conditional distributions, inference in the simple linear regression model, multivariate regression, ordinary and weighted least squares, categorical predictor variables, transformations, residual analysis, model selection, and logistic regression.
Course Prerequisites: Statistics, (Calculus based) Probability, Linear Algebra.
Textbook: Applied Linear Regression Models, fourth edition by Kutner, Nachtsheim and Neter.

Homework: There will be six homework assignments, approximately evenly spaced throughout the semester. The homework will be posted on CourseWork. We highly recommend using Piazza for discussion. We will use CourseWork for submitting and grading.Homeworks submitted after the deadline will not be considered, so please plan in advance. In the case of an emergency (sudden sickness, family problems, etc.), a reasonable extension will be assigned. But we emphasize that this is reserved for true emergencies.

Evaluation: 50% for min{Homework average, Exam average} + 30% for 2 Midterms + 20% for Final.

Schedule

Date	Topic	Note
Mon 09/13	Introduction	Review Materials
Wed 09/15	R Tutorial	HW1 out
Mon 09/20	Simple Linear Regression
Wed 09/23	Simple Linear Regression Cont'd	HW2 out
Mon 09/27	Gaussian SLR: Inference and Prediction	HW1 due
Wed 09/29	Gaussian SLR: Hypothesis Testing
Mon 10/04	HW1 Session
Wed 10/06	code for SLR , Multivariate Linear Regression	HW3 out
Mon 10/11	Multivariate Linear Regression: Inference and Estimation	HW2 due
Wed 10/13	Multivariate Linear Regression: Hypothesis Testing
Mon 10/18	Multicollinearity; Diagnostics and Modification
Wed 10/20	Polynomial Regression	HW4 out
Mon 10/25	HW2 and Midterm1 Review Session	HW3 due
Wed 10/27	Midterm 1
Mon 11/01	No Class
Wed 11/03	Categorial Variables: Transformations and Interactions	HW5 out
Mon 11/08	HW3 and Midterm1 Session	HW4 due
Wed 11/10	Weighted Least Squares
Mon 11/15	Generalized Linear Regression
Wed 11/17	Influential Points and Outliers
Mon 11/22	No Class
Wed 11/24	No Class
Mon 11/29	Midterm2 Review Session
Wed 12/01	Midterm 2	HW5 due
Mon 12/06	Modern Regression
Wed 12/08	Review Session
Mon 12/13	Study and Exam Days
Wed 12/15	Study and Exam Days
Mon 12/20	Final Exam	Pupin 329 1:10 - 4:00

Logistics

Policy on Late Homework: All homework submitted after the solution is not going to be graded and you will receive zero credit for that homework.

Policy on Collaboration: You are encouraged to work together on the homework. Discussing the homework problems with one another can be a valuable learning experience. However, it is a violation of the rules on academic integrity to copy another student's solution and submit it as your own. You should write up your solutions separately, not referring to a common document. Furthermore, you should not submit any work that you do not fully understand. You should be able to start with a clean sheet of paper and without notes or assistance write out the solution to any homework solution you submit. If you will do that with every homework you submit, the similarity between your solutions and those of other students will not arouse suspicion. More importantly, you will be well prepared for the exams. You are not permitted to use homework solutions for this course from previous years or solutions you find from other sources, including the internet.

Take Care of Yourself:It is easy for me to say and hard for all of us, including me, to do, but taking care of your physical and mental health is essential, especially during the COVID-19 pandemic. Life is a marathon, and you need to pace yourself. Do your best to maintain a healthy lifestyle by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.
If you or anyone you know experiences extreme academic stress, difficult life events, or feelings of anxiety or depression, I strongly encourage you to seek support. Counseling and Psychological Services is here to help 24/7, and everything will be confidential: call 212-854-2878 or visit here.
In addition, consider reaching out to a friend, faculty or family member you trust for help getting connected to the support. Keep in mind that for serious psychological issues, the first counselor you meet with may not be the right one for you, but this does not mean you should give up on counseling. Keep looking for someone who can help you.
If you or someone you know is feeling suicidal or in danger of self-harm, call immediately, day or night:

On campus: Columbia Police: 212-854-2797
Off campus: 911

If you have questions about this advice, your coursework, or anything else about which I might be helpful, please let me know.